Abstract
In recent years, customized bus (CB), as a complementary form of urban public transport, can reduce residents’ travel costs, alleviate urban traffic congestion, reduce vehicle exhaust emissions, and contribute to the sustainable development of society. At present, customized bus travel demand information collection method is passive. There exist disadvantages such as the amount of information obtained is less, the access method is relatively single, and more potential travel demands cannot be met. This study aims to combine mobile phone signaling data, point of interest (POI) data, and secondary property price data to propose a method for identifying the service areas of commuter CB and travel demand. Firstly, mobile phone signaling data is preprocessed to identify the commuter’s location of employment and residence. Based on this, the time-space potential model for commuter CB is proposed. Secondly, objective factors affecting commuters’ choice to take commuter CB are used as model input variables. Logistic regression models are applied to estimate the probability of the grids being used as commuter CB service areas and the probability of the existence of potential travel demand in the grids and, further, to dig into the time-space distribution characteristics of people with potential demand for CB travel and analyze the distribution of high hotspot service areas. Finally, the analysis is carried out with practical cases and three lines are used as examples. The results show that the operating companies are profitable without government subsidies, which confirms the effectiveness of the method proposed in this paper in practical applications.
Highlights
As a new innovative public transport mode, the customized bus (CB) advocates energy saving and emission reduction, green travel, alleviating urban traffic congestion, and providing people with high-quality travel services in a “point-to-point” way [1, 2]
In view of the existing problems and combined with big data processing technology, this paper proposes commuter CB service areas and travel demand identification method based on mobile phone signaling data
The commuter CB travel demand and service areas identification method is proposed in the paper. e method is applied to a real case in the central city of Chongqing, China. e distribution of commuters’ occupational and residential locations is identified and visualized based on the commuter OD identification algorithm
Summary
As a new innovative public transport mode, the CB advocates energy saving and emission reduction, green travel, alleviating urban traffic congestion, and providing people with high-quality travel services in a “point-to-point” way [1, 2]. CB originated from the idea of “car-sharing.” It was introduced in 1948 by the organization “Sefage” in Sweden to save transportation costs for families who did not own a car [3]. Travel demand is an important part of customized bus route planning. Before most scholars study the route planning framework, they need to analyze the travel demand initially. Qiu et al [5] investigated a method to improve the performance of flexible route buses in an operational environment with uncertain travel demand. Lyu et al [9] proposed a CB-Planner method for a bus line planning framework with multiple travel data sources and designed a heuristic solution framework. The commuter CB travel demand and service areas identification method is proposed in the paper.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
