Models, algorithms, and evaluation for autonomous mobility-on-demand systems

This tutorial paper examines the operational and economic aspects of autonomous mobility-on-demand (AMoD) systems, a rapidly emerging mode of personal transportation wherein robotic, self-driving vehicles transport customers in a given environment. We address AMoD systems along three dimensions: (1) modeling - analytical models capable of capturing the salient dynamic and stochastic features of customer demand, (2) control - coordination algorithms for the vehicles aimed at stability and subsequently throughput maximization, and (3) economic - fleet sizing and financial analyses for case studies of New York City and Singapore. Collectively, the models and algorithms presented in this paper enable a rigorous assessment of the value of AMoD systems. In particular, the case study of New York City shows that the current taxi demand in Manhattan can be met with about 8,000 robotic vehicles (roughly 70% of the size of the current taxi fleet), while the case study of Singapore suggests that an AMoD system can meet the personal mobility need of the entire population of Singapore with a number of robotic vehicles that is less than 40% of the current number of passenger vehicles. Directions for future research on AMoD systems are presented and discussed.

[1]  Leonidas J. Guibas,et al.  A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[2]  Dimitris Bertsimas,et al.  Stochastic and Dynamic Vehicle Routing in the Euclidean Plane with Multiple Capacitated Vehicles , 1993, Oper. Res..

[3]  Emilio Frazzoli,et al.  Asymptotically Optimal Algorithms for Pickup and Delivery Problems with Application to Large-Scale Transportation Systems , 2012, ArXiv.

[4]  Patrick Jaillet,et al.  Online Routing Problems: Value of Advanced Information as Improved Competitive Ratios , 2006, Transp. Sci..

[5]  Terry L. Friesz,et al.  Dynamic Network Traffic Assignment Considered as a Continuous Time Optimal Control Problem , 1989, Oper. Res..

[6]  Anne Olhoff,et al.  The Emissions Gap Report 2013: A UNEP Synthesis Report , 2013 .

[7]  Emilio Frazzoli,et al.  Rebalancing the rebalancers: optimally routing vehicles and drivers in mobility-on-demand systems , 2013, 2013 American Control Conference.

[8]  K. Mani Chandy,et al.  Open, Closed, and Mixed Networks of Queues with Different Classes of Customers , 1975, JACM.

[9]  Gilbert Laporte,et al.  Dynamic pickup and delivery problems , 2010, Eur. J. Oper. Res..

[10]  Leen Stougie,et al.  On-line single-server dial-a-ride problems , 2001, Theor. Comput. Sci..

[11]  Oar,et al.  Greenhouse Gas Equivalencies Calculator , 2015 .

[12]  Emilio Frazzoli,et al.  Asymptotically Optimal Algorithms for One-to-One Pickup and Delivery Problems With Applications to Transportation Systems , 2012, IEEE Transactions on Automatic Control.

[13]  Micha Sharir,et al.  Vertical Decomposition of Shallow Levels in 3-Dimensional Arrangements and Its Applications , 1999, SIAM J. Comput..

[14]  Marco Pavone,et al.  Control of robotic mobility-on-demand systems: A queueing-theoretical perspective , 2014, Int. J. Robotics Res..

[15]  Emilio Frazzoli,et al.  Multi-vehicle Routing , 2015, Encyclopedia of Systems and Control.

[16]  Emilio Frazzoli,et al.  Models and asymptotically optimal algorithms for pickup and delivery problems on roadmaps , 2012 .

[17]  Ludger Riischendorf.  The Wasserstein distance and approximation theorems , 1985 .

[18]  Marco Pavone,et al.  Dynamic Vehicle Routing for Robotic Networks , 2010 .

[19]  Ludger Rüschendorf,et al.  The Wasserstein distance and approximation theorems , 1985 .

[20]  P. Holtberg,et al.  International Energy Outlook 2016 With Projections to 2040 , 2016 .

[21]  Athanasios K. Ziliaskopoulos,et al.  Foundations of Dynamic Traffic Assignment: The Past, the Present and the Future , 2001 .

[22]  D. Schrank,et al.  2012 Urban Mobility Report , 2002 .

[23]  Emilio Frazzoli,et al.  Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore , 2014 .

[24]  A. Santos,et al.  Summary of Travel Trends: 2009 National Household Travel Survey , 2011 .

[25]  P. S. Hu,et al.  Summary of Travel Trends , 2004 .

[26]  Hai-Jun Huang,et al.  Modeling and solving the dynamic user equilibrium route and departure time choice problem in network with queues , 2002 .

[27]  Jonathan Reades,et al.  Reinventing the automobile: personal urban mobility for the 21st century , 2013 .

[28]  Dimitris Bertsimas,et al.  A Stochastic and Dynamic Vehicle Routing Problem in the Euclidean Plane , 1991, Oper. Res..

[29]  Emilio Frazzoli,et al.  Fundamental performance limits and efficient polices for Transportation-On-Demand systems , 2010, 49th IEEE Conference on Decision and Control (CDC).

[30]  Simchi-LeviDavid,et al.  A New Generation of Vehicle Routing Research , 1996 .

[31]  David Simchi-Levi,et al.  A New Generation of Vehicle Routing Research: Robust Algorithms, Addressing Uncertainty , 1996, Oper. Res..

[32]  Emilio Frazzoli,et al.  Robotic load balancing for mobility-on-demand systems , 2012, Int. J. Robotics Res..

[33]  R. Serfozo.  Introduction to Stochastic Networks , 1999 .

[34]  Marco Pavone,et al.  Autonomous Mobility-on-Demand Systems for Future Urban Mobility , 2016 .

[35]  Dimitri P. Bertsekas,et al.  Data Networks , 1986 .

[36]  Michael I. Jordan,et al.  Revisiting k-means: New Algorithms via Bayesian Nonparametrics , 2011, ICML.

[37]  Cathy H. Xia,et al.  Fleet-sizing and service availability for a vehicle rental system via closed queueing networks , 2011, Eur. J. Oper. Res..

[38]  Marco Pavone,et al.  A queueing network approach to the analysis and control of mobility-on-demand systems , 2014, 2015 American Control Conference (ACC).

[39]  Richard C. Larson,et al.  Urban Operations Research , 1981 .