A Headway Adjustment Method Based on Bus Arrival Time Prediction

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Jiaojiao Zhu ◽  
Zhirui Ye ◽  
Chao Wang ◽  
Yu Yan
Keyword(s):  
Arrival Time ◽  
Adjustment Method ◽  
Time Prediction ◽  
Arrival Time Prediction ◽  
Bus Arrival Time Prediction

scholarly journals Bus arrival time prediction and measure of uncertainties using survival models

2020 ◽  
Vol 14 (8) ◽  
pp. 900-907
Author(s):  
R.B. Sharmila ◽  
Nagendra R. Velaga ◽  
Pushpa Choudhary
Keyword(s):  
Arrival Time ◽  
Survival Models ◽  
Time Prediction ◽  
Arrival Time Prediction ◽  
Bus Arrival Time Prediction

scholarly journals A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 42372-42383 ◽  
Author(s):  
Qingwen Han ◽  
Ke Liu ◽  
Lingqiu Zeng ◽  
Guangyan He ◽  
Lei Ye ◽  
...  
Keyword(s):  
Arrival Time ◽  
Prediction Method ◽  
Time Prediction ◽  
Position Calibration ◽  
Arrival Time Prediction ◽  
Bus Arrival Time Prediction

Dynamic Prediction Method with Schedule Recovery Impact for Bus Arrival Time

2005 ◽  
Vol 1923 (1) ◽  
pp. 208-217 ◽  
Author(s):  
Mei Chen ◽  
Xiaobo Liu ◽  
Jingxin Xia
Keyword(s):  
Arrival Time ◽  
Recent Observation ◽  
Control Factor ◽  
Automatic Vehicle Location ◽  
Time Prediction ◽  
Schedule Recovery ◽  
Vehicle Location ◽  
Arrival Time Prediction ◽  
Bus Arrival Time Prediction ◽  
The Impact

This study develops a dynamic bus arrival time prediction model using the data collected by the automatic vehicle location and automatic passenger counter systems. It is based on the Kalman filter algorithm with a two-dimensional state variable in which the prediction error in the most recent observation is used to optimize the arrival time estimate for each downstream stop. The impact of schedule recovery is considered as a control factor in the model to reflect the driver's schedule recovery behavior. The algorithm performs well when tested with a set of automatic vehicle location–automatic passenger counter data collected from a real-world bus route. The algorithm does not require intensive computation or an excessive data preprocessing effort. It is a promising approach for real-time bus arrival time prediction in practice.

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