公交车辆在公交站点停留时间的估算
Estimating Transit Vehicle Dwell Times at Bus Stops
摘要：
这项工作提出了一种定量的方法来估计总的时间在附近的一个巴士站，包括减速时间，上车和下车时间（在早期的研究中开发），加速时间，并重新进入时间（所需的时间合并到相邻的车道）。使用不同的统计模型来计算减速、加速和合并时间。使用运动学方程计算典型的减速和加速水平，然后使用运动学方程计算减速和加速时间。采用平均绝对百分比误差（MAPE）和均方根误差（RMSE），利用Blacksburg Transit（BT）的过境数据，对所采用的方法进行了验证，以估计减速时间和加速时间。减速时间的MAPE和RMSE值分别为0.3%和13.3%，加速时间的MAPE和RMSE值分别为0.42%和2.72%。使用多元线性回归和贝叶斯回归方法，估计再入时间是相邻道路交通密度的函数。两种方法在估计合并时间模型系数时表现出一致性。所提出的模型可以与公交应用程序集成，以估计公交车辆的行程时间。
英文摘要：
This work presents a quantitative approach to estimate the total time spent in the vicinity of a bus stop including the deceleration time, the boarding and alighting time (developed in an earlier study), the acceleration time, and re-entry time (time required to merge into the adjacent lane). Different statistical models were used to compute the deceleration, acceleration and merge times. Typical deceleration and acceleration levels were computed using kinematic equations that were then used to compute both the deceleration and acceleration times. The adopted method to estimate both the deceleration time and the acceleration time was validated utilizing transit data from Blacksburg Transit (BT) using the mean absolute percentage error (MAPE) and root mean square error (RMSE). The MAPE and RMSE values were calculated to be 0.3 and 13.3 percent for the deceleration time and 0.42 and 2.72 percent for the acceleration time, respectively. The re-entry time was estimated to be a function of the adjacent roadway traffic density using both a multiple linear regression and Bayesian regression approach. Both methods showed consistency in estimating the merge-time model coefficients. The proposed models can be integrated with transit applications to estimate transit vehicle travel times.
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