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

• 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
• ISSN: 1226-7988; 1976-3808
• DOI: 10.1007/s12205-023-0696-6

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.