Comparing paradigms for strategy learning of route choice with traffic information under uncertainty

被引:10
作者
Ma, Tai-Yu [1 ]
Di Pace, Roberta [2 ]
机构
[1] LISER, Maison Sci Humaines, 11 Porte Sci, L-4366 Esch Sur Alzette, Luxembourg
[2] Univ Salerno, Via Giovanni Paolo 2,132, I-84084 Fisciano, SA, Italy
关键词
Advanced traveller information system; Fictitious play; Bayesian learning; Reinforcement learning; Compliance; Route choice; DAY-TO-DAY; TRAVEL-TIME INFORMATION; FICTITIOUS PLAY; RATIONAL CHOICE; REPEATED GAMES; FORM GAMES; BEHAVIOR; MODEL; EQUILIBRIUM; DYNAMICS;
D O I
10.1016/j.eswa.2017.07.008
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper aims to model the traveller's day-to-day route choice in the case of an Advanced Traveller Information System (ATIS) through two learning paradigms: reinforcement-based and belief-based. The reinforcement learning approach is adopted in both a basic version and an extended one. Similarly, the belief-learning approach is adopted in both a Joint Strategy Fictitious Play version and in a Bayesian-learning one. All the models are compared and validated based on data collected by means of a stated preference experiment. The models explicitly account for the reliability of the information system, as this interacts with the inherent dispersion of network travel times and determines the overall level of uncertainty affecting the travellers' adaptive learning behaviour. The experiment is then designed to simulate different levels of reliability for the ATIS. Results show that for intermediate and high levels of information accuracy, joint strategy fictitious play best predicts the respondents' route choice behaviour under information provision, suggesting that a best-reply strategy is used by travellers for their route choices. In low information accuracy, the result suggests the payoff variability moves the choice behaviour toward randomness. The proposed approach provides useful tools to model travellers' adaptive route choice behaviour and contributes to the support of effective ATIS design. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:352 / 367
页数:16
相关论文
共 74 条
[1]  
[Anonymous], FRONTIERS ECONOMETRI
[2]  
[Anonymous], 1998, THEORY LEARNING GAME
[3]   Modeling learning and adaptation processes in activity-travel choice - A framework and numerical experiment [J].
Arentze, T ;
Timmermans, H .
TRANSPORTATION, 2003, 30 (01) :37-62
[4]   Information gain, novelty seeking and travel: a model of dynamic activity-travel behavior under conditions of uncertainty [J].
Arentze, TA ;
Timmermans, HJP .
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, 2005, 39 (2-3) :125-145
[5]  
ARTHUR WB, 1994, AM ECON REV, V84, P406
[6]   Sensitivity to travel time variability: Travelers' learning perspective [J].
Avineri, E ;
Prashker, JN .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2005, 13 (02) :157-183
[7]   The impact of travel time information on travelers' learning under uncertainty [J].
Avineri, Erel ;
Prashker, Joseph N. .
TRANSPORTATION, 2006, 33 (04) :393-408
[8]  
AXHAUSEN KW, 1991, ADVANCED TELEMATICS IN ROAD TRANSPORT, VOLS 1 AND 2, P1020
[9]   Response to Travel Information: A Behavioural Review [J].
Ben-Elia, Eran ;
Avineri, Erel .
TRANSPORT REVIEWS, 2015, 35 (03) :352-377
[10]   The impact of travel information's accuracy on route-choice [J].
Ben-Elia, Eran ;
Di Pace, Roberta ;
Bifulco, Gennaro N. ;
Shiftan, Yoram .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2013, 26 :146-159