Dynamic route and departure time choice model based on self-adaptive reference point and reinforcement learning

2018 ◽  
Vol 502 ◽  
pp. 77-92 ◽  
Author(s):  
Xue-yan Li ◽  
Xue-mei Li ◽  
Lingrun Yang ◽  
Jing Li
Keyword(s):  
Reinforcement Learning ◽  
Reference Point ◽  
Choice Model ◽  
Model Based ◽  
Departure Time ◽  
Departure Time Choice ◽  
Dynamic Route ◽  
Self Adaptive

scholarly journals ARRIVAL TIME VALUATION OF COMMUTERS IN URBAN RAIL TRANSIT

Transport ◽  
2019 ◽  
Vol 34 (3) ◽  
pp. 383-393
Author(s):  
Yan Cheng ◽  
Xiafei Ye ◽  
Zhi Wang
Keyword(s):  
Reference Point ◽  
Arrival Time ◽  
Choice Model ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Value Functions ◽  
Schedule Delay ◽  
Urban Rail ◽  
Departure Time ◽  
Departure Time Choice

Departure time choice of commuters is one of key decisions affecting the crowding of urban rail transit network during peak hours. It is influenced by arrival time value, the additional psychological pressure caused by in-vehicle crowding, and time uncertainty. This paper aims at investigating how commuters in urban rail transit value their arrival time at work/school. Three valuation frameworks are proposed based on the reference point approach of prospect theory. Non-linear value functions with different reference point alternatives are estimated using data from a survey and stated choice study of users of Shanghai Metro system. Results show that schedule delay with work/school start time as the only reference point cannot properly reflect the arrival time valuation of urban rail transit commuters. Instead, the valuation framework with preferred arrival time as a reference point fits best, which hits as much as 85.64% of the cases. The asymmetrical response to early-side and late-side arrivals is identified. The findings of this study provide an essential basis for the development of departure time choice model.

scholarly journals Identifying Crowding Impact on Departure Time Choice of Commuters in Urban Rail Transit

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yan Cheng ◽  
Xiafei Ye ◽  
Taku Fujiyama
Keyword(s):  
Reference Point ◽  
Arrival Time ◽  
Choice Model ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Peak Time ◽  
Urban Rail ◽  
Departure Time ◽  
Departure Time Choice ◽  
The Impact

Crowding in urban rail transit is an inevitable issue for most of the high-density cities across the world, especially during peak time. For commuters who have considerably fixed destination arrival times, departure time choice is an important tool to adjust their trips. The ignorance of crowding impact on commuters’ departure time choice in urban rail transit may cause errors in forecasting dynamic passenger flow during peak time in urban rail transit. The paper develops a mixed logit model to identify how crowding impacts the departure time choice of commuters and their taste variation. Arrival time value was firstly measured in a submodel by applying the reference point approach and then integrated to the main model. Considering the characteristics of human perception, we divided crowding into five grades with distinct circumstances. All parameter distributions were assumed based on their empirical distributions revealed through resampling. The data from Shanghai Metro used for estimation were collected by a specifically designed survey, which combines revealed preference questions and stated preference experiments to investigate the willingness and extent of changing departure time choice of passengers who experienced various grades and duration of crowding in the most crowded part. The result shows that an asymmetric valuation model with preferred arrival time as the only reference point best captured commuters’ responses to arrival time. The departure time choice model clearly identified that only crowding ranging from Grades 3 to 5 had an impact on commuters’ departure time choice. The parameters of crowding costs can be assumed to follow transformed lognormal distributions. It is found that the higher the grade of crowding is, the bigger the impact each unit of crowding cost has on commuters’ departure time choice, while commuters’ tastes get more concentrated when crowded situation upgrades. The model in this paper can help policymakers better understand the interaction between commuters’ departure time choice and crowding alleviation.

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.

Exploration of Maximum-to-Minimum Shifts in Generic Methods for Departure Time Choice Models

2021 ◽  
pp. 036119812110133
Author(s):  
Amit Daly ◽  
Hillel Bar-Gera
Keyword(s):  
Choice Model ◽  
Minimum Cost ◽  
Exact Formula ◽  
Time Interval ◽  
Maximum Cost ◽  
Basic Model ◽  
Wide Range ◽  
Departure Time ◽  
Departure Time Choice ◽  
Complex Models

Vickrey’s seminal departure time choice model is based on a penalty function which is a linear combination of travel time, earliness, and lateness. The original model depicts a single link from a single origin to a single destination, serving homogeneous travelers by a deterministic point-queue regime. Numerous variants of the basic model, relaxing one or more of these assumptions, have been used in a wide range of contexts. The equilibrium solution of the basic model can be computed directly by exact formula. Specific convergent methods have been proposed for certain variants. One of the troubling challenges in this model is the need for a generic iterative numeric approach, that may address complex models in which departure time choice is embedded. Natural candidates were shown to fail even on the basic model. In this paper we explore a fairly naive approach, where, in each iteration, demand is shifted from the maximum cost time interval to the minimum cost time interval. Results for the basic model are promising, demonstrating that, with a fixed shift, solutions converge to a deviation which is proportional to the shift size and that semi-adaptive or adaptive shift size may offer convergence to any desirable level of approximation of the exact equilibrium.

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