Travel mode recognition of urban residents using mobile phone data and MapAPI
Bibliographic Data
| ID | 21247054 |
|---|---|
| Authors | Zhenghong Peng (Wuhan University), Guikai Bai (Wuhan University), Hao Wu (0000-0001-5323-516X, Wuhan University, corresponding author), Lingbo Liu (0000-0002-9876-8506, Wuhan University), Yang Yu (0000-0001-5353-6165, Wuhan University) |
| Year | 2021 |
| Volume | 48 |
| Issue | 9 |
| Pages | 2574-2589 |
| Publication date | 2021-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning B Urban Analytics and City Science (JOURNAL) |
| Journal identifiers | ISSN: 2399-8083 • E-ISSN: 2399-8091 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/2399808320983001 |
| OpenAlex | W3119997239 |
| Language | EN |
| Citations received | 4 |
| References cited | 34 |
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
Cluster analysis · Data mining · DBSCAN · Fuzzy clustering · Geography · Human–computer interaction · Mobile phone · Telecommunications · Transport engineering · Travel behavior · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Impact of Light on Environment and Health · Urban Transport and Accessibility · Artificial Intelligence
Exploring Spatial Nonstationarity in Determinants of Intercity Commuting Flows
Enhancing intercity transportation will improve the equitable distribution of high-quality health care in China
Using Large-Scale Truck Trajectory Data to Explore the Location of Sustainable Urban Logistics Centres—The Case of Wuhan
Exploring population spatiotemporal structure of cities with cellular data
Limits of Predictability in Human Mobility
Human mobility and socioeconomic status
Origin–destination trips by purpose and time of day inferred from mobile phone data
Understanding monthly variability in human activity spaces
Does polycentric and compact development alleviate urban traffic congestion? A case study of 98 Chinese cities
Discovering urban activity patterns in cell phone data
| Citations per year | 1 |
|---|---|
| Citation span | 2022 - 2025 (4) |
| Highly cited | No |