Bus arrival time prediction using mixed multi-route arrival time data at previous stop

    Xuedong Hua Affiliation
    ; Wei Wang Affiliation
    ; Yinhai Wang Affiliation
    ; Min Ren Affiliation


The primary objective of this paper is to develop models to predict bus arrival time at a target stop using actual multi-route bus arrival time data from previous stop as inputs. In order to mix and fully utilize the multiple routes bus arrival time data, the weighted average travel time and three Forgetting Factor Functions (FFFs) – F1, F2 and F3 – are introduced. Based on different combinations of input variables, five prediction models are proposed. Three widely used algorithms, i.e. Support Vector Machine (SVM), Artificial Neutral Network (ANN) and Linear Regression (LR), are tested to find the best for arrival time prediction. Bus location data of 11 road segments from Yichun (China), covering 12 bus stops and 16 routes, are collected to evaluate the performance of the proposed approaches. The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy: the prediction errors reduce by around 20%. The algorithm comparison demonstrates that the SVM and ANN outperform the LR. The FFFs can also affect the performance errors: F1 is more suitable for ANN algorithm, while F3 is better for SVM and LR algorithms. Besides, the virtual road concept in this paper can slightly improve the prediction accuracy and halve the time cost of predicted arrival time calculation.

First published online 02 May 2017

Keyword : bus arrival time prediction, multiple routes; support vector machine (SVM), artificial neutral network (ANN), linear regression (LR), forgetting factor function (FFF)

How to Cite
Hua, X., Wang, W., Wang, Y., & Ren, M. (2018). Bus arrival time prediction using mixed multi-route arrival time data at previous stop. Transport, 33(2), 543–554.
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Jan 26, 2018
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This work is licensed under a Creative Commons Attribution 4.0 International License.


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