Evaluation method and influence factors of network travel time reliability

Xi Qun Chen, Jiao Kun Liu, Hao Qiang Hu, Er Jia Cui, Shuai Chao Zhang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The probability distribution of network travel time rate was studied based on the regional division approach, and the travel time reliability indexes based on OD pairs were proposed to appraise the reliability of urban traffic. The multivariate linear regression model was established by choosing the relevant factors that influenced the travel time reliability indexes. The model was solved by the stepwise regression method, and the significance test was conducted to verify the estimated model parameters. The network travel time reliability indexes were calculated by Hangzhou and Beijing ride-hailing data and compared with the peak congestion delay indexes, then the temporal and spatial distributions of network travel time reliability indexes were analyzed. Research result shows that in multivariate linear regression models, the fitting determination coefficient between the planning travel time rate and five independent variables, including the waiting time, cost, distance, travel time, and number of trips for OD pairs is 0.772, and the fitting determination coefficient between the average travel time rate and five independent variables is 0.857, so both models have better fitting degrees and statistical significance. In the regression model of planning travel time rate, the regression coefficients of waiting time, travel time, and travel distance are 0.386, 0.399, and -1.286, respectively. In the regression model of average travel time rate, the regression coefficients of waiting time, travel time, and travel distance are 0.162, 0.177, and -0.676, respectively. The two traffic reliability indexes are positively correlated for the waiting time and travel time, and negatively correlated for the actual travel distance. The proposed network travel time reliability indexes are consistent with the peak congestion delay index and reflect the traffic reliability characteristics from various perspectives. They provide decision support for transportation planning and help residents choose propitious routes. 5 tabs, 7 figs, 26 refs.

Original languageEnglish (US)
Pages (from-to)132-142
Number of pages11
JournalJournal of Traffic and Transportation Engineering
Volume18
Issue number4
StatePublished - Aug 1 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018, Editorial Department of Journal of Traffic and Transportation Engineering. All right reserved.

Keywords

  • Multiple linear regression
  • Peak congestion delay index
  • Reliability index
  • Significance test
  • Transportation planning
  • Travel time rate

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