Understanding the Mediating Role of Artificial Intelligence in Urban Transportation Planning for Smart City Development and its Implications for the United States


Authors : Emmanuel Ogundare

Volume/Issue : Volume 9 - 2024, Issue 12 - December

Google Scholar : https://tinyurl.com/4z2r7p7w

Scribd : https://tinyurl.com/js6y2k35

DOI : https://doi.org/10.5281/zenodo.14613884

Abstract : Artificial intelligence (AI) is revolutionizing urban transportation planning in the United States, playing a pivotal role in optimizing transportation systems for smart city development. This research digs into the pivotal role of AI as a mediator in integrating diverse technological components to optimize urban operations and enhance residents' quality of life. Faced with challenges such as congestion, pollution, and infrastructure deficits, cities in the United States are increasingly leveraging AI to revolutionize transportation planning. Through case studies like Smart Columbus, LA Metro, One Center City, and SFMTA, the study illustrates AI's transformative potential in fostering sustainable, efficient, and inclusive urban transportation networks. Policy analysis reveals a multifaceted regulatory landscape at local, state, and federal levels, emphasizing safety, equity, and interoperability. While existing policies lay foundational frameworks, there's a pressing need for adaptive regulations to harness AI's full potential responsibly. This research emphasizes the significance of AI- driven innovations in reshaping urban mobility and offers insights for policymakers to navigate the complexities of smart city development effectively.

Keywords : Artificial Intelligence (AI), Urban Transportation Planning, Smart city Development, Urbanization, United States.

References :

  1. Alberto Dianin, E. R. (2021). Implications of Autonomous Vehicles for Accessibility and .   Transport  Equity: A Framework Based on Literature.
  2. Alexandros Nikitas, K. M. (2020, April). Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era.
  3. Anabel Ortega-Fernández, R. M.-R.-M. (2020). Artificial Intelligence in the Urban Environment: Smart Cities as Models for Developing Innovation and Sustainability.
  4. Andre Wirjo, S. C. (2022, November ). Artificial Intelligence. Asia-Pacific Economic Cooperation.
  5. Anjali Mazumder, D. L. (2021). Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?
  6. Booker, C. (2020, july 10). Four years later, Ohio State research boosts Smart Columbus. Retrieved from OSU.EDU: https://news.osu.edu/four-years-later-ohio-state-research-boosts-smart-columbus/
  7. Bucchiarone, A. B. (2021). Autonomous Shuttle-as-a-Service (ASaaS): Challenges, Opportunities, and Social Implications.
  8. Burden, M. (2016, December 9). Snyder signs new Michigan self-driving vehicles law. Retrieved from https://www.detroitnews.com/story/business/autos/2016/12/09/autonomous-car-law/95199544/
  9. Califonia Department Of Motor Vehicles. (n.d.). Retrieved from https://www.dmv.ca.gov/portal/
  10. Casey Bergh, R. A. (2005 , September ). Continued Reliance on Traffic Signals: The Cost of Missed Opportunities to Improve Traffic Flow and Safety at Urban Intersections.
  11. City of Pittsburgh. (n.d.). Retrieved from Autonomous Technology: https://pittsburghpa.gov/domi/autonomous
  12. Cui, Q., Yingze Wang, K.-C. C., Lin, I.-C., & Xiaofeng Tao, P. Z. (2019, April 02). Big Data Analytics and Network Calculus Enabling Intelligent Management of Autonomous Vehicles in a Smart City. pp. 2021 - 2034.
  13. D. Pritima, S. S. (2021, December 02 ). Artificial Intelligence-Based Energy Management and Real-Time Optimization in Electric and Hybrid Electric Vehicles.
  14. David Iyanuoluwa Ajiga, N. L. (2024). “AI-Driven Predictive Analytics in Retail: A Review of Emerging Trends and Customer Engagement Strategies.
  15. David Wasserman, A. a. (n.d.). Artificial Intelligence and Planning Practice. APA American Planning Association, 18.
  16. Downtown Seattle Association. (n.d.). Retrieved from Downtown Seattle Association: https://downtownseattle.org/
  17. Flannery, L. (2020, October 29). Studying AI's Potential to Optimize Public Transit Systems. Retrieved from Planetizen: https://www.planetizen.com/news/2020/10/111035-studying-ais-potential-optimize-public-transit-systems
  18. Harle, S. (2024). Advancements and challenges in the application of artificial intelligence in civil engineering: a comprehensive review.
  19. Herring/WESA, A.-L. (2022, JANUARY 21, ). Self-driving tech companies in Pittsburgh push for looser rules on vehicle testing. Retrieved from https://www.witf.org/2022/01/21/self-driving-tech-companies-in-pittsburgh-push-for-looser-rules-on-vehicle-testing/
  20. Ignesa. (2023, August 02). Predictive Analytics in Urban Planning. Retrieved from https://ignesa.com/insights/predictive-analytics-in-urban-planning/#
  21. Ionescu, D. (2024, February 6). USDOT Launches AI Initiative for Complete Streets. Retrieved from Planetizen: https://www.planetizen.com/news/2024/02/127352-usdot-launches-ai-initiative-complete-streets
  22. King County Metro. (n.d.). Retrieved from https://kingcounty.gov/depts/transportation/metro/schedules-maps/one-center-city.aspx
  23. Kuru, K. a. (2020). A framework for the synergistic integration of fully autonomous ground vehicles with smart city.
  24. LA Metro. (n.d.). Retrieved from Metro: https://www.metro.net/
  25. McKinsey Global Inst. (2018). SMART CITIES: Digital Solutions for a more Livable Future. Retrieved from PwC: https://medium.com/mckinsey-global-institute/smart-cities-c0d557ff42c1
  26. MCL Section 257.665. (n.d.). Retrieved from Michigan Legislature, Michigan Compiled Laws Complete Through PA 35 of 2024: MCL - Section 257.665
  27. Metro’s Sustainability . (n.d.). Retrieved from metro.net: https://www.metro.net/about/sustainability/
  28. Muni Forward. (2023). Retrieved from SFMTA: https://www.sfmta.com/projects/transit-effectiveness-project-tep-muni-forward
  29. National Highway Traffic Safety Administration. (n.d.). Retrieved from Automated Vehicles for Safety: https://www.nhtsa.gov/vehicle-safety/automated-vehicles-safety
  30. Oakil, A. H. (2023, October 01). Smart Transportation Systems in Smart Cities: Practices, Challenges, and Opportunities for Saudi Cities.
  31. Obiora A. Nnene, J. W. (2023, January 18). A simulation-based optimization approach for designing transit networks. Retrieved from https://link.springer.com/article/10.1007/s12469-022-00312-5
  32. One Center City. (n.d.). Retrieved from Seattle Department of Transportation: https://www.seattle.gov/transportation/projects-and-programs/programs/transportation-planning/one-center-city
  33. One Center Mobility Plan. (n.d.). Retrieved from https://onecentercity.org/
  34. Petra Hurtado, B. G. (n.d.). Smart Cities Integrating Technology, Community and Nature. APA American Planning Association, 111.
  35. Rudolf Giffinger, C. F. (2007). Smart cities - Ranking of European medium-sized cities.
  36. San Francisco Municipal Transportation Agency. (n.d.). Retrieved from SFMTA Muni Forward: https://www.sfmta.com/projects/muni-forward
  37. Sayed A. Sayed, Y. A.-H. (2023). Artificial intelligence-based traffic flow prediction: a comprehensive review.
  38. Seattle Department of Transportation. (n.d.). Retrieved from Downtown Seattle Association: https://downtownseattle.org/programs-and-services/mobility/one-center-city-vision/
  39. Smart Columbus. (2020). Retrieved from Smart Columbus Overview: https://smartcbus.com/
  40. SMRT Columbu's. (n.d.). Retrieved from https://smartcolumbus.com/
  41. Sound Transit . (n.d.). Retrieved from https://www.soundtransit.org/system-expansion
  42. Sustainability Strategic Plan 2020. (n.d.). Los Angeles County Metropolitan Transportation Authority, We Are Moving Beyond Sustainability.
  43. The Winner: Columbus, Ohio. (2016, September 28). Retrieved from U.S. Department of Transportation: https://www.transportation.gov/smartcity/winner
  44. Thomas W. Sanchez, p. (n.d.). Planning With Artificial Intelligence. APA American Planning Association, 68.
  45. Traffic Congestion and Reliability. (n.d.). Retrieved from U.S Department of Transportation: https://ops.fhwa.dot.gov/congestion_report/executive_summary.htm
  46. U.S. Department of Transportation . (n.d.). Retrieved from U.S. Department of Transportation Announces Columbus as Winner of Unprecedented $40 Million Smart City Challenge:  Funding and Grants: Smart Columbus secured significant funding from various sources. Columbus received a $40 million grant from the U.S. Department of Transportation as the winner of the Smart City Challenge, supplemented by $10 million from the Vulcan
  47. USDOT. (2017, June 29). Smart City Challenge. Retrieved from U.S. Department of Transportation: https://www.transportation.gov/smartcity
  48. Xiaojian Zhanga, Q. K. (2023). Travel Demand Forecasting: A Fair AI Approach.
  49. Yassine Maleh, Y. B. (2021). Artificial Intelligence and Blockchain for Future Cybersecurity Applications.
  50. Zaib Ullah, F. A.-T. (2020, March). Applications of Artificial Intelligence and Machine learning in smart cities.

Artificial intelligence (AI) is revolutionizing urban transportation planning in the United States, playing a pivotal role in optimizing transportation systems for smart city development. This research digs into the pivotal role of AI as a mediator in integrating diverse technological components to optimize urban operations and enhance residents' quality of life. Faced with challenges such as congestion, pollution, and infrastructure deficits, cities in the United States are increasingly leveraging AI to revolutionize transportation planning. Through case studies like Smart Columbus, LA Metro, One Center City, and SFMTA, the study illustrates AI's transformative potential in fostering sustainable, efficient, and inclusive urban transportation networks. Policy analysis reveals a multifaceted regulatory landscape at local, state, and federal levels, emphasizing safety, equity, and interoperability. While existing policies lay foundational frameworks, there's a pressing need for adaptive regulations to harness AI's full potential responsibly. This research emphasizes the significance of AI- driven innovations in reshaping urban mobility and offers insights for policymakers to navigate the complexities of smart city development effectively.

Keywords : Artificial Intelligence (AI), Urban Transportation Planning, Smart city Development, Urbanization, United States.

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