Abstract
The incorporation of disruptive innovations into the transportation industry will inevitably cause major upheavals in the transportation sector. However, existing research lacks systematic theories and methodologies to represent the underlying characteristics of future urban transport systems. Furthermore, emerging modes in urban mobility have not been sufficiently studied. The National Natural Science Foundation of China (NSFC) officially approved the Basic Science Center project titled “Future Urban Transport Management” in 2022. The project members include leading scientists and engineers from Beijing Jiaotong University, Beihang University, and Beijing Transport Institute. Based on a wide range of previous projects by the consortium on urban mobility and sustainable cities, this project will encompass transdisciplinary and interdisciplinary research to explore critical issues affecting future urban traffic management. It aims to develop fundamental theories and methods based on social and technological developments in the near future and explores innovative solutions to implement alongside these emerging developments in urban mobility.
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Acharya S, Singleton P A (2022). Associations of inclement weather and poor air quality with non-motorized trail volumes. Transportation Research Part D: Transport and Environment, 109: 103337
Alghamdi T, Elgazzar K, Mostafi S, Ng J (2021). Improving spatiotemporal traffic prediction in adversary weather conditions using hierarchical Bayesian state space modeling. In: IEEE International Intelligent Transportation Systems Conference (ITSC). Indianapolis, IN: IEEE, 3252–3258
Alonso A, Monzón A, Cascajo R (2015). Comparative analysis of passenger transport sustainability in European cities. Ecological Indicators, 48: 578–592
Avila A M, Mezić I (2020). Data-driven analysis and forecasting of highway traffic dynamics. Nature Communications, 11(1): 2090
Bao Y, Xu M, Dogterom N, Ettema D (2020). Effectiveness investigation of travel demand management measures in Beijing: Existing measures and a potential measure-tradable driving credit. Transportation Research Part F: Traffic Psychology and Behaviour, 72: 47–61
Barthélemy M (2011). Spatial networks. Physics Reports, 499(1–3): 1–101
Bertsimas D, Delarue A, Jaillet P, Martin S (2019). Travel time estimation in the age of big data. Operations Research, 67(2): 498–515
Bhoopalam A K, Agatz N, Zuidwijk R (2018). Planning of truck platoons: A literature review and directions for future research. Transportation Research Part B: Methodological, 107: 212–228
Bi H, Ye Z, Zhu H (2022). Data-driven analysis of weather impacts on urban traffic conditions at the city level. Urban Climate, 41: 101065
Fagnant D J, Kockelman K (2015). Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77: 167–181
Furuhata M, Dessouky M, Ordóñez F, Brunet M E, Wang X, Koenig S (2013). Ridesharing: The state-of-the-art and future directions. Transportation Research Part B: Methodological, 57: 28–46
Gao Y, Sun S, Shi D (2011). Network-scale traffic modeling and forecasting with graphical lasso. In: 8th International Symposium on Neural Networks: Advances in Neural Networks. Guilin: Springer, 151–158
Guo Q, Li L, Ban X (2019). Urban traffic signal control with connected and automated vehicles: A survey. Transportation Research Part C: Emerging Technologies, 101: 313–334
Kasliwal A, Furbush N J, Gawron J H, McBride J R, Wallington T J, de Kleine R D, Kim H C, Keoleian G A (2019). Role of flying cars in sustainable mobility. Nature Communications, 10(1): 1555
Kirkley A, Barbosa H, Barthelemy M, Ghoshal G (2018). From the betweenness centrality in street networks to structural invariants in random planar graphs. Nature Communications, 9(1): 2501
Larson W, Zhao W (2020). Self-driving cars and the city: Effects on sprawl, energy consumption, and housing affordability. Regional Science and Urban Economics, 81: 103484
Ledoux C (1997). An urban traffic flow model integrating neural networks. Transportation Research Part C: Emerging Technologies, 5(5): 287–300
Liu Z, Chen Z, He Y, Song Z (2021). Network user equilibrium problems with infrastructure-enabled autonomy. Transportation Research Part B: Methodological, 154: 207–241
Liu Z, Lyu C, Wang Z, Wang S, Liu P, Meng Q (2023). A Gaussian-process-based data-driven traffic flow model and its application in road capacity analysis. IEEE Transactions on Intelligent Transportation Systems, 24(2): 1544–1563
Lu K, Liu J, Zhou X, Han B (2021). A review of big data applications in urban transit systems. IEEE Transactions on Intelligent Transportation Systems, 22(5): 2535–2552
Malta L, Miyajima C, Takeda K (2009). A study of driver behavior under potential threats in vehicle traffic. IEEE Transactions on Intelligent Transportation Systems, 10(2): 201–210
Meyer J, Becker H, Bösch P M, Axhausen K W (2017). Autonomous vehicles: The next jump in accessibilities? Research in Transportation Economics, 62: 80–91
Miglani A, Kumar N (2019). Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges. Vehicular Communications, 20: 100184
Mourad A, Puchinger J, Chu C (2019). A survey of models and algorithms for optimizing shared mobility. Transportation Research Part B: Methodological, 123: 323–346
Qin H, Guan H, Wu Y J (2013). Analysis of park-and-ride decision behavior based on Decision Field Theory. Transportation Research Part F: Traffic Psychology and Behaviour, 18: 199–212
Saberi M, Hamedmoghadam H, Ashfaq M, Hosseini S A, Gu Z, Shafiei S, Nair D J, Dixit V, Gardner L, Waller S T, González M C (2020). A simple contagion process describes spreading of traffic jams in urban networks. Nature Communications, 11(1): 1616
Sayegh A S, Connors R D, Tate J E (2018). Uncertainty propagation from the cell transmission traffic flow model to emission predictions: A data-driven approach. Transportation Science, 52(6): 1327–1346
Schönhof M, Helbing D (2007). Empirical features of congested traffic states and their implications for traffic modeling. Transportation Science, 41(2): 135–166
Simini F, Barlacchi G, Luca M, Pappalardo L (2021). A deep gravity model for mobility flows generation. Nature Communications, 12(1): 6576
Stefaniec A, Hosseini K, Xie J, Li Y (2020). Sustainability assessment of inland transportation in China: A triple bottom line-based network DEA approach. Transportation Research Part D: Transport and Environment, 80: 102258
Sun X, Feng S, Lu J (2016). Psychological factors influencing the public acceptability of congestion pricing in China. Transportation Research Part F: Traffic Psychology and Behaviour, 41: 104–112
US Census Bureau (2011). Statistical Abstract of the United States: 2012
Vazifeh M M, Santi P, Resta G, Strogatz S H, Ratti C (2018). Addressing the minimum fleet problem in on-demand urban mobility. Nature, 557(7706): 534–538
Vlahogianni E I, Karlaftis M G, Golias J C (2014). Short-term traffic forecasting: Where we are and where we are going. Transportation Research Part C: Emerging Technologies, 43: 3–19
Wang Y, Jiang R, Nie Y, Gao Z (2021). Impact of information on topology-induced traffic oscillations. Transportation Science, 55(2): 475–490
Wang Y, Szeto W Y, Han K, Friesz T L (2018). Dynamic traffic assignment: A review of the methodological advances for environmentally sustainable road transportation applications. Transportation Research Part B: Methodological, 111: 370–394
Wu J, Li D, Si S, Gao Z (2021). Special issue: Reliability management of complex system. Frontiers of Engineering Management, 8(4): 477–479
Yang J, Wen Y, Wang Y, Zhang S, Pinto J P, Pennington E A, Wang Z, Wu Y, Sander S P, Jiang J H, Hao J, Yung Y L, Seinfeld J H (2021). From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model. Proceedings of the National Academy of Sciences of the United States of America, 118(26): e2102705118
Yildirimoglu M, Sirmatel I I, Geroliminis N (2018). Hierarchical control of heterogeneous large-scale urban road networks via path assignment and regional route guidance. Transportation Research Part B: Methodological, 118: 106–123
Zhang J, Wang F Y, Wang K, Lin W H, Xu X, Chen C (2011). Data-driven intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 12(4): 1624–1639
Zhang Z, Li M, Lin X, Wang Y, He F (2019). Multistep speed prediction on traffic networks: A deep learning approach considering spatiotemporal dependencies. Transportation Research Part C: Emerging Technologies, 105: 297–322
Zhao S, Zhang K (2021). Online predictive connected and automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions. Transportation Research Part B: Methodological, 145: 80–117
Zheng F, van Zuylen H, Liu X (2017). A methodological framework of travel time distribution estimation for urban signalized arterial roads. Transportation Science, 51(3): 893–917
Zheng N N, Tang S, Cheng H, Li Q, Lai G, Wang F Y (2004). Toward intelligent driver-assistance and safety warning systems. IEEE Intelligent Systems, 19(2): 8–11
Zheng Z, Qi X, Wang Z, Ran B (2021). Incorporating multiple congestion levels into spatiotemporal analysis for the impact of a traffic incident. Accident, Analysis and Prevention, 159: 106255
Zhou Y, Ahn S, Wang M, Hoogendoorn S (2020). Stabilizing mixed vehicular platoons with connected automated vehicles: An H-infinity approach. Transportation Research Part B: Methodological, 132: 152–170
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This work was supported by the National Natural Science Foundation of China (Grant No. 72288101).
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Gao, Z., Huang, Hj., Guo, J. et al. Future urban transport management. Front. Eng. Manag. 10, 534–539 (2023). https://doi.org/10.1007/s42524-023-0255-3
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DOI: https://doi.org/10.1007/s42524-023-0255-3