scholarly journals Sequential Route Choice Modeling Based on Dynamic Reference Points and Its Empirical Study

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xueqin Long ◽  
Chenxi Hou ◽  
Shanshan Liu ◽  
Yuejiao Wang
Keyword(s):  
Prospect Theory ◽  
Travel Time ◽  
Real Time ◽  
Route Choice ◽  
Cumulative Prospect Theory ◽  
Time Travel ◽  
Reference Points ◽  
Travel Time Prediction ◽  
Route Choice Behavior ◽  
Types Of Information

Aiming at the influence of information, we investigate and analyze the sequential route choice behavior under dynamic reference points based on cumulative prospect theory in this paper. An experiment platform collecting the sequential route choices based on C/S structure is designed and four types of information are released to participants, respectively. Real-time travel time prediction methods are then proposed for travelers’ decision-making. Using nonlinear regression method, the parameters of the value function and weight function of cumulative prospect theory are estimated under different types of information, respectively. It is found that travelers’ behavior showed obvious characteristic of risk pursuit under the circumstance where real-time travel time information is released. Instead, when they have access to descriptive information, they tend to be more conservative.

Real-time travel time predictor for route guidance consistent with driver behavior

2012 ◽  
Vol 39 (10) ◽  
pp. 1113-1124 ◽  
Author(s):  
Tian-dong Xu ◽  
Yuan Hao ◽  
Zhong-ren Peng ◽  
Li-jun Sun
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Estimation ◽  
Field Testing ◽  
Pilot Project ◽  
Time Travel ◽  
Guidance System ◽  
Route Guidance ◽  
Travel Time Prediction ◽  
Traffic Conditions

Providing reliable real-time travel time information is a critical challenge to all existing traffic routing systems. This study develops a new model for estimating and predicting real-time traffic conditions and travel times for variable message signs-based route guidance system. The proposed model is based on real-time limited detected traffic data, stochastic nonlinear macroscopic traffic flow model, and adaptive Kalman filtering theory. The method has the following main features: (1) real-time estimation and prediction of traffic conditions on a network level using limited traffic detectors, (2) travel time prediction in free flow and congested flow, and (3) prediction of drivers’ en-route diversion behavior. Field testing is conducted based on the Route Guidance Pilot Project sponsored by the National Science and Technology Ministry of China. The achieved testing results are satisfactory and have potential use for future works and field applications.

Real-Time Travel Time Prediction Framework for Departure Time and Route Advice

2015 ◽  
Vol 2490 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Simeon C. Calvert ◽  
Maaike Snelder ◽  
Taoufik Bakri ◽  
Bjorn Heijligers ◽  
Victor L. Knoop
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Travel ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Departure Time

Drivers’ Route Choice Behavior Analysis Based on Cumulative Prospect Theory

2014 ◽  
Vol 1030-1032 ◽  
pp. 2231-2234
Author(s):  
Wei Yan Zong ◽  
En Jian Yao ◽  
Yang Yang
Keyword(s):  
Prospect Theory ◽  
Choice Behavior ◽  
Route Choice ◽  
Expected Utility Theory ◽  
Cumulative Prospect Theory ◽  
Route Choice Behavior ◽  
Route Network ◽  
Behavior Characteristics ◽  
Study Designs ◽  
Different Levels

Due to different travel experiences, travel attitude varies from people of different levels of familiarity with urban route network. Expected utility theory (EUT) is usually used to describe travelers’ route choice behavior. But there is huge discrepancy between reality and the results under the condition with network uncertainty when the cumulative prospect theory (CPT) is more attractive to describe the travelers’ route choice behavior. Based on CPT this study designs scenarios at different levels of familiarity with urban road network, analyzes route choice behavior in all scenarios and conclude travelers’ behavior characteristics compared with reality investigation results. It shows that CPT is really more powerful to describe the travelers’ behavior under risk and people of high familiarity level are more relied on constraint time and tend to take a risk for more gain.

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