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A platform for research: civil engineering, architecture and urbanism
Understanding Urban Mobility Pattern with Cellular Phone Data: A Case Study of Residents and Travelers in Nanjing
The rapid development of urban metropolises has attracted a growing number of immigrants and travelers, increasing the burden on transportation systems. Previous research on urban mobility patterns have ignored the temporal variations and heterogeneity in divergent urban trip makers due to the limited data resolution and coverage. In this paper, we analyzed cellular phone data of more than five million travelers for one month in Nanjing, China and proposed a method to extract trip origin and destination information from cellular phone signal data. We found that mobility patterns are different for urban residents, short-term travelers, and transfer travelers, and that trip length distributions can best be described by gamma and exponential distributions. In addition to the daily trip length distribution models, we utilized the agglomerative hieratical clustering method in order to group similar hourly trip patterns and further proposed within-day trip length distribution models under different times of the day and days of the week.
Understanding Urban Mobility Pattern with Cellular Phone Data: A Case Study of Residents and Travelers in Nanjing
The rapid development of urban metropolises has attracted a growing number of immigrants and travelers, increasing the burden on transportation systems. Previous research on urban mobility patterns have ignored the temporal variations and heterogeneity in divergent urban trip makers due to the limited data resolution and coverage. In this paper, we analyzed cellular phone data of more than five million travelers for one month in Nanjing, China and proposed a method to extract trip origin and destination information from cellular phone signal data. We found that mobility patterns are different for urban residents, short-term travelers, and transfer travelers, and that trip length distributions can best be described by gamma and exponential distributions. In addition to the daily trip length distribution models, we utilized the agglomerative hieratical clustering method in order to group similar hourly trip patterns and further proposed within-day trip length distribution models under different times of the day and days of the week.
Understanding Urban Mobility Pattern with Cellular Phone Data: A Case Study of Residents and Travelers in Nanjing
Fan Yang (author) / Zhenxing Yao (author) / Fan Ding (author) / Huachun Tan (author) / Bin Ran (author)
2019
Article (Journal)
Electronic Resource
Unknown
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