A Multiagent-Based Approach for Vehicle Routing by Considering Both Arriving on Time and Total Travel Time

Arriving on time and total travel time are two important properties for vehicle routing. Existing route guidance approaches always consider them independently, because they may conflict with each other. In this article, we develop a semi-decentralized multiagent-based vehicle routing approach where vehicle agents follow the local route guidance by infrastructure agents at each intersection, and infrastructure agents perform the route guidance by solving a route assignment problem. It integrates the two properties by expressing them as two objective terms of the route assignment problem. Regarding arriving on time, it is formulated based on the probability tail model, which aims to maximize the probability of reaching destination before deadline. Regarding total travel time, it is formulated as a weighted quadratic term, which aims to minimize the expected travel time from the current location to the destination based on the potential route assignment. The weight for total travel time is designed to be comparatively large if the deadline is loose. Additionally, we improve the proposed approach in two aspects, including travel time prediction and computational efficiency. Experimental results on real road networks justify its ability to increase the average probability of arriving on time, reduce total travel time, and enhance the overall routing performance.

[1]  Jin Li,et al.  Route Guidance Mechanism with Centralized Information Control in Large-scale Crowd's Activities , 2009, 2009 International Joint Conference on Artificial Intelligence.

[2]  Nicholas Jing Yuan,et al.  Sensing the Pulse of Urban Refueling Behavior , 2015, ACM Trans. Intell. Syst. Technol..

[3]  Daniel Krajzewicz,et al.  SUMO - Simulation of Urban MObility An Overview , 2011 .

[4]  Karine Zeitouni,et al.  Proactive Vehicular Traffic Rerouting for Lower Travel Time , 2013, IEEE Transactions on Vehicular Technology.

[5]  Jie Zhang,et al.  Finding the Shortest Path in Stochastic Vehicle Routing: A Cardinality Minimization Approach , 2016, IEEE Transactions on Intelligent Transportation Systems.

[6]  Stephen P. Boyd,et al.  1 Trend Filtering , 2009, SIAM Rev..

[7]  Y. Nie,et al.  Shortest path problem considering on-time arrival probability , 2009 .

[8]  Jie Zhang,et al.  Multiagent-Based Route Guidance for Increasing the Chance of Arrival on Time , 2016, AAAI.

[9]  Bo An,et al.  Optimizing Efficiency of Taxi Systems: Scaling-up and Handling Arbitrary Constraints , 2015, AAMAS.

[10]  Sascha Ossowski,et al.  Route guidance: Bridging system and user optimization in traffic assignment , 2015, Neurocomputing.

[11]  Xin Zhao,et al.  Research on Dynamic Route Guidance for an Emergency Vehicle Considering the Intersection Delay , 2014 .

[12]  Rong Yang,et al.  Scaling-up Security Games with Boundedly Rational Adversaries: A Cutting-plane Approach , 2013, IJCAI.

[13]  Xing Xie,et al.  Learning travel recommendations from user-generated GPS traces , 2011, TIST.

[14]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[15]  R. Kalaba,et al.  Arriving on Time , 2005 .

[16]  Daniela Rus,et al.  Practical Route Planning Under Delay Uncertainty: Stochastic Shortest Path Queries , 2012, Robotics: Science and Systems.

[17]  Ali Selamat,et al.  Modeling of route planning system based on Q value-based dynamic programming with multi-agent reinforcement learning algorithms , 2014, Eng. Appl. Artif. Intell..

[18]  Hari Balakrishnan,et al.  Stochastic motion planning and applications to traffic , 2011, Int. J. Robotics Res..

[19]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[20]  Jennifer McManis,et al.  A Multi-Agent based vehicles re-routing system for unexpected traffic congestion avoidance , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[21]  Danny Weyns,et al.  A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multiagent Systems , 2011, IEEE Transactions on Intelligent Transportation Systems.

[22]  Jie Zhang,et al.  Maximizing the Probability of Arriving on Time: A Practical Q-Learning Method , 2017, AAAI.

[23]  Hongliang Guo,et al.  A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion , 2017, IEEE Transactions on Intelligent Transportation Systems.

[24]  Ali A. Ghorbani,et al.  A multiagent system for optimizing urban traffic , 2003, IEEE/WIC International Conference on Intelligent Agent Technology, 2003. IAT 2003..

[25]  Nicholas R. Jennings,et al.  Intention-aware routing to minimise delays at electric vehicle charging stations: the research related to this demonstration has been published at IJCAI 2013 [1] , 2013, AIIP '13.

[26]  Jie Zhang,et al.  Improving the Efficiency of Stochastic Vehicle Routing: A Partial Lagrange Multiplier Method , 2016, IEEE Transactions on Vehicular Technology.

[27]  Ana L. C. Bazzan,et al.  Agent-based simulation of mobility in real-world transportation networks: effects of acquiring information and replanning en-route , 2012, AAMAS.

[28]  Matthew Brand,et al.  Stochastic Shortest Paths Via Quasi-convex Maximization , 2006, ESA.

[29]  Koichi Kurumatani,et al.  Smooth traffic flow with a cooperative car navigation system , 2005, AAMAS '05.

[30]  Yu Zheng,et al.  Travel time estimation of a path using sparse trajectories , 2014, KDD.

[31]  Licia Capra,et al.  Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.

[32]  Terry L. Zimmerman,et al.  Multi-Agent Management of Joint Schedules , 2006, AAAI Spring Symposium: Distributed Plan and Schedule Management.

[33]  Manfred Morari,et al.  Lifted Evaluation of mp-MIQP Solutions , 2015, IEEE Transactions on Automatic Control.

[34]  Jie Zhang,et al.  Routing Multiple Vehicles Cooperatively: Minimizing Road Network Breakdown Probability , 2017, IEEE Transactions on Emerging Topics in Computational Intelligence.

[35]  Stephen F. Smith,et al.  Incremental Management of Oversubscribed Vehicle Schedules in Dynamic Dial-A-Ride Problems , 2012, AAAI.

[36]  Stephen F. Smith,et al.  A few good agents: multi-agent social learning , 2008, AAMAS.

[37]  Xing Xie,et al.  T-drive: driving directions based on taxi trajectories , 2010, GIS '10.

[38]  Jie Zhang,et al.  A pheromone-based traffic management model for vehicle re-routing and traffic light control , 2014, AAMAS.

[39]  Stephen P. Boyd,et al.  Enhancing Sparsity by Reweighted ℓ1 Minimization , 2007, 0711.1612.

[40]  Mikhail Chester,et al.  Can Disruptive Technologies, On-Demand Mobility, and Biofuels Improve Transportation Environmental Sustainability? A Review of Recent Research , 2015 .

[41]  Zilu Liang,et al.  A route guidance system with personalized rerouting for reducing traveling time of vehicles in urban areas , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[42]  Markos Papageorgiou,et al.  Dynamic modeling, assignment, and route guidance in traffic networks , 1990 .

[43]  Ana L. C. Bazzan,et al.  A review on agent-based technology for traffic and transportation , 2013, The Knowledge Engineering Review.