Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China

Understanding commuting patterns has been a classic research topic in the fields of geography, transportation and urban planning, and it is significant for handling the increasingly serious urban traffic congestion and air pollution and their impacts on the quality of life. Traditional studies have used travel survey data to investigate commuting from the aspects of commuting mode, efficiency and influence factors. Due to the limited sample size of these data, it is difficult to examine the large-scale commuting patterns of urban citizens, especially when exploring the spatial structure of commuting. This study attempts to understand the spatial structure characteristics generated by human commutes to work by using massive mobile phone datasets. A three-step workflow was proposed to accomplish this goal, which includes extracting the home and work locations of phone users, detecting the communities from the commuting network, and identifying the commuting convergence and divergence areas for each community. A case study of Shenzhen, China was implemented to determine the commuting structure. We found that there are thirteen communities detected from the commuting network and that some of the communities are in accordance with urban planning; moreover, spatial polycentric polygons exist in each community. These findings can be referenced by urban planners or policy-makers to optimize the spatial layout of the urban functional zones. Document type: Articl

Exploring universal patterns in human home-work commuting from mobile phone data

Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country and city scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)--despite substantial spatial and infrastructural differences. Furthermore, a comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors--as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance

MOBILITY AND ACTIVITY SPACE: UNDERSTANDING HUMAN DYNAMICS FROM MOBILE PHONE LOCATION DATA

Studying human mobility patterns and people’s use of space has been a major focus in geographic research for ages. Recent advancements of location-aware technologies have produced large collections of individual tracking datasets. Mobile phone location data, as one of the many emerging data sources, provide new opportunities to understand how people move around at a relatively low cost and unprecedented scale. However, the increasing data volume, issue of data sparsity, and lack of supplementary information introduce additional challenges when such data are used for human behavioral research. Effective analytical methods are needed to meet the challenges to gain an improved understanding of individual mobility and collective behavioral patterns. This dissertation proposes several approaches for analyzing two types of mobile phone location data (Call Detail Records and Actively Tracked Mobile Phone Location Data) to uncover important characteristics of human mobility patterns and activity spaces. First, it introduces a home-based approach to understanding the spatial extent of individual activity space and the geographic patterns of aggregate activity space characteristics. Second, this study proposes an analytical framework which is capable of examining multiple determinants of individual activity space simultaneously. Third, the study introduces an anchor-point based trajectory segmentation method to uncover potential demand of bicycle trips in a city. The major contributions of this dissertation include: (1) introducing an activity space measure that can be used to evaluate how individuals use urban space around where they live; (2) proposing an analytical framework with three individual mobility indicators that can be used to summarize and compare human activity spaces systematically across different population groups or geographic regions; (3) developing analytical methods for uncovering the spatiotemporal dynamics of travel demand that can be potentially served by bicycles in a city, and providing suggestions for the locations and daily operation of bike sharing stations

Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data

This study proposes a framework to impute travel mode for trips identified from cellphone traces by developing a deep neural network model. In our framework, we use the trips from a home interview survey and transit smartcard data, for which the travel mode is known, to create a set of artificial pseudo-cellphone traces. The generated artificial pseudo-cellphone traces with known mode are then used to train a deep neural network classifier. We further apply the trained model to infer travel modes for the cellphone traces from cellular network data. The empirical case study region is Montevideo, Uruguay, where high-quality data are available for all three types of data used in the analysis: a large dataset of cellphone traces, a large dataset of public transit smartcard transactions, and a small household travel survey. The results can be used to create an enhanced representation of origin-destination trip-making in the region by time of day and travel mode