Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes.

Cong Bai,Qing-Chang Lu,Zhong-Ren Peng,Jian Sun

doi:10.1155/2015/432389

Cong Bai, Qing-Chang Lu + Show 2 more

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https://doi.org/10.1155/2015/432389

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Abstract

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

Highlights

Providing reliable and accurate bus travel and arrival times would be an effective way to improve the service of bus transit systems [1]
Since studies in recent years proved that SVM and artificial neural network (ANN) models outperformed other models in prediction accuracy, in this study five models, including pure ANN, pure SVM, pure Kalman, ANN-Kalman (ANN-Kalman model refers to the model based on ANN and Kalman filtering-based algorithm), and SVM-Kalman (SVM-Kalman model is short for the model based on SVM and Kalman filtering-based algorithm) models, are proposed for bus travel time prediction on road with multiple bus routes
This paper investigated the dynamic travel time prediction models for buses on road with multiple bus routes

Summary

Introduction

Providing reliable and accurate bus travel and arrival times would be an effective way to improve the service of bus transit systems [1]. Few previous studies addressed the specific situation of multiple bus routes sharing the same road segments and bus stops to predict the bus travel times. The remainder of this paper is organized as follows: Section 2 reviews the related literature; Section 3 details the case of buses on road with multiple bus routes and provides the basic theory of the dynamic bus travel time prediction models, together with the input factors of the models; Section 4 presents a case study in Shenzhen, China, with the performance comparison of the five models; and Section 5 gives the conclusions and the suggestions for further study. Previous studies proved that ANN models had the ability to solve complex nonlinear relationships and they are very effective in bus travel time prediction

Literature Review

Problem Descriptions and Model Developments

Case Study

Conclusions