Application of Stochastic Learning Automata for Modeling Departure Time and Route Choice Behavior

2002 ◽  
Vol 1807 (1) ◽  
pp. 154-162 ◽  
Author(s):  
Kaan Ozbay ◽  
Aleek Datta ◽  
Pushkin Kachroo
Keyword(s):  
Learning Automata ◽  
Route Choice ◽  
User Equilibrium ◽  
Learning Behavior ◽  
Reinforcement Scheme ◽  
Route Choice Behavior ◽  
Long Run ◽  
Stochastic Learning ◽  
Departure Time ◽  
Departure Time Choice

Stochastic learning automata (SLA) theory is used to model the learning behavior of commuters within the context of the combined departure time route choice (CDTRC) problem. The SLA model uses a reinforcement scheme to model the learning behavior of drivers. A multiaction linear reward-ϵ-penalty reinforcement scheme was introduced to model the learning behavior of travelers based on past departure time choice and route choice. A traffic simulation was developed to test the model. The results of the simulation are intended to show that drivers learn the best CDTRC option, and the network achieves user equilibrium in the long run. Results indicate that the developed SLA model accurately portrays the learning behavior of drivers, while the network satisfies user equilibrium conditions.

scholarly journals User Equilibrium Analysis Considering Travelers’ Context-Dependent Route Choice Behavior on the Risky Traffic Network

2020 ◽  
Vol 12 (17) ◽  
pp. 6706
Author(s):  
Qinghui Xu ◽  
Xiangfeng Ji
Keyword(s):  
High Probability ◽  
Choice Behavior ◽  
Route Choice ◽  
User Equilibrium ◽  
Equilibrium Analysis ◽  
Risk Averse ◽  
Risk Seeking ◽  
Route Choice Behavior ◽  
Risk Neutral ◽  
Context Dependent

This paper studies travelers’ context-dependent route choice behavior in a risky trafficnetwork from a long-term perspective, focusing on the effect of travelers’ salience characteristics. In particular, a flow-dependent salience theory is proposed for this analysis, where the flow denotes the traffic flow on the risky route. In the proposed model, travelers’ attention is drawn to the salient travel utility, and the objective probabilities of the state of the world are replaced by the decision weights distorted in favor of this salient travel utility. A long-run user equilibrium will be achieved when no traveler can improve his or her salient travel utility by unilaterally changing routes, termed salient user equilibrium, which extends the scope of the Wardropian user equilibrium. Furthermore, we prove the existence and uniqueness of this salient user equilibrium. Finally, numerical studies demonstrate our theoretical findings. The equilibrium results show non-intuitive insights into travelers’ route choice behavior. (1) Travelers can be risk-seeking (the travel utility of a risky route is small with a relatively high probability), risk-neutral (in special situations), or risk-averse (the travel utility of a risky route is large with a relatively high probability), which depends on the salient state. (2) The extent of travelers’ risk-seeking or risk-averse behavior depends on their extent of salience bias, while the risk-neutral behavior is irrelative to this salience bias.

Stochastic Traffic Assignment Model Based on Travel Time Reliability

2011 ◽  
Vol 130-134 ◽  
pp. 3716-3720
Author(s):  
Yi Ran Cheng ◽  
Yin Han ◽  
Xin Kai Jiang ◽  
Jia Lei Gu
Keyword(s):  
Travel Time ◽  
Equilibrium Model ◽  
Choice Behavior ◽  
Transportation Networks ◽  
Route Choice ◽  
User Equilibrium ◽  
Travel Time Reliability ◽  
Stochastic User Equilibrium ◽  
Model Based ◽  
Route Choice Behavior

Considering the un-deterministic transportation networks, the paper proposes the change of the route choice decisions under the stochastic transportation networks. The route choice behavior is described as a choice for a time shortest route which is subject to a time-reliability level. The paper also considered this new route choice behavior in the stochastic user equilibrium model, and proposed stochastic user equilibrium model based on the optimized reliability travel time route choice behavior in the stochastic networks. The equivalence and uniqueness of the solution of the model are demonstrated. Numerical results of a small network show that the proposed model can reflect the real traveler’s route choice behavior in stochastic transportation networks.

scholarly journals A Dynamic Day-To-Day Departure Time and Route Choice Model for Bounded-Rational Individuals

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lingjuan Chen ◽  
Yu Wang ◽  
Dongfang Ma
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Choice Model ◽  
Route Choice ◽  
User Equilibrium ◽  
Navigation Systems ◽  
Dynamic Learning ◽  
Traffic State ◽  
Departure Time ◽  
Navigation Information

Accurate prediction of travellers’ day-to-day departure time and route choice is critical in advanced traffic management systems. There have been several related works about route choice with the assumption that the departure time for individual travellers is known beforehand. With real-time traffic state information provided by navigation systems and previous historical experience, travellers will dynamically update their departure time, which is neglected in existing works. In this study, we aim to describe travellers’ spatial-temporary choice behaviour taking navigation information into account and propose a bounded-rational day-to-day dynamic learning and adjustment model. The new model contains three steps. First, the real-time navigation guidance on each discrete day is obtained, and the self-learned experience of travellers’ choices with navigation information is presented; then, the day-to-day revision process of the choices is derived to maximize departure and route choice prospect; next, by aggregating each individual’s behaviour and calculating route choice probability, a bounded-rational continuous day-to-day dynamic model is provided. Numerical experiments suggest that the proposed model converges to a spatial-temporal oscillating equilibrium not a fixed-point stable status, and the final equilibrium trend is different from classical user equilibrium. The findings of the study are helpful to improve the prediction accuracy of traffic state in urban street networks.

scholarly journals A Reliability-Based Network Equilibrium Model with Adaptive Risk-Averse Travelers

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Lei Zhao ◽  
Hongzhi Guan ◽  
Junze Zhu ◽  
Yunfeng Wei
Keyword(s):  
Travel Time ◽  
Time Distribution ◽  
Choice Behavior ◽  
Route Choice ◽  
Great Influence ◽  
User Equilibrium ◽  
Probability Density Distribution ◽  
Risk Averse ◽  
Route Choice Behavior ◽  
Travel Time Distribution

In this paper, route free-flow travel time is taken as the lower bound of route travel time to examine its impacts on budget time and reliability for degradable transportation networks. A truncated probability density distribution with respect to route travel time is proposed and the corresponding travel time budget (TTB) model is derived. The budget time and reliability are compared between TTB models with and without truncated travel time distribution. Under truncated travel time distribution, the risk-averse levels of travelers are adaptive, which are affected by the characteristics of the used routes besides the confidence level of travelers. Then, a TTB-based stochastic user equilibrium (SUE) is developed to model travelers’ route choice behavior. Moreover, its equivalent variational inequality (VI) problem is formulated and a route-based algorithm is used to solve the proposed model. Numerical results indicate that route travel time boundary produces a great influence on decision cost and route choice behavior of travelers.

Departure Time Choice Equilibrium and Tolling Strategies for a Bottleneck with Stochastic Capacity

2021 ◽  
Author(s):  
Jiancheng Long ◽  
Hai Yang ◽  
W. Y. Szeto
Keyword(s):  
User Equilibrium ◽  
Single Step ◽  
Numerical Results ◽  
General Distribution ◽  
Time Varying ◽  
Bottleneck Model ◽  
Schedule Delay ◽  
Departure Time ◽  
Stochastic Capacity ◽  
Departure Time Choice

This paper develops a bottleneck model in which the capacity of the bottleneck is assumed to be stochastic and follow a general distribution that has a positive upper bound. The user equilibrium principle in terms of mean trip cost is adopted to formulate commuters’ departure time choice in the stochastic bottleneck. We find that there exist five possible equilibrium departure patterns, which depend on both commuters’ unit costs of travel time, schedule delay early and late, and the uncertainty of the stochastic capacity of the bottleneck. All possible equilibrium departure patterns are analytically derived. Both the analytical and numerical results show that increasing the uncertainty of the capacity of the bottleneck leads to an increase of commuters’ individual mean trip cost. In addition, both a time-varying toll scheme and a single-step coarse toll scheme are designed within the proposed stochastic bottleneck model. We provide an analytical method to determine the detailed toll-charging schemes for both toll strategies. The numerical results show that the proposed toll schemes can indeed improve the efficiency of the stochastic bottleneck in terms of decreasing mean total social cost, and the time-varying toll scheme is more efficient than the single-step coarse toll scheme. However, as the uncertainty of the capacity of the bottleneck increases, the efficiency of the time-varying toll scheme decreases, whereas the efficiency of the single-step coarse toll scheme fluctuates slightly.

Analysis of Time Allocation, Departure Time, and Route Choice Behavior Under Congestion Pricing

2000 ◽  
Vol 1725 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Toshiyuki Yamamoto ◽  
Satoshi Fujii ◽  
Ryuichi Kitamura ◽  
Hiroshi Yoshida
Keyword(s):  
Time Allocation ◽  
Choice Model ◽  
Stated Preference ◽  
Congestion Pricing ◽  
Route Choice ◽  
Multinomial Logit Model ◽  
Allocation Model ◽  
Departure Time ◽  
Departure Time Choice ◽  
Pricing Schemes

Driver behavior under congestion pricing is analyzed to evaluate the effects of alternative congestion pricing schemes. The analysis, which is based on stated preference survey results, focuses on time allocation, departure time choice, and route choice when a congestion pricing scheme is implemented on toll roads in Japan. A unique feature of the model system of this study is that departure time choice and route choice are analyzed in conjunction with the activities before and after the trip. A time allocation model is developed to describe departure time choice, and a route and departure time choice model is developed as a multinomial logit model with alternatives representing the choice between freeways and surface streets and, for departure time, the choice from among before, during, or after the period when congestion pricing is in effect. The results of the empirical analysis suggest that departing during the congestion pricing period tends to have higher utilities and that a worker and a nonworker have quite different utility functions. The comparative analysis of different congestion pricing schemes is carried out based on the estimated parameters. The results suggest that the probability of choosing each alternative is stable even if the length of the congestion pricing period changes, but a higher congestion price causes more drivers to change the departure time to before the congestion pricing period.

scholarly journals Agent-Based Route Choice with Learning and Exchange of Information

Urban Science ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 58 ◽  
Author(s):  
Shanjiang Zhu ◽  
David Levinson
Keyword(s):  
Route Choice ◽  
User Equilibrium ◽  
Spatial Knowledge ◽  
Stochastic User Equilibrium ◽  
Agent Based ◽  
Route Choice Behavior ◽  
Arc Model ◽  
Planning Models ◽  
Behavioral Preferences ◽  
Levels Of Service

Planning models require consideration of travelers with distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g., willingness to switch routes with potential savings) in a differentiated market (where routes have varying tolls and levels of service). This paper proposes to explicitly model the formation and spreading of spatial knowledge among travelers, following cognitive map theory. An agent-based route choice (ARC) model was developed to track choices of each individual decision-maker in a road network over time and map individual choices into macroscopic flow pattern. ARC has been applied to both the Sioux Falls and Chicago sketch networks. Comparisons between ARC and existing models (user equilibrium (UE) and stochastic user equilibrium (SUE)) on both networks show ARC is valid and computationally tractable. In brief, this paper specifically focuses on the route choice behavior, while the proposed model can be extended to other modules of transportation planning under an integrated framework.

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