TY - JOUR
T1 - Comparing paradigms for strategy learning of route choice with traffic information under uncertainty.
AU - Ma, Tai-Yu
AU - Di Pace, Roberta
PY - 2017/12
Y1 - 2017/12
N2 - 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.
AB - 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.
KW - Advanced traveller information system
KW - bayesian learning
KW - compliance
KW - fictitious play
KW - reinforcement learning
KW - route choice
UR - http://www.sciencedirect.com/science/article/pii/S0957417417304785?via%3Dihub
U2 - 10.1016/j.eswa.2017.07.008
DO - 10.1016/j.eswa.2017.07.008
M3 - Article
SN - 0957-4174
VL - 88
SP - 352
EP - 367
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 1
ER -