Which road do I take? A learning-based model of route-choice behavior with real-time information

2010 ◽  
Vol 44 (4) ◽  
pp. 249-264 ◽  
Author(s):  
Eran Ben-Elia ◽  
Yoram Shiftan
Keyword(s):  
Real Time ◽  
Choice Behavior ◽  
Route Choice ◽  
Route Choice Behavior ◽  
Real Time Information ◽  
Time Information

Modeling Inertia and Compliance Mechanisms in Route Choice Behavior Under Real-Time Information

2000 ◽  
Vol 1725 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Karthik K. Srinivasan ◽  
Hani S. Mahmassani
Keyword(s):  
Real Time ◽  
Travel Behavior ◽  
Choice Behavior ◽  
Information Quality ◽  
Route Choice ◽  
Probit Model ◽  
Choice Data ◽  
Route Choice Behavior ◽  
Real Time Information ◽  
Time Information

This research examines route choice, in the presence of real-time information, as a consequence of two underlying behavioral mechanisms: compliance and inertia. The compliance mechanism reflects the propensity of a user to comply with the information supplied by advanced traveler information systems (ATIS). The inertial mechanism represents the tendency of users to continue on their current paths. These two mechanisms in route choice are neither mutually exclusive nor collectively exhaustive. A framework is proposed to model these mechanisms explicitly. The proposed framework decomposes the route choice into two states by exploiting the user’s path choice structure (resulting from the current choice prior to the decision of interest) and the information supplied by ATIS. In each state, the mechanisms are incorporated by associating their utilities with those that reflect the specific attributes of the alternative paths. The resulting nested choice structure is implemented using the multinomial probit model. This framework is illustrated using route choice data obtained from dynamic interactive simulator experiments. The empirical results strongly support the simultaneous presence of both the compliance and inertia mechanisms in route choice behavior. The results also indicate that information quality, network loading and day-to-day evolution, level-of-service measures, and trip-makers’ prior experience are significant determinants of route choice through the inertial and compliance mechanisms. These findings have important implications in travel behavior forecasting, ATIS design and evaluation, demand management, and network state prediction.

Route Choice Decision based on Real Time Information

2010 ◽  
Vol 10 (19) ◽  
pp. 2304-2309
Author(s):  
F. Shokri ◽  
A. Ismail ◽  
Riza Atiq O.K. Rahmat
Keyword(s):  
Real Time ◽  
Route Choice ◽  
Choice Decision ◽  
Real Time Information ◽  
Time Information

Dynamic control strategy based on passenger choice behavior with real-time information

SIMULATION ◽  
2017 ◽  
Vol 95 (9) ◽  
pp. 809-822
Author(s):  
Wensi Wang ◽  
Zhihui Tian ◽  
Yonglei Jiang ◽  
Lan Wu ◽  
Jianqiao Gao
Keyword(s):  
Real Time ◽  
Waiting Time ◽  
Control Strategy ◽  
Choice Behavior ◽  
Dynamic Control ◽  
Holding Time ◽  
Level Model ◽  
Real Time Information ◽  
Time Information ◽  
Passenger Choice

Real-time control strategies are important methods for high-frequency transit to counteract the effects of bus bunching in passenger waiting time. This paper extends previous literature with the development of an optimization model for multiple lines in a corridor capable of executing a dynamic control strategy based on passenger choice behavior with real-time information. The bi-level model integrates “passenger perceptions,”“service selection,” and “control strategy” effectively. The upper level model is a control model with the objective of minimizing the total waiting time of passengers in the system composed of common lines to decide whether a bus arriving at the hub should be held and its holding time. The lower level model is an allocation model with the utilization of a Nested Logit model to study passenger choice behavior. In addition, a heuristic algorithm is introduced to solve the problem. The effectiveness of the model is evaluated with the data of two lines in Dalian city of China. The results show that the control strategy proposed in this paper outperforms the simple control strategy without passenger choice behavior, where the waiting time of passengers, the number of buses that need to hold, and bus holding time are all reduced.

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