TY - JOUR
T1 - Methodology to Detect Bus Stop Influence Zones Utilizing Facebook Prophet Changepoint Detection Method
AU - Maltas, Abdullah
AU - Ozen, Halit
AU - Saracoglu, Abdulsamet
N1 - Publisher Copyright:
© 2023, Korean Society of Civil Engineers.
PY - 2023/10
Y1 - 2023/10
N2 - 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.
AB - 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.
KW - Automatic vehicle location
KW - Bus stop influence zone
KW - Changepoint detection
KW - Facebook prophet
KW - Geographic information system
UR - http://www.scopus.com/inward/record.url?scp=85167677751&partnerID=8YFLogxK
U2 - 10.1007/s12205-023-0696-6
DO - 10.1007/s12205-023-0696-6
M3 - Article
AN - SCOPUS:85167677751
SN - 1226-7988
VL - 27
SP - 4472
EP - 4484
JO - KSCE Journal of Civil Engineering
JF - KSCE Journal of Civil Engineering
IS - 10
ER -