A Data-Driven Analysis for Operational Vehicle Performance of Public Transport Network

The operational stability of public transport is significant for both passengers and operators. Affected by many stochastic factors, such as traffic congestion, traffic signals and passenger demand at stops, the headway always become uneven, which greatly reduces the service quality. This paper used...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.96404-96413
Main Authors: Zhang, Hui, Cui, Houdun, Shi, Baiying
Format: Article
Language:English
Subjects:
Data analysis
data-driven analysis
Dwell time
Global Positioning System
Global positioning systems
GPS
GPS trajectory data
Headways
Network topology
Operators
Public transport network
Public transportation
Quality of service
Satellite navigation systems
Stability analysis
Stability criteria
stability of headway
Stations
Statistical analysis
Suburban areas
Synchronization
Traffic congestion
Traffic signals
Trajectory
Trajectory analysis
Transportation networks
Travel demand
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Summary:The operational stability of public transport is significant for both passengers and operators. Affected by many stochastic factors, such as traffic congestion, traffic signals and passenger demand at stops, the headway always become uneven, which greatly reduces the service quality. This paper used the big global positioning systems (GPS) trajectory data to analyze the headway stability of bus system from the perspective of network. A statistical method is proposed to analyze the operational vehicle performance of bus network. The GPS trajectory data of Jinan is used to test the model. The results show that the average dwell time, actual headway, and headway stability index of stations follow lognormal distributions with obvious right tails. Moreover, the seriously unstable situations do not appear in the peak hours, but in the time periods before peak hours. In addition, the stations with most unstable headway are located in the suburbs and the fringe area of downtown. The outcomes suggest that operators should pay more attention to the suburbs and the fringe area of downtown, and the time periods before peak hours to efficiently improve the service quality.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2930279