Big Data in Transportation Planning

Big Data in Transportation Planning The Decision-making process requires parties involved to have good information about the issues involved. Decisions about transport projects in Barcelona are impacted by big data, especially with regard to determining how to implement a transport network and decongest the city. Big data on the flow of traffic in the city is used to help policymakers to come up with measures that are effective. According to Ukkusuri and Yang (2019), big data is used to predict passenger volumes at a given time. The data can be captured from a wide range of sources such as online booking apps and social media. By forecasting travel volume, the transport industry can make decisions about the fleet of passenger vehicles or trains that are required.

Big data has also been used to assess the effect of pollution from motor vehicles in the cities of Barcelona and Singapore, which has enabled city administration to come up with measures to enhance the use of eco-friendly means of transport such as electric cars, trains, and bicycles. In Mexico City, big data is used to control traffic.

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The city has a traffic jam problem due to rapid development and an increased number of automobiles. The city management relies on data on the flow of traffic to guide motorists on which routes to avoid (Ukkusuri & Yang, 2019). Furthermore, road expansion projects are influenced by big data. Subsequently, big data help the city administration to recognise areas that have high traffic and infective road network.

In Singapore, big data analytics is used to implement targeted increases in transit passes.

Transport firms use the system-based forecast of passenger increase, using the previous data to predict future trends. Online booking and ticketing systems make it easier for transport organisations to gather and analyse big data about their customers (Antoniou et al., 2019).

Targeted increases help firms to also maximise their revenues. In this respect, big data can be used to manage transit passes in cities by predicting future trends in passenger flow.

Moreover, big data can also be used to determine the demand for cars for hire or private cars use in a city, impacting the decisions to come up with public transport systems that are more effective. Big data on transport is used to create innovative ideas such as bicycle sharing schemes in the city of Barcelona. The data is gathered to establish common trends among the people as they move from one place to another. Their preferred mode of transport is also analysed through big data (Nguyen, Liu, & Chen, 2017). The setting of bicycle centres is influenced by trends on demand and use, enabling the operators to strategically position the bicycle parking.

In the aviation sector, big data is used to assess passengers’ habits and determine the scheduling of flights on particular routes. The use of air transport is linked to other transport systems such as roads and railways. Big data on air transport traffic helps in the strategic planning of road and railway systems to support airports (Hausladen & Schosser, 2020). New Delhi has been able to improve its transport infrastructure around the Indira Gandhi International

Airport with big data support. In this respect, big data is important in planning and implementing measures to ease the flow of traffic in cities.

Moreover, big data also is used in improving safety in the transportation sector. Data sensors installed on vehicles and other means of transport such as trains offer real-time information on their performance, enabling the management to assess their safety in relation to the routes they use (Bachechi, Po & Rollo, 2021). Information on vehicles’ travel speeds and the time they spend idling in transit is used for security planning. The city of Brisbane leverages smart technology to conduct a real-time overview of the transport network to ease congestion and reduce pollution. Therefore, big data can be used to attain real-time management of traffic jams in cities.

Big data can also be used in scheduling transport vehicles and trains within the city to reduce traffic. Data on the flow of traffic is necessary for planning transport routes (Nguyen, Liu & Chen, 2017). The number of vehicles and trains can be varied according to the traffic flow, which can be determined from data capture on the movement of people in various directions at a specific time of the day, week, or months (Maeda, Shiode, Zhong, Mori & Sakimoto, 2019). The information can help players in the transport sector to decide when to avail more vehicles or trains and when to reduce the time spent in transit and how long they are idle for Difficulties and Challenges in Collecting Transport Data The city of the private sector collecting transport data may face quality issues that may arise when collecting data about transport. The reliability of data is dependent on its quality, an issue that the city government or private entities may face while gathering big data on the transport sector in Saudi Arabia (Badu-Marfo, Farooq & Patterson, 2019). The utility of data is enhanced when it is dependable, hence the importance of data quality.

Data complexity is another issue that government or private sector officials are likely to encounter in gathering big data on the transport sector. Transport in cities relies on various means, some of which cannot be accurately captured through technology. It becomes challenging for data collectors to capture all the information because of the complexities that are involved.

Moreover, transport involves passengers, goods, and services, which are impacted differently by problems such as congestion.

Error-handling in big data collection is another issue that is likely to occur in gathering information about transport in Saudi Arabia. Errors in data should be well managed to guarantee quality and also to eliminate hitches that may affect its use (Hausladen & Schosser, 2020). When using data, one should be aware of possible errors that may compromise the decisions made using the data.

Another issue that may arise is privacy rights, which may be breached during the collection and use of data. Data is generally captured through technology that can interfere with people's privacy. In this respect, the use of traffic data should take into account possible breaches of privacy (Badu-Marfo, Farooq & Patterson, 2019). To accurately capture information about traffic movement, especially in predicting passenger behaviours, data collectors may rely on online applications used in ticketing and booking, which contain private information.

Saudi Arabia Focus and Invest Areas Saud Arabia should focus on a number of factors to ensure that many entities focusing on technology and digital transformation applicable in transport and other sectors are made a reality.

One of the major areas to invest in is human resource development. Investing in technology and digital transformation cannot be successful without creating the necessary human resource to man it. Therefore, Saudi Arabia still needs experts in information technology (IT), computer

science, data analytics, programming, and other fields relevant to the use of big data and technology at large.

Saud should also invest in technology infrastructure to boost data transformation in the country. Technology and digital transformation require an expansion of internet infrastructure, focusing on efficiency and reliability. The growing demand for computer and internet technology to support digital transformation requires an internet connection. Most digital technologies function well when supported by the internet. Although KSA has a stout digital infrastructure that is speeding up the digital transformation in the country, there is still room for expansion. The growth of businesses, educational, citizen needs, and the use of the online platform to perform activities such as shopping has made technology infrastructure increasingly on-demand. A robust digital network is handy in sustaining the country’s transformation.

Saudi Arabia should also invest in research to boost the development of technology and digital transformation in the country. Research should target specific areas of interest, focusing on the country’s unique needs and future needs. Research is important in any given sector, including big data. The gathering of big data on the transport sector alone is not enough to make sound decisions. Further research is needed to examine various trends captured in data. The research should lead to recommendations on what can be done to address the challenges that were recognised.

The Kind of Data, Algorithms, and Equipment The kind of data that is collected in the transport sector includes information on passenger traffic, vehicle traffic, fuel consumption, pollution by automobiles, the demand for vehicles, and transit goods at a given time. The data can also be captured on the basis of the

season and means of transport used. There can be data on the road, railway, and air transport in this respect.

The collection of big data in the transport sector requires the use of various equipment that government or private sector involved in the exercise need to have for efficiency and effectiveness. The equipment used to gather data include sensors, computers, and closed-circuit cameras (CCTV) (Antoniou et al., 2019). The choice of equipment for collecting data is impacted by the type of data collection chosen.

The algorithm used to collect transport data includes the KNN algorithm, Smart Traffic Light Scheduling, and KD tree. Mikheeva (2014) holds that there are also algorithms for people transportation, good transportation, control of automobiles, and public transportation. An optimised signal coordination algorithm and general-purpose algorithm are also used in the transport sector. Algorithms can be used to determine a particular set of data from a large database to collect information and analysis.

In conclusion, I feel upon completing this course; I will be able to replicate such data processing algorithms for a city in Saudi Arabia. Furthermore, the course will equip me will the necessary skills and knowledge needed to apply the algorithm in data collection and manipulation. By the time I complete the course, I will also understand the importance of algorithms in big data, particularly with respect to the transport sector.

References Antoniou, C., Dimitriou, L., & Pereira, F. B. (2019). Mobility patterns, big data, and transport analytics: Tools and applications for modelling. Elsevier.

Bachechi, C., Po, L., & Rollo, F. (2021). Big Data Analytics and Visualization in Traffic Monitoring. Big Data Research, 27, 100292. DOI: 10.1016/j.bdr.2021.100292.

Badu-Marfo, G., Farooq, B., & Patterson, Z. (2019). A Perspective on the Challenges and Opportunities for Privacy-Aware Big Transportation Data. Journal Of Big Data Analytics In Transportation, 1(1), 1-23. DOI: 10.1007/s42421-019-00001-z.

Hausladen, I., & Schosser, M. (2020). Towards a maturity model for big data analytics in airline network planning. Journal Of Air Transport Management, 82, 101721. DOI: 10.1016/j.jairtraman.2019.101721.

Maeda, T., Shiode, N., Zhong, C., Mori, J., & Sakimoto, T. (2019). Detecting and understanding urban changes through decomposing the numbers of visitors’ arrivals using human mobility data. Journal Of Big Data, 6(1). DOI: 10.1186/s40537-019-0168-5.

Mikheeva, T. (2014). intelligent transport systems: methods, algorithms, realisation. SL: Lap Lambert Academic Publ.

Nguyen, H., Liu, W., & Chen, F. (2017). Discovering Congestion Propagation Patterns in SpatioTemporal Traffic Data. IEEE Transactions On Big Data, 3(2), 169-180. DOI: 10.1109/tbdata.2016.2587669.

Ukkusuri, S. V. S. K., & Yang, C. (2019). Transportation analytics in the era of big data.

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