Methodology to Detect Bus Stop Influence Zones Utilizing Facebook Prophet Changepoint Detection Method

Travel time of buses, a major part of the urban public transit system, is affected by various factors and foremost are the bus stops. In addition to the dwell time at the stop, the deceleration and acceleration zones reduce average speed, particularly in mixed traffic, and increase travel time. Seve...

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Bibliographic Details
Published in:KSCE journal of civil engineering 2023, 27(10), , pp.4472-4484
Main Authors: Maltas, Abdullah, Ozen, Halit, Saracoglu, Abdulsamet
Format: Article
Language:English
Subjects:
Acceleration
Automatic vehicle location
Bus stops
Buses
Buses (vehicles)
Civil Engineering
Cluster analysis
Clustering
Deceleration
Detection
Dwell time
Energy consumption
Engineering
Geographic information systems
Geographical information systems
Geotechnical Engineering & Applied Earth Sciences
Industrial Pollution Prevention
Information systems
Methods
Public transportation
Remote sensing
Route selection
Social networks
Transportation Engineering
Transportation planning
Travel
Travel time
Vector quantization
토목공학
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Summary:Travel time of buses, a major part of the urban public transit system, is affected by various factors and foremost are the bus stops. In addition to the dwell time at the stop, the deceleration and acceleration zones reduce average speed, particularly in mixed traffic, and increase travel time. Several approaches are used in estimating the link-based travel time of public transit systems in transportation planning, but Bus Stop Influence Zones (BSIZs) are ignored. Moreover, fuel consumption and pollutant emissions increase in BSIZs, and this demonstrates the importance of BSIZs in measuring the performance of public transit systems as well as planning. Data obtained from the Automatic Vehicle Location system was used and visualized on Geographic Information System and data preprocessing steps were performed. Finally, changepoint detection method of Facebook Prophet (FBP-CDM) was exploited to identify changepoints in location-speed data on the selected route. Results were validated with real-life data and expert opinion, and then compared with the findings acquired by the K-means clustering. Based on the conclusions, FBP-CDM was found quite effective in detecting and predicting BSIZs accurately and the proposed methodology is useful for studies in transportation planning and operations.
ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-023-0696-6