Conference Paper

Route Classification Using Cellular Handoff Patterns

DOI: 10.1145/2030112.2030130 Conference: UbiComp 2011: Ubiquitous Computing, 13th International Conference, UbiComp 2011, Beijing, China, September 17-21, 2011, Proceedings
Source: DBLP


Understanding utilization of city roads is important for urban planners. In this paper, we show how to use handoff patterns from cellular phone networks to identify which routes people take through a city. Specifically, this paper makes three contributions. First, we show that cellular handoff patterns on a given route are stable across a range of conditions and propose a way to measure stability within and between routes using a variant of Earth Mover's Distance. Second, we present two accurate classification algorithms for matching cellular handoff patterns to routes: one requires test drives on the routes while the other uses signal strength data collected by high-resolution scanners. Finally, we present an application of our algorithms for measuring relative volumes of traffic on routes leading into and out of a specific city, and validate our methods using statistics published by a state transportation authority.

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Available from: Ji Meng Loh, Jul 17, 2014
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    • "Previous studies have presented the use of cellular networks handover related data (double handover, cell dwell time, and CDRs) for traffic parameter estimation (Alger et al., 2005; Bar- Gera, 2007; Caceres et al., 2007; Herrera et al., 2010; Liu et al., 2008), OD estimation (Pan et al., 2006; White and Wells, 2002), analysis of urban dynamics (Becker et al., 2011b; Calabrese et al., 2010; Calabrese et al., 2011; Ratti et al., 2005), congestion detection (Hongsakham et al., 2008; Thajchayapong et al., 2006), and to understanding people's movements and for urban planning purposes (Ahas et al., 2010; Becker et al., 2011a; González et al., 2008; Järv et al., 2012). Even though many experts are convinced on the usefulness of cellular networks information for the analysis of urban mobility, some important limitations remain to be addressed. "
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    ABSTRACT: The progressive trend of urbanization involving changes in the activities of a city has created several problems. Addressing these problems requires reliable and detailed information regarding the urban structure and its dynamics. Previous studies have tried to explore cellular networks data for urban analysis, yet little attention has been given in exploring mobility related events of cellular networks. This study uses handover, which is the process of transferring an ongoing call from one cell to the other, to capture urban dynamics. The handover data was collected from cellular towers in Lisbon, Portugal. First, our method started with a pre-processing of the handover data. Then, experiments were carried out to understand the city dynamics through GIS visualization and statistical analysis. The visualizations provided a qualitative explanation of how the movement of calls is useful in highlighting the flow of people in urban infrastructures. Using statistical analysis, two important relationships were proved: there is a significant association between cell towers with a high number of incoming handovers and a high presence of people in their vicinity; and a greater proximity to the main road links of cell towers characterized by a high number of incoming as well as outgoing handovers thus towers denoting more movement. Our results suggest that the handover information, taking the advantage of its pervasiveness, can provide ways to analyze city dynamics at a larger scale. This approach complements the effort of traditional urban data collection methods, which are usually made available less frequently to urban planners and policy makers.
    Journal of Transport Geography 07/2013; DOI:10.1016/j.jtrangeo.2013.06.016 · 2.54 Impact Factor
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    • "An average coefficient of correlation of 0.76 was obtained from all the 12 counters. The result proved the existence of good correlation between the handover and the traffic volume which is in accordance to previous studies (Vaccari et al., 2009; Becker et al., 2011). While the overall correlation values obtained in the previous as well as in our study seem impressive, this is not enough to say that handovers give site-specific traffic profile. "
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    ABSTRACT: The methods currently used for primary road traffic data collection have prohibitive costs which compromise coverage of the entire transportation network in a city. Failure to collect information from the road traffic stream leads traffic management authorities to rely on an incomplete picture of the traffic status. This study explores a complementary method to gauge the status of road traffic conditions through the use of cellular networks handover count. To test this method, hourly handover counts were obtained in Lisbon, Portugal, from 39 cellular towers in the vicinity of arterial roads that have 12 traffic counters with an average daily traffic size of 20,500 vehicles. An initial correlation analysis proved the existence of a good relationship between handover and traffic volumes. However, the number of vehicles to handovers ratio at different sites can change up to 10 folds, which has limited the expansion of our model to estimate the absolute traffic volumes based on handover counts. Hence we have classified the hourly traffic counts into three categories: high, medium, and low traffic levels using the 50th and 80th percentiles. Then, half of the data was used to build a multinomial logit (MNL) model and to train an artificial neural network (ANN) in order to relate traffic and handover. The other half of the data was used to validate both models. The MNL and ANN models gave an overall correct classification accuracy of 76.4% and 78.1% respectively. Both models outperformed the accuracy of 70.8% obtained from a City-wide time-of-day traffic profile. The results demonstrate the feasibility of handover based models providing better accuracy in capturing site-specific traffic profile compared with the typical City-wide time-of-day traffic profile. It can be concluded that this study encourages the exploration of the use of cellphone handover information in estimating the road traffic status.
    Transportation Research Part C Emerging Technologies 07/2013; 32:76-78. DOI:10.1016/j.trc.2013.03.010 · 2.82 Impact Factor
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    ABSTRACT: Models of human mobility have broad applicability in fields such as mobile computing, urban planning, and ecology. This paper proposes and evaluates WHERE, a novel approach to modeling how large populations move within different metropolitan areas. WHERE takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs) from a cellular telephone network, and produces synthetic CDRs for a synthetic population. We have validated WHERE against billions of anonymous location samples for hundreds of thousands of phones in the New York and Los Angeles metropolitan areas. We found that WHERE offers significantly higher fidelity than other modeling approaches. For example, daily range of travel statistics fall within one mile of their true values, an improvement of more than 14 times over a Weighted Random Waypoint model. Our modeling techniques and synthetic CDRs can be applied to a wide range of problems while avoiding many of the privacy concerns surrounding real CDRs.
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