Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research.


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Literature Cited

  1. 1. 
    UN Dep. Econ. Soc. Aff 2018. 68% of the world population projected to live in urban areas by 2050, says UN. United Nations Department of Economic and Social Affairs May 16. https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html
    [Google Scholar]
  2. 2. 
    UN 2021. Cities and pollution. United Nations https://www.un.org/en/climatechange/climate-solutions/cities-pollution
    [Google Scholar]
  3. 3. 
    Schrank D, Eisele B, Lomax T. 2019. Urban mobility report 2019 Rep., Tex. A&M Transp. Inst. College Station:
  4. 4. 
    Mitchell WJ, Borroni-Bird CE, Burns LD. 2010. Reinventing The Automobile: Personal Urban Mobility for the 21st Century Cambridge, MA: MIT Press
  5. 5. 
    Pavone M 2015. Autonomous mobility-on-demand systems for future urban mobility. Autonomes Fahren: technische, rechtliche und gesellschaftliche Aspekte M Maurer, J Gerdes, B Lenz, H Winner 399–416 Berlin: Springer
    [Google Scholar]
  6. 6. 
    City of New York 2021. OneNYC 2050: building a strong and fair city Rep., City of New York http://onenyc.cityofnewyork.us/reports-resources/
  7. 7. 
    Berger T, Chen C, Frey CB 2018. Drivers of disruption? Estimating the Uber effect. Eur. Econ. Rev. 110:197–210
    [Google Scholar]
  8. 8. 
    Rogers B. 2015. The social costs of Uber. Univ. Chicago Law Rev. Online 82:85–102
    [Google Scholar]
  9. 9. 
    AAA Found. Traffic Saf 2021. New American driving survey: updated methodology and results from July 2019 to June 2020 Tech. Rep., AAA Found. Traffic Saf. Washington, DC: https://aaafoundation.org/new-american-driving-survey-updated-methodology-and-results-from-july-2019-to-june-2020
  10. 10. 
    Hancock PA, Nourbakhsh I, Stewart J 2019. On the future of transportation in an era of automated and autonomous vehicles. PNAS 116:7684–91
    [Google Scholar]
  11. 11. 
    Becker H, Becker F, Abe R, Bekhor S, Belgiawan PF et al. 2020. Impact of vehicle automation and electric propulsion on production costs for mobility services worldwide. Transp. Res. A 138:105–26
    [Google Scholar]
  12. 12. 
    Iagnemma K. 2020. 100,000 self-driving rides strong. Aptiv Feb. 11. https://www.aptiv.com/en/insights/article/100000-Self-Driving-Rides-Strong
    [Google Scholar]
  13. 13. 
    Schwall M, Daniel T, Victor T, Favaro F, Hohnhold H. 2020. Waymo public road safety performance data. arXiv:2011.00038 [cs.RO]
  14. 14. 
    Yigitcanlar T, Wilson M, Kamruzzaman M 2019. Disruptive impacts of automated driving systems on the built environment and land use: an urban planner's perspective. J. Open Innov. Technol. Mark. Complex. 5:24
    [Google Scholar]
  15. 15. 
    Kim G, Ong YS, Heng CK, Tan PS, Zhang NA. 2015. City vehicle routing problem (city VRP): a review. IEEE Trans. Intell. Transp. Syst. 16:1654–66
    [Google Scholar]
  16. 16. 
    Psaraftis HN, Wen M, Kontovas CA. 2016. Dynamic vehicle routing problems: three decades and counting. Networks 67:3–31
    [Google Scholar]
  17. 17. 
    Eksioglu B, Vural AV, Reisman A. 2009. The vehicle routing problem: a taxonomic review. Comput. Ind. Eng. 57:1472–83
    [Google Scholar]
  18. 18. 
    Bullo F, Frazzoli E, Pavone M, Savla K, Smith SL 2011. Dynamic vehicle routing for robotic systems. Proc. IEEE 99:1482–504
    [Google Scholar]
  19. 19. 
    Gendreau M, Potvin JY 1998. Dynamic vehicle routing and dispatching. Fleet Management and Logistics TG Crainic, G Laporte 115–26 Berlin: Springer
    [Google Scholar]
  20. 20. 
    Schiffer M, Schneider M, Walther G, Laporte G 2019. Vehicle routing and location routing with intermediate stops: a review. Transp. Sci. 53:319–43
    [Google Scholar]
  21. 21. 
    Zhao L, Malikopoulos A. 2020. Enhanced mobility with connectivity and automation: a review of shared autonomous vehicle systems. IEEE Intell. Transp. Syst. Mag 14:187–102
    [Google Scholar]
  22. 22. 
    Narayanan S, Chaniotakis E, Antoniou C 2020. Shared autonomous vehicle services: a comprehensive review. Transp. Res. C 111:255–93
    [Google Scholar]
  23. 23. 
    Hyland MF, Mahmassani HS. 2017. Taxonomy of shared autonomous vehicle fleet management problems to inform future transportation mobility. Transp. Res. Rec. 2653:26–34
    [Google Scholar]
  24. 24. 
    Mourad A, Puchinger J, Chu C. 2019. A survey of models and algorithms for optimizing shared mobility. Transp. Res. B 123:323–46
    [Google Scholar]
  25. 25. 
    Golbabaei F, Yigitcanlar T, Bunker J 2021. The role of shared autonomous vehicle systems in delivering smart urban mobility: a systematic review of the literature. Int. J. Sustain. Transp. 15:731–48
    [Google Scholar]
  26. 26. 
    Markov I, Guglielmetti R, Laumanns M, Fernández-Antolín A, de Souza R. 2021. Simulation-based design and analysis of on-demand mobility services. Transp. Res. A 149:170–205
    [Google Scholar]
  27. 27. 
    Agatz N, Erera A, Savelsbergh M, Wang X. 2012. Optimization for dynamic ride-sharing: a review. Eur. J. Oper. Res. 223:295–303
    [Google Scholar]
  28. 28. 
    Motzkin T. 1956. The assignment problem. Proc. Symp. Appl. Math. 6:109–25
    [Google Scholar]
  29. 29. 
    Kuhn HW. 1955. The Hungarian method for the assignment problem. Naval Res. Logist. Q. 2:83–97
    [Google Scholar]
  30. 30. 
    Bertsekas DP, Castañon DA. 1993. Parallel asynchronous Hungarian methods for the assignment problem. ORSA J. Comput. 5:261–74
    [Google Scholar]
  31. 31. 
    Peeta S, Ziliaskopoulos AK. 2001. Foundations of dynamic traffic assignment: the past, the present and the future. Netw. Spat. Econ. 1:233–65
    [Google Scholar]
  32. 32. 
    Toth P, Vigo D. 2014. Vehicle Routing: Problems, Methods, and Applications Philadelphia: Soc. Ind. Appl. Math.
  33. 33. 
    Berbeglia G, Cordeau JF, Laporte G. 2010. Dynamic pickup and delivery problems. Eur. J. Oper. Res. 202:8–15
    [Google Scholar]
  34. 34. 
    Laporte G. 2009. Fifty years of vehicle routing. Transp. Sci. 43:408–16
    [Google Scholar]
  35. 35. 
    Pavone M, Smith SL, Frazzoli E, Rus D 2012. Robotic load balancing for mobility-on-demand systems. Int. J. Robot. Res. 31:839–54
    [Google Scholar]
  36. 36. 
    Ruch C, Lu C, Sieber L, Frazzoli E. 2021. Quantifying the efficiency of ride sharing. IEEE Trans. Intell. Transp. Syst. 22:5811–16
    [Google Scholar]
  37. 37. 
    Santi P, Resta G, Szell M, Sobolevsky S, Strogatz SH, Ratti C. 2014. Quantifying the benefits of vehicle pooling with shareability networks. PNAS 111:13290–94
    [Google Scholar]
  38. 38. 
    Alonso-Mora J, Samaranayake S, Wallar A, Frazzoli E, Rus D 2017. On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. PNAS 114:462–67
    [Google Scholar]
  39. 39. 
    Spieser K, Treleaven K, Zhang R, Frazzoli E, Morton D, Pavone M 2014. Toward a systematic approach to the design and evaluation of automated mobility-on-demand systems: a case study in Singapore. Road Vehicle Automation G Meyer, S Beiker 229–45 Cham, Switz: Springer
    [Google Scholar]
  40. 40. 
    Vazifeh MM, Santi P, Resta G, Strogatz SH, Ratti C. 2018. Addressing the minimum fleet problem in on-demand urban mobility. Nature 557:534–38
    [Google Scholar]
  41. 41. 
    Barrios JA, Godier JD. 2014. Fleet sizing for flexible carsharing systems: simulation-based approach. Transp. Res. Rec. 2416:1–9
    [Google Scholar]
  42. 42. 
    Boesch PM, Ciari F, Axhausen KW 2016. Autonomous vehicle fleet sizes required to serve different levels of demand. Transp. Res. Rec. 2542:111–19
    [Google Scholar]
  43. 43. 
    Fagnant DJ, Kockelman KM. 2018. Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas. Transportation 45:143–58
    [Google Scholar]
  44. 44. 
    Wallar A, Schwarting W, Alonso-Mora J, Rus D. 2019. Optimizing multi-class fleet compositions for shared mobility-as-a-service. 2019 IEEE Intelligent Transportation Systems Conference (ITSC)2998–3005 Piscataway, NJ: IEEE
    [Google Scholar]
  45. 45. 
    Zardini G, Lanzetti N, Censi A, Frazzoli E, Pavone M 2020. Co-design to enable user-friendly tools to assess the impact of future mobility solutions. arXiv:2008.08975 [eess.SY]
  46. 46. 
    Zardini G, Milojevic D, Censi A, Frazzoli E 2021. Co-design of embodied intelligence: a structured approach. arXiv:2011.10756 [cs.RO]
  47. 47. 
    Choudhury S, Solovey K, Kochenderfer MJ, Pavone M. 2021. Efficient large-scale multi-drone delivery using transit networks. J. Artif. Intell. Res. 70:757–88
    [Google Scholar]
  48. 48. 
    Ruch C, Ehrler R, Hörl S, Balac M, Frazzoli E 2021. Simulation-based assessment of parking constraints for automated mobility on demand: a case study of Zurich. Vehicles 3:272–86
    [Google Scholar]
  49. 49. 
    Zhang W, Guhathakurta S. 2017. Parking spaces in the age of shared autonomous vehicles: How much parking will we need and where?. Transp. Res. Rec. 2651:80–91
    [Google Scholar]
  50. 50. 
    Zhang H, Sheppard CJ, Lipman TE, Moura SJ. 2019. Joint fleet sizing and charging system planning for autonomous electric vehicles. IEEE Trans. Intell. Transp. Syst. 21:4725–38
    [Google Scholar]
  51. 51. 
    Chen TD, Kockelman KM 2016. Management of a shared autonomous electric vehicle fleet: implications of pricing schemes. Transp. Res. Rec. 2572:37–46
    [Google Scholar]
  52. 52. 
    Chen TD, Kockelman KM, Hanna JP. 2016. Operations of a shared, autonomous, electric vehicle fleet: implications of vehicle & charging infrastructure decisions. Transp. Res. A 94:243–54
    [Google Scholar]
  53. 53. 
    Rossi F, Iglesias R, Alizadeh M, Pavone M 2019. On the interaction between autonomous mobility-on-demand systems and the power network: models and coordination algorithms. IEEE Trans. Control Netw. Syst. 7:384–97
    [Google Scholar]
  54. 54. 
    Estandia A, Schiffer M, Rossi F, Luke J, Kara EC et al. 2021. On the interaction between autonomous mobility on demand systems and power distribution networks—an optimal power flow approach. IEEE Trans. Control Netw. Syst. 8:1163–76
    [Google Scholar]
  55. 55. 
    Rossi F, Zhang R, Hindy Y, Pavone M. 2018. Routing autonomous vehicles in congested transportation networks: structural properties and coordination algorithms. Auton. Robots 42:1427–42
    [Google Scholar]
  56. 56. 
    Salazar M, Lanzetti N, Rossi F, Schiffer M, Pavone M. 2019. Intermodal autonomous mobility-on-demand. IEEE Trans. Intell. Transp. Syst. 21:3946–60
    [Google Scholar]
  57. 57. 
    Wollenstein-Betech S, Salazar M, Houshmand A, Pavone M, Paschalidis IC, Cassandras CG 2021. Routing and rebalancing intermodal autonomous mobility-on-demand systems in mixed traffic. IEEE Trans. Intell. Transp. Syst. In press. https://doi.org/10.1109/TITS.2021.3112106
    [Crossref] [Google Scholar]
  58. 58. 
    Levinson J, Thrun S 2013. Automatic online calibration of cameras and lasers. Robotics: Science and Systems IX P Newman, D Fox, D Hsu, pap 29 N.p.: Robot. Sci. Syst. Found.
    [Google Scholar]
  59. 59. 
    Coutinho RW, Boukerche A. 2019. Guidelines for the design of vehicular cloud infrastructures for connected autonomous vehicles. IEEE Wirel. Commun. 26:6–11
    [Google Scholar]
  60. 60. 
    Basu R, Araldo A, Akkinepally AP, Nahmias Biran BH, Basak K et al. 2018. Automated mobility-on-demand versus mass transit: a multi-modal activity-driven agent-based simulation approach. Transp. Res. Rec. 2672:608–18
    [Google Scholar]
  61. 61. 
    Wen J, Chen YX, Nassir N, Zhao J 2018. Transit-oriented autonomous vehicle operation with integrated demand-supply interaction. Transp. Res. C 97:216–34
    [Google Scholar]
  62. 62. 
    Alizadeh M, Wai HT, Chowdhury M, Goldsmith A, Scaglione A, Javidi T. 2016. Optimal pricing to manage electric vehicles in coupled power and transportation networks. IEEE Trans. Control Netw. Syst. 4:863–75
    [Google Scholar]
  63. 63. 
    Turan B, Pedarsani R, Alizadeh M 2020. Dynamic pricing and fleet management for electric autonomous mobility on demand systems. Transp. Res. C 121:102829
    [Google Scholar]
  64. 64. 
    Lanzetti N, Schiffer M, Ostrovsky M, Pavone M. 2020. On the interplay between self-driving cars and public transportation: a game theoretic perspective Work. Pap., Cah. GERAD G-2020-24, HEC Montréal Montreal, Can: https://www.gerad.ca/en/papers/G-2020-24
  65. 65. 
    Zardini G, Lanzetti N, Guerrini L, Frazzoli E, Dörfler F 2021. Game theory to study interactions between mobility stakeholders. 2021 IEEE 24th International Conference on Intelligent Transportation Systems (ITSC)pp. 2054–61 Piscataway, NJ: IEEE
    [Google Scholar]
  66. 66. 
    Neuburger H. 1971. The economics of heavily congested roads. Transp. Res. 5:283–93
    [Google Scholar]
  67. 67. 
    Ford LR Jr. 1956. Network flow theory Tech. Rep. P-923, RAND Corp. Santa Monica, CA:
  68. 68. 
    Guo G, Xu T. 2020. Vehicle rebalancing with charging scheduling in one-way car-sharing systems. IEEE Trans. Intell. Transp. Syst. In press. https://doi.org/10.1109/TITS.2020.3043594
    [Crossref] [Google Scholar]
  69. 69. 
    Kang D, Levin MW. 2021. Maximum-stability dispatch policy for shared autonomous vehicles. Transp. Res. B 148:132–51
    [Google Scholar]
  70. 70. 
    Nair R, Miller-Hooks E. 2011. Fleet management for vehicle sharing operations. Transp. Sci. 45:524–40
    [Google Scholar]
  71. 71. 
    Duan L, Wei Y, Zhang J, Xia Y. 2020. Centralized and decentralized autonomous dispatching strategy for dynamic autonomous taxi operation in hybrid request mode. Transp. Res. C 111:397–420
    [Google Scholar]
  72. 72. 
    Zgraggen J, Tsao M, Salazar M, Schiffer M, Pavone M. 2019. A model predictive control scheme for intermodal autonomous mobility-on-demand. 2019 22nd IEEE Intelligent Transportation Systems Conference (ITSC)1953–60 Piscataway, NJ: IEEE
    [Google Scholar]
  73. 73. 
    Tsao M, Milojevic D, Ruch C, Salazar M, Frazzoli E, Pavone M 2019. Model predictive control of ride-sharing autonomous mobility-on-demand systems. 2019 International Conference on Robotics and Automation (ICRA)6665–71 Piscataway, NJ: IEEE
    [Google Scholar]
  74. 74. 
    Chu KF, Lam AY, Li VO. 2021. Joint rebalancing and vehicle-to-grid coordination for autonomous vehicle public transportation system. IEEE Trans. Intell. Transp. Syst. In press. https://doi.org/10.1109/TITS.2021.3067044
    [Crossref] [Google Scholar]
  75. 75. 
    James J, Lam AY. 2017. Autonomous vehicle logistic system: joint routing and charging strategy. IEEE Trans. Intell. Transp. Syst. 19:2175–87
    [Google Scholar]
  76. 76. 
    Boewing F, Schiffer M, Salazar M, Pavone M. 2020. A vehicle coordination and charge scheduling algorithm for electric autonomous mobility-on-demand systems. 2020 American Control Conference (ACC)248–55 Piscataway, NJ: IEEE
    [Google Scholar]
  77. 77. 
    Hu L, Dong J. 2022. An artificial-neural-network-based model for real-time dispatching of electric autonomous taxis. IEEE Trans. Intell. Transp. Syst. 23:1519–28
    [Google Scholar]
  78. 78. 
    Jaillet P, Wagner MR. 2006. Online routing problems: value of advanced information as improved competitive ratios. Transp. Sci. 40:200–10
    [Google Scholar]
  79. 79. 
    Hyland M, Mahmassani HS. 2018. Dynamic autonomous vehicle fleet operations: optimization-based strategies to assign AVs to immediate traveler demand requests. Transp. Res. C 92:278–97
    [Google Scholar]
  80. 80. 
    George DK. 2012. Stochastic modeling and decentralized control policies for large-scale vehicle sharing systems via closed queueing networks PhD Thesis, Ohio State Univ. Columbus:
  81. 81. 
    Zhang R. 2016. Models and large-scale coordination algorithms for autonomous mobility-on-demand PhD Thesis, Stanford Univ. Stanford, CA:
  82. 82. 
    Zhang R, Pavone M. 2016. Control of robotic mobility-on-demand systems: a queueing-theoretical perspective. Int. J. Robot. Res. 35:186–203
    [Google Scholar]
  83. 83. 
    Ruch C, Gächter J, Hakenberg J, Frazzoli E. 2020. The +1 method: model-free adaptive repositioning policies for robotic multi-agent systems. IEEE Trans. Netw. Sci. Eng. 7:3171–84
    [Google Scholar]
  84. 84. 
    Treleaven K, Pavone M, Frazzoli E 2013. Asymptotically optimal algorithms for one-to-one pickup and delivery problems with applications to transportation systems. IEEE Trans. Autom. Control 58:2261–76
    [Google Scholar]
  85. 85. 
    Levina E, Bickel P. 2001. The Earth Mover's distance is the Mallows distance: some insights from statistics. 2001 8th IEEE International Conference on Computer Vision (ICCV) 2251–56 Piscataway, NJ: IEEE
    [Google Scholar]
  86. 86. 
    Albert M, Ruch C, Frazzoli E. 2019. Imbalance in mobility-on-demand systems: a stochastic model and distributed control approach. ACM Trans. Spat. Algorithms Syst. 5:1–22
    [Google Scholar]
  87. 87. 
    Babonneau F, Vial JP. 2008. An efficient method to compute traffic assignment problems with elastic demands. Transp. Sci. 42:249–60
    [Google Scholar]
  88. 88. 
    Dafermos SC, Sparrow FT. 1969. The traffic assignment problem for a general network. J. Res. Natl. Bur. Stand. B 73:91–118
    [Google Scholar]
  89. 89. 
    Gartner NH. 1980. Optimal traffic assignment with elastic demands: a review part I. Analysis framework. Transp. Sci. 14:174–91
    [Google Scholar]
  90. 90. 
    Gartner NH. 1980. Optimal traffic assignment with elastic demands: a review part II. Algorithmic approaches. Transp. Sci. 14:192–208
    [Google Scholar]
  91. 91. 
    US Bur. Public Roads 1964. Traffic Assignment Manual for Application with a Large, High Speed Computer 2 Washington, DC: US Gov. Print. Off.
  92. 92. 
    Salazar M, Tsao M, Aguiar I, Schiffer M, Pavone M. 2019. A congestion-aware routing scheme for autonomous mobility-on-demand systems. 2019 18th European Control Conference (ECC)3040–46 Piscataway, NJ: IEEE
    [Google Scholar]
  93. 93. 
    Zhang R, Rossi F, Pavone M. 2018. Analysis, control, and evaluation of mobility-on-demand systems: a queueing-theoretical approach. IEEE Trans. Control Netw. Syst. 6:115–26
    [Google Scholar]
  94. 94. 
    Carron A, Seccamonte F, Ruch C, Frazzoli E, Zeilinger MN 2021. Scalable model predictive control for autonomous mobility-on-demand systems. IEEE Trans. Control Syst. Technol. 29:635–44
    [Google Scholar]
  95. 95. 
    Yang K, Tsao MW, Xu X, Pavone M. 2020. Planning and operations of mixed fleets in mobility-on-demand systems. arXiv:2008.08131 [eess.SY]
  96. 96. 
    Belakaria S, Ammous M, Smith L, Sorour S, Abdel-Rahim A. 2019. Multi-class management with sub-class service for autonomous electric mobility on-demand systems. IEEE Trans. Veh. Technol. 68:7155–59
    [Google Scholar]
  97. 97. 
    Tucker N, Turan B, Alizadeh M. 2019. Online charge scheduling for electric vehicles in autonomous mobility on demand fleets. 2019 IEEE Intelligent Transportation Systems Conference (ITSC)226–31 Piscataway, NJ: IEEE
    [Google Scholar]
  98. 98. 
    Cenedese C, Fabiani F, Cucuzzella M, Scherpen JM, Cao M, Grammatico S 2019. Charging plug-in electric vehicles as a mixed-integer aggregative game. 2019 IEEE 58th Conference on Decision and Control (CDC)4904–9 Piscataway, NJ: IEEE
    [Google Scholar]
  99. 99. 
    Fagnant DJ, Kockelman KM. 2015. Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. A 77:167–81
    [Google Scholar]
  100. 100. 
    Sieber L, Ruch C, Hörl S, Axhausen KW, Frazzoli E. 2020. Improved public transportation in rural areas with self-driving cars: a study on the operation of Swiss train lines. Transp. Res. A 134:35–51
    [Google Scholar]
  101. 101. 
    Ma J, Li X, Zhou F, Hao W. 2017. Designing optimal autonomous vehicle sharing and reservation systems: a linear programming approach. Transp. Res. C 84:124–41
    [Google Scholar]
  102. 102. 
    Levin MW, Kockelman KM, Boyles SD, Li T. 2017. A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application. Comput. Environ. Urban Syst. 64:373–83
    [Google Scholar]
  103. 103. 
    Liu Z, Miwa T, Zeng W, Bell MG, Morikawa T. 2019. Dynamic shared autonomous taxi system considering on-time arrival reliability. Transp. Res. C 103:281–97
    [Google Scholar]
  104. 104. 
    Li L, Pantelidis T, Chow JY, Jabari SE. 2021. A real-time dispatching strategy for shared automated electric vehicles with performance guarantees. Transp. Res. E 152:102392
    [Google Scholar]
  105. 105. 
    James J, Yu W, Gu J 2019. Online vehicle routing with neural combinatorial optimization and deep reinforcement learning. IEEE Trans. Intell. Transp. Syst. 20:3806–17
    [Google Scholar]
  106. 106. 
    Cordeau JF, Laporte G, Potvin JY, Savelsbergh MW. 2007. Transportation on demand. Handb. Oper. Res. Manag. Sci. 14:429–66
    [Google Scholar]
  107. 107. 
    Dell'Amico M, Hadjicostantinou E, Iori M, Novellani S. 2014. The bike sharing rebalancing problem: mathematical formulations and benchmark instances. Omega 45:7–19
    [Google Scholar]
  108. 108. 
    Montané FAT, Galvao RD. 2006. A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service. Comput. Oper. Res. 33:595–619
    [Google Scholar]
  109. 109. 
    Smith SL. 2009. Task allocation and vehicle routing in dynamic environments PhD Thesis, Univ. Calif. Santa Barbara:
  110. 110. 
    Sayarshad HR, Chow JY. 2017. Non-myopic relocation of idle mobility-on-demand vehicles as a dynamic location-allocation-queueing problem. Transp. Res. E 106:60–77
    [Google Scholar]
  111. 111. 
    Wallar A, Van Der Zee M, Alonso-Mora J, Rus D. 2018. Vehicle rebalancing for mobility-on-demand systems with ride-sharing. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)4539–46 Piscataway, NJ: IEEE
    [Google Scholar]
  112. 112. 
    Pavone M, Bisnik N, Frazzoli E, Isler V 2009. A stochastic and dynamic vehicle routing problem with time windows and customer impatience. Mobile Netw. Appl. 14:350–64
    [Google Scholar]
  113. 113. 
    Pavone M. 2010. Dynamic vehicle routing for robotic networks PhD Thesis, Mass. Inst. Technol. Cambridge:
  114. 114. 
    Lin K, Zhao R, Xu Z, Zhou J. 2018. Efficient large-scale fleet management via multi-agent deep reinforcement learning. KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining1774–83 New York: ACM
    [Google Scholar]
  115. 115. 
    Guériau M, Cugurullo F, Acheampong RA, Dusparic I. 2020. Shared autonomous mobility on demand: a learning-based approach and its performance in the presence of traffic congestion. IEEE Intell. Transp. Syst. Mag. 12:4208–18
    [Google Scholar]
  116. 116. 
    Mao C, Shen Z 2018. A reinforcement learning framework for the adaptive routing problem in stochastic time-dependent network. Transp. Res. C 93:179–97
    [Google Scholar]
  117. 117. 
    Han M, Senellart P, Bressan S, Wu H 2016. Routing an autonomous taxi with reinforcement learning. CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management2421–24 New York: ACM
    [Google Scholar]
  118. 118. 
    Smith SL, Pavone M, Schwager M, Frazzoli E, Rus D 2013. Rebalancing the rebalancers: optimally routing vehicles and drivers in mobility-on-demand systems. 2013 American Control Conference2362–67 Piscataway, NJ: IEEE
    [Google Scholar]
  119. 119. 
    Martin L. 2020. Rebalancing in shared mobility systems PhD Thesis, Tech. Univ. München Munich:
  120. 120. 
    Iglesias R, Rossi F, Wang K, Hallac D, Leskovec J, Pavone M 2018. Data-driven model predictive control of autonomous mobility-on-demand systems. 2018 IEEE International Conference on Robotics and Automation (ICRA)6019–25 Piscataway, NJ: IEEE
    [Google Scholar]
  121. 121. 
    Dandl F, Hyland M, Bogenberger K, Mahmassani HS. 2019. Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets. Transportation 46:1975–96
    [Google Scholar]
  122. 122. 
    Tsao M, Iglesias R, Pavone M. 2018. Stochastic model predictive control for autonomous mobility on demand. 2018 21st IEEE International Conference on Intelligent Transportation Systems (ITSC)3941–48 Piscataway, NJ: IEEE
    [Google Scholar]
  123. 123. 
    Zhang R, Rossi F, Pavone M. 2016. Model predictive control of autonomous mobility-on-demand systems. 2016 IEEE International Conference on Robotics and Automation (ICRA)1382–89 Piscataway, NJ: IEEE
    [Google Scholar]
  124. 124. 
    Iacobucci R, McLellan B, Tezuka T. 2019. Optimization of shared autonomous electric vehicles operations with charge scheduling and vehicle-to-grid. Transp. Res. C 100:34–52
    [Google Scholar]
  125. 125. 
    Solovey K, Salazar M, Pavone M 2019. Scalable and congestion-aware routing for autonomous mobility-on-demand via Frank-Wolfe optimization. Robotics: Science and Systems XV A Bicchi, H Kress-Gazit, S Hutchinson, pap 66 N.p.: Robot. Sci. Syst. Found.
    [Google Scholar]
  126. 126. 
    Miao F, Han S, Hendawi AM, Khalefa ME, Stankovic JA, Pappas GJ. 2017. Data-driven distributionally robust vehicle balancing using dynamic region partitions. 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems261–72 Piscataway, NJ: IEEE
    [Google Scholar]
  127. 127. 
    Gammelli D, Yang K, Harrison J, Rodrigues F, Pereira FC, Pavone M. 2021. Graph neural network reinforcement learning for autonomous mobility-on-demand systems. arXiv:2104.11434 [eess.SY]
  128. 128. 
    Levin MW. 2017. Congestion-aware system optimal route choice for shared autonomous vehicles. Transp. Res. C 82:229–47
    [Google Scholar]
  129. 129. 
    Spieser K, Samaranayake S, Frazzoli E 2016. Vehicle routing for shared-mobility systems with time-varying demand. 2016 American Control Conference (ACC)796–802 Piscataway, NJ: IEEE
    [Google Scholar]
  130. 130. 
    Miller J, How JP. 2017. Predictive positioning and quality of service ridesharing for campus mobility on demand systems. 2017 IEEE International Conference on Robotics and Automation (ICRA)1402–8 Piscataway, NJ: IEEE
    [Google Scholar]
  131. 131. 
    Ma TY, Rasulkhani S, Chow JY, Klein S. 2019. A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers. Transp. Res. E 128:417–42
    [Google Scholar]
  132. 132. 
    Fielbaum A, Bai X, Alonso-Mora J. 2021. On-demand ridesharing with optimized pick-up and drop-off walking locations. Transp. Res. C 126:103061
    [Google Scholar]
  133. 133. 
    Horni A, Nagel K, Axhausen KW. 2016. The Multi-Agent Transport Simulation MATSim London: Ubiquity
  134. 134. 
    Adnan M, Pereira FC, Azevedo CML, Basak K, Lovric M et al. 2016. SimMobility: a multi-scale integrated agent-based simulation platform Paper presented at the Transportation Research Board 95th Annual Meeting Washington, DC: Jan. 10–14
  135. 135. 
    Krajzewicz D, Hertkorn G, Rössel C, Wagner P. 2002. SUMO (Simulation of Urban MObility): an open-source traffic simulation. Proceedings of the 4th Middle East Symposium on Simulation and Modelling (MESM2002) A Al-Akaidi 183–87 Ghent, Belg: SCS
    [Google Scholar]
  136. 136. 
    Keimer A, Bayen A. 2020. Routing on traffic networks incorporating past memory up to real-time information on the network state. Annu. Rev. Control Robot. Auton. Syst. 3:151–72
    [Google Scholar]
  137. 137. 
    Azevedo CL, Marczuk K, Raveau S, Soh H, Adnan M et al. 2016. Microsimulation of demand and supply of autonomous mobility on demand. Transp. Res. Rec. 2564:21–30
    [Google Scholar]
  138. 138. 
    Auld J, Hope M, Ley H, Sokolov V, Xu B, Zhang K. 2016. Polaris: agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations. Transp. Res. C 64:101–16
    [Google Scholar]
  139. 139. 
    Jäger B, Brickwedde C, Lienkamp M. 2018. Multi-agent simulation of a demand-responsive transit system operated by autonomous vehicles. Transp. Res. Rec. 2672:764–74
    [Google Scholar]
  140. 140. 
    Payne HJ. 1979. FREFLO: a macroscopic simulation model of freeway traffic. Transp. Res. Rec. 722:68–77
    [Google Scholar]
  141. 141. 
    Ruch C, Hörl S, Frazzoli E 2018. AMoDeus, a simulation-based testbed for autonomous mobility-on-demand systems. 2018 21st IEEE International Conference on Intelligent Transportation Systems (ITSC)3639–44 Piscataway, NJ: IEEE
    [Google Scholar]
  142. 142. 
    Hörl S. 2017. Agent-based simulation of autonomous taxi services with dynamic demand responses. Procedia Comput. Sci. 109:899–904
    [Google Scholar]
  143. 143. 
    Miller JJL. 2017. Demand estimation and fleet management for autonomous mobility on demand systems PhD Thesis, Mass. Inst. Technol. Cambridge:
  144. 144. 
    Paull L, Tani J, Ahn H, Alonso-Mora J, Carlone L et al. 2017. Duckietown: an open, inexpensive and flexible platform for autonomy education and research. 2017 IEEE International Conference on Robotics and Automation (ICRA)1497–504 Piscataway, NJ: IEEE
    [Google Scholar]
  145. 145. 
    Haklay M, Weber P. 2008. OpenStreetMap: user-generated street maps. IEEE Pervasive Comput 7:12–18
    [Google Scholar]
  146. 146. 
    McHugh B 2013. Pioneering open data standards: the GTFS story. Beyond Transparency: Open Data and the Future of Civic Innovation B Goldstein, L Dyson 125–36 San Francisco: Code Am.
    [Google Scholar]
  147. 147. 
    Donovan B, Work D. 2016. New York City taxi trip data (2010–2013) Illinois Data Bank, Univ. Ill. Urbana-Champaign deposited May 19. https://doi.org/10.13012/J8PN93H8
  148. 148. 
    Matyas MB. 2020. Investigating individual preferences for new mobility services: the case of “mobility as a service” products PhD Thesis, Univ. Coll London, London:
  149. 149. 
    Kamel J, Vosooghi R, Puchinger J, Ksontini F, Sirin G 2019. Exploring the impact of user preferences on shared autonomous vehicle modal split: a multi-agent simulation approach. Transp. Res. Procedia 37:115–22
    [Google Scholar]
  150. 150. 
    Rossi F. 2018. On the interaction between autonomous mobility-on-demand systems and the built environment: models and large scale coordination algorithms PhD Thesis, Stanford Univ. Stanford, CA:
  151. 151. 
    Gawron JH, Keoleian GA, De Kleine RD, Wallington TJ, Kim HC 2018. Life cycle assessment of connected and automated vehicles: sensing and computing subsystem and vehicle level effects. Environ. Sci. Technol. 52:3249–56
    [Google Scholar]
  152. 152. 
    Bonanni TM, Zardini GJ, Seccamonte F. 2021. System and method for updating map data US Patent Appl. 17/129,420
  153. 153. 
    Kim SW, Liu W, Ang MH, Frazzoli E, Rus D 2015. The impact of cooperative perception on decision making and planning of autonomous vehicles. IEEE Intell. Transp. Syst. Mag. 7:339–50
    [Google Scholar]
  154. 154. 
    Beojone CV, Geroliminis N. 2021. On the inefficiency of ride-sourcing services towards urban congestion. Transp. Res. C 124:102890
    [Google Scholar]
  155. 155. 
    Zardini G, Censi A, Frazzoli E 2021. Co-design of autonomous systems: from hardware selection to control synthesis. 2021 European Control Conference (ECC)pp. 682–89 Piscataway, NJ: IEEE
    [Google Scholar]
  156. 156. 
    Zardini G, Lanzetti N, Salazar M, Censi A, Frazzoli E, Pavone M 2020. On the co-design of AV-enabled mobility systems. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) Piscataway, NJ: IEEE https://doi.org/10.1109/ITSC45102.2020.9294499
    [Crossref] [Google Scholar]
  157. 157. 
    Censi A. 2017. Uncertainty in monotone codesign problems. IEEE Robot. Autom. Lett. 2:1556–63
    [Google Scholar]
  158. 158. 
    Ostrovsky M, Schwarz M. 2019. Carpooling and the economics of self-driving cars. EC '19: Proceedings of the 2019 ACM Conference on Economics and Computation581–82 New York: ACM
    [Google Scholar]
  159. 159. 
    Paccagnan D, Gairing M. 2021. congestion games, taxes achieve optimal approximation. arXiv:2105.07480 [cs.GT]
  160. 160. 
    Dandl F, Bogenberger K, Mahmassani HS. 2019. Autonomous mobility-on-demand real-time gaming framework. 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) Piscataway, NJ: IEEE https://doi.org/10.1109/MTITS.2019.8883286
    [Crossref] [Google Scholar]
  161. 161. 
    Lanzetti N, Schiffer M, Ostrovsky M, Pavone M. 2021. On the interplay between self-driving cars and public transportation. arXiv:2109.01627 [eess.SY]
    [Google Scholar]
  162. 162. 
    Psaraftis HN, Kontovas CA. 2010. Balancing the economic and environmental performance of maritime transportation. Transp. Res. D 15:458–62
    [Google Scholar]
  163. 163. 
    Yan A, Howe B 2020. Fairness in practice: a survey on equity in urban mobility. Bull. IEEE Comput. Soc. Tech. Comm. Data Eng. 42:49–63
    [Google Scholar]
  164. 164. 
    Jalota D, Solovey K, Zoepf S, Pavone M 2021. Balancing fairness and efficiency in traffic routing via interpolated traffic assignment. arXiv:2104.00098 [eess.SY]
  165. 165. 
    Angelelli E, Morandi V, Speranza MG. 2020. Minimizing the total travel time with limited unfairness in traffic networks. Comput. Oper. Res. 123:105016
    [Google Scholar]
  166. 166. 
    He BY, Chow JY. 2019. Optimal privacy control for transport network data sharing. Transp. Res. Procedia 38:792–811
    [Google Scholar]
  167. 167. 
    Isaac M. 2017. How Uber deceives the authorities worldwide. New York Times Mar. 3. https://www.nytimes.com/2017/03/03/technology/uber-greyball-program-evade-authorities.html
    [Google Scholar]
  168. 168. 
    Li Y, Ouyang K, Li N, Rahmani R, Yang H, Pei Y 2020. A blockchain-assisted intelligent transportation system promoting data services with privacy protection. Sensors 20:2483
    [Google Scholar]
  169. 169. 
    Tsao M, Yang K, Zoepf S, Pavone M 2021. Trust but verify: cryptographic data privacy for mobility management. arXiv:2104.07768 [cs.CR]
  170. 170. 
    Iglesias R, Rossi F, Zhang R, Pavone M. 2019. A BCMP network approach to modeling and controlling autonomous mobility-on-demand systems. Int. J. Robot. Res. 38:357–74
    [Google Scholar]
  171. 171. 
    Lazar DA, Byk E, Sadigh D, Pedarsani R. 2019. Learning how to dynamically route autonomous vehicles on shared roads. arXiv:1909.03664 [math.OC]
  172. 172. 
    Gaechter J, Zanardi A, Ruch C, Frazzoli E, Pavone M 2021. Image representation of a city and its taxi fleet for end-to-end learning of rebalancing policies. 2021 International Conference on Robotics and Automation (ICRA)pp. 8076–82 Piscataway, NJ: IEEE
    [Google Scholar]
  173. 173. 
    Bischoff J, Maciejewski M. 2016. Simulation of city-wide replacement of private cars with autonomous taxis in Berlin. Procedia Comput. Sci. 83:237–44
    [Google Scholar]
  174. 174. 
    Maciejewski M, Bischoff J, Nagel K. 2016. An assignment-based approach to efficient real-time city-scale taxi dispatching. IEEE Intell. Syst. 31:68–77
    [Google Scholar]
  175. 175. 
    Chen X, Miao F, Pappas GJ, Preciado V. 2017. Hierarchical data-driven vehicle dispatch and ride-sharing. 2017 IEEE 56th Annual Conference on Decision and Control (CDC)4458–63 Piscataway, NJ: IEEE
    [Google Scholar]
  176. 176. 
    Li M, Zheng N, Wu X, Huo X. 2019. An efficient matching method for dispatching autonomous vehicles. 2019 22nd IEEE Intelligent Transportation Systems Conference (ITSC)3013–18 Piscataway, NJ: IEEE
    [Google Scholar]
  177. 177. 
    Braverman A, Dai JG, Liu X, Ying L. 2019. Empty-car routing in ridesharing systems. Oper. Res. 67:1437–52
    [Google Scholar]
  178. 178. 
    Wen J, Zhao J, Jaillet P. 2017. Rebalancing shared mobility-on-demand systems: a reinforcement learning approach. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)220–25 Piscataway, NJ: IEEE
    [Google Scholar]
  179. 179. 
    Spieser K, Samaranayake S, Gruel W, Frazzoli E 2016. Shared-vehicle mobility-on-demand systems: a fleet operator's guide to rebalancing empty vehicles Paper presented at the Transportation Research Board 95th Annual Meeting Washington, DC: Jan. 10–14
  180. 180. 
    Marczuk KA, Soh HS, Azevedo CM, Lee DH, Frazzoli E. 2016. Simulation framework for rebalancing of autonomous mobility on demand systems. MATEC Web Conf 81:01005
    [Google Scholar]

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