Title

EXPERIMENTAL STUDY OF REAL-TIME BUS ARRIVAL TIME PREDICTION WITH GPS DATA

Authors

W-H LIN
J Zeng

Document Type

Journal Article

Publication Date

1999

Subject Area

infrastructure - vehicle, planning - signage/information, ridership - forecasting, ridership - forecasting, technology - passenger information, place - rural, mode - bus

Keywords

Vehicle locating systems, Traveler information and communication systems, Scenarios, Rural transit, Projections, Precision, Passenger information, Headways, GPS, Global Positioning System, Forecasting, Dwell time, Blacksburg (Virginia), AVL, Automatic vehicle location, Automatic location systems, Arrival time, Algorithms

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.