Experimental Study of Real-Time Bus Arrival Time Prediction with GPS Data

Author:

Lin Wei-Hua1,Zeng Jian1

Affiliation:

1. Department of Civil and Environmental Engineering, 200 Patton Hall, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061

Abstract

Bus headway in a rural area usually is much larger than that in an urban area. Providing real-time bus arrival information could make the public transit system more user-friendly and thus enhance its competitiveness among various transportation modes. As part of an operational test for rural traveler information systems currently ongoing in Blacksburg, Virginia, an experimental study has been conducted on forecasting the arrival time of the next bus with automatic vehicle location techniques. The process of developing arrival time estimation algorithms is discussed, including route representation, global positioning system (GPS) data screening for identifying data quality and delay patterns, algorithm formulation, and development of measures of performance. Whereas GPS-based bus location data are adopted in all four algorithms presented, the extent to which other information is used in these algorithms varies. In addition to bus location data, information relevant to the performance of an algorithm includes scheduled arrival time, delay correlation, and waiting time at time-check stops. The performance of an algorithm using different levels of information is compared against three criteria: overall precision, robustness, and stability. Results show that at the site where the study is being conducted, the dwell time at time-check stops is most relevant to the performance of an algorithm.

Publisher

SAGE Publications

Subject

Mechanical Engineering,Civil and Structural Engineering

Reference5 articles.

Cited by 84 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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3. GPS-based Arrival Time Prediction for Bus Schedules: Enhancing Commuter Experience;2023 12th International Conference on Advanced Computing (ICoAC);2023-08-17

4. Improving bus arrival time predictors using only public transport API data;Transportation Letters;2023-08-13

5. Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations;Railway Engineering Science;2023-06-07

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