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

Zhenghong Peng · Guikai Bai · Hao Wu · Lingbo Liu · Yang Yu

25 citations0/0 files storedEnvironment and Planning B Urban Analytics and City Science

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Human Mobility and Location-Based Analysis Transportation Planning and Optimization Urban Transport and Accessibility

Abstract

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.

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References

2010Limits of Predictability in Human Mobility 2015Origin–destination trips by purpose and time of day inferred from mobile phone data 2011Estimating Origin-Destination Flows Using Mobile Phone Location Data 2012Understanding Road Usage Patterns in Urban Areas 2013Understanding monthly variability in human activity spaces: A twelve-month study using mobile phone call detail records 2018Human mobility and socioeconomic status: Analysis of Singapore and Boston 2015Discovering urban activity patterns in cell phone data 2010Transportation mode inference from anonymized and aggregated mobile phone call detail records

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2022Machine-Learning Approaches to Identify Travel Modes Using Smartphone-Assisted Survey and Map Application Programming Interface 2022Exploring Spatial Nonstationarity in Determinants of Intercity Commuting Flows: A Case Study of Suzhou–Shanghai, China 2023Enhancing intercity transportation will improve the equitable distribution of high-quality health care in China 2022Detection of Traffic Pattern Based on Fuzzy Clustering and Wavelet Analysis Model at Different Signaling Positioning Frequencies

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