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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2018  >  18(4): 132-142
CHEN Xi-qun, LIU Jiao-kun, HU Hao-qiang, CUI Er-jia, ZHANG Shuai-chao. Evaluation method and influence factors of network travel time reliability[J]. Journal of Traffic and Transportation Engineering, 2018, 18(4): 132-142. doi: 10.19818/j.cnki.1671-1637.2018.04.014
Citation: CHEN Xi-qun, LIU Jiao-kun, HU Hao-qiang, CUI Er-jia, ZHANG Shuai-chao. Evaluation method and influence factors of network travel time reliability[J]. Journal of Traffic and Transportation Engineering, 2018, 18(4): 132-142. doi: 10.19818/j.cnki.1671-1637.2018.04.014
shu

Evaluation method and influence factors of network travel time reliability

doi: 10.19818/j.cnki.1671-1637.2018.04.014

  • College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, Zhejiang, China

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  • Author Bio:

    CHEN Xi-qun(1986-), male, professor, PhD, chenxiqun@zju.edu.cn

  • Received Date: 2018-03-18
  • Publish Date: 2018-08-25
    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 fiveindependent 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.

     

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