mobilityPLUS-Issue 1-July 2021 by S+M.ai

Issue 1 July 2021

Have We MET?

perspective. To provide a few examples of what will be coming up, I will write about the material footprint of electric vehicles, the sustainability of automated and electrified heavy-duty trucks, the potential environmental impacts of fuel cell electric buses, and battery technologies that will power all our vehicles in the (near) future.

In 1713, Hans Carl von Carlowitz first coined the term Nachhaltigkeit, meaning ‘sustainability’ in German, when he realized that unsustainable mining practices would jeopardize the supply of wood. At the time, wood was an essential resource for many economic activities from construction to mining and was also used as a source of fuel. Therefore, as a mine administer, von Carlowitz particularly warned about the unsustainable use of timber in building and carpentry practices across Germany and brought up, for the first time, the supply risk of timber and its implications for the German economy. Hence, it was a material critical to human development that led to the spark of sustainability in both production and consumption.

Before anything else, I would like to congratulate S-plus-M.ai on their launch as a smart mobility hub, intent on bringing all relevant stakeholders together around this crucial topic.

I will start this series of columns off with a more overarching subject, the material-energytransportation (MET) nexus. My main reason behind this is the overlooked significance of the materiality aspect of sustainability transition in energy and transportation sectors. Additionally, I would like this first column to lead readers towards asking questions about this aspect of smart mobility. In my later col-umns, I will dive deeper into more specific subjects, and the technologies within each component of this nexus as is relevant for smart mobility, in an attempt to detail the sustainability

Fast forward to today, the potential consequences of climate crisis urge scientists and policymak-ers around the world to establish a carbon-neutral economy by 2050 by rapidly phasing out the use of fossil fuels in human socioeconomic activities. The carbon-neutrality goal requires, first and foremost, the transformation of our current energy system into one that is fueled entirely by renewable energy sources. To that end, efforts with respect to transitioning to renewable energy systems have appeared high on the global sustainable development agenda and are viewed as a vital strategy to address global energy and environmental challenges in all sectors, including the transportation1. Countries all around the world have started investing heavily on wind and solar energy to substantially transform their energy and transportation infrastructure.

However, achieving such a transition is a challenging task and requires a profound understanding of the costs and benefits of deploying various transportation and renewable energy technologies. This challenge is

Burak Sen 5 min

exacerbated by the fact that enabling such a transition is of a dynamic, complex, and inter-connected nature. As an example, the potential future of the energy sector will have a direct impact on the sustainability profile of the transportation sector. Likewise, the potential future of the transportation sector will determine how much energy will be needed to fuel the transportation sector. Hence, transpor-tation must be made an integral part of the planning for renewable and sustainable energy, and vice ver-sa. Furthermore, the future of energy and transportation enabled by new technologies emerging in these sectors will exhibit different raw material profiles, a fact that is often overlooked in their sustainability assessments. The technoeconomic availability of critical materials used in these technologies is an im-portant factor, influencing their scaling-up and deployment. To that end, this interconnectedness between transportation, energy, and materials forming the MET Nexus must be taken into consideration in transforming the transportation and energy systems worldwide.

Technologies that enable smart mobility and sustainable and renewable energy necessitate the use of advanced products that rely on the stable supply of a number of critical and precious materials. For example, rare earth elements (REEs) are extensively used in electric motors deployed in wind turbines and electric vehicles (EVs). While platinum group metals (PGMs), lithium, and cobalt are some of the indispensable materials used in mobile and stationary energy storage systems. These materials, among others, have been recently listed by the European Commission due to several reasons2, including, but not limited to, the geographical concentration of materials (e.g. about 90% of global PGM reserves being in South Africa)3, host metal dependence (e.g. PGMs being dependent on copper-nickel mining)4, a lack of compatible and reliable substitutes5, and the inefficient and insufficient recycling of those materials6. These circumstances in turn raise serious concerns over potential resource constraints that may pose a bottleneck for the deployment of needed technologies; and ultimately, for the transition to renewable energy.

These important perspectives are still missing in the current discussion on transportation and energy planning and climate crisis mitigation strategies. Considering the lifetime of large investments in renewable energy and smart mobility infrastructures, understanding of the dynamic interactions between the energy sector, transportation sector, and the raw material supply under various climate crisis mitiga-tion scenarios is urgently needed. It is hoped that scientists and engineers in all fields will keep these aspects in mind for their work.

References

(1) Mancini, L.; Nuss, P. Responsible Materials Management for a Resource-Efficient and Low-Carbon Society. Resources. MDPI AG June 1, 2020, p 68. https://doi.org/10.3390/RESOURCES9060068.

(2) European Commission Joint Research Center. Critical Raw Materials https://rmis.jrc.ec.europa.eu/?page=crm-list-2020-e294f6 (accessed Mar 28, 2021).

(3) Rasmussen, K. D.; Wenzel, H.; Bangs, C.; Petavratzi, E.; Liu, G. Platinum Demand and Potential Bottlenecks in the Global Green Transition: A Dynamic Material Flow Analysis. Environ. Sci. Technol. 2019. https://doi. org/10.1021/acs.est.9b01912.

(4) Nassar, N. T.; Graedel, T. E.; Harper, E. M. By-Product Metals Are Technologically Essential but Have Problematic Supply. Sci. Adv. 2015, 1 (3). https://doi.org/10.1126/sciadv.1400180.

(5) Graedel, T. E.; Harper, E. M.; Nassar, N. T.; Reck, B. K. On the Materials Basis of Modern Society. Proc Natl Acad Sci U S A 2015, 112 (20), 6295–6300. https://doi.org/10.1073/pnas.1312752110.

(6) Ciacci, L.; Vassura, I.; Cao, Z.; Liu, G.; Passarini, F. Recovering the “New Twin”: Analysis of Secondary Neodymium Sources and Recycling Potentials in Europe. Resour. Conserv. Recycl. 2019, 142, 143–152. https:// doi.org/10.1016/j.resconrec.2018.11.024.

What about the materiality of Electric Vehichles?

4 min

The sustainability of vehicle electrification has been assessed by many researchers and compared to that of conventional vehicles (i.e. internal combustion engine vehicles) that run on gasoline. There seems to be a consensus that electric vehicles outperform conventional vehicles in terms of the potential life cycle environmental and economic impacts. However, most of these assessments fail to provide insights into the materiality aspect of vehicle electrification. This may be an important limitation to our understanding of the sustainability implications of electric vehicles to the full extent, since they are substantially differ-ent from conventional vehicles in terms of their drivetrains and infrastructural needs (i.e., electricity re-fueling station vs. gasoline station). Hence, in this column, I will attempt to cover this aspect.

Given its significance in terms of global sustainability, improving the environmental and socio-economic performances of the transportation sector has been given a high priority towards the mitigation and management of climate crisis, globally. The dependence of the transportation sector on fossil fuels has increased the reliance on fuel imports for countries that do not possess the needed resources, which, in turn, raised concerns over energy security and fuel supply risks. The economic consequences of such a reliance were experienced around the world during the oil crisis of 1970s, which led many countries to redesign their energy and transportation policies.

Sen

Burak

Most importantly, the combustion of fossil fuels consumed by vehicles has caused adverse environmental impacts while posing serious risks for public health, as well as the health of urban ecosystems, especially in countries without proper air and environmental quality control measures. In addition, the estimated increase in the number of registered vehicles around the world — especially in countries like the United States, Europe, China, and India — is likely to cause greater environmental and socioeconomic impacts due to increased vehicle use. These concerns and potential consequences have led scientists and decision-makers in both public and private organizations to reconsider the electrification of transportation, even more seriously than ever before.

In practice, electrifying the transportation sector means transforming the way we build and main-tain the related infrastructure, as well as the way we generate and supply fuel (i.e. electricity). There is a growing consensus — even acknowledged by fossilminded politicians — on the findings of many scien-tific studies, concluding that the world has already run out of its carbon budget, and cannot afford to use fossil fuels in any manner. Therefore, we also need to reconsider the role of energy infrastructure in transforming the transportation sector, since the electricity needed to power our vehicles must come from renewable energy sources utilized in the most sustainable way.

At this junction, it is critical to take into account the materiality aspect of this transformation, since both the electric vehicle and clean energy technologies that will be used to power these vehicles rely on the stable supply of materials that are critical for such a transformation. Additionally, majority of countries are dependent on the imports of such materials. The question of how criticality in this context is defined can be naturally raised; however, that is a topic for another time. An indicator that can provide insights into the materiality aspect of electric vehicles is the material footprint. The material footprint of a product can be defined as the amount of materials involved in the production of the given product along its entire supply chain, from raw material extraction to final consumption. Material footprint analysis aids in effectively managing the natural resources, which is

crucial to the deployment of electric vehicles around the world.

Using this indicator, I have investigated the material footprints of U.S. electric vehicles, and ob-served that when the materiality aspect is of concern, the results draw a different picture compared to other sustainability indicators (e.g., global warming potential) used to assess the benefits and costs of these vehicles.

Given their additional needs such as relatively larger batteries to store and provide power and recharging stations, electric vehicles underperform conventional vehicles in terms of their material requirements, leading to large differences between their material footprints and those of a conventional vehicle.

For example, the material footprint of a battery electric vehicle is 60% higher than that of a con-ventional vehicle. Plug-in hybrid electric vehicles also have a similar material footprint profile as battery electric vehicles, with their material footprint being 40% to 55% higher than that of a conventional vehi-cle. In both cases, battery manufacturing is a critical factor driving up their life cycle material footprints. My investigation has also confirmed the concerns over the supply risks of critical materials, as the U.S. is moderately to heavily dependent on the imports of many such materials embedded in the production of U.S. electric vehicles. The expected increase in the demand for electric vehicles, as pushed by environ-mental constraints and government regulations, is likely to result in an increase in the demand for these critical materials.

All in all, there are at least four important takeaways related to the materiality of electrifying the transportation sector, and they apply to all countries. Firstly, the material efficiency of electric vehicles should be paid a due attention from a life cycle perspective. Secondly, the share of electricity generated from renewable energy sources should be increased at the margin as much as possible, preferably by 100%, and urgently. Thirdly, batteries are critical to the material footprints of electric vehicles, and the importance of batteries becomes clearer as the material footprint attributed to the use phase of a conventional vehicle is larger than electric vehicles. Last but not least, countries should heavily invest in material recycling in order to diversify their material supplies while increasing domestic supply capacity.

Expansion of EV Charging network for long-distance trips

The subject of connected automated, shared, and electrical vehicles, is a multidisciplinary issue with several different stakeholders engaged in their implementation. There are multiple teams, from federal and state agencies, OEMs and academia to startups and larger consortiums, working on this complex subject. User attitudes and a lack of market demand for CASE vehicles, insurance and liability challenges, regulatory issues, infrastructure needs, and technological challenges, are among the significant hurdles currently identified to the use of these vehicles. To overcome these social and technological concerns, states should shift towards creating a more productive, sustainable, and smart environment. The transition from conventional vehicles to CASE vehicles depends on the development of technology, as well as consumer acceptance and policies.

At the time of writing this column, the automotive industry is moving away from the CASE vehicle hype. A more realistic understanding of the hurdles regarding their implementation and what kinds of automation will be available and when, has developed. There have been several industry consolidations and partnerships among network and automobile companies to develop a critical mass for new CASE technologies. However, investments by federal agencies remain less than private investments. Developers and investors tend to focus on narrowly defined use cases. Empirical evidence suggests we are far from level 5 automation (fully autonomous vehicles).

Bahar Azin

3 min

In the past decade, electric vehicles (EV) have drawn more customers due to their lower environmental footprint, and greater financial benefit compared to conventional vehicles. Many automotive market trends indicate that the EV sales rate has increased significantly in the past few years, and is forecasted to continue to grow in the upcoming years. On the other hand, to satisfy this rising demand, the number of charging stations has grown with greater user consumption. Among the different charger types, level 3 DC fast chargers are the most practical for EV users, in order to charge their vehicles efficiently and quickly. Depending on the vehicle’s battery capacity, one can charge their car in about half an hour. This type of charger becomes handy in dense population areas (e.g., apartments and condominiums), where access to a home charger is limited, and overall demand is higher compared to suburban areas. Despite the breakthroughs and improvements in charging availability, EVs still face significant hurdles; namely, the limited driving range. These current limitations are a deterrent for new potential customers; and it’s believed that ‘EV are not suitable for long distance trips.’ However, the combination of an extended charging network, and fast chargers, could facilitate longer trips for users.

The existing literature on EVs and charging stations mainly focuses on urban trips, and developing a charging network corresponding to inter-city areas with a limited EV driving range. Moreover, developers design charging stations by focusing on transportation networks, or power distribution networks exclusively; whereas both networks are involved in developing an integrated charging infrastructure. In a recent study by UTRAIL at the University of Utah, a framework for allocating charging stations in the state of Utah was created which tries to capture most EV traffic demand, while distributing the power load on the distribution

network evenly. This recent study uses a heuristic approach to find the optimal locations for chargers using scaled GPS trajectory data. Results show that the morning and afternoon peak hours are when the demand for charging is highest. With consideration given to power network constraints, the equivalent power load of charging sessions can be normalized. Assuming a 130 mile range of driving for the sample EV, 68% of the traffic could be powered by 12 stations all over the state. Sensitivity analysis of the driving range demonstrates that, increasing the range to 200 miles, could broaden coverage up to 89%. However, most available EVs with a higher driving range (more than 200 miles) are notably pricier than vehicles with a more limited range, which is also a barrier. The findings of this study also demonstrate that the available resources for charging facilities are not sufficient for the upcoming surge of EV users; as it will only cover 18% the demand when the EV market share reaches 10%.

The development of an expanded network could bring more customers to EVs, and a more optimal charging network for distribution and transportation networks. Yet, the enlargement of the EV user base requires a comprehensive demand management plan for more efficient utilization of the available charging stations, based on the study of EV drivers’ behavior. Users’ behavior could help planners to find the travel alternatives that offer users the most payoffs. Providing an alternate charging plan through policies for travelers could reduce the average waiting time for charging in public stations. Furthermore, it could keep the distribution network in balance, so no power shortage will be experienced, and charger posts can operate under their capacity for a fast and consistent charging session. Meanwhile, the growing EV penetration rate entails updating the demand management plan, considering the changing behavior of users. The later framework, includes a sustainable planning strategy that contemplates the long-term urban infrastructure development and policies, according to users’ travel behavior. The outcome of the proposed plan could improve accessibility, and lead to perspectives that are socially supported for allocating charging points for EV users in smart and connected urban areas.

Various EMSs have been proposed for the application of electrified vehicles, and are generally categorized into methods based on rules and logic, or the approaches used to integrate optimization tools.

Electrified Vehichles

Energy management strategies: Summary & Trends

The past decade has seen the rapid development of electrified vehicles. To manage the use of energy sources in an electrified drivetrain, a supervisory controller is essential. An Energy Management Strategy (EMS) distributes power between the energy sources in an electrified vehicle, while meeting the driver’s power demand. Monitoring power consumption is critical for the different aspects of electrified vehicles; from minimizing the fuel/electricity purchase, to the vehicle’s overall performance. Various EMSs have been proposed for the application of electrified vehicles, and are generally categorized into methods based on rules and logic, or the approaches used to integrate optimization tools. Rule-based methods are real-time implementable, easy to understand, and suitable for commercial applications. Although EMSs are the Original Equipment Manufacturers (OEMs) confidential documents, assessing the data recorded to integrate optimization tools. Rule-based methods are real-time implementable, easy to understand, and suitable for commercial applications. Although EMSs are the Original Equipment Manufacturers (OEMs) confidential documents, assessing the data recorded during tests of the electrified vehicles has shown that OEMs have widely deployed rule-based EMSs for their vehicles. These rules are developed based on engineering expertise and best practices, and may deviate the EMS’s performance from optimal levels. Additionally, rule-based control methods cannot be easily modified for a different powertrain architecture.

Reihane Ostadian

3 min 10

Many automotive market trends indicate that the EV sales rate has increased significantly in the past few years and is forecasted to continue this growth in the same direction in upcoming years.

To further improve the performance of rulebased controllers’, research efforts have been directed towards incorporating optimizationbased methods to rule-based approaches in order to maximize the EMS controller’s capability. Optimization-based algorithms distribute the power, along with the drivetrain, in an optimal way. This optimal distribution requires having a complete knowledge of the drive cycle beforehand, partially accessing the future drive mission, or the real-time drive cycle information. To enable the real-time capability of the controller, methods that require a complete knowledge of the drive cycle would not be reasonable for actual in-vehicle implementation. Prediction-based optimization algorithms however, show outstanding performance, embedding the future drive cycle profile to the controller. Although optimization-based algorithms provide profound performance, their implementation can be very challenging for OEMs due to the high computational resources, real-time implementation issues, and the optimization method’s complexity. Consequently, the vehicle industry has not completely welcomed prediction-based methods due to reliability issues with the predicted parameters’.

With the rapid advancement of Intelligent Transportation Systems (ITS) and autonomous vehicles’ progress, the accessibility of traffic data, road conditions, and vehicle information is more feasible, which renders prediction-based algorithms a viable strategy for the future of electrified vehicle embedded controllers.

V2X communication systems offer valuable knowledge on charging station locations, traffic lights, traffic congestion, and route distance information in order to plan the power distribution for the electrified drivetrain more efficiently. This information is integrated into optimization-based algorithms, which enhances prediction accuracy and reliability. Another benefit of V2X communication systems for the EMSs is their capacity to utilize connected vehicles and cloud-based EMS controllers, thus reducing the need for high computational resources. Data availability through V2X and ITS has also paved the way for the enablement of machine-learning-based EMSs. Critical parameters that affect the power distribution can be easily integrated into machine learning-based EMSs. Examples of these parameters include the trip distances, driver’s behavior, and road geometry. Although machine learning algorithms may require a good deal of offline training that can be timeconsuming, they are implementable in real-time. The quality of offline training data will therefore directly impact the real-time EMS controller performance. At the offline stage, optimization-based algorithms can be utilized to prepare a training dataset that enables the machine learning EMS controllers to have a nearoptimal performance.

Identifying new locations of EVs charging stations

For Baltimore city and Baltiomre county to encourage more EV users

3 min

a charging station, consideration should be given to selecting a location that most effectively considers the route choice behavior, and the charging demand of EV users (Hanabusa & Horiguchi, 2011). Different studies have found that the inappropriate location of charging stations eventually results in voltage fluctuation, and power quality-related problems (Foosnæs, Jensen, & Nordentoft, 2017, Aghaei, Nezhad, Rabiee, & Rahimi, 2016).

Nashid Khadem

The world is seeing a massive rise in electric vehicle (EV) mobility. In 2020, EV numbers climbed to 10.9 million, up more than three million from 2019. A survey by the Centre for Solar Energy and Hydrogen Research BadenWürttemberg (ZSW) showed China is leading this electric vehicle boom with five million e-cars, the USA with 1.77 million, and Germany with 570,000 EVs on the road. All of these vehicles need electrical power, but currently all EV owners, or future owners, will not have charging facilities at their homes. Obviously, with the increasing number of EVs, the number of charging stations also needs to be increased, in suitable locations. Charging stations put in inappropriate places is a significant concern for both operating, and expanding the number of EVs. A survey of car buyers showed that the first concern for EV owners is the location of charging facilities, and the time to recharge (Bonges et al., 2016). When locating

To encourage more people to buy EVs, the author of this column conducted a study to see if the EV users of Baltimore county have charging stations where they live or work. The study also tried to identify which areas in Baltimore need more charging stations for EV users. Two data sets were used in this study; first EV charging location data was obtained from the US Department of Energy website, which provided all alternative energy sources in Maryland. Second, was a set of EV user data collected from the National Transportation Center at Morgan State University. NTC had a project titled “Electric Vehicle Ownership Factors and Commuting Behavior in the United States”, where they collected the house and work zip codes of EV users in Maryland. That study mapped the data into three categories: zip codes based on EV user frequency, EV charging stations in the station area, and suggestions for new EV charging stations, or possible extensions of EV charging stations.

In the map analysis, zip code data and existing EV charging station data in Baltimore County, and the city, were incorporated together. From the map analysis, we can see five or six users’ zip codes where most of the EV stations are concentrated. Some zip codes don’t have any EV stations, although EV user’s frequency is high. Again, a prominent northern and western part of Baltimore County has zero EV charging stations. These results show the existing EV charging stations are concentrated around particular zip codes. On the other hand, the EV user’s frequency map shows that most EV users have homes, or commute to work, in other zip codes where no EV charging station is available. Overlaying the EV charging station map over the user density area, zip codes with high usage, and locations with no EV charging station, areas were selected as an appropriate place to install new EV charging stations (zip codes: 21131, 21136). Another set of zip codes (21030, 21206, 21208) have a medium density of users, but a low number of EV stations, and were selected as potential

locations to expand the EV charging station service. As this study suggests, consumers might buy more EVs, if the charging stations are available (Bonges et al., 2016). The author hopes the appropriate and convenient location of EV charging stations, will encourage the citizens of Baltimore to use more EVs.

Bibliography:

1. Center for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW). Retrieved from: https://www.zsw-bw.de/en/newsroom/news/newsdetail/news/detail/News/electric-cars-on-the-rise-global-count-climbsto-109-million.html

2. Bonges III, H. A., & Lusk, A. C. (2016). Addressing electric vehicle (EV) sales and range anxiety through parking layout, policy, and regulation. Transportation Research Part A: Policy and Practice, 83, 63-73.

3. Hanabusa, H., & Horiguchi, R. (2011). A study of the analytical method for the location planning of charging stations for electric vehicles. KnowledgeBased and Intelligent Information and Engineering Systems, 596–605

4. Foosnæs, A. H., Jensen, A. N., & Nordentoft, N. C. (2017). Report: Case studies on grid impacts of fast charging. Edison D 4.4.3 & D 4.4.4 .

5. Aghaei, J., Nezhad, A. E., Rabiee, A., & Rahimi, E. (2016). Contribution of plug-in hybrid electric vehicles in power system uncertainty management. Renewable and Sustainable Energy Reviews, 59, 450–458

Figure 1: Map analysis showing EVs user’s frequency zip codes, zip codes with existing charging stations, and proposed area for new EV charging stations.

The impact of a Eco-SpeedControl system on driver behavior and emissions reduction at signalized in sections using a driver simulator

2.5 min

Samira Ahangari

This study represents the drivers’ behavior regarding the implementation of an in-vehicle Eco-Speed-Control (ESC) system in order to provide real-time speed guidance in the vicinity of an intersection by using a full-scale high-fidelity 3D driving simulator. Infrastructure-to-vehicle communication is assumed, and the vehicles have full knowledge of the traffic light timings in the driving horizon. The advantage of the ESC system used in this study, compared to other eco-driving systems, is that it considers the roadway grade (uphill vs downhill). The ESC system is also applicable for different types of vehicles (light-duty, heavy-duty, and hybrid electric vehicles) and calculates the speed trajectory and fuel- more efficiently by considering real-time traffic. The system includes a visual dashboard display or audio guidance that provides moment-by-moment speed guidance aimed at improving fuel efficiency based on the drivers’ current performance, and the actions required to improve fuel economy whenever possible. Since it may take a long time for most vehicles road network to be equipped with level 4 or 5 automation, this system allows the vehicle to take over and traverse the intersection and equips vehicles with a speed guidance system till the road network will be ready for full automation. However, the success of such a system partially depends on how well it can direct the driver. To study the driver’s behavior a network was simulated in the driving simulator. The study area used was a medium-size road network of the Baltimore metropolitan area, which consists of three signalized intersections, as shown in Figure 1. Seventeen scenarios were used for different road characteristics, traffic conditions, and Eco-Speed-Guidance (ESG) (Table 1) to investigate drivers’ behavior and the reduction of CO2 emissions. Participants began by driving in a base scenario, with no guidance to provide a general measure of driving behavior for purpose of comparison, before using ESG systems. Participants then drove

different ESG scenarios on a midsize road network in the Baltimore metropolitan area, including three intersections with uneven roads (uphill and downhill). In each scenario, the ESG was provided 200 meters before, and 200 meters after each intersection.

In the above-mentioned ESG area, at each intersection the participants were given the “Recommended Speed” or “Speed Change” via Voice, Text, and Graphic/Color. In “Recommended Speed” scenarios, the exact speed was announced; while in “Speed Change” scenarios, participants were given general statements such as “Increase Speed”, “Decrease Speed”, and “No Change”. Participants were expected to drive at a speed limit of 30 mph, and change their speed in response to the information provided via ESG (except in the base scenario) and go through the signalized intersection without stopping. The goal of the

study was to measure the ability of drivers to follow the ESG.

The seventeen scenarios include no information as a benchmark, aside from providing “Recommended Speed” and “Speed Change” (Increase or Decrease or No Change) via Voice, Text, and Graphic/Color (Figure 2). Uphill versus Downhill was differentiated (due to differences in emission), along with “No Traffic” versus “Mild Traffic” (Traffic Intensity = 0.5E)

The overall outcome of this study confirmed the effectiveness of an ESC system in reducing fuel consumption and emissions. In addition, the Speed Change-Color was identified as one of the best forms of Eco-Speed-Guidance when considering safety and emissions.

Fig.1 Study Area

Table.1 Simulated Scenarios

Figure.2 ESG

Finding the Balance

A little context about my background: I am a Transportation Planner and Engineer. After my graduation with a bachelors in civil engineering, I pursued my masters in Urban Transportation Planning; and worked in an Urban Development Authority where I was involved in drafting the Development Plan (DP) for a city. A DP basically identifies the future growth areas of the city, and specifies development regulations for planned and manageable growth. I am now pursuing my Ph.D. in civil engineering. My research focusses on using connected technology in transportation to help make people’s lives better. This blend of planning, and engineering in my background, helps me see the two sides that are very important and crucial in shaping the urban environment. I am using this platform to share some of the lessons that I have come to realize during this time.

Cities have a trend of having overly packed downtowns, with the population density thinning as you go farther from the center. People prefer to live outside of the city for various reasons; like- lack of affordable housing in the core, lack of an efficient public transportation system, the dream of owning a bigger home, etc. With the addition of newer and larger homes on the outskirts, the city limits keep expanding. People travel longer distances for work and recreational purposes. To help people get a faster more comfortable commute, we make highways. More and more vehicles keep adding to the highways as the city expands. As a result, we add more lanes to the highway to accommodate the ever-increasing traffic demands; till we come to the point where highways can no longer be expanded.

Dhwani Shah

3 min 18

Also, the pandemic has made travelling in a personal vehicle the mode of transport of choice for many commuters.

New ways are being explored to accommodate the increasing number of vehicles on the road. With the help of new technologies and advanced vehicles, , we are now trying to reduce the spacing between vehicles, to accommodate more and more. But, there is a limit to the benefits offered.

We need to find a better way to optimize this increasing vehicular traffic. Public transport and ride sharing have been considered as alternatives to tackle this problem; but unfortunately, the uptake of these alternatives is not significant enough to make a substantial difference on traffic congestion. The comfort of owning and travelling by one’s own vehicle is still preferred by many. Additionally, the pandemic has made travelling in a personal vehicle the transportation of choice for many commuters. For the people who are willing to use public transport (PT), providing affordable and convenient PT is not always feasible for the government of a sprawled city. Getting enough ridership to run buses every 10 to 15 mins is also difficult. Other related transportation modes such as cycle/scooter sharing are not as effective if people do not wish to use this public transport. A better planning for shared mobility can be done using connected technology, but again, to what extent?

The problem is, to implement any type of service, there is a minimum amount of ridership that is required to make the system convenient for users, and affordable for authorities and users both. If it is not possible to provide an affordable and convenient system, it’s not going to be sustainable in the longer run. It is very important that we find a balance.

Mixed land use, having more than one urban hub (not just one city center), densification of the cities, making walkable/ cyclable cities, coupled with better public transportation systems, are some of the key solutions that could help us break the status quo.

Safety & operational benefits of AVs

The World Health Organization (WHO) reported 1.35 million fatalities due to roadway crashes in 2018. In addition, roadway crash injury was the 8th leading cause of death in the world (WHO, 2018). The numbers of roadway crash fatalities in the United States were 37,133 and 40,000 in 2017 and 2018, respectively (Highway Traffic Safety Administration and Department of Transportation, 2017; U.S. Department of TransportationNHTSA, 2018). These statistics have made roadway safety a critical concern. AVs are considered an effective solution to significantly enhance traffic safety, as they reduce or even eliminate drivers which contribute to 94% of the crashes in the U.S. (Gora and Rüb, 2016; Morando et al., 2018; Wayomo, 2018).

Maryam Mousavi

To address the operational and safety concerns associated with roadway traffic, various conventional solutions, including capacity expansion, have been implemented. However, due to a lack of space and funding resources, conventional solutions cannot fully address the traffic issues anymore (Ajitha et al., 2015). Therefore, the focus has been shifting towards improving the performance of the current infrastructure by using operational tools, and implementing intelligent transportation system (ITS) technologies. Among those, autonomous vehicles (AVs) have been considered as an effective solution. Hence, the safety and operational benefits of AVs on various roadway components should be evaluated comprehensively, before implementing them on the current roadway infrastructure.

On the other hand, there are traffic operation obstacles that deteriorate roadway performance Among different roadway components, urban arterials experience traffic congestion on a daily basis, as they connect major activity centers, and carry high traffic volumes. Moreover, urban arterials are associated with closely spaced signalized and unsignalized intersections; among which, unsignalized intersections are more complicated, as driving behaviors are entirely dependent upon the drivers’ judgment. This high traffic congestion and complexity not only diminishes traffic operation, but also results in higher exposure, and consequently, lower traffic safety (American Association of State Highway and Transportation Officials (AASHTO), 2011; Federal Highway Administration (FHWA), 2010; Khan et al., 2017). Hence, it is important to analyze how AVs perform in urban arterials nearby unsignalized intersections or driveways.

4 min

World Health Organization (WHO) reported 1.35 million fatalities due to roadway crashes in 2018. In addition, the crash injury was indicated as the 8th leading cause of death for the entire world (WHO, 2018).

Due to a lack of real-world data, the operational and safety effects of AVs are mainly determined through implementing micro-simulation software packages. VISSIM was used to assess how different levels of AV MPR, and traffic congestion, affect traffic safety and operational performance nearby a driveway on an urban arterial (Mousavi et al., 2021). A total of 24 scenarios were developed, considering four levels of traffic level of service (LOS); including LOS A, LOS B, LOS C, LOS D, and six levels of AV MPRs; 0%, 10%, 25%, 50%, 75%, and 100%.

Traffic operation was analyzed using two different performance measures; one, traffic density; and two, traffic speed. The analysis was done for each combination of traffic LOS and AV MPR, and each traffic lane separately. The results indicated that autonomous vehicles have the capability to improve traffic operation by reducing the overall traffic density, especially for higher traffic LOS (including LOS C and LOS D). At LOS D, in the fully AV environment, highdensity points could be observed along the lanes, due to the fact that the AVs are not connected. In other words, since the AVs do not communicate, the arterial vehicles do not get notified when a driveway vehicle intends to merge. Hence, the density increased in some situations where there was not enough gap. Moreover, analyzing the average operating speed of the network indicated that at the same LOS, any increase in the AV MPR resulted in an increase in the average speed of the network. Hence, AVs provide a smoother traffic flow and consequently, a higher average speed.

Autonomous Vehicle Infrastructure: Defining the Domain

4 min

While excitement has been building in recent years for having fully autonomous vehicles (AVs) on our roads, the delivery has proven to be challenging for many vehicle manufacturers. When the conversation surrounding AVs first started, many manufacturers focused on achieving fully autonomous vehicles (SAE Level 5), where human driving is eliminated. That goal is still years in the future, but today’s drivers are already benefitting from many of the incremental steps the industry has taken toward full autonomy. And continued collaboration between private industry and the public sector is helping to deliver the technology and infrastructure improvements AVs will need to operate safely in more locations.

Here’s a quick refresher on the AV levels established by SAE International, a global leader in developing standards for the mobility industry:

• Level 0 represents no automation, much like a 1968 pickup truck.

•Levels 1 and 2 include varying degrees of driver assistance features that are becoming increasingly common in today’s vehicles, such as adaptive cruise control, lane keeping assistance and emergency breaking.

• Level 3 represents conditional automation, in which the human must retake control when the vehicle determines it is no longer capable of driving.

• Level 4 represents conditional automation in which the vehicle handles all driving functions within its operational design domain (more on this below).

• Level 5 represents a fully automated vehicle. In terms of planning and designing for AVs, it is time to shift the focus to level 4 automation. Vehicles at levels 1 through 3 will have a minimal effect on transportation infrastructure design, and level 5 simply represents the most technologically advanced version of level 4. Preparing for this level of automation first requires a solid understanding of the operational design domain.

Bobby Cottam

Finding the Use Case: What Is the Operational Domain?

The operational domain is the area in which level 4 AVs can safely operate. Vehicles that fit into level 4 of the SAE International scale will be able to provide completely autonomous functionality, but only in specific circumstances. How we define these circumstances, or the operational domain for the vehicle, will determine how and where a vehicle can operate — and there are many ways to define that domain.

A domain could be defined geographically, such as a warehouse, a specific route, or an entire city. It could also be defined by roadway features, such as a dedicated lane or set of streets with highly precise GPS, well-defined lane markings and high-reflectivity signs. It could even be defined environmentally, allowing the vehicle to operate only during the day or not in snow.

Level 4 automation is not incredibly difficult to achieve if the domain is strictly controlled. However, the trade-off is that the more strictly defined the domain, the less useful and applicable the vehicle becomes. Technically, level 4 AVs are already in use, such as farm tractors that follow a lead vehicle, automated guided vehicles in warehouses with specific paths, and airport people movers that operate on tracks in a separated right-of-way. These types of vehicles and their functions are not useful in all situations, but as their domains grow so does the complexity that the vehicles must be able to handle.

The Connecticut Department of Transportation recently received three level 4 autonomous buses; however, the operational domain of these buses is not entirely clear. Will they only be autonomous during docking — pulling up to the curb to pick up passengers — or will their operational domain include certain road segments or even entire routes? Since buses operate on particular routes and make predetermined stops to pick up and drop off passengers, they operate in a much smaller domain than most vehicles that operate in traffic. A private vehicle operating in the same city would require a domain that covers an entire region and many different routes for a wide variety of trips. With a dedicated and trained operator — such as in a bus — the vehicle could have a narrow domain in which to operate at level 4, but could operate at level 2 or 3 everywhere else. To achieve more rapid deployment, some manufacturers have discussed designing domains to avoid certain complex situations. For example, if the domain were the entire city of Boston, the AV would likely encounter roundabouts and large traffic circles. If those roadway features are too complex for the AVs, the domain could be defined to avoid those features in Boston and provide alternative routes.

Preparing for Deployment: How Is the Domain Facilitated?

By determining where AVs can be most useful, and then defining domains accordingly, it is possible to deploy these vehicles in certain areas sooner and also inform how they will function in the future. At the present state of AV technology, significant infrastructure improvements will be needed to achieve broad level 4 deployments. These improvements may include physical separation, operating speed requirements, high-definition maps of the area, precise GPS, improved signing and striping, or any number of other site-specific or manufacturer-specific provisions.

Robust collaboration among vehicle manufacturers and transportation planners will be needed to design effective domains that incorporate the necessary technology and infrastructure

improvements to support AV technology. And these improvements will work in concert with the adoption of connected vehicle (CV) and electric vehicle (EV) technologies, which compound the safety and economic benefits as well.

As we consider the many factors that impact AVs, focusing on the domains at the intersection of usefulness and readiness will bring the most value, making it clear that private industry and publicsector agencies will need to work closely together to make AVs a reality for more drivers, more quickly.

NLP and EVs: A Notable Move Toward Automated Vehicles

Just 20 years ago, the idea of owning a smart car was nothing more than a dream. Today, artificial intelligence (AI) permeates every part of our lives. As AI and data sources mature every day at an exceptional rate, researchers are now focused on designing different models to select the most optimal solutions to a variety of problems under varying conditions. They are accomplishing this by developing machine learning (ML) algorithms. By combining ML, cloud computing, 5G technology, and vehicle automation into electric vehicles (EVs), the future of transportation is looking bright. Among the diverse applications of ML, natural language processing (NLP) is designed to solve higher skill problems that deal with sequence models such as audio or text. NLP in EVs, with the incorporation of the technologies mentioned above, represents a remarkable leap in EV automation.

ML can be incorporated into many different parts of EVs, from low-level internal energy management controllers, battery systems, and temperature analysis, to high-level systems dealing with cameras, radar, light detection, and ranging (LiDAR); and even to model a community of EV users and their interactions with the power grid.

Amin Zahedi

min

NLP is a branch of ML that deals with sequence models such as speech recognition, sentiment classification, word embedding, and machine translation. With NLP, several new features can be added to the vehicles. Passengers can talk with the vehicle AI engine enabled with speech recognition and even ask for a ride to a particular destination. If they are far from the vehicle, they can call and request the vehicle to come to pick them up, which could be done by text message or with a phone call through human-AI interactions. Moreover, users can request entertainment services such as displaying the weather, showing the local news, or playing requested music. NLP can also work in the tourism industry by adding features to sight-seeing buses and providing informative videos and audios or the operating hours of various sites. With machine translation, tourists can also have a more comfortable trip and improved communication with other nationalities in real-time.

In addition to the cited utilities, NLP can offer technical services for drivers and passengers. In Hybrid Electric Vehicles (HEVs) that may face two or more forms of energy sources or storage, drivers can communicate with the core of the vehicle and command how to provide the requested power demand at each time step. For example, in an HEV with an engine and electric

motor, the driver can use EV mode for city driving, while switching to the HEV mode for highways or wherever higher power is needed.

NLP works based on Recurrent Neural Networks (RNN) that, in comparison with the naive architecture of neural networks, each cell receives the previous cell’s information in addition to its own input, as shown in Fig. 1. For text recognition, it can break down the text into sentences, assign each RNN cell to a word input, and use a vocabulary set or word dictionary for the word representation and the model training. An easy way to represent each word is to use a one-hot vector for each word, with the size of the vocabulary set represented with zeros, except the index of that specific word, which is represented by a one. With RNN, previous words’ parameters in a sentence influence the learning of the current word. For EVs, it is possible to provide a particular dictionary with words with a higher frequency of usage, which may hasten the learning process.

Gated Recurrent Unit (GNU) and Long Short Term Memory (LSTM) are two architectures that can add memory to the RNN. Even if many words are separating the two related words in a sentence, these two architectures allow the network to recognize them and aid with gradient vanishing.

In speech recognition, the input is audio that predicts and generates the output transcript. Audio can be seen as air pressure variation over time, but audio modeling can be considered the intensity of different frequencies over time, visible on a spectrogram. By computing spectrogram features, input features can be generated, passing it through an RNN to reach the transcript. Today, researchers in academia train their models with over 300 – 3,000 hours of data sets, with end-to-end deep learning networks, although this number can go up to 100,000 hours for commercial systems. Usually, a bidirectional LSTM or GRU network with more than one layer will be selected for the speech recognition architecture. One crucial feature of speech recognition is enabling the computer with a trigger word detection system which makes it do something in response to activation words. This can be seen with technology like Alexa for Amazon Echo, Okay Google for Google Home, or Hey Siri for Apple Siri. The strategy is similar to the speech recognition steps. It is enough to set the target label “y” to zero for each RNN cell output, corresponding to not detecting the trigger word. Right after receiving the trigger word, set the target label for that cell to one.

The color in the spectrogram shows the degree to which different frequencies are present (loud) in the audio at different points in time.

Green means a certain frequency is more active or more present in the audio clip (louder).

Blue squares denote less active frequencies.

The dimension of the output spectrogram depends upon the hyperparameters of the spectrogram software and the length of the input.

All the concepts mentioned can be distributed through a cloud-based system, and the vehicles or run independently in each vehicle. Several other abilities can be added to the vehicle with a cloud-based system, such as a third party can interact and control the vehicle remotely during an emergency situation by talking or commanding the vehicle. Passengers can also play virtual games with each other in a vehicle or play with other cars through the cloud. With 5G, these features can be all happening in real-time. All in all, NLP is one of the hot topics in ML, and there is no surprise to see that more gravely in the near future of EVs.

Fig.1 Basic RNN model

Frequency Time

Fig. 2 Spectrogram of an audio recording [1].

[1] – “Deep Learning Specialization” by Andrew Ng 28

Reference:

interview Sasan Tavakkol

Sasan Tavakkol, Software Engineer at Google AI, elaborates the rə platform and explains how spatial data analysis and AI could be employed for a more intelligent city and tomorrow’s mobility systems.

1. Hey, Sasan! Thanks for joining us today. Please tell us about your projects from 2018 to the present:

Let me introduce myself briefly, and then we will get to the questions. My name is Sasan Tavakkol, and I’m a software engineer at Google Research New York, or Google AI, as it is known externally. Before that, I was a Ph.D. student at the University of Southern California. I started with Google about two years ago. Let me tell you a bit about my projects. My first project at Google was an open-source project called Kartta Labs. Kartta Labs mission is to create a 4D map of the world. We want to have the 3D buildings, the 3D model of buildings, plus a time dimension — think of Google Earth with a time-slider. Such that you have the 3D world, but you also have the time dimension; you can go back in time in Kartta Labs and see Manhattan, say 100 years ago.

2. About the rə platform; the platform that you and your google AI team members initiated. Please tell us about that project:

So Kartta Labs, as I said, is an open-source project and an open beta project, which aims to create a 3D map of the world with a time dimension. We have different modules, different components that I’ll explain. One module — our major module — is Kartta Labs map services. So to have the 3D buildings, we need first to have the map. We want to know where that building is located, right? So we want to have a map server — think of, again, Google Maps — with a time-slider. So you could go back in time and see the map of the 1940s of, say, New York or Los Angeles. This is our first module. The second module is the 3D reconstruction part, where some machine learning algorithms would look at a photo and try to make sense of it and create and reconstruct that photo in 3D.

3. Could you please elaborate on the crowdsourcing platform of rə city?

In Kartta Labs, we relied on crowdsourcing and machine learning to achieve our goal. There are two parts; the maps and the 3D reconstruction. In the maps module, we relied on people; we relied on crowdsourcing to collect these maps, upload maps to georectify them, and vectorize them. By georectifying I mean that we have this historical map, somebody uploaded that map, but we need to know where that map is located. And we need a correspondence between every point on historical maps and contemporary maps. So we want to know if this point on this map corresponds to this specific point in Manhattan. That’s called rectification or geo-referencing.

We have some automated algorithms to roughly guess the map’s location to help our users get started. After you upload the map, our website will roughly guess the location of the map; it will say that “okay, this looks like downtown Manhattan.” And then, the user will start putting points on the historical map and the corresponding point on a contemporary map. Then, based on these, as we call them to control points, the historical map would be warped or will be georectified

such that each point of it corresponds to a point on contemporary maps. And the second step, we relied on our users to vectorize. This map vectorization means that we see that there’s a building here; we see the footprint of this building. But that’s an image, it’s not very useful, we cannot search it, we cannot provide that data to our users in a useful way. So we want to know exactly what are the boundaries of this footprint. And that’s called vectorization. So users will choose an image to

rectify, and then they will start tracing artifacts on the map, tracing the building, tracing the roads, the rivers, and that’s called vectorization. After this step, this data is stored in our backend map services and is available, open data, open-source.

4. Could you please elaborate on the temporal map server of rǝ city?

So that was all about the map platform. But a subtle but important difference between our map and the ordinary maps was this temporal aspect. When users trace a map or upload a map, they will say, “okay, this map is from the 1940s”. Or when they trace the footprint, they will try to add some time aspect; if that information is available on the map, they will say “this building was built in 1935, and it was standing in 1980” if that information is available. Of course, if it’s not available, they will take their best guess they will say, “okay, I see that there’s a building here in the 1940s. I don’t see the same building on a map from the 1970s. So that means that probably that building was torn down”. And I will put the end date for the 1970s. So that’s about the temporal aspect of our map.

5. Could you please elaborate on the 3D experience platform of rə city?

As I said, our primary goal was to provide a 3D experience, as we call it, a collaborative time travel experience for our users. We want to take our users back in time, and we want to help them see, say, their childhood neighborhood in 3D to achieve this goal. We had the 2D maps, so we know where those buildings are, we have the footprint of the buildings, and we needed a bit of extra information to create the 3D model for the buildings. If we didn’t have much information, we rely on the height of that building if that information was available. Or maybe the number of floors of the building. And then, we will just extrude the rough 3D geometry of the building. But if we had a photo of that building, a user — users as I said, they could upload historical photos to our system — they could say that “okay, this photo belongs to this building”, then our

deep learning algorithm would look at this photo and do a segmentation job on it to recognize; where are the windows, where are the doors, and we’ll find all those instances. And we will try to recreate — the algorithm will create a 3D model based on that information, and also the footprint of the building from the maps module. So we will have a rough geometry of the building based on the footprint and the artifacts on each facade, like the windows, the doors, etc., and then we will create the 3D building and put it on the map.

So Kartta Labs, as I said, our major goal is to create this for the map of the world. But the applications of Kartta Labs are endless. We want Kartta Labs to act as a platform where users can make any application on top of it. They could add their data or add another layer of information on top of our data. And that’s kind of our primary goal. And we are excited to see what people will come up with, what applications they will come up with this kind of historical data. One possible use case is studying land use and how it changes over time. Because as I said, we have this information, we have the maps of the different cities over time. So a researcher can put this together and study how land use has changed over time. And they can augment this data with data from elsewhere, like population data or transportation data. They can augment this data on our platform and then make a better conclusion about whatever they are interested in. Users can get creative with this kind of data. And we hope to see these kinds of applications. It can have a lot of applications for transportation, especially if users can augment transportation data on our system. And they can study over time how adding streets or maybe blocking the street has changed the transportation pattern. And that can definitely help researchers to make informed decisions about what they should do next.

Recreating Historical Streetscapes Using Deep Learning and Crowdsourcing: Image Credits: https:// ai.googleblog.com/2020/10/recreating-historicalstreetscapes.html

6. Could your platform be employed to forecast the land use and city development around NY and NJ to find the required transportation infrastructure? And What type of data should be employed?

One interesting pattern that I think researchers can get out of our historical map data is urbanization. I think if we study how cities are getting larger and suburbs are getting smaller, we can figure out how people — especially if you augment this information with the population information from US Census — have moved from suburbs and from rural areas, to the cities and to downtown. We can learn a lot from this pattern.

7. How important is it for transport authorities to forecast land use patterns and visualize these predictions to glean insights from them?

But something that pandemic showed us, urbanization didn’t really help when we have a pandemic. So the cities were overcrowded, and we saw how a disease like COVID-19 could be spread in the cities very quickly. But it also showed us something else

moving forward —and I see that it will be an option — a lot of tech companies already gave their employees this option to work remotely. This may cause another migration of population from urban areas to suburbs and it has already started, but this will probably be accelerated, when we have true autonomous cars when the mass of the population can have access to these kinds of autonomous cars. I can see that people — even those people that have to work in urban areas — may decide to work in suburbs and commute to their work every day. Because first, they are not driving. Second, they can be productive in their car, they can start checking their emails, maybe answering some phone calls. And I also expect that autonomous cars will hopefully reduce traffic jams and will help with congestion. If this is true, then we will be able to live in the suburb but work in downtown; I think by studying the pattern of urbanization, say on our platform re.city, we can get some insight about the reverse thing that is going to happen, and that’s that people are actually going to move back to the suburb.

8. For people in the civil engineering and transportation industry who like to follow your journey to become a Software Engineer, what steps you suggest:

This is a great question. I started my journey as a civil engineer; I got a Bachelor’s and a Master’s in Civil Engineering from Tehran Polytechnic. And then, I earned my Ph.D. in computational hydrodynamics, which falls somewhere between civil engineering and computer science. But today, I work for Google AI as a software engineer in research. I know that many people like to take this path, and I will be happy to discuss how I did it. I ended up doing a formal education in computer science. I got a Master’s from USC while I was working toward my Ph.D. But I will say that’s not required. The cool thing about the software engineering industry and the computer science industry is that you don’t need to have a degree in computer science to work. If you know what you are supposed to know, you will pass the interviews, and the companies will happily hire you. And you won’t really feel like you are different. The companies will be happy to hire you if you know what you’re supposed to know, even if you don’t have the degree. And that’s pretty good about CS and software engineering.

To give some pointers about how you want to do the switch, I will say the first thing that you need to think about is whether you should do it or not. Don’t just follow the crowd. Don’t just say that, “yeah, I want to be a software engineering because that’s like the hot thing today.” Make sure that you love this, make sure that this job is what you like — I love it. I know a lot of people who love it. But I also know a lot of people just don’t like sitting behind the computer and coding; it’s not for everyone. But if you like solving problems, if you like having a large impact, a scalable impact, this is a very cool industry. And there’s a lot of job opportunities in computer science. So if you have a love for solving math problems and serious coding problems, this is absolutely a good decision for you.

And how should you do the switch? Well, you have the internet today, and you can find anything on it. You can find all the online courses; you can find all the resources that you need to get prepared for an interview. Just go on YouTube and search “how to become a computer scientist” or “how to become a software engineer,” you’ll find a lot of resources. I’m not going to introduce specific resources, but I will say that there are a few things that you should focus on. You want to know the fundamentals, you want to know the algorithms, you want to know data structures. And don’t just skip them, don’t think that the cool thing is actually coding. The algorithm and data structures are what we use every day. And you cannot become a good software engineer without knowing about the basics. So algorithms, data structures, and you definitely need to know, coding. You need to know how to code in a language of your choice; language doesn’t really matter that much. Better to choose an object-oriented language because that’s also important for where you want to work and also what you want to do for the interviews. To count it again: data structure, algorithms, object-oriented programming, and a language of your choice.

It might be easy to get lost. But try just to focus on one aspect of software engineering, then move to the next one. Once you are ready, just prepare a resume and start applying to different companies. Because if you don’t have the educational background or job experience, you will have a hard time getting the interviews. It helps create some artifacts; it helps to have a GitHub account where you work on open source projects or contribute to other people’s open-source projects. Or maybe create your own thing. These artifacts will help you get the interviews, but to pass the interviews, you need to code. Usually, the interviews are primarily technical, and they will ask you tough programming questions, and you will usually code it on the spot. So that’s all about it. So thanks so much for having me.

CASE Vehicles’ Emergence

Sam Mahdavian

4 min 35

A few experimental automation product use cases include the following:

• low-speed urban first- or last-mile transit access,

• low-speed urban package delivery such as buses in protected busways,

• trucks on low-density rural motorways, whether individually or as platoon followers (including protected sites such as mines and ports),

• taxi services in retirement communities for low-density Sunbelt suburbs,

• the limited number of Tesla autopilot cases for conventional personal cars.

It should also be noted that Tesla is currently only at automation level 2. Tesla’s cars require the driver to be hands-off for no more than 30 seconds, and never have their eyes off the road; while level 5 is completely hands-off, and eyes off on any road, and in any weather and traffic conditions.

The most advanced level 2 car currently available is General Motor’s SuperCruise that was designed for limited access divided highways and works on 70,000 miles of road in the U.S., in good weather. There have been several advances in fleet operations in the past few years; and strong momentum is building behind driverless human transport by robo-taxi, and goods movement by robo-delivery and robo-truck. Goods movement autonomy could be categorized into four types: streets, controlled environments, resource roads, and highways. Regarding the automated street movement of goods, for business-to-business (B2B) parcel delivery, Waymo is working with UPS, while GATIK is working with Walmart and Loblaw. Einride is also

working with Oatly, Lidl, and Coca-Cola. Also, with regard to automated business-to-consumer (B2C) parcel delivery, Nuru is working with Fry’s Food, Kroger, and CVS.

There is a small market for controlled environments, mainly for industrial use and logistics yards, but big OEMs have not invested much in this area. Concerning resource roads (unpaved roads and remote areas), companies such as FPInnovations employ automated trucks for timber-hauling. Ultimately, when it comes), companies such as FPInnovations employ automated trucks for timber-hauling. Ultimately, when it comes to highways, there are several players in both platooning and solo driverless vehicles. In the case of platooning, active startups include Peloton, Locomation, Robotic Research, while active OEMs include Traton Group, Volvo, and Daimler. With regard to solo driverless vehicles, several companies (such as Utobon, Ike, Waymo, tusimple, Embark, Kodiak, Aurora, Ainride, pony. ai, plus.ai, Navistar, Tesla, Volvo, Traton, and Daimler) are working to enable both ramp-to-ramp and dock-todock driverless trips.

Although there have been several concerns related to this technology, user attitudes, and regulations of CAVs, software safety engineering, and software verification and validation (V&V) have been evolving significantly over the past decade [96]. However, there are still some issues that must be solved to encourage the smooth adoption of CASE. In addition, there is regulatory uncertainty regarding distributed decisionmaking. Finally – but most importantly – users who might buy CASE vehicles have mixed attitudes about them. The unresolved challenges for successful CASE implementation are discussed in the following sections: technological obstacles, user attitude problems, and regulatory challenges.

First, concerning unresolved technological obstacles, there are several factors for EVs, AVs, CVs and SAVs that currently require more attention. The high cost of battery technology, short battery life, the limited number of charging stations, and the long wait times for charging, are among the biggest concerns for fleet electrification that researchers must solve. Regarding AVs, most ADAS users are satisfied with the technology, and the main stakeholders must, therefore, better present this automated technology to gain consumers’ trust. Regarding CVs, specific determinants impact the effectiveness of these systems, including the number of DSRC-equipped cars, the cellular coverage quality, and the presence of additional ITS facilities. Currently, for SAVs, most citizens view car ownership and driving as symbols of freedom and prestige, and the user mindset must change from an asset-focused environment to a digitally focused and asset-less environment that supports mobility as a service (MaaS).

Second, there exists a gap between car owners, companies, and public parties, and their willingness to accept and pay for the new CASE technologies. Moreover, there are still trust issues and critical doubts about the safety and costs of these vehicles. Policymakers need to better identify potential policies that can increase the level of trust among users.

Third, concerning unresolved regulatory challenges, issues such as privacy and security, licensing, and insurance and liability are among the main factors that still need further investigation. All stakeholders in the regulatory environment – whether federal, state, or local – must work together and pass the required laws needed for the smooth implementation of CASE vehicles.

By considering the influences examined in this work and removing the barriers to the implementation and adoption of CASE vehicles, the government can then leverage the benefits of these vehicles. By so doing, the roads’ level of service will ultimately be enhanced.

Article source: https://ieeexplore.ieee.org/abstract/document/9343324

EVs in Norway

As you may know, climate change and the challenges that are related partially or completely to this matter attract persons to move toward advanced technology which assists the condition to be environmentally friendlier than before. In the transportation sector, giant steps have been taken to achieve this aim that we can consider the tendency to the Electrical Vehicles (EVs) as one of effective endeavor in this part. Basically, EVs now includes cars, transit buses, trucks of all sizes, and even big-rig tractor-trailers that are at least partially powered by electricity (1). Today, I want to seize this opportunity to talk more about the status of EVs and its spread in Norway as one of the premier countries in this field among not only Europe but also the world.

Almost all European cities have been suffering from air pollution resulting from road traffic, and Oslo is no exception. “Transport accounts for 50 % of greenhouse gas emissions in Oslo. It is also the main source of local air pollution in the city.” The Norwegian government has developed compulsive targets: “the Norwegian government and the EU have drawn up regulations aimed at reducing fossil CO2 emissions from road traffic.”

Moving from fossil-fueled to EVs as a significant perspective has attracted Norway’s attention to focus on in recent years. Although research on EVs commenced in Norway in the 1970s, usage has only begun this gotten going this century. Assistance in reducing local emissions,

5 min

improving air quality, and reducing noise in the city has been considered as the main consequences which can be resulted in by spreading the EVs’ usage among users in Norway. Moreover, a number of motivators, including exemption from road tolls and lower taxes have been used to attract users’ glances to this critical topic (2).

On behalf of the Ministry of Transport, the energy companies organized a resource group in 2009. It presented a continuous plan of action for the electrification of road transport, which assumed that it would be possible to reach a 10 percent share for EVs and plug-in hybrids in the passenger car fleet in 2020. Marianne Mølmen said that even with the target of 10 %, “we really didn’t know what is going to happen in the future.” She mentioned that the technology was still nascent. “At that time [2009] we only had small EVs that were produced in Norway. They were like plastic cars!”. Nevertheless, support came from more than just the government. “The Norwegian EV Association supported their members’ endeavors to get the most out of the vehicles, by compiling and making available information on charging facilities. New EV drivers through test drives were recruited. In addition, knowledge transfer on an internet user forum was facilitated by them.” (3)

Another important discussion is that a consensus about the benefits of EVs has existed among the political parties in Norway. Although there have been some differences across parties around what the incentives should be to get the users’ attention to the EVs’ topic, the government has supported politically this matter to achieve its target in this long-term perspective.

Furthermore, this positive view of the subject of EVs has been observed among citizens. However, there are some challenges that have not been solved properly that I would mention just some of them in this part:

Some Norwegian citizens are concerned with the fact that “the country’s vehicle-charging infrastructure had not maintained the same pace with the number of new electric cars on the road.” (4)

To consider EVs as a convincing option, the process of charging them at home must be possible for Norwegians. However, “fewer than five percent of

building owners or strata councils in Norway are even considering installing charging infrastructure”. (5)

Although the Manufacturers have kept up with the demand for electric cars, the production of electric vanes has not experienced the same trend. “Norway has not had anywhere near the same success with electric vans as it has had with cars, with sales in the low hundreds. However, this may have to do with the limited market availability of such vehicles.” (5)

On the other hand, a policy that is the combination of financial incentives, free parking/no toll road, access to bus lanes, and charging infrastructure can be considered as the incentives of EVs spread in Norway. Moreover, a special car taxing system in Norway that provides a suitable condition for the EVs users should not be ignored in this success. (6)

In sum, the future outlook for EVs in not only Norway but also all around the world is hopeful. The main factor that assures the success of EVs relates to the evergrowing volume of investment in the development of new automotive technology. Although Norway is a significant early market, it is far too small to be able to be profitable for the big automakers. Therefore, serious determination of other countries in investing in EVs can directly assist EVs’ success and spread in Norway. Undoubtedly, we should eagerly wait for hearing great news about this issue in near future!

References:

(1) https://earthjustice.org/features/electric-vehicles

(2) Case Study: The Electric Vehicle Capital of the World, 16 June 2014, C40 Cities

(3) Electricity for road vehicles in Norway, Institute of Transport Economics (TØI)

(4) Norway Is a Model for Encouraging Electric Car Sales, DAVID JOLLY, 16 October 2015, The New York Times

(5) Norway’s electric vehicle revolution: Lessons for British Columbia, Leigh Phillips, The Pacific Institute for Climate Solutions, 26 October 2015

(6) MalviK. H, Wensaas. G, Hannisdahl. O, the future is electric! The EV revolution in Norway – explanations and lessons learned

EVs in China

5 min

Today, there is no doubt that China is one of the leading players in the electric vehicle (EV) industry. Markets in both Europe and the United States hope to compete with China; while China remains in the first place, even after the COVID-19 pandemic. The pandemic has had severe effects on China’s EV market, including 81.2% and 48.9% drops in February and March of 2020. This was not the first year that the Chinese car market has experienced a decline. In fact, 2020 was actually the third year China’s car sales declined, after only 29 million sales in 2017. Despite these issues, China still has an excellent EV market due to the strong presence of more than 500 EV companies and startups in the country.

The future of the Chinese car industry will consist of new-energy vehicles (NEVs); including battery electric vehicles (BEVs), plug-in hybrid vehicles (PHEVs), and hydrogen-powered fuel cell electric vehicles (FCEVs). In spite of problems caused by COVID-19, there is also some good news. The Chinese car industry is getting healthier. Although support for NEVs is going down, and tax breaks for small-engine vehicles have been eliminated, these policies are still expected to help the market consolidate over time due to the presence of hundreds of national manufacturers. The market was in fact consolidating, even before the pandemic. Most of the new cars sold on the market are related to top brands, whose sales grew from 48% in 2016, to 54% in 2019. While Chinese manufacturers are consolidating, China’s car market is also dominated by foreign brands, such as Volkswagen and Toyota. While the top 8 Chinese companies had 79% of the market share in 2019; this percentage was 64% in 2016, and this trend is expected to accelerate.

Xi Jinping, the president of China, has announced that the country plans to cut China’s carbon dioxide emissions to nearly zero by 2060. They also plan to limit the sale of fossil-fuel cars by 2035. The government said that all new vehicles sold after 2035 should be ecofriendly NEVs. In this case, 50% of new vehicles should be either electric, plug-in hybrid, or fuel cell vehicles; and the other 50% should be at least conventional hybrids, which still use some gasoline. Therefore, it is not strange that Tesla, and other well-known companies, have tried

for so long to become major players in the Chinese market. In 2018, about 1.1 million EVs were sold in China, or 4.2% of all vehicles; and at the end of 2019, more than 3.5 million EVs were on the road in this country. More than 5 million low-speed electric vehicles (LSEVs, electric vehicles that cannot move faster than 70 km/hr.) were also on China’s roads at the time. EV sales in China then continued to grow in 2020, with more than 1.3 million EVs sold. This number was roughly 41% of global EV sales, and put China just behind Europe, with 42% of global EV sales in 2020.

Despite the growing number of EV sales, this industry has had to face multiple challenges. Charging infrastructure being one of the main problems. Charging has always been a concern for EV consumers, so the number of charging stations has been increasing in China in order to satisfy EV consumers. According to the Chinese Electric Vehicle Charging Infrastructure Promotion Agency (EVCIPA), the number of charging stations in China was approximately 808,000 in 2019 — a more than an 80% increase in one year. 330,000 of these were public chargers, and the other 480,000 were home chargers.

China’s EV market consists of several auto manufacturers, including Tesla, Volkswagen, BMW, BYD, NIO, Geely, Xpeng Motors, Li Auto, and others. Each of these competitors tries to produce improved vehicles, and enhance the quality of their products in order to obtain more customers. Furthermore, they offer a variety of models to appeal to consumers with different tastes. Tesla’s Model 3 is one of the best-selling EVs in China; but before this model entered China’s market, most of the available EVs were based on traditional platforms. They contained many disadvantages, such as high price, inconvenient charging system, and a low driving range. However, Tesla’s Model 3 changed the customers’ view of EVs, and caused Chinese EV manufacturers to enhance their products’ overall quality. In addition to Tesla, NIO currently offers three premium electric SUVs on the market; while Xpeng Motors produces premium EVs. They are the major rivals of Tesla’s Model Y SUV, and Model 3 sedan, and are available on China’s market today. The company sold more than 8,500 EVs in 2020

as well. Of China’s other major automobile companies, Li Auto also produces and sells hybrid EVs. This company’s SUV, the Li ONE, was one of the top-selling EV SUVs in China in September 2020. These three recent Chinese companies are expected to further challenge Tesla as global rivals in the near future. Other automakers to consider are BYD, which is another Chinese auto manufacturer that produces EVs. Last year, more than one-third of BYD sales (about 131,000 vehicles) were EVs. Automakers like Volkswagen, BMW, and Geely are also going to launch their new EV models. For instance, Volkswagen is expected to launch its ID.4, with a range of 550km, in the near future. The presence of these traditional manufacturers in the EV market is expected to raise market quality.

Despite all the challenges faced, China’s EV market has shown that it still has much room to grow. Today, 5% of the total auto market in China consists of EVs. Even if the government suddenly stops supporting the national Chinese EV industry, the strict zero-emission policies still persuade foreign EV manufacturers to invest in China’s EV market, allowing it to grow. A majority of experts believe that the future of electric vehicles in China depends heavily on intelligent mobility, especially autonomous vehicles. As most of the well-known car manufacturers are developing autonomous vehicle technology, its likely these cars will dominate the market in the future. As autonomous vehicles also use electricity, they may play a key role in the future expansion of EVs in general, and allow the EV industry to grow rapidly in the coming decades. In addition, China’s EV sales in 2021 are expected to reach over 1.9 million vehicles, in line with recent predictions.

References

[1] “Guide to Chinese Climate Policy”, Columbia University

[2] “China plans 2035 gas car ban that doesn’t actually ban gas cars”, Jameson Dow, electrek

[3] “Why China’s electric vehicle market is at full throttle”, Schroders

[4] “The Chinese electric vehicle industry is on a rebound after an almost year-long slump, but startups BYD and NIO are taking initiative to shield against market volatility”, Insider

EVs in the U.S.

Sooner or later, every country in the world will hopefully come to understand the benefits of developing the electric vehicle (EV) market, and the potential benefits; such as their effects on reducing greenhouse gas (GHG) emissions. The U.S. is no exception. Although EV market growth in the U.S. is slower than in China or Europe, it still has the third largest EV market in the world. Currently, transportation is the largest source of GHG emissions in the U.S., and 91% of transportation energy consumption is related to fossil fuels. Consequently, transportation GHG emissions are about 28% of the nation’s GHG. In this case, the government must play an essential role in developing EV infrastructure and raising the total number of EVs driven. This outcome is more likely now, because of the presence of Joe Biden in the White House, and also a Democratic leader in the U.S. Congress.

On the 15th of April, President Joe Biden stated that the United States must accelerate the production of electric vehicles to exceed that of China. In the last year, more than 1.3 million passenger EVs were sold in China, while this number was only 328,000 in the United States. Still, 2018 was a great year for the U.S. EV market. Tesla’s Model 3, a best-selling electric sedan on the U.S. market today, had just entered the market. It lead to high demand in the market due to its low price, and high range. However, everything changed in 2020, and the EV market became stagnant. The new U.S. administration knows that it has to face this problem. According to Biden’s recent statements, he plans to allocate $174 billion for manufacturing zero-emission cars and buses, and for building EV charging stations. In addition to the government, EV manufacturers also have plans for the future of the U.S. EV market. General Motors Company announced that they are going to be fully electric by 2035. Currently, they are following their electrified path with ambition; and they are using Environmental Defense Fund consultations to meet their goals. Ford, another active company in the U.S. EV market, is also producing an electronic version of iconic brands, such as the Mustang and F-150 truck. In addition, Volkswagen said that they will invest $35.8 billion in mobility. This company also announced that they will offer more than 70 different EVs by the end of 2028. Meanwhile, Tesla has a plan to build a new plant in Austin, Texas. Right now, electric trucks are another focus, particularly of EV startups. As an example, Rivian, an e-truck innovator company, has signed a contract with Amazon to provide them with Rivin’s all-electric delivery vans.

Today, people rely on gasoline cars much more than EVs. However, gasoline is no longer a reliable fuel source, and consumers’ attitudes will need to change as well to keep up. Although the expected improvements to rechargeable battery technology could allow consumers to one day charge their EV by roof solar panels, or a charging instrument in their garage; at the present, charging batteries for EVs remains the biggest concern for vehicle consumers, which may create doubts about purchasing an EV. Charging station infrastructure is one of the critical bottlenecks in the EV industry. If a government wants people to purchase and use EVs, it has to build enough charging stations to

6 min

alleviate the people’s concerns. While Tesla believes that providing its own superchargers for its customers could be helpful for sales, it doesn’t seem that future charging stations will be marked by special brands. Instead, charging stations are expected to be built through the cooperation of automakers and private firms, with the financial assistance of the government. There are more than 27,000 public charging stations in the U.S. today, which is not enough to support the government policy to increase EVs on U.S. roads. To overcome this charging station issue, the Biden administration plans to build 28,000 charging stations, containing 500,000 individual chargers; this would be five times more than what exists now. With the implementation of this program, more than half of the U.S. charging demand could be met by 2030.

The U.S. administration also understands that to encourage customers to use EVs, providing several credits and financial incentives could be very helpful. Therefore, different states have offered a variety of financial incentives, such as tax credits, rebates, and registration fee reductions. For example, Connecticut suggested reducing the biennial vehicle registration fee of $38 for EVs. As another example, Colorado implemented a $4,000 tax credit through 2021, on the purchase of light-duty EVs. Moreover, a $2.7 billion Environmental Mitigation Trust Fund was provided to reduce diesel emissions for all 50 states, by Volkswagen’s 2016 Clean Air Act civil settlement. Federal incentives are also available to aid in decreasing the number of diesel vehicles. The $7.500 federal tax credit for purchasing a new EV is one example of this type of aids. However, this aid has a limitation of 200,000 EVs for each company, and after that, it will expire; as it now expired for Tesla and General Motors because they sold more than 200,000 vehicles. Based on recent surveys, Americans are more

interested in trucks and SUVs than passenger cars. Since most of the available EVs today are passenger cars, offering more EV truck and SUV models is another main challenge for the government and private companies. To solve this problem, more companies are going to start to offer EV trucks and SUVs; and 2021 will be the first year in which EV vehicles from all three main categories (passenger, SUV, and truck) will be available on the U.S. market.

The future of the EV market in the U.S. is expected to be a bright one. 30 EV models from 21 brands are anticipated to become available in 2021. There will be 11 passenger cars, 13 SUVs, and 6 trucks on the U.S. EV market. The number of EVs on the roads is expected to rise from 1 million in 2018, to 18.7 million in 2030. The annual sales of EVs is predicted to be more than 3.5 million vehicles in 2030, which is more than 20% of all annual vehicle sales in 2030. All these numbers suggest that the U.S. EV market is growing rapidly, and it doesn’t show any signs of slowing down.

References

[1] “EV Turning Point: Momentum Builds for U.S. Electric Vehicle Transition”, John Paul MacDuffie and Sarah E. Light, Yale School of Environment, 2021

[2] “Biden: U.S. must boost EV production to surpass China”, David Shepardson, Reuters, 2021

[3] “EEI CELEBRATES 1 MILLION ELECTRIC VEHICLES ON U.S. ROADS”, EEI

[4] “U.S. Electric Vehicle Market Poised for Record Sales in 2021, According to Edmunds”, CISION PR Newswire

[5] “State Policies Promoting Hybrid and Electric Vehicles”, Kristy Hartman and Laura Shields, NCSL, 2021

[6] “Why China is so far ahead of the U.S. in electric vehicle production”, Erin Black, CNBC, 2021

Xpeng Motors

5 min

supercharging stations, offering exclusive access to vehicle owners. Xpeng has also established access to more than 200,000 charging stations across China, and it provides free installation of home-based chargers for buyers.

XPeng Motors is one of China’s leading smart electric vehicle (EV) companies. This company was founded by Xiaopeng He, the founder of UCWeb Inc. and a former Alibaba executive, in 2015. The co-founder of the company also are Henry Xia (Xia Heng) and He Tao, former senior executives at Guangzhou Automobile Group (GAC Group). The headquarters of Xpeng Motors Company is located in Guangzhou, China. It also contains three other main branches in Beijing, Shanghai, and Silicon Valley. The main goal of Xpeng Motors is to design, develop, manufacture and market smart EVs that are seamlessly integrated with advanced Internet, AI, and autonomous driving technologies. Currently, this company contains 3676 employees that 43% of them have focused on in-house research and development areas (R&D) since June 2020. In addition, to provide ing vehicles, Xpeng is also building out a network of

Xpeng Motors has been producing two EV models since 2018. The first Xpeng EV model was G3 SUV, which was introduced in November 2018. Its NEDC driving range is 580 km and its battery can be charged from 30% to 80% in 30 minutes. It also has the IP68 confirmation which indicates that it is a waterproof and dustproof vehicle. Besides, Xpeng G3 SUV achieved the highest C-NCAP safety score and it also became one of the top-three best-selling electric SUVs in China based on the HIS market data in 2019. The AI-empowered intuitive voice assistant and the Adaptive Cruise Control (ACC) function are the most popular features of G3 based on customers’ opinions. The Xpeng P7, a four-door electronic sedan, is Xpeng Motors another EV model that was introduced in April 2019. This vehicle NEDC range is 706 km which is the longest one in China, based on MIIT NEV catalogs in April 2020.

Fast charging and high battery performance are the other advantages of this vehicle. By charging P7 for 10 minutes, a 120 km driving range can be reached. This manufacture was also equipped with a new-generation permanent magnet synchronous motor with a power density of 2kW/kg, maximum power of 316kW, and a peak torque of 655N.m which allows P7 to respond faster to green traffic lights and overtake faster when there is a lane change. China is a hotbed of EV innovation, and Xpeng is keeping up the pace to compete. In January 2021, the company released an over-the-air upgrade of its vehicle operating system to P7 customers in China, adding 40 new functions and more than 200 optimized

features for the vehicle. One of the most important is the beta version of Xpeng’s navigation-guided pilot, as owners used the new system to drive more than 1 million kilometers by late February.

“We are very encouraged by the growing customer enthusiasm towards features that are more than what a conventional electric vehicle does. The data shows us, customers, in China are keen to experience a more leisurely style of driving, and see their vehicles more than just a transportation machine to bring them from A to B,” said He Xiaopeng. As it can be revealed from the recent facts, Xpeng focuses heavily on autonomous driving technology. In the founder’s opinion, the intelligence system should be as important as the power electric system which can be seen from the Xpilot, Xpeng’s autonomous driving system, technology. This system is developing at a high speed and Xpeng also has a plan to launch a series of advanced autonomous driving functions in the near future. Although Xpeng’s autonomous driving technology is not as strong as world-leading players like Tesla but compared with Tesla, NIO, and most of its competitors, Xpeng is more aggressive in the application of autonomous driving systems.

New EV companies have problems in vehicle quality control at their starting most of the time and Xpeng is not an exception. Some negative feedbacks about the vehicle’s durability were also received by Xpeng. The contract manufacturing model inevitably sacrifices Xpeng’s control over production details. Compared with Tesla, which has already owned independent product lines, Xpeng and other Chinese emerging electric vehicle companies are still at a disadvantage in competition. However, Xpeng is now in the transmission from contract manufacturer to self-production. According

to the company’s news, it has been manufacturing the latest model of P7 independently since May 2020 which is a great step for this company to follow its dreams to be one of the pioneer EV manufacturers in the world.

Xpeng started generating significant revenue in early 2019, but its rapid growth has been magnificent. In the third quarter of 2020, the company delivered 8,578 vehicles, more than triple the 2,345 it had delivered in the year-earlier period. That’s led to big financial gains. Revenue climbed 342% year over year in the third quarter of 2020, on a 376% jump in vehicle sales. The annual revenue of $524M, put Xpeng Motors next to NIO, Li Auto, and Kandi Technologies, the other growing Chinese competitors of Tesla in Chine’s EV market. The global market for electric vehicles is currently about $200 billion in size and is expected to grow to $800 billion by 2027. In this case, Xpeng has released electric sedans to much fanfare, and the capacity for EVs to replace tens of millions of internal combustion vehicles offers a long upslope for potential growth. It currently operates in china and has started delivering manufactures to Norway, however, it has not revealed any plans to enter the United States market yet. Although Xpeng Motors is not as large as some especial companies like Tesla and NIO, it has shown significant improvement during recent years which is telling that it can challenge these companies’ dominance in the domestic and also international market in the near future.

References

[1] XPENG, https://en.xiaopeng.com/

[2] “Tesla competitors growing in China: NIO, Xpeng, and more”, Scooter Doll, electrek,https://electrek.co/2021/02/16/tesla-competitorsgrowing-in-china-nio-xpeng-and-more/

[3] XPENG, Owler, https://www.owler.com/company/xiaopeng

Uber

Uber Technologies, Inc. is an American ridesharing company that provides services like food delivery, package delivery, couriers, freight transportation, electric bicycles, and motorized scooter rental. But ridesharing is probably the first thing that comes to mind when we hear somebody say “Uber.” The company is based in San Francisco and was founded 12 years ago in 2009. In their first official launch in 2011, the Uber app only allowed users to hail a luxury car, and it cost 1.5 times the price of a taxi ride. One year after that, Uber started a service in Chicago where users could request a regular taxi or an Uber driver via its mobile app. In 2019, after multiple improvements, Uber became a public company via an initial public offering (IPO). Unfortunately, Uber’s shares dropped 11% on the first day – a record in US history. This trend did not stop there and led to the company selling its Indian Uber Eats operations. Uber also started to lay off many employees and close multiple offices. In November of 2020, Uber announced that it had lost $5.8 billion.

After such a dramatic loss, Uber fought its way back to the market by expanding its service variability. For instance, it purchased Drizly, the Bostonbased alcohol delivery service. And, of course, it had to offer some free services to improve its image, like COVID-19 vaccine delivery, for those who live in underserved communities. Recently, Uber declared that its employees are expected to return to the office by September 13 and to work at least three days per week in person. We are now going further to explore the reasons for all these ups and downs. First, we have to define better what exact services they offer and what technologies they use. Second, we will assess the pros and cons of the company, in general, to understand Uber’s journey better.

Perhaps Uber owes its popularity to its user-friendly procedure. The process is simple. A rider opens the app and is matched with a driver; the driver picks the rider up and takes him/her to their destination, and they both leave ratings and reviews. Uber offers various additional services to customers, including:

6 min

UberX: affordable rides, all to yourself;

Uber Pool: shared rides, door to door or within a short walking distance;

Uber Comfort: newer cars with extra legroom;

Uber Black: premium rides in luxury cars;

Scooters: electric scooters to help you get around your city;

Uber WAV: rides in wheelchair-accessible vehicles. You probably won’t see such a variation in services from any other ridesharing company. There are also other options like Uber Green, Bikes, UberXL, and many others.

Uber Eats: Food delivery on demand! Uber Eats widens the number of drivers. This is because some vehicles are not qualified to drive people, but they are in the case of food.

Drive or Deliver With Uber: anyone can earn money by driving other people or delivering their food orders or other items.

Uber Health: providing members and patients with access to healthcare by offering them flexible, ridescheduling options.

Uber Transit: Uber is acquiring Routematch to support cities in providing more accessible public

transportation. Uber’s expertise in on-demand, global mobility technologies will be combined with Routematch’s proven capabilities across paratransit, payments, fixed-route tools, and trip planning services.

Uber for Business: offers a clear option for employee trip activities, automated billing, expensing, and reporting.

Uber Freight: an app that matches carriers with shippers.

Uber Self-Driving Cars: in 2015, Uber started to work on self-driving cars — mainly with Volvo — not as a developer, but for use in its fleet. Due to multiple issues, Uber paused its self-driving vehicle testing. In 2019, it changed its approach and collaborated with General Motors and Waymo. Recently, Uber ATG was acquired by Aurora, and Uber invested in Aurora instead.

Uber Self-Driving Trucks: Uber acquired Otto in 2016 to develop autonomous trucks. However, due to legal issues, the project was canceled in 2018.

Uber Elevate: collaborating with HeliFlight, Uber started to offer helicopter Taxis in New York. Then, in 2020, Joby Aviation acquired Uber Elevate.

Uber Works: an app connecting workers seeking temporary jobs. It was canceled in 2020.

Another interesting service provided by Uber is Uber Movement. It is likely to be a fantastic option for urban transportation planning. Currently, Uber gathers trip data in more than 10,000 cities across the world and gives planners access to their aggregated data. As a transportation engineer, I think this is the most exciting option for Uber. In addition, Uber recently declared that they are ready to deliver marijuana if allowed. This shows the flexibility of Uber to expand in the future. Finally, Uber is working on a new app to get people or things from one point to another as fast as possible. As CEO Dara Khosrowshahi said, “The way I think about the new Uber is this: if Amazon owns ‘next day, we want to own ‘next hour.’”

One could also say, Uber is popular because of the diversity of services it offers. Undoubtedly this is true, but there is also a particular inherent characteristic about Uber which makes it unique. It is the reason they changed their service from a luxury one to an affordable one in the early stages. Accessibility! This is Uber’s most advantageous quality. Another benefit of Uber is its flexibility for both the drivers and users. While Uber is convenient, this feature is less unique because

other services like Uber also provide convenience. However, from a psychological perspective, we often choose services based on cons rather than pros. So, it’s important to talk about Uber’s negative points.

Surge Pricing: a key annoyance for most customers. It is a pricing method that involves increasing or decreasing prices based on supply and demand.

Trip Cancellations: sometimes, drivers cancel the trip due to the unavailability of taxis or other reasons. This causes disruptions to the passengers’ plans.

Negative Impact on Driver’s Earning: drivers rely on surge charges to compensate for infrequent trips and low fares. However, due to price competition and the regular hiring of new drivers, Uber impacts drivers’ average earnings and morale. They need to work for longer hours to earn a sustainable income.

Adverse Impact on Traditional Taxis: due to its affordable services, Uber is providing tough competition to the traditional car and taxi services, resulting in the loss of customers for taxi drivers, impairing their ability to earn a regular income.

Inappropriate Policies Against Drivers: Uber does not classify its drivers as employees. But, due to myriads of lawsuits across the world, in many countries, Uber has been obliged to classify drivers as employees or workers. Moreover, in many countries, drivers do not get paid the minimum wage. But actually, this can be an advantage. A study in 2019 by Chen et al. showed that Uber drivers could earn more than minimum wage workers with less flexible work hours.

Safety: since most of the time, people put a great deal of trust in their drivers, it’s very damaging when Uber drivers commit crimes. This problem is not limited to Uber, but it is more noticeable because of Uber’s fame.

We know that Uber’s market segments are large enough to accommodate multiple rivals. While local taxi services have experienced a dramatic decrease in revenue, they always remain a traditional competitor to ridesharing services because they can negotiate costs with clients. Another indirect competitor would be public transit. As they are a different mode of transportation, their impact on ridesharing services is noticeably less, however. If we evaluate the market based specifically on other ridesharing services, many companies could be considered direct rivals:

Lyft: this company started business only several weeks before Uber, and it operates mainly in North America. It is Uber’s main competitor but operates at a much lower scale. For example, in 2018, Uber’s revenue was more than five times that of Lyft.

Curb: this is an old company with a long history in the paratransit industry. In 1992, they developed the Metrometer 21R, the first credit card taximeter. Since 2018, it has separated from Verifone and started to work in the ridesharing industry.

Since Uber is an international company, it also has its own worldwide competitors:

DidiChuxing: is a dangerous rival to Uber. Since it is based in China, they inherently have a larger market. Moreover, Baidu and Alibaba are their investors. Undoubtedly, it could prove troublesome for Uber, at least in Asia.

Grab: is a Singapore-based company, started in 2016. They are currently providing service in six countries. While not a main competitor, due to Singapore’s extreme economic improvement, it could become a serious rival in the future.

Ola: was founded in 2010 in India. It knows the large Indian market better than Uber and is currently investing in artificial intelligence-based technology. Without a doubt, it’s a serious competitor to Uber in India.