Abstract
Obtaining the distribution of home and work locations is essential for city planning, as it defines the structure and mobility pattern of a city. With the development of telecommunication networks, mobile network data, having the advantages of large coverage and strong followability, have produced large amounts of information about human activities. Thus, it has become a popular research subject for human position detection. In this study, we proposed a new method to detect home and work locations based on the extraction of focal points in traces, identifying an individual’s working and resting hours, and analyzing the characteristics of city grids using mobile phone cellular signaling data (CSD). At the individual level, we validated the algorithm on ground-truth volunteer data and achieved a small deviation of under 500 and 565 m for home and work location detection 85% of the time. At the aggregate level, we tested it on a city-wide anonymized CSD set and found a high Pearson correlation between our result and the census data of 0.93. Compared to existing studies, this study improved the granularity and location accuracy of home and work location detection, as well as validated the method using both individually labeled ground-truth data and aggregate data for the first time. Applying the algorithm in a city, we captured the population distribution, commuting patterns, and job-housing balance of the city and demonstrated the potential in using mobile network data for urban planning and policy formulation.
Highlights
Detecting home and work locations is of great importance to modern city and transportation planning, as it aids the understanding of the relationship of jobs-housing [1], the design of public transportation [2], and the optimization of urban land use [3]
Survey data have a low update frequency, a small sample size, and a high implementation cost, while smart card data are confined to people using public transportation, which can possibly result in the sample being unrepresentative
Ground-Truth Volunteer Data. e anonymized cellular signaling data (CSD) set could not be matched to real users for algorithm validation. erefore, we developed an app for Android phones to collect CSD from the volunteers that were recruited
Summary
Detecting home and work locations is of great importance to modern city and transportation planning, as it aids the understanding of the relationship of jobs-housing [1], the design of public transportation [2], and the optimization of urban land use [3]. E model considered the cluster features, including days, durations, and the number of events during working and resting hours It was validated on data from 19 volunteers and achieved median errors of 0.9 and 0.83 miles for home and work location detection, respectively. (1) Investigating the unique schedule of each user by analyzing the variation of activity intensity of the user, distinguishing users with unusual working schedules and avoiding the biases from setting uniform timeframes (2) Improving the spatial accuracy of the home and work location detection by comprehensively analyzing the attributes of city grids (3) Evaluating the home and work location detection algorithm using both individually labelled groundtruth data and aggregate data for the first time e rest of the paper is structured as follows.
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