Improvement of the first-mile service based on passengers’ choice of travel mode

2021 ◽  
pp. 1-34
Author(s):  
Junsheng Huang ◽  
Baohua Mao ◽  
Gang Wu
Keyword(s):  
Travel Mode

scholarly journals From attitude to satisfaction: introducing the travel mode choice cycle

2021 ◽  
pp. 1-18
Author(s):  
Jonas De Vos ◽  
Patrick A. Singleton ◽  
Tommy Gärling
Keyword(s):  
Mode Choice ◽  
Travel Mode ◽  
Travel Mode Choice

Travel mode recognition of urban residents using mobile phone data and MapAPI

2021 ◽  
pp. 239980832098300
Author(s):  
Zhenghong Peng ◽  
Guikai Bai ◽  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu
Keyword(s):  
Urban Planning ◽  
Mobile Phone ◽  
Traffic Flow ◽  
Road Traffic ◽  
Urban Traffic ◽  
Phone Call ◽  
Traffic Information ◽  
Mobile Phone Data ◽  
Urban Residents ◽  
Travel Mode

Obtaining the time and space features of the travel of urban residents can facilitate urban traffic optimization and urban planning. As traditional methods often have limited sample coverage and lack timeliness, the application of big data such as mobile phone data in urban studies makes it possible to rapidly acquire the features of residents’ travel. However, few studies have attempted to use them to recognize the travel modes of residents. Based on mobile phone call detail records and the Web MapAPI, the present study proposes a method to recognize the travel mode of urban residents. The main processes include: (a) using DBSCAN clustering to analyze each user’s important location points and identify their main travel trajectories; (b) using an online map API to analyze user’s means of travel; (c) comparing the two to recognize the travel mode of residents. Applying this method in a GIS platform can further help obtain the traffic flow of various means, such as walking, driving, and public transit, on different roads during peak hours on weekdays. Results are cross-checked with other data sources and are proven effective. Besides recognizing travel modes of residents, the proposed method can also be applied for studies such as travel costs, housing–job balance, and road traffic pressure. The study acquires about 6 million residents’ travel modes, working place and residence information, and analyzes the means of travel and traffic flow in the commuting of 3 million residents using the proposed method. The findings not only provide new ideas for the collection and application of urban traffic information, but also provide data support for urban planning and traffic management.

scholarly journals Exploring the impact of COVID-19 on individual's travel mode choice in China

2021 ◽  
Vol 106 ◽  
pp. 271-280
Author(s):  
Siliang Luan ◽  
Qingfang Yang ◽  
Zhongtai Jiang ◽  
Wei Wang
Keyword(s):  
Mode Choice ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
The Impact

Suburban Commuter's Choice Behavior of Travel Mode: A Case Study in Nanjing

ICTE 2013 ◽  
2013 ◽  
Author(s):  
Tingting Lv ◽  
Li Lin ◽  
Yu Gu
Keyword(s):  
Choice Behavior ◽  
Travel Mode ◽  
Mode A

A Selective Review of Travel-Mode Choice Models

1982 ◽  
Vol 8 (4) ◽  
pp. 370 ◽  
Author(s):  
Richard Barff ◽  
David Mackay ◽  
Richard W. Olshavsky
Keyword(s):  
Mode Choice ◽  
Choice Models ◽  
Travel Mode ◽  
Selective Review ◽  
Travel Mode Choice
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