Assessing the effects of stochastic perception error under travel time variability

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• 作者：Xiangdong Xu (1) (2)
  Anthony Chen (2) (3)
  Lin Cheng (1)
  
• 关键词：Mean ; excess travel time ; Stochastic perception error ; Travel time variability ; Moment analysis
• 刊名：Transportation
• 出版年：2013
• 出版时间：May 2013
• 年：2013
• 卷：40
• 期：3
• 页码：525-548
• 全文大小：894KB
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• 作者单位：Xiangdong Xu (1) (2)
  Anthony Chen (2) (3)
  Lin Cheng (1)
  
  1. School of Transportation, Southeast University, Nanjing, 210096, People鈥檚 Republic of China
  2. Department of Civil and Environmental Engineering, Utah State University, Logan, UT, 84322-4110, USA
  3. Key Laboratory of Road and Traffic Engineering, Tongji University, Shanghai, 201804, People鈥檚 Republic of China
  
• ISSN：1572-9435

文摘

Perceived mean-excess travel time is a new risk-averse route choice criterion recently proposed to simultaneously consider both stochastic perception error and travel time variability when making route choice decisions under uncertainty. The stochastic perception error is conditionally dependent on the actual travel time distribution, which is different from the deterministic perception error used in the traditional logit model. In this paper, we investigate the effects of stochastic perception error at three levels: (1) individual perceived travel time distribution and its connection to the classification by types of travelers and trip purposes, (2) route choice decisions (in terms of equilibrium flows and perceived mean-excess travel times), and (3) network performance measure (in terms of the total travel time distribution and its statistics). In all three levels, a curve fitting method is adopted to estimate the whole distribution of interest. Numerical examples are also provided to illustrate and visualize the above analyses. The graphical illustrations allow for intuitive interpretation of the effects of stochastic perception error at different levels. The analysis results could enhance the understanding of route choice behaviors under both (subjective) stochastic perception error and (objective) travel time uncertainty. Some suggestions are also provided for behavior data collection and behavioral modeling.