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Social and Behavioral Sciences Commons

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Public Affairs, Public Policy and Public Administration

Singapore Management University

Research Collection School Of Computing and Information Systems

Arriving on time

Publication Year

Articles 1 - 2 of 2

Full-Text Articles in Social and Behavioral Sciences

Using Reinforcement Learning To Minimize The Probability Of Delay Occurrence In Transportation, Zhiguang Cao, Hongliang Guo, Wen Song, Kaizhou Gao, Zhengghua Chen, Le Zhang, Xuexi Zhang Mar 2020

Using Reinforcement Learning To Minimize The Probability Of Delay Occurrence In Transportation, Zhiguang Cao, Hongliang Guo, Wen Song, Kaizhou Gao, Zhengghua Chen, Le Zhang, Xuexi Zhang

Research Collection School Of Computing and Information Systems

Reducing traffic delay is of crucial importance for the development of sustainable transportation systems, which is a challenging task in the studies of stochastic shortest path (SSP) problem. Existing methods based on the probability tail model to solve the SSP problem, seek for the path that minimizes the probability of delay occurrence, which is equal to maximizing the probability of reaching the destination before a deadline (i.e., arriving on time). However, they suffer from low accuracy or high computational cost. Therefore, we design a novel and practical Q-learning approach where the converged Q-values have the practical meaning as the actual …


A Multiagent-Based Approach For Vehicle Routing By Considering Both Arriving On Time And Total Travel Time, Zhiguang Cao, Hongliang Guo, Jie Zhang Dec 2017

A Multiagent-Based Approach For Vehicle Routing By Considering Both Arriving On Time And Total Travel Time, Zhiguang Cao, Hongliang Guo, Jie Zhang

Research Collection School Of Computing and Information Systems

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 …