mobilityPLUS-Issue 2-March 2023

Dear Esteemed Reader,

We are delighted to present the second edition of MobilityPLUS magazine, focused on exploring advancements in transportation accessibility and equity.

S+M is a pioneering technology startup dedicated to providing state-of-the-art digital solutions and products in the mobility sector. Our talented team of experts in smart mobility, artificial intelligence, and software development collaborates to create decision-making applications, mobile apps, and other digital tools aimed at promoting accessible mobility for all, carbon neutrality, and zero accidents.

Additionally, S+M offers a collaborative platform for exchanging perspectives and insights on smart mobility with academic institutions, federal and local transportation agencies, and the broader industry. Our knowledge hub primarily aims to raise awareness and foster education in the realm of smart mobility. By partnering with subject matter experts, we are committed to delivering the most relevant transportation topics to you in an easily digestible format.

Our goal is to establish a central hub for academics, industry professionals, federal agencies, and transportation enthusiasts to exchange ideas and opinions on smart mobility. This will ultimately benefit transportation engineers, students, and fellow researchers by deepening their understanding of smart mobility and artificial intelligence.

In this edition, we will delve into topics related to smart and connected communities, as well as the transportation accessibility requirements of both urban and rural areas.

We look forward to spreading awareness about smart mobility and paving the road for the transition to a smarter mobility network.

On behalf of the S+M team,

S+M Founder | mobilityPLUS Editor

contents

Transportation & Socio-demography

4 min

Dr. Sam Mahdavian

There are numerous hypotheses about how food deserts emerged in the United States. One hypothesis has been linked to stores opening and closing (Guy et al., 2004). According to Miller et al. (2016), 291 grocery stores were expected to close in the U.S. in 2015. As grocery stores leave city cores, suburban retail food establishments are rising (Pine & Bennett, 2014). Another explanation for the development of inner-city food deserts relates to shifts in the population of bigger American cities between 1970 and 1988. Economic segregation is thought to have increased during this time as more affluent households moved out of inner cities and into the suburbs (Walker et al., 2010). Other significant factors include higher expenses involved with healthy food and residents’ constrained access to healthful meals in many metropolitan areas due to a supermarket shortage. Most importantly, living in a food desert can be much more detrimental for inhabitants without access to private transportation, and hence, without access to food outlets outside their nearby community (Lake and Townshend, 2006). Overall, areas identified as food deserts are not only defined by grocery access, but also by other socio-demographic factors (e.g., income, education, vehicle ownership, public transit connectivity, etc.). It was identified that regardless of the rural or urban area, the lower the income is, the higher the chances of falling into a food desert area. In addition, dense urban areas with higher minority occupants, distance from the store, and population growth also play an important role in defining food deserts (2012 USDA). Therefore, the primary criteria for food deserts

include Lower income, Percentage minority residents, Access to privately-owned vehicles, Lower education level, Access to public transport, Distance from the supermarket, and Number of abandoned buildings and low-income housing.

Significant transportation barriers to accessing healthy foods

The literature on food deserts primarily focuses on topics, such as ‘health,’ socio-demographics,’ and ‘access to grocery stores.’ There are very few articles that examine solely transportation-related problems and solutions. Therefore, SPLUSM has attempted to investigate those sources that consider transportationrelated barriers in a food desert.

Impact of Transportation Disparities on Sociodemographic Status

In the socio-demographic context, food disparity also reveals inequality in society’s access to food. Repurposing urban districts for high- and lowincome housing without enhancing access to public transportation or products and services has contributed significantly to the emergence of modern food deserts (Deener, 2017). In the past, zoning laws and land use restrictions have been adopted throughout the United States to preserve property values and lower crime rates. However, urban sprawl mainly caused by the Third Industrial Revolution resulted in zoning rules, shifting their focus from zoning to urban planning, separating incompatible land uses like industrial, commercial, residential, and recreational. As a result, conflicting land use grew inside residential zoning to divide high-, middle-, and low-income housing, which has continued to structurally disadvantage poorer populations by dividing the areas that they can afford to live in from the larger community (Serkin, 2020). As a result, food deserts and oases are becoming more prevalent, and violence is disproportionately distributed between and within communities as a result of this practice (Battin & Crowl, 2017; Brantingham, 2016; Jones & Pridemore, 2018).

The United States also differs significantly from other high-gross domestic income nations like Canada, New Zealand, Australia, and the United Kingdom regarding food supply and availability (Beaulac et al., 2009). In contrast to the United States, which frequently suffers food disparities based on class and race, other developed countries’ experiences with food-related inequality in underprivileged areas are rare and limited (Alkerwi et al., 2015). The percentage of black and Hispanic families living in poverty is more than double that of white households (Lynch, 2016; Matthew, 2018). Communities, primarily minorities, with more significant percentages of black Americans, tend to have higher concentrations of food deserts (Kane 2011). Due to the way that food insecurity exacerbates other types of structural disadvantages, the United States Centers for Disease Control and Prevention (CDC) refers to this phenomenon as “deprivation amplification.”

According to the 2021 USDA household food security report till 2020, 89.5% of households were food secured, whereas 10.5% (13.8 million) were insecure. 7.6% (2.9 million homes) of families with children were food insecure in 2020, increasing from 6.5% in 2019. On average, food-secure households in the same demographic group spent 18% more money than food-insecure households did. Approximately 55% of families responded that they participated in one of the three most extensive federal food support systems in the month before the survey. During the pandemic, from mid-November to mid-December (30 days), food insecurity was higher than the national average of 5.7%. It was 16.4% for households with a reference person who was unable to work and 20.4% for those who were unemployed (Coleman-Jensen et al., 2021).

Transportation is crucial to access food and is a significant factor for those residing in food deserts. Different studies have revealed transportation as an obstacle for residents to access food. Low-cost grocery stores were frequently located far from participants’ homes and were unreachable without a car or public transit (Vahabi and Damba 2013).

Facilities for public transit and personal vehicle

usage are critical factors for resource accessibility in urban areas. For residents of food deserts, access to services like supermarkets, open spaces, decent work, medical services, and quality education is restricted; however, these resources continue to be significant draws for wealthy newcomers who can afford private transportation to and from them (Acevedo-Garcia et al., 2020). In addition, the choice of low-income, racial minorities to buy unhealthful, calorie-dense foods are greatly influenced by how easily they can reach stores and groceries that sell affordable food options (Walker et al., 2011).

Thus, public transportation becomes increasingly crucial when inhabitants need to access grocery stores and other food sellers, especially in locations where grocery stores are limited (Hardman, 2016; Safe Routes to School National Partnership, 2017). A study on Supplemental Nutrition Assistance Program (SNAP) users found that experts thought lack of transportation to a farmer’s market was the biggest hurdle for lowincome consumers. Farmer markets are often found in wealthy neighborhoods, according to stakeholders, who are worried that low-income customers without access to reliable transportation may not even be capable of walking to the farmer’s market or conveniently transporting products home (Ritter 2019).

Rural Accessibility

4 min

Rural residents frequently express transportation anxiety. Rural inhabitants’ access to leisure, entertainment, and other activities has given the availability of an adequate transportation infrastructure increasing foster community engagement. Public and private transportation is essential for people with disabilities, low-income people, older people, and others who might not have consistent access to mobility (e.g., owning a car) or safely drive themselves to access goods (e.g., food) or services (e.g., healthcare), and other necessities, and interact with their communities.

Nashid Khadem, MSc

In rural areas, efficient and affordable transportation is a crucial factor in economic growth and ensures that residents can access services and fully participate in society. Rural residents rely more on privately owned, singledriver cars for transportation than their urban counterparts (Bonifas 2020). However, many people who live in rural areas cannot depend on this method of transportation. Some locals might not have driver’s licenses and owning and

In rural areas, efficient and affordable transportation is a crucial factor in economic growth and ensures that residents can access services and fully participate in society.

maintaining a personal vehicle might be prohibitively expensive. Furthermore, people who live in rural areas and have physical or mobility issues might not be able to drive.

Good transportation is essential to the movement of people and commodities in rural areas and for access to jobs, health care, educational opportunities, and social services. Many businesses depend on transportation, a key consideration when deciding where to establish or grow operations. Transportation is the vital link between visitors and attractions for communities that rely on tourism and natural resources. Public transportation is a form of transit that is regularly scheduled and accessible to the general public. However, there are many disparities in how often people use public transportation between urban and rural locations. For example, even though 20% of Americans live in rural areas, just 11% of federal transportation grant funding is given to these areas (Rural Health Information Hub 2022). In 2014, urban transit agencies accounted for slightly more than 98% of passenger journeys, whereas rural transit agencies delivered just 1.5%, according to the American Public Transportation Association’s Public Transportation Fact Book (Rural Health Information 2022). Nonprofits provided the remaining 0.5% of passenger journeys.

Some crucial aspects of rural transportation barriers’ background are described below:

Connectivity: The inability to considerably improve the country’s rural transportation system and providing good connections impedes potential future economic growth in many rural communities. Sixty-six American cities with 50,000 or more residents lack a direct link

to the Interstate Highway System (Bonifas 2020). For commercial sectors that depend on transportation, such as the expanding energy production and extraction industries, advanced manufacturing, and tourism, accessibility, and connectivity to rural transportation are essential. Numerous urban occupations rely on economic contributions from rural communities. More than 100,000 miles of rail lines have been abandoned in recent decades, especially in rural areas, which limits access to many rural villages and increases reliance on trucking for freight transportation (Bonifas 2020). According to research by the American Association of State Highway and Transportation Officials (AASHTO), connectivity in rural areas of Western states is especially bad, due to the considerable distances between interstate highway routes and the absence of effective rail service. Public transit only exists in 60% of rural counties nationally. Twenty-eight percent of these have minimal service (AASTHO 2010). People living in rural areas must frequently travel further to obtain healthcare, tap into educational opportunities, socialize, or go shopping. Living in places that may be further from emergency response services, such as police, fire, or medical aid, exposes rural residents to significant risks.

Access to Education and Employment: In remote areas, the ability to use transportation services to commute to work is crucial. A dependable mode of transportation to a place of employment is essential for many rural residents to maintain economic security. One study (Rural Health Information Hub 2022) indicated that 34% of all public transit trips, for small-town and rural residents, had worked as the primary objective. Longer travel times and a lack of transportation choices are frequent work impediments for rural residents. Due to long travel times and low population densities, transportation support services in rural areas may be more expensive per user than in metropolitan areas.

Increasing a community’s access to education enhances its livability and economic competitiveness.

There is a critical need for transportation to and from schools in remote areas to upgrade education access across all levels. According to the American Public Transportation Association (Firestine 2011), twelve percent of all public transportation trips are to and from schools. As a result, children in remote areas must travel long distances, over an extended period of time, to get to school.

Access to Health Services: The health and well-being of rural residents are impacted by their access to safe and dependable transportation. In rural areas where walking or cycling may not be practical for reaching a healthcare practitioner, transport is essential for access to healthcare services. In remote areas, the travel distance for medical or dental services is typically about 9 miles longer (Rural Health Information Hub 2022). When gas prices rise, lengthier trips can be costly for those who own a personal automobile. Longdistance travel can be extremely taxing for people without a private automobile. Those without access to a car frequently rely on public transportation, although not all locations are found on a route served by public transportation.

The inability to go to medical appointments, delays in receiving treatment, and the failure to take necessary drugs on time can all adversely impact disease management. Accessing dependable transportation can affect how people in remote communities choose their healthcare providers. Long-distance travel can harm their health even when they have access to transportation.

Access to Social Services and Other Community

Activities: Access to social services and consumer requirements may hinder rural residents from activities, such as running errands or shopping, due to a lack of transportation choices. Transportation may be necessary for more rural areas to ensure civic participation and other involvement in community life. Due to a lack of polling locations and transportation choices, voting in local, state, and federal elections

can be difficult for residents in remote communities. Indigenous people do not have enough access to polling sites (Bonifas 2020). Increased and moderate access to transportation programs can help children access education, health, childcare, libraries, afterschool, and sports. Likewise, senior citizens can benefit from health, social programming, and community participation. These services can also help veterans, indigenous people, the disabled, and low-income access their daily needs.

Transportation accessibility is crucial for maintaining and improving rural communities’ health, education and welfare worldwide. The government needs to consider investing more resources to improve rural

transportation. Responsible authorities need to build a partnership with existing rural transportation services to potential users and increase programs between states to improve the situation.

Accessibility Score Modeling

5 min

Food deserts are geographic areas where residents’ access to affordable and fresh food sources is restricted due to the absence of groceries within a convenient distance. According to USDA, food deserts are “low-income, urban areas where a significant number of people live farther than a mile away from the nearest grocery store” or “low-income, rural areas where people live more than 10 miles away from the nearest grocery store.” Based on the area measured and the distance definitions of a food desert, 11.5 million people, or 4.1 percent of the U.S. population, live in low-income areas more than 1 mile from a supermarket (Pleog et al. 2009).

Accessibility to healthy food options is a crucial factor in defining food deserts, yet it is challenging to quantify and measure accessibility in a way that reflects residents’ actual travel behavior/preferences (Smith et al., 2010). Traditional accessibility definitions are associated with location-based measures to quantify access from a given zone/neighborhood to opportunities or attractions in other zones while accounting for travel costs. Shimbel (Shimbel, 1951), for example, defined accessibility based on distance (or the number of links) from a given node to all other network nodes. Ingram (Ingram, 1971) also proposed a similar accessibility measure while accounting for the deterrent effect of travel costs. In another study, accessibility is measured as the total number of opportunities within a given travel time or distance (Vickerman, 1974).

Dr. Yalda Rahmati

The majority of food desert research is also built upon pure distance- and/or density-based accessibility measures. Kane et al. (2017), for example, analyzed accessibility patterns from individual dwelling units to different types of neighborhood businesses, including grocery stores, where accessibility is simply defined as the street network distance between a dwelling unit and the target business establishment. In another study, Steiniger et al. (2016) evaluated urban accessibility using the OpenStreetMap database where food accessibility was defined based on the distance to grocery stores, as well as the number of stores at the destination.

The major challenge posed by using pure location-based accessibility measures in food desert analysis is centered around the assumption that increasing food supply availability would improve residents’ access to healthy food. However, there is not enough empirical support for this assumption. Kolak et al. (2018) argued that while the total number of grocery stores in Chicago increased between 2007 and 2014, areas with low access to healthy food did not benefit and food desert patterns persisted. In another study, Cummins et al. (2014) investigated whether opening a new supermarket in a low-income neighborhood in Philadelphia had changed food accessibility. Their analysis indicated no clear relation between proximity to the new supermarket and people’s dietary habits. Sadler et al. (2013) and Lee (2012) also found similar results from their empirical analysis in Michigan and California, respectively. In a study of Detroit’s low-income neighborhoods, Ledoux and Vojnovic (2013) found a significant share of residents who do grocery shopping outside their neighborhoods, accepting higher temporal and financial costs. A major explanation of these results is the overstated role of distance to, and density of, food retailers in food desert analysis. Focusing on pure location-based accessibility measures can neglect some major factors that affect healthy food access and people’s real consumption choices.

Over the past decades, research on food accessibility measurement has evolved significantly. Beyond pure spatial components, recent studies have emphasized the importance of complex social and behavioral factors in people’s access to healthy food (Widener et al., 2017; Chen et al., 2013). A review of more recent studies indicates that the socioeconomic status of residents, the availability of transportation options, and the characteristics of food retailers themselves can significantly influence food provisioning patterns.

Investigating food deserts in southwestern Ontario, Canada, Sadler et al. (2011) argued how incomplete methods of measuring food access patterns can lead to mischaracterizing food deserts. Rather than computing distance to the nearest grocery store, authors categorized food retailers into grocery stores, fast food, fruit and vegetable sources, grocery stores plus fruit and vegetable sources, and variety stores. Stores were also examined in terms of price buckets. Food accessibility was then measured based on the network distance to the first, second, and third nearest food retailer of a given category. A socioeconomic distress index was also defined based on low educational attainment, unemployment rate, single-parent families, and then the incidence of low income, and the association between residents’ socioeconomic status and food access was further investigated.

Another view in characterizing food deserts takes into account the travel behavior of residents (Moniruzzaman et al., 2015, Gundlach et al., 2016) and the number of commuters between two nodes (Piovani et al., 2018). Moniruzzaman et al. (Moniruzzaman et al., 2015), for instance, reviewed accessibility from a different perspective and incorporated individual differences in travel behavior into their accessibility computation. They developed a multilevel linear regression to examine the relationship between trip distance and the socio-demographic attributes of residents and then calculated an accessibility index based on their travel behavior. In a more recent study that adopted a similar perspective based on travel behavior differences, Van der Veen et al. (2020) first defined a cumulative accessibility score based on the number of opportunities that can be reached within a predefined amount of time or travel cost. Then residents were categorized into different groups based on their location, income, mode availability, and other socioeconomic features, and a potential mobility indicator (PMI) is computed based on group-specific travel times. The cumulative accessibility score and PMI were then used to identify regions with a higher need for improved healthy food accessibility.

Emphasizing the impact of public transportation on food accessibility, Aman and Smith-Colin (2020) introduced the “transit deserts” concept by applying the idea of food deserts to transit equity. The authors defined transit deserts as regions with high transit dependency (high demand) and low transit supply and indicated the relationship between transit deserts and food accessibility. A comprehensive set of spatial and temporal measures, including network connectivity, connectivity to destinations, service frequency, flexibility, and time efficiency, were utilized to compute a score that reflects accessibility to transit for a given region, which can then be used to identify and improve access to services (e.g., healthy food supplies) in transit-dependent areas. In a similar study, Widener et al. (2017) investigated temporal changes in food accessibility over a 24-h period, incorporating the impact of available transportation options (i.e., automobiles, walking, and public transit). Results indicated a significant decrease in access to grocery stores for large parts of the city during the late night and early morning when higher and more variable public transit travel times were also reported. These findings highlight the impact of public transit on residents’ access to food supplies.

In another study, Gundlach et al. (2013) proposed a new approach to identify food deserts based on the characteristics of both food retailers as well as the public transportation system. They defined a scoring system to analyze food accessibility which had two components: a supermarket score based on the price, availability, and quality of healthy food, and a bus score that takes into account the number of routes, frequency, and the number of food deserts served. The authors indicated how accounting for public transit in measuring accessibility can redefine food desert borders. In addition, by examining the food selection and price in each food store, as well as the number and frequency of the transit system that services these stores, authors were able to define an accessibility score that can more realistically characterize food deserts.

The Tip of the Iceberg: Health Impacts of COVID-19 through Changes in Transportation

4 min

The COVID-19 outbreak was a health crisis that caused an unprecedented number of deaths. But the impacts of the pandemic go beyond killing people, where some indirect health impacts, namely, mental health issues, substance abuse disorders, and limited access to primary health care, were observed. This urged international, federal, and local agencies to develop pandemic preparedness and response. Transportation agencies are no exception and should identify the COVID-19 health implications through the changes in transportation systems to support planning, decision-making, and innovation as we move from crisis to recovery.

Evidently, the transportation system, particularly public transit, and shared mobility, is responsible for a portion of the COVID-19 outbreak. But where can COVID-19 impact transportation, and how will these impacts be translated into public health implications? Figure 1 illustrates six (interconnected) areas through which transportation can be impacted by COVID-19. These impacts can be further linked to the public health consequences through ten pathways.

Mobility demand: COVID-19 has changed the way we live. A survey study showed 50% increase in working from home after the COVID-19 outbreak among employed adults; of those respondents, 53% are willing to continue working from home after the pandemic is over. Data shows the Quarterly share of e-commerce sales of total U.S. retail sales experienced a 4% increase after the COVID-19 pandemic. This potential reduction

in work and shopping trips can be translated into fewer motor vehicles on the roads. It was found that the total vehicle miles traveled (VMT) at the county and state level had declined by 61% to 90% following the various government stay-at-home orders. Consequently, a reduction in air pollutants, contaminations, noise, heat, and motor vehicle crashes is expected–.

Modal change: Many commuters stopped riding New York City’s subway system during the early days of the COVID-19 outbreak. Although mostly ventured back after a few months, an analysis of ridership trends by the American Public Transportation Association shows a slow recovery in public transit usage. The modal shift from public transportation to private cars would result in more VMT in the system, and therefore, higher vehicle emissions, noise, and heat. A study on bike-share users in San Antonio, TX, showed that 43%

Figure 1. Health impacts of COVID-19 pandemic through the changes in transportation

of survey respondents who were unemployed due to the pandemic reported increasing use ridership. The decrease in active transportation would result in physical inactivity which was associated with obesity, cardiovascular disease, and dementia.

Transportation equity: The global pandemic exposed persistent inequity in communities. Low-income households often rely on public transportation to get to do their essential activities and access healthy food and health care. The costs of lost fares–due to an overall decrease in transit ridership–and sanitizing procedures portend financial challenges for transit agencies. To maintain the financial viability of the system, many transit agencies cut their services, usually in the form of reduced schedules. These challenged low-income communities’ access to reliable public transit, and if accessed, expose them to a higher risk of contracting CVOID-19 while using a packed transit system. The elderly and people with disabilities are other groups that faced difficulties in accessing their needs, mainly because of limitations in public transit and paratransit during the pandemic and the risk associated with riding with transportation network systems.

Land-use: The changes in transportation demand and modal shifts may encourage urban sprawl. There is evidence that urban sprawl increases total VMT and negatively influences accessibility in an urban area.

Traffic safety: The changes in travel demand and VMT during the pandemic affected traffic safety. Although the rate of motor vehicle crashes decreased during the global pandemic, the severity of crashes increased, according to the National Highway Traffic Safety Administration (NHTSA). A further investigation of NHTSA showed a 7.2% increase in road fatalities during the pandemic and blames riskier driving patterns and behavior–including speeding, failing to wear seat belts, and driving under the influence of drugs or alcohol.

Transportation jobs: Shifting from public transit and ride-hailing to driving private cars would result in losing driving jobs. The transportation sector saw a

peak unemployment rate of 15.7% in July 2020, 5.2 percentage higher than the overall U.S. unemployment rate at the time. Moreover, the reduction in road crashes and VMT would affect transportation service jobs. Job loss can lead to public health issues such as stress and mental health problems. We, however, expect some of these driving jobs can be replaced by delivery jobs.

The proper reaction to COVID-19 impacts and efficient planning for its permanent consequences is contingent upon a precise recognition and evaluation of the health consequences of the pandemic. The transportation sector is responsible for defining and supporting research to help better identify the health implications of the global pandemic through the changes in transportation and quantify the extent of impacts. The six areas of impact and the ten health pathways lay out a research agenda for future investigations.

To lessen the adverse health impacts of COVID-19 through the changes in transportation and reinforce the positive effects, both public and private transportation agencies need to intervene. Despite the limitations in transportation sector funding sources–because of the reduction in “user fees” such as fuel tax revenue, vehicle registration fees, and toll and congestion pricing–federal agencies need to support public transit to maintain their services in low-income communities. Advancing innovations and technology, e.g., self-driving cars and shared mobility, can help overcome mobility barriers for individuals with a disability.

Transportation plays a vital role in the e-commerce supply chain. Employing emerging transportation technologies–such as self-driving delivery vehicles, drone delivery, and High-speed rail developments–by the private transportation sector and facilitating the use of these new technologies by public agencies would contribute to efficient e-commerce. Imposing traffic demand management policies (e.g., road pricing) and creating urban development boundaries are some strategies that can control urban sprawl. Last

but not least, as improving traffic safety has been a recurrent concern of transportation agencies, policies, and strategies are required to deal with the riskier road users’ driving behavior. Moreover, Transportation agencies need to dedicate themselves to advancing the lifesaving potential of new vehicle technologies.

Bike Sharing

You may have already seen or heard about bike-sharing programs that have been launched in different countries; but to elaborate, bike-sharing relies on a system of self-service bike stations. Users usually check out a bike using a membership or credit/debit card. They can then ride to their destination and park their bike in a nearby docking station. Bike share bikes are comfortable, have integrated locks and cargo baskets, and usually include gearing, fenders, and lights that make urban biking safe and enjoyable. Many of them are accessed by a mobile app, so you can usually find a bike nearby from wherever you are at the time (1).

Bike-sharing can encourage new demographics – those who wouldn’t normally ride a bike – to start using bikes for transportation. According to the National Association of City Transportation Officials (NACTO), bike-sharing programs increase the visibility of cyclists, making riding safer for everyone. Studies also show that more people riding bikes in urban areas leads to improved bicycling and walking infrastructure.

MohammadMahdi Miraj, MSc

Another Washington D.C. study suggested that bike-sharing programs have a positive economic impact on commercial areas. Based on these findings, bike share stations attract more customers to nearby businesses, and bike share users were more likely to spend money within four blocks of a bike share station. In more congested areas, like downtown centers, bike-share users spent

Bike-sharing can encourage new demographics – those who wouldn’t normally ride a bike – to start using bikes for transportation.

considerably less time finding parking, and more time patronizing nearby businesses (1).

There are several factors that can be effective in evaluating the success or failure of a bike-sharing scheme;

The first one relates to the geographical condition in which they are supposed to be applied. In order to find suitable sites, it is necessary to evaluate the topography, typical temperature and weather of the candidate city. For instance, the hills cannot be a suitable and popular site to apply bike sharing scheme, and implementing this program can result in gathering a huge number of returned bikes in low-lying areas.

The second factor relates to the compatibility of the scheme with the existing transportation system of that city. In other words, the capability of a city in providing better infrastructures such as providing safe routes for cycling everywhere or increasing cycling access for the users will directly affect the success chance of this scheme in order to provide a real alternative to other carbon-intensive modes. (4)

The number of shared bicycle platforms has doubled in the past four years. Mobility giants like Lyft and Uber both acquired bike-share companies, and dockless and dock-based cycles are now commonplace in major cities (2). Let’s take a look at the top five bikeshare providers:

1) Mobike

Mobike is the largest shared bicycle operator by the number of bikes, making Shanghai the world’s largest bike-sharing city. Like most bike-sharing platforms, their bikes are activated by downloading a QR code via the company’s app. In 2017, the company began selling bicycles in Singapore, which was the company’s first international initiative. This Chinese company has since

expanded to 15 countries, finding a global foothold outside of China’s saturated bike-share market. (2).

2) Lime

Lime, formerly known as Lime Bike, operates a bicycle and scooter sharing platform in more than 40 United States cities, and four cities in Europe. In October 2017, Lime had 150,000 users. It costs $1 for a 30-minute ride on a traditional Lime bike, while the electric alternatives cost $1 to unlock plus 15 cents a minute to use. Following a $335m funding round in 2018, Lime became a Unicorn company, with a valuation of more than $1bn. Since its incorporation, the platform has raised a total of $467m in funding. Aside from electric and standard bicycles, Lime has also rolled out pay-peruse scooters, which could eventually become more popular than bikes (at least in the US) (2).

3) JUMP Bikes

JUMP Bikes provides users with traditional and electric bikes that are unlocked via an app. The platform presents 15,000 non-electric bicycles in 40 cities, while its pedal helps electric bikes currently operate in eight US locations. In the summer of 2018, JUMP was bought by Uber in an acquisition that demonstrated just how important bike-sharing has become. Users can now rent JUMP bikes directly through the Uber app, filling the transportation first and last-mile gap. JUMP has avoided many of the issues faced by competitors,

by developing dock-based units that don’t leave abandoned bicycles clogging up city streets — however, this has limited accessibility (2).

4) Motivate

The US-focused company Motivate is owned by Lyft, arguably Uber’s main rival in the mobility sphere. It is ‘North America’s bike share leader,’ with two million members as of 2017, and an annual total of 26.5 million trips. Motivate currently operates in nine states across the US, managing a variety of different systems such as Citi Bike, which runs in Manhattan, Brooklyn, Queens, and Jersey City. Although some bike-share initiatives have been held back by friction with city authorities, Motivate has formed various beneficial partnerships with local governments and organizations. Building these relationships will help companies like Motivate to establish themselves ahead of competitors (2).

5) Ofo

Ofo claims to be the world’s first and biggest stationfree (dockless) bike share platform and app. By 2017, Ofo had deployed more than 10 million bicycles in 20 countries, with more than 62 million users. This year, Ofo has worked hard to maintain its existing services, ending operations in Australia, Austria, Germany, India, and more. In addition, the company only ordered 80,000 bikes in 2018 instead of the expected five million units. Ofo now plans to focus on select locations,

The Global Bike-Sharing Service Market was valued at about $ 3.7 billion in 2020 and is expected to reach $ 21.3 billion by 2030

including London and several unspecified cities in the United States. According to an Ofo spokesperson, the company’s withdrawal is not representative of the wider market (2).

The bike-sharing point with rental bicycles parked at docking stations and payment terminals drawn with contour lines on white background. Urban transport. Vector illustration in modern linear style Image ID: 117297067 Media Type: Vector Copyright: goodstudio

Bike share platforms are clearly becoming more and more popular, and efforts are being made to remove barriers that limit their adoption. The support of mobility leaders and continued investment is helping bike-share businesses to improve their services, partner with other organizations, and extend their influence. The surge of interest in shared bikes makes sense, given the ongoing first and last-mile transportation dilemma. An autonomous, electric vehicle can only get you so far, which is precisely why Uber and Lyft have bought their own cycle solutions. Despite worries over the attitudes of city authorities towards bikesharing, companies like Motivate have established strong partnerships with local governments and businesses to increase users. Even well-known automakers like Ford have invested in their own bike-share service. Ofo may be scaling back, but its competitors are kicking it up a gear (2).

The Global Bike-Sharing Service Market was valued at about $ 3.7 billion in 2020 and is expected to reach $ 21.3 billion by 2030, with an estimated annual growth rate of 10.4%. Furthermore, the European Union (EU) encourages the use of such facilities because they are environmentally friendly, and help to minimize traffic congestion. Therefore, with a growing number of people that are looking for costeffective regular commuting alternatives, bike-sharing programs are expected to continue to grow in popularity (3), which can cause improvement the access and quality of life for everyone all over the world.

References:

(1) commuteoptions.org

(2) foundry4.com

(3) globenewswire.com

(4) bbc.com/futureplanet

Gentrification

4 min

Gentrification is the process of changing the character of a neighborhood through the influx of more affluent residents and businesses. It is a common and controversial topic in urban politics and planning. Gentrification often increases the economic value of a neighborhood, but the resulting demographic displacement may itself become a major social issue. Gentrification often sees a shift in a neighborhood’s racial or ethnic composition and average household income as housing and businesses become more expensive and resources that had not been previously accessible are extended and improved. The gentrification process is typically the result of increasing attraction to an area by people with higher incomes spilling over from neighboring cities, towns, or neighborhoods.

Dr. Samira Ahangari

Gentrification is an old phenomenon in the United States; however, from the year 2000 onwards, the rate of eligible tract gentrification in the 50 largest U.S. cities has more than doubled (20%) compared to the 1990 rate (8.6%) [1]. According to the National Community Reinvestment Coalition, more than 135,000 people were displaced between 2000 and 2012 [2]. Different reasons trigger gentrification in different areas. New transportation investments in neighborhoods are one of these reasons. Transportation investments in gentrified neighborhoods serve many purposes, such as increasing mobility and accessibility through the deployment of advanced transportation technologies, increasing public transportation ridership, reducing residents’ automobile dependence, and cutting commute distances to create livable, meaningful, opportunity-inducing, socially stimulating, and inclusive communities. New transportation infrastructure provides citizens with mobility options, while simultaneously influencing landuse changes and supporting or redirecting economic development. However,

these goals are sometimes in conflict when considering the far-reaching impact of transportation investment. Transit-oriented development (TOD) has been increasingly adopted worldwide, particularly around light-rail transport (LRT) and bus rapid transit (BRT), through various combinations and tactics. Transportation projects are widely promoted as effective methods for increasing the transportation system’s efficiency and sustainability performance, while simultaneously spurring local development and improving quality of life in otherwise declining areas. These new transportation improvements in low-income areas may increase residents’ access to options that could improve their economic prospects and are associated with improved labor market results [3,4]. However, increased accessibility may raise demand for property near these areas, putting tremendous pressure on housing values and rents, potentially leading to a disproportionate outflow of existing inhabitants who would benefit the most from transportation system infrastructure upgrades.

As transportation improvement encourages real estate investment, land values are likely to rise, thereby limiting low-income populations’ access to housing and their ability to maintain current residences. As a result, low-income families may be displaced by wealthier groups. Some scholars have delved deeper into the issue of equality and used prices as a proxy for gentrification consequences [5]. Researchers and policymakers have suggested in different studies that TOD interventions could lead to gentrification and the eventual relocation of low-income people [6-8].

The negative consequence of gentrification starts as transportation investment and interventions aim to increase ridership (for transit authorities and operators) and property tax revenue (for local governments) while also addressing the high costs associated with requirements, such as zoning and regulatory changes, coordination with transit agencies, public space design, and the provision of local amenities (place-making). Newly improved transportation facilities are intended to attract investments by private-led developments that must

be financed through housing construction aimed at upper-income residents [7]. In addition, these new, well-designed urban spaces attract young professionals [8] for the newly added transit system and the associated attributes of this newly built and social environment. Some attractive characteristics include land use mix, amenities, lifestyle services, open space, and green areas [9]. Even if gentrifiers rarely utilize public transportation, innovative development, and new urbanism approaches can contribute to a modern and progressive image of the places that adopt them. Such approaches are regularly used as a strategy to recruit qualified workers and employment [10]. Thus, gentrification has been primarily viewed as a negative phenomenon, raising concerns about the possible relocation of low-income groups and a rise in or escalation of local disputes due to increased housing costs, rising food prices, and a loss of community identity [11]. Gentrification also leads to a loss of people’s geographical link to their employers. Displaced workers’ jobs may be jeopardized, and this problem may be worsened by the expected rise in transportation expenses associated with longer commute distances. In addition, it has been challenging to demonstrate displacement and the new home sites chosen by displaced people are mostly unknown [12]. People who have been displaced may accept more expensive, risky, or overcrowded accommodations. They may have negative psychological issues as a result of the prospect of relocation [13], the need to relocate to urban outskirts and become more car-dependent, a lack of access to services and facilities, or living in less health-supportive built environments [14].

As a result, newly constructed transportation infrastructure (e.g., new transit lines, intelligent transportation systems, etc.) may ultimately fail to give accessibility benefits to people who most require it. First, the transit-related displacement theory suggests that those who most need transit would lose access to stations, resulting in lower transit use among poorer demographics. Second, better-off households moving into TOD-served areas would have more cars than the now displaced prior inhabitants, especially if on-

and off-street parking is still available [9]. Third, although wealthier inhabitants frequently switch to non-car modes after moving to a TOD region, their presence would not compensate for income losses caused by the displacement of the poorest households, because they frequently prefer cycling to public transportation [10].

Research is needed to examine and define the specific role of transportation investment in contributing to the negative effects of gentrification and the investment decision-making processes that lead to them.

References

1. Maciag,Mike. (2015). Gentrification in America Report. Governing: The future of State and Localities. Retrieved: https://www.governing.com/archive/gentrification-in-cities-governing-report.html

2. Richardson, J., Mitchell, B., & Edlebi, J. (2020). Gentrification and disinvestment 2020. Retrieved from: https://ncrc. org/gentrification20/#:~:text=Nationally%2C%20out%20of%20the%2072%2C668,time%20period%20(Table%202)

3. Andersson, F., Haltiwanger, J. C., Kutzbach, M. J., Pollakowski, H. O., & Weinberg, D. H. (2017). Job Displacement and the Duration of Joblessness: The Role of Spatial Mismatch. The Review of Economics and Statistics, 100(2), 203–218.

4. Jin, J., & Paulsen, K. (2018). Does accessibility matter? Understanding the effect of job accessibility on labour market outcomes. Urban Studies, 55(1), 91-115.

5. Immergluck, D., & Balan, T. (2018). Sustainable for whom? Green urban development, environmental gentrification, and the Atlanta Beltline. Urban Geography, 39(4), 546–562.

6. Jones, C. E., & Ley, D. (2016). Transit-oriented development and gentrification along metro Vancouver’s lowincome SkyTrain corridor. The Canadian Geographer / Le Géographe canadien, 60(1), 9–22.

7. Cappellano, F., & Spisto, A. (2014). Transit oriented development & social equity: From mixed use to mixed framework. Advanced Engineering Forum, 11, 314–322.

8. Rayle, L. (2015). Investigating the connection between transit-oriented development and displacement: Four hypotheses. Housing Policy Debate, 25(3), 531–548.

9. Chatman, D. G. (2013). Does TOD need the T? Journal of the American Planning Association, 79(1), 17– 31

10. Danyluk, D., & Ley, D. (2007). Modalities of the new middle class ideology and behavior in the journey to work in gentrified neighborhoods in Canada. Urban Studies, 44(11), 2195–2210.

11. Clagett, M. T. (2015). If it’s not mixed-income, it won’t be transit-oriented: Ensuring our future developments are equitable & promote transit. Transportation, 41(1), 1–32.

12. Chapple, K., Loukaitou-sideris, A., Chatman, D., Waddell, P., & Ong, P. (2017). Developing a new methodology for analyzing potential displacement. Sacramento, CA: California Air Resources Board.

13. Twigge-Molecey, A. (2014). Exploring resident experiences of indirect displacement in a neighbourhood undergoing gentrification: The case of Saint-Henri in Montréal. Canadian Journal of Urban Research, 23(1), 1–22.

14. Cole, H. V. S., Garcia Lamarca, M., Connolly, J. J. T., & Anguelovski, I. (2017). Are green cities healthy and equitable? Unpacking the relationship between health, green space and gentrification. Journal of Epidemiology and Community Health, 71(11), 1118–1121.

Impact on Health Due to Transportation Disparities in A Food Desert

2 min

The existence of higher numbers of restaurants, convenience stores, and liquor stores increases the population’s risk of food insecurity (Freeman, 2015; Hilmers et al., 2012; Hipp, 2010). Families who are impoverished or struggling financially tend to have a poor diet and eat less nutrient-rich meals (Alkerwi et al., 2015; Basu et al., 2016; Heflin, 2017). It seems lower-class city dwellers consume meals high in processed sugar, carbohydrates, and fat, but low in micronutrients and lacking in fresh fruits and vegetables (Basu et al., 2016; Walker et al., 2011).

Gregory and Coleman-Jensen (2017) showed an association between food security and health outcomes in their USDA food insecurity report. Their data analysis shows lower food security and higher possibilities of chronic diseases like - hypertension, coronary heart disease (CHD), hepatitis, stroke, cancer, asthma, diabetes, arthritis, chronic obstructive pulmonary disease (COPD), and kidney disease. In some cases, chronic diseases can define food security better than income. For example, adults in very low food-secure households are 10.5% more likely than adults in high food-secure homes to be diagnosed with hypertension. Adults in very low food security households were 15.3% more likely to have any chronic illness than those with high food security. Adults in homes with marginal food security were 9% less likely to report excellent health than those in households with increased food security and 1.3% more likely to report poor health. The number of chronic conditions for adults in homes with low food security is, on average, 18% higher than those in high food secure households.

Owens et al. (2020) conducted a similar study on food deserts in Alabama and identified some common issues, such as a lack of grocery stores, transportation (un)availability and affordability, limited income, limited skills, tools, and space

for growing fresh food. They also focused on the impact of these problems on the obesity epidemic and chronic diseases. Poor diets are disruptive to cognitive functioning and reduce productivity, which has resulted in increased incidences of diabetes and cardiovascular disease. They suggested programs to combat this food desert issue: the farm-to-school program, Tuskegee University Urban Farm Program, Community Gardens, the Mobile Food Market, and the healthy food financing act. They also suggested redesigning transportation routes, expanding the accessibility of mobile food markets and nutritional food education, expanding food co-ops, establishing municipally owned grocery stores, expanding farmto-school programs, and increasing the number of community gardens.

Food deserts and health-related literature also emphasize childhood obesity and mental trauma due to a lack of nutritious foods. According to micro-level observations, childhood obesity is more common in underprivileged neighborhoods with an abundance of low-nutritional-value foods. This leads to bullying, which causes an increase in anger, depression, and violence (Issner et al., 2017; Walters, 2020). The study also showed that healthful meals vary among U.S. populations, particularly in socially and economically underprivileged neighborhoods. Race, ethnicity, social support, culture and language, access to care, and living environment contribute to cardiovascular diseases and type two diabetes (Ferdinand 2017).

Rodriquez and Maraj Grahame (2016) showed that access to the food supply, a component of the mesosystem, influences people’s dietary decisions. Participants remarked that they would like to consume fresh fruits and vegetables but frequently do not have the opportunity to do so, according to survey answers. It was challenging to get to a place where people could buy healthy food due to transportation shortages. Due to a lack of mobility, people were forced to eat only what was offered at nearby food sources, such as convenience stores. Convenience stores typically offered less nutrient-dense foods and carried few to no fresh goods.

References

Alkerwi, A. A., Crichton, G. E., & Hébert, J. R. (2015). Consumption of readymade meals and increased risk of obesity: findings from the Observation of Cardiovascular Risk Factors in Luxembourg (ORISCAV-LUX) study. British Journal of Nutrition, 113(2), 270-277.

Rodriguez, R. M., & Maraj Grahame, K. (2016). Understanding food access in a rural community: an ecological perspective. Food, Culture & Society, 19(1), 171-194.

Owens, A., Knox, L., LaGrone, S., Smith, S., Hawkins, M., Duncan, T. (2020). Food Desert in Alabama. Certified Public Manager Program - Solutions Alabama 2020. (https://www.aum.edu/continuingeducation/wp-content/uploads/ sites/9/2020/11/Food-Deserts-in-Alabama-1.pdf)

Gregory, C. A., & Coleman-Jensen, A. (2017). Food insecurity, chronic disease, and health among working-age adults (No. 1477-2017-3689).

Ferdinand, K. C., & Mahata, I. (2017). Food deserts: Limited healthy foods in the land of plenty. Circulation: Cardiovascular Quality and Outcomes, 10(9), e004131.

Issner, J. H., Mucka, L. E., & Barnett, D. (2017). Increasing positive health behaviors in adolescents with nutritional goals and exercise. Journal of Child and Family Studies, 26(2), 548-558.

Walters, G. D. (2020). Unraveling the bidirectional relationship between bullying victimization and perpetration: A test of mechanisms from opportunity and general strain theories. Youth Violence & Juvenile Justice, 18, 395-411.

Basu, S., Wimer, C., & Seligman, H. (2016). Moderation of the relation of county-level cost of living to nutrition by the supplemental nutrition assistance program. American Journal of Public Health, 106(11), 2064-2070.

Heflin, C. M. (2017). The role of social positioning in observed patterns of material hardship: New evidence from the 2008 survey of income and program participation. Social Problems, 64(4), 513-531.

Walker, R. E., Keane, C. R., & Burke, J. G. (2010). Disparities and access to healthy food in the United States: A review of food deserts literature. Health & place, 16(5), 876-884.

Alkerwi, A. A., Crichton, G. E., & Hébert, J. R. (2015). Consumption of readymade meals and increased risk of obesity: findings from the Observation of Cardiovascular Risk Factors in Luxembourg (ORISCAV-LUX) study. British Journal of Nutrition, 113(2), 270-277.

Freeman, A. (2015). Transparency for food consumers: Nutrition labeling and food oppression. American journal of law & medicine, 41(2-3), 315-330. Hilmers, A., Hilmers, D. C., & Dave, J. (2012). Neighborhood disparities in access to healthy foods and their effects on environmental justice. American journal of public health, 102(9), 1644-1654.

Kane, K., Hipp, J. R., & Kim, J. H. (2017). Analyzing accessibility using parcel data: is there still an access–space trade-off in long beach, California?. The Professional Geographer, 69(3), 486-503.

Impact of Transportation Disparities on Access to Healthy Foods

Social norms influence nutritional food consumption, as do individual-level characteristics, neighborhood location, and design features that limit availability and access. There are several reasons behind a food desert, but lack of transportation is one of the major ones (Lindsey et al., 2015; Freedman et al., 2016; Briggs et al., 2018).

USDA ERA (2009) found that distance and unavailability of transportation modes are important reasons driving a lack of access to nutritious food. Small groceries that are easily accessible charge higher prices than more distant supermarkets. Low-income communities tend to shop from supermarkets with lower prices than small grocery or corner stores. As a result, Supplemental Nutrition Assistance Program (SNAP) participants tend to buy canned food, vegetables, and milk from supermarkets. Reports have suggested the importance of subsidizing programs and restructuring zonal policies for supermarkets. Along with that, a public health campaign to educate people about building nutritious knowledge and utilizing food for community development can help inclusive growth.

Lower-income people spend a far greater percentage of their income on transportation than middle- or high-income households. A huge part of the country has been labeled “transit deserts.” Infrastructure and land use policy has often made car ownership a necessity, particularly in areas where the public transportation system has been neglected (USDOT 2022).

Conducting a survey, Chrisinger (2016) discovered that depending on their mode of transportation, thirty-two respondents altered their purchasing habits in Philadelphia, Pennsylvania. For instance, several participants reported making smaller purchases if they planned to take the city bus. Other shoppers also decided to buy wheeled luggage to transport goods from the grocery to work, saving transportation time. Other customers spread their weekly grocery store visits to avoid carrying as much each time. In worstcase circumstances, research participants had to rely on family members or neighbors to get them to the grocery shop. The study also found that in some cases, people had to employ a cab driver to take them to and from food stores, which negatively impacted their limited income. Accordingly, respondents had to adjust their buying patterns to take advantage of readily available transportation. This research also interviewed vehicle owners and revealed that petrol prices frequently restricted how far a customer would go to a grocery store.

Sohi et al. (2014) did a study in which residents of eight South Carolina counties with poor access to food had to travel three miles or more to go to their primary grocery shop. The total number of miles these folks spend shopping each week was higher (more than 10 miles) than that of their counterparts who lived in places with better food access. They also found that access to a supermarket was not always a determining factor in consumers’ food-buying habits. According to researchers, the availability of specific foods and their cost were significant factors for some people living in food desert settings. They suggested that people who live in places with limited access to healthy meals would benefit from better public

transit and closer food outlets since they would save time and money. The expense of longer commutes to supermarkets was aggravated, and this cost was frequently related to the amount of time and money needed to travel to a food outlet.

Leclair and Aksan (2014) conducted a study in Connecticut where both on-the-ground observations and computer-based GIS mapping were analyzed. Approximately 60,000 people live in areas only serviced by small stores offering a limited selection of affordable, nutritious foods. Moreover, the high economic cost of taking a bus to a supermarket might erase the actual cost savings, leading locals to opt for unhealthy but readily available goods that are close.

Gauging et al. (2013) conducted semi-structured, qualitative interviews with 16 older individuals to research healthier eating options. Because the resident did not have access to private transportation and was immobile due to a chronic sickness or disease, residents in this study were forced to purchase their meals from neighborhood corner stores. Chavis and Jones (2020) studied the Baltimore food desert and found that 38% of those surveyed did not own a car. The careless relied on various means to get to the store, including getting rides, walking, taking public transit, and hiring private vehicles, such as taxis or hacks. Transit was not a substitute for transportation by car in Baltimore; 44.5% of people who did not own a vehicle had taken transit to the store the previous month, but only 25% stated that it was their primary mode of transportation.

The study by McDermot et al. (2017) evaluated physical access to Women, Infants, and Children (WIC)

merchants based on rural and urban contexts, mode of transportation, and level of availability. Researchers discovered that many people in low-income neighborhoods resided closer to a WIC merchant than a supermarket. Therefore, when a farmer’s market or giant supermarket is not nearby, the WIC program can offer people access to nutritious food options. When there are few options for acquiring proper nutrition in a food desert, accessing food through a WIC merchant is a practical option.

In some cases, travel can be expensive, and some people might not shop at stores offering the best deals. Several studies, including Rose et al. (2009), have estimated that depending on the mode of transportation, total travel, and time costs between census tracts, with poor supermarket access, and those that have access to supermarkets range from over $5 to almost $60 per trip. This estimate was based on 2007 data from New Orleans. These expenses are not insignificant and may necessitate budgetary compromises that worsen food insecurity.

Fitzpatrick, Greenhalgh-Stanley, and Ver Ploeg (2015) suggested that a senior’s mobility constraints may more significantly influence food and material hardship than whether or not they reside in a food desert. For example, without a vehicle, elderly residents in food deserts are 12 percent more likely to lack access to food than elderly residents in food deserts who own cars. Therefore, the referred study suggested that policies should target older adults living in food deserts with limited transportation options. It was further suggested that offering meals to people who cannot go to a food source may increase their access to adequate nourishment.

Two studies worked on transportation modeling for food deserts using a mixed model and GIS. The first one is Abel and Faust (2017), who simulated how autonomous occupants would act and travel to and from a store/food source. They created a mixed agent-based and GIS FD model (by walking by choice, walking as a last resort, or driving to the store). They found the use of object-oriented programming for

simulating food deserts’ testing disruptors (such as new stores and extended hours of operation) and adjusting variables like one’s desire to walk a certain distance to reach a store. However, this study does not account for the public transport option. The second is a study by Abel and Faust (2018), which extended their previous research and added public transport as a variable. They looked at how modifications to the public transportation system, such as bus frequency, the quantity and location of bus stops, and operating hours, affect bus ridership and food access in the case study region. As a result, public transportation systems were identified to significantly reduce the number of individuals who must walk to the supermarket as a last resort, thereby increasing food accessibility by up to 23% in the case study region of Austin, Texas. This outcome highlights the significance of considering public transportation networks in food deserts models, as well as links between networks of the food system and the built environment.

In the context of food delivery systems in food deserts, one study used the 2014 farm bill, which included an Online Purchase Pilot (OPP) project that allowed SNAP providers to purchase online and get deliveries. More than 90% of SNAP households and urban food desert census tracts in the eight states that took part in the USDA’s OPP had access to online grocery shopping and delivery services. Still, similar benefits were infrequently available in rural food desert census tracts. Study findings imply that combining current supermarket delivery networks and online grocery shopping may increase access to groceries in many places where it is most deficient. However, SNAP does not pay for supermarket delivery fees, which may discourage online shopping (Brandt et al., 2019). Nordquist (2022) studied the food delivery system and found that more than 90% of Americans have access to one of the services - Walmart, Amazon, Instacart, and/or Uber Eats. However, delivery cost, broadband price, and the internet are some reasons why these delivery systems are underutilized. Khadem et al. (2022) studied six delivery methods. They discovered that delivery by truck is the most cost-effective option for delivering fresh items, while third-party delivery

ranks second. Shared-ride services and electric cargo bicycles are far more expensive than truck delivery. A driver’s hourly rate is a significant factor in determining operating costs for providing items late at night. At the same time, other variables associated with delivery time have less impact on total costs.

Although some studies reviewed the impact of the lack of suitable transportation options on the development of food deserts, there is a need for more detailed studies and tools that could help decisionmakers make more effective and efficient investment decisions in this area.

References

Freedman, D. A., Vaudrin, N., Schneider, C., Trapl, E., Ohri-Vachaspati, P., Taggart, M., ... & Flocke, S. (2016). Systematic review of factors influencing farmers’ market use overall and among low-income populations. Journal of the Academy of Nutrition and Dietetics, 116(7), 1136-1155. Freeman, A. (2015). Transparency for food consumers: Nutrition labeling and food oppression. American journal of law & medicine, 41(2-3), 315-330. Briggs S, Fisher A, Loot M, Miller S, Tessman N. (2018).Real food, real choice: connecting SNAP recipients with farmers markets. Retrieved from: http:// farmersmarketcoalition.org/wp-content/uploads/2013/10/Real-Food-RealChoice-FINAL.pdf

Chrisinger, B. (2016). A mixed-method assessment of a new supermarket in a food desert: contributions to everyday life and health. Journal of Urban Health, 93(3), 425-437.

Sohi, I., Bell, B. A., Liu, J., Battersby, S. E., & Liese, A. D. (2014). Differences in food environment perceptions and spatial attributes of food shopping between residents of low and high food access areas. Journal of Nutrition Education and Behavior, 46(4), 241-249. doi:10.1016/j.jneb.2013.12.006

LeClair, M. S., & Aksan, A. M. (2014). Redefining the food desert: Combining GIS with direct observation to measure food access. Agriculture and Human Values, 31(4), 537-547.

Chavis, C., Jones, A., & Center, E. (2020). Understanding Access to Grocery Stores in Food Deserts in Baltimore City.

McDermot, D., Igoe, B., & Stahre, M. (2017). Assessment of healthy food availability in Washington State—Questioning the food desert paradigm. Journal of Nutrition Education and Behavior, 49(2), 130-136.

Rose, D., Bodor, J. N., Swalm, C. M., Rice, J. C., Farley, T. A., & Hutchinson, P. L. (2009). Deserts in New Orleans? Illustrations of urban food access and implications for policy. Ann Arbor, MI: University of Michigan National Poverty Center/USDA Economic Research Service Research.

Fitzpatrick, K., Greenhalgh-Stanley, N., & Ver Ploeg, M. (2016). The impact of food deserts on food insufficiency and SNAP participation among the elderly. American Journal of Agricultural Economics, 98(1), 19-40.

Abel, K. C., & Faust, K. M. (2017). MODELING FOOD DESERT DISRUPTORS: AN OBJECT ORIENTED PROGRAMMING APPROACH. In Presented at Canadian Society for Civil Engineering (CSCE) annual conference and general meeting.

Abel, K. C., & Faust, K. M. (2018). Modeling food desert disruptors: Impact of public transit systems on food access. In Construction Research Congress 2018 (pp. 362-372).

Brandt, E. J., Silvestri, D. M., Mande, J. R., Holland, M. L., & Ross, J. S. (2019). Availability of grocery delivery to food deserts in states participating in the online purchase pilot. JAMA Network Open, 2(12), e1916444-e1916444 Nordquist, C. (2022, May 18). New study maps digital access to food across the U.S., how delivery apps can help in food deserts. Khadem, N. K., Shin, H. S., Lee, Y. J., Choi, Y., & Schonfeld, P. M. (2022). Optimized Options for Fresh Food Deliveries in Baltimore Food Deserts. International Journal of Urban Planning and Smart Cities (IJUPSC), 3(1), 1-18.

Digital Twins: A Way to Discover New Opportunities in Smart Cities

What is Digital Twin? And what components is it made of? How can Digital Twins pave our way in effectively responding to a city’s needs? Are Digital Twins being used effectively today? What should be done to advance them?

Hossein Naderi, MSc Dr. Alireza Shojaei

According to the world bank report, currently, more than half of the world’s population lives in cities. This trend is expected to continue by 2050 when nearly 70% of people will live in cities. This trend of growing cities raises concerns about limited resources and their influence on the quality of living. Under such a circumstance, cities require more optimized systems and effective designs to provide all citizens with better services. To this end, various technological advancements can be utilized to make more informed decisions in smart cities. Digital Twins have been introduced as one of the promising solutions for accelerating the transition to smarter and more effective cities. But before looking into how Digital Twins can improve our future smart cities, we need to take a look at its definition and how it works.

Digital Twin is defined as a virtual replica of a physical asset. This virtual replica should be connected to the physical entity in real-time to reflect one or multiple behaviors of the physical entity. A Digital Twin also can be connected to other Digital Twins in the lower, same, or upper-level systems, which altogether can potentially create a system of systems. After defining Digital Twin, we need to discuss the main technologies or components of constructing Digital Twin to understand the overall function of Digital Twin. As a digital replica abstracting physical counterpart, Digital Twin development requires four components:

(1) an information model abstracting information of physical entities, (2) a data acquisition mechanism that collects and bi-directionally transfers data between a Digital Twin and its physical counterpart,

(3) a data processing module extracting hidden knowledge behind heterogeneous multi-source data and enabling insightful decisions (4) a synchronization mechanism between three mentioned components to guarantee a smooth data distribution between various modules and the physical entity.

To address the second question, we need to take a look at how Digital Twin works. Data is constantly, and in real-time, transmitted from a physical entity to its virtual replica (Digital Twin). In this stage, data is harnessed and processed in various modules to

generate new information. This information, then, comes back to the physical entity to support more insightful decision-making or effective services. This brings lots of opportunities that can change how we manage cities and provide services in them. Existing cities contain a massive number of services that constantly interact with each other, creating a super complex system of systems. Overall, Digital Twins, fueled by data, can give us a deeper understanding of how this complex system actually works and furthermore serve as a test bed to see how our new ideas will eventually play out in such a system. In a way, Digital Twins treat data as raw material for analyzing and creating more effective services in smart cities.

Next, let’s look at some ways that Digital Twins can be applied to build effective solutions at different levels within the context of smart cities. City-level Digital Twins can solve macro-level problems. For example, a city-level Digital Twin can harness city data to reduce traffic, minimize waste, and increase energy efficiency at the city level. In a more specific way, an infrastructure-level Digital Twin can improve services at the level of the infrastructure. For example, Digital Twins of self-driving vehicles can provide access for minorities in smart cities, leading to more diverse and inclusive communities in the future. Furthermore, the Digital Twin idea can be implemented at a more microscopic level, such as the Digital Twins of humans. For example, the Digital Twins of citizens in smart cities can provide them with insightful information about their interactions with cities’ infrastructures. This can raise lots of opportunities to save time and perhaps more healthy behaviors in society. Moreover, Digital Twins, on different levels, can interact with each other to resemble the actual complex system of systems in cities and build more smart solutions. For example, vehicles’ Digital Twins can interact with roads’ Digital Twins in cities to report all problems, enabling predictive maintenance and considerable cost saving in city operations. This should not be restricted to same-level Digital Twins. As an example, Digital Twins of citizens can be connected to upper-level Digital Twins, such as city-level Digital Twins, to improve user experience in interacting with services in smart cities.

Despite the opportunities discussed in the previous paragraph, we need to take a look at the current state of Digital Twin development to build a realistic expectation of this technology. Except for a few real projects, most Digital Twins are developed to mirror a middle-level physical entity, such as buildings and vehicles. Although the current state of Digital Twin development faces different open issues, increasing advancements in twinning technologies and interoperability promise more advanced interaction with physical twins and more accurate Digital Twins in the near future. For example, Microsoft has created Azure Digital Twins as an open-source platform, a JASON-like language based on Digital Twin Definition Language (DTDL). With DTDL, we can define different physical and virtual systems, and the platform allows us to incorporate more IoTs, as the core technology for data acquisition. Bentley also has developed an open-source environment based on JavaScript (Typescript) language that enables practitioners to develop more specialized applications for their Digital Twins. In addition to these technologies, NVIDIA introduced Omniverse as an open platform for virtual collaboration based on Universal Scene Description (USD). This twinning tool can provide practitioners with a collaborative environment to work on their Digital Twins while each of them is responsible for replicating a given element of the physical twin. In this situation, Digital Twins can be utilized to model a highly dynamic environment like the construction and design phases of buildings and infrastructures.

Even though advancements have been made in the context of Digital Twin development, interoperability remains one of the most unexplored issues. This means that existing Digital Twins are less capable of integrating with other Digital Twins to generate a comprehensive solution for smart cities. Different standards are currently under development that will provide solutions for this challenge in the near future. BuildingSMART is developing the IFC5 standard, which will move from file-based information silos to a scalable solution. Open Geospatial Consortium is working on CityGML 3.0 as a new BIM-compatible solution that will allow data to be encoded in more open schemas, such as JSON. Furthermore, Digital Twin Consortium proposed a framework for creating complex systems interoperable at scale.

Spatial Accessibility Assessment of COVID-19 Patients to Healthcare Facilities: A Case Study of Florida

5 min

The World Health Organization (WHO) announced more than 94 million confirmed COVID-19 cases worldwide as of January 19, 2021. The U.S. with approximately 24 million COVID-19 cases and over 400,000 total deaths ranked first in comparison to other countries. Among U.S. states, Florida is among the top three states regarding the high number of cases. On January 19, 2021, the Florida Department of Health announced 1,589,097 cases and 24,436 deaths due to coronavirus throughout the state, which have been gradually increasing. According to the Centers for Disease Control and Prevention (CDC), the older population (65+) and those with serious medical conditions such as lung disease, diabetes, liver disease, and other chronic issues are at a higher risk to get infected with COVID-19. Especially, since Florida is a state with a substantial aging population and people living in assisted living facilities or independently, the issue becomes even more challenging. As such, understanding the extent to which Florida healthcare facilities are available to the public in both urban and rural areas is crucial.

There are several studies in the literature that have focused on measuring transportation-based accessibility to different public services facilities such as healthcare facilities, libraries, supermarkets, shelters, and urban parks. However, there is still a research gap in the literature regarding assessing the spatial access to healthcare facilities during a global pandemic such as the COVID-19 outbreak in which the demand for this type of facility increases dramatically. As such, my team measured the spatial accessibility of COVID-19 patients to healthcare facilities in the State of Florida (Ghanbarzadeh et al. 2021). For this purpose, the two-step floating catchment area (2SFCA) and the enhanced twostep floating catchment area (E2SFCA) methods were utilized in order to identify the areas with high and low levels of accessibility to healthcare services given the

Dr. Mahyar Ghorbanzadeh

number of confirmed coronavirus cases (demand) and the number of ICU beds (supply). More specifically, our team aimed to answer the following research question: To what extent do potential COVID-19 patients in Florida have access to healthcare resources, and which areas may experience potential resource shortages during the pandemic?

We included four main steps to measure the spatial accessibility of Floridians to healthcare providers during the COVID-19 pandemic. In the first step, the data related to healthcare facilities with the corresponding ICU beds as well as the number of COVID-19 patients were extracted for the entire state. Second, the travel times between the centroids of zip codes and each healthcare facility were calculated using the O-D cost matrix function of

the ArcGIS Network Analyst. The travel times in the roadway network were obtained via the Florida Standard Urban Transportation Model Structure (FSUTMS) model built-in CUBE software. The congested travel times on the roadways were used. In the next step, the 2SFCA and E2SFCA methods were applied to obtain the accessibility scores at the zip code level in order to identify the areas with high and low levels of accessibility to healthcare resources in Florida. Ultimately, a metric, namely the Accessibility Ratio Difference (ARD), was developed to compare the level of access obtained through the models. It is important to note that, the healthcare facilities that hospitalize COVID-19 patients and are equipped with ICU beds in Florida were selected.

As stated earlier, the 2SFCA and the E2SFCA methods were utilized to measure the spatial accessibility of COVID-19 patients to healthcare services in the State of Florida. Fig. 1a and Fig. 1b show the results obtained by the 2SFCA and E2SFCA models, respectively. In these figures, the green and red colors represent the higher and lower accessibility ratios obtained by the models, respectively. Both methods approximately reveal the same accessibility patterns over the entire state. As shown in Fig. 1a and Fig. 1b, those regions are mainly located in the northwest and southern portions of Florida and seem to have low spatial accessibility ratios which are shown in red. Note that the areas in northwest Florida are mostly considered rural areas. In contrast to northwest Florida, there are many healthcare facilities along with more ICU beds in southern Florida. However, the high number of COVID-19 patients in these areas led to findings of low access in these regions given the low computed ratios. On the other hand, the areas with higher access are mainly located in central Florida and close to the cities of Tampa and Orlando (shown in green). Therefore, it can be concluded that the people in northwest and southern Florida are more likely to experience resource shortages due to an imbalance between supply and demand.

and E2SFCA methods. The results of this approach are presented in Fig. 2. In this figure, the higher value of difference, the higher the accessibility ratio obtained by the E2SFCA method (shown in green). As seen, the 2SFCA method showed higher access ratios in most parts of the state (shown clearly with the yellow color) due to the negative ARD values. On the other hand, the E2SFCA model shows the higher access ratios in the regions with a higher number of ICU beds which appears in green. One explanation for this finding could be related to the distance decay effect within the catchment area which was considered in the E2SFCA method. According to the results, it can be concluded that the 2SFCA method overestimates accessibility in the areas with a low number of ICU beds due to the equal access of the population within the catchment area.

From a policy perspective, exploratory analyses such as the present effort can provide key information that could be used by health officials to formulate educational agendas aimed at promoting safety and well-being regarding the risks associated with COVID-19. The problem is so critical that even one or two neglected locations can have dire consequences. Specifically, the 2SFCA and E2SFCA analyses and their comparison, and insights presented in this paper could be a part of efforts to raise awareness of safety issues and make health officials more cognizant of locations near them that might require further care in providing access and support. In addition, with regard to COVID-19 cases, there are several communityoriented organizations charged with assisting them to meet their daily needs. The types of insights produced may have the potential to assist them in their efforts to help people, especially those vulnerable, find the health assistance they need. The obtained knowledge and insights can be useful for public health planners and decision-makers. This information can also help officials to better identify those areas with low access to the healthcare resources that are equipped with ICU beds.

Additionally, in order to evaluate the spatial access difference between the models, the ARD metric was used to provide a detailed comparison of the 2SFCA

Fig. 1. (a) Results of the 2SFCA method; (b) Results of the E2SFCA method.

Fig. 2. Results of the accessibility ratio difference (ARD).

Factors Influencing Shared Micromobility Services: An Analysis of E-Scooters and Bikeshare

5 min

Dr. Aryan Hosseinzadeh

Micro-mobility services fill a gap in demand by offering convenient and flexible transportation options, including first and last-mile trips. Micromobility services offer meaningful benefits to peoples’ transportation options by enhancing mobility and accessibility while improving communities through a reduction in fuel consumption and pollution. Considering the worldwide adoption rate of micro-mobility services, comprehensive research attention needs to be devoted to investigating the spatial and temporal factors influencing different micro-mobility shared services.

Although some recent studies have studied the impact of specific weather conditions and day of the week on specific micro-mobility services (Bai and Jiao, 2020,

Noland, 2019, Hosseinzadeh et al., 2021), there is still room to understand the impact of other major factors, such as major holidays and special events and a wider range of weather-related factors, on the usage of micro-mobility services, such as shared e-scooter and bike-share. In addition, to the best knowledge of the authors, only a few studies targeted how different factors impact shared e-scooter and bike-share simultaneously (Zhu et al., 2020, Younes et al., 2020).

Therefore, this study extends prior works with two main objectives: (1) comprehensively exploring the impact of the day of the week, various weatherrelated factors, major holidays, and special events on shared e-scooter and bike-sharing systems, and (2) compare and contrast the impact of these factors on trips. Finally, to examine how different factors impact shared e-scooter and bike-share simultaneously, trip distribution ratios were calculated for the estimated models. The results of this study can be used to inform how shared micro-mobility providers distribute vehicles and how cities manage e-scooter policies.

To conduct this study, trip records of shared e-scooters and bike-share were collected from Louisville Metro

Micromobility modes of transportation such as shared e-scooter and bike-sharing are critical elements in the development of sustainable communities.

Government’s Open Data platform for the period of January 2019 to January 2020. The e-scooter system emerged in Louisville in August 2018. As of July 2020, a total of 538,956 e-scooter trips were recorded in Louisville. In this study, Negative Binomial GAM models were estimated to model e-scooters and bikeshare trips. Negative Binomial Generalized Additive Models (NBGAM) are based on a Negative Binomial Generalized Linear Model formulation. NBGAM is appropriate for counting data with overdispersion.

This research assumed that the daily number of micro-mobility trips has a non-linear interaction with time. The effective degree of freedom (edf) was found to be 8.026 and 8.104 for shared e-scooter and bikeshare models, respectively. That is, there exists nonlinearity in the temporal trend of micro-mobility trips, both of which show approximately the same degree of not linearity. Also, the 8th degree of smooth function in the estimated models indicates the existence of eight different coefficients for the smooth function of each model. In addition, the p-values estimated for the smooth functions for both shared e-scooter and bikeshare were significant (p-value < 0.001). This indicates that the temporal autocorrelation effect exists for both sets of shared micro-mobility trips across different days.

Among the weather-related factors, rainfall, mist, glaze, higher wind speed, and higher temperature index decreased trips in both shared micro-mobility services. The day of the week significantly influenced both shared e-scooter trips and bike-share trips. That is, by considering Monday as the base condition, bike-share trips were significantly higher during all other days of the week except for Sundays. E-scooter trips were

only significantly higher on Thursdays, Fridays, and Saturdays relative to the base case on Mondays. Major holidays and special events had an impact on only e-scooter trips. On average, the number of e-scooter trips increased by 15% during major holidays and special events compared to normal daily trips.

Fig. 1 (a) and 1 (b) shows the smooth function highlighting the underlying temporal trend across the study period for bike-share and e-scooter trips, respectively. Based on this figure, the smooth function is at its peak in July 2019 for e-scooter trips and April 2019 for bike-share trips. In July, the smooth function increased the number of e-scooter trips by 64% (i.e., exp(0.5)) relative to the intercepted number of trips in the e-scooter model, and in April, the smooth function adds about 22% (i.e., exp(0.2)) compared to the intercept of the bike share model.

In order to simultaneously evaluate the impact of different factors on e-scooter and bike-share trips, trip distribution ratios were estimated. The trip distribution ratio shows how different factors influence e-scooters and bike-share trips simultaneously.

Fig. 2 (a) shows the log number of trips in both micromobility modes on Saturdays versus non-Saturdays. Based on this figure, Saturdays increase the number of trips in both services. Fig. 2 (b) demonstrates how Saturdays impact the ratio of e-scooter to bike-share trips. To better interpret this figure and trip distribution

ratio, initially, the coefficients of the developed models are explored. Based on the trip distribution ratios illustrated in Fig. 2 (b), it can be identified that Saturdays increase the total number of e-scooter trips more than bike-share trips across all months of the study period. This is because the line for Saturdays (solid line) in Fig. 2 (b) is upper than the non-Saturdays line (dashed line) and at the same time the coefficient for Saturdays is positive for both models. Considering February as an example, on Saturdays the trip distribution ratio of e-scooter to bike-share trips is about 30 while on non-Saturdays this ratio is about 25. This means that although the trips for both services increased on Saturdays, bike-share trips had a larger share of this increase. Similar trends can be observed for other months as well.

Micromobility modes of transportation such as shared e-scooter and bike-sharing are critical elements in the development of sustainable communities. Both shared e-scooters and bike-sharing systems are quick, convenient, and environmentally friendly modes of transportation for taking shorter trips, including first and last-mile trips. In addition, these environmentally friendly modes of transportation are able to bridge the gap between public transportation, prevent congestion and air pollution caused by personal vehicles, and mitigate parking issues in metropolitan areas. The benefits of micro-mobility services have been widely shown by many studies around the world. Considering the rate of worldwide adoption of these

Fig.1. Smooth function of time; (a) shared e-scooter, and (b) bike-share

Fig.2. (a) log number of micro-mobility trips on Saturdays vs. non-Saturdays

(b) ratio of e-scooter trips to bike-share trips on Saturdays vs. non-Saturdays

systems and the significant modal shifts from other transportation modes, it is crucial to understand how different factors might impact the usage of these services.

The findings of this study could support planners at metropolitan planning organizations (MPOs) with the operation, management, and environmental sustainability of micro-mobility services. That is, using the finding of this study city planners can proactively plan ahead of any special events, understand the need for each micro-mobility service, manage its demand, and strategically increase the usage of nonpersonal vehicles, which will potentially lead to mobility improvement and environmental sustainability. More specifically, the results will support city planners in better understanding how weather-related factors, special events, seasons, and days of the week might impact the usage of e-scooter and bike-share.

In addition, the comparison results of bike-share and e-scooter could further promote the effective integration of different micro-mobility services. Planners can proactively take advantage of the knowledge gained in this study by ensuring they are able to sufficiently meet demand. For instance, the findings indicate that during special events and holidays, people tend to use more shared e-scooter

rather than bike-share. Therefore, city planners can deploy additional or rebalance the number of vehicles around the targeted areas to better manage the travel demand. This will also encourage the use of micromobility rather than personal vehicles.

References

Bai, S., Jiao, J., 2020. Dockless E-scooter usage patterns and urban built environments: a comparison study of Austin, TX, and Minneapolis, MN. Travel Behav. Soc. 20, 264–272.

Hosseinzadeh, Aryan, Algomaiah, Majeed, Kluger, Robert, Li, Zhixia, 2021a. E-scooters and sustainability: Investigating the relationship between the density of E-scooter trips and characteristics of sustainable urban development. Sustain. Cities Soc. 66, 102624. https://doi.org/10.1016/j. scs.2020.102624.

Noland, R.B., 2019. Trip Patterns and Revenue of Shared E-Scooters in Louisville, Kentucky. Transport Findings.

Younes, H., Zou, Z., Wu, J., Baiocchi, G., 2020. Comparing the temporal determinants of dockless scooter-share and station-based bike-share in Washington, DC. Transp. Res. Part A: Policy Practice 134, 308–320.

Zhu, R., Zhang, X., Kondor, D., Santi, P., Ratti, C., 2020. Understanding spatiotemporal heterogeneity of bike-sharing and scooter-sharing mobility. Comput. Environ. Urban Syst. 81, 101483.