Generating Origin–Destination Matrices from Mobile Phone Trajectories

Friedrich, Markus; Immisch, K; Jehlicka, P; Otterstätter, Thomas; Schlaich, Jörg

doi:10.3141/2196-10

Cited by 43 publications

(21 citation statements)

References 6 publications

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“…(Gonzalez et al 2008;Schneider et al 2013;Ratti et al 2006;Ratti et al 2007;Sevtsuk and Ratti 2010;Calabrese et al 2013;Hoteit et al 2014), origin-destination flows, e.g. (Tettamanti and Varga 2014;Wang et al 2013;Caceres et al 2012;Calabrese et al 2011;Friedrich et al 2010), and road usage patterns (Wang et al 2012). More detailed reviews of the use of cell phone data in traveler information systems and travel behavior studies can be found in (Qiu and Cheng 2007;Yue et al 2014;Wang et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Discovering urban activity patterns in cell phone data

et al. 2015

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Massive and passive data such as cell phone traces provide samples of the whereabouts and movements of individuals. These are a potential source of information for models of daily activities in a city. The main challenge is that phone traces have low spatial precision and are sparsely sampled in time, which requires a precise set of techniques for mining hidden valuable information they contain. Here we propose a method to reveal activity patterns that emerge from cell phone data by analyzing relational signatures of activity time, duration, and land use. First, we present a method of how to detect stays and extract a robust set of geolocated time stamps that represent trip chains. Second, we show how to cluster activities by combining the detected trip chains with land use data. This is accomplished by modeling the dependencies between activity type, trip scheduling, and land use types via a Relational Markov Network. We apply the method to two different kinds of mobile phone datasets from the metropolitan areas of Vienna, Austria and Boston, USA. The former data includes information from mobility management signals, while the latter are usual Call Detail Records. The resulting trip sequence patterns and activity scheduling from both datasets agree well with their respective city surveys, and we show that the inferred activity clusters are stable across different days and both cities. This method to infer activity patterns from cell phone data allows us to use these as a novel and cheaper data source for activity-based modeling and travel behavior studies.

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Section: Introductionmentioning

confidence: 99%

Discovering urban activity patterns in cell phone data

et al. 2015

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“…For example, typical mobile phone data only have the location when a person exposes his or her location by using the phone. Extracting origins and destinations of each meaningful trip from mobile phone data needs extra work compared to taxi trip data but has attracted many researchers (Friedrich et al 2010, Calabrese et al 2011. It is also an open issue to define and reveal the semantics of human trajectories (Parent et al 2013).…”

Section: Discussionmentioning

confidence: 99%

Incorporating spatial interaction patterns in classifying and understanding urban land use

Liu

Kang

Gong

et al. 2015

International Journal of Geographical Information Science

135

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Land use classification has benefited from the emerging big data, such as mobile phone records and taxi trajectories. Temporal activity variations derived from these data have been used to interpret and understand the land use of parcels from the perspective of social functions, complementing the outcome of traditional remote sensing methods. However, spatial interaction patterns between parcels, which could depict land uses from a perspective of connections, have rarely been examined and analysed. To leverage spatial interaction information contained in the above-mentioned massive data sets, we propose a novel unsupervised land use classification method with a new type of place signature. Based on the observation that spatial interaction patterns between places of two specific land uses are similar, the new place signature improves land use classification by trading off between aggregated temporal activity variations and detailed spatial interactions among places. The method is validated with a case study using taxi trip data from Shanghai. ARTICLE HISTORY

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“…Another application of probe vehicle systems was updating digital road maps, which had the advantages of low cost, short updating circle and high accuracy Vartziotis et al, 2012). Some studies have also applied probe vehicle data to estimate dynamic OD for traffic demand forecasting purpose (Baek et al, 2010;Friedrich et al, 2010). Recently, the extended floating car systems (xFCD) that were based on the floating cars equipped with extra visual cameras and on-board Electronic Control Units have appeared to be a more efficient way for traffic state estimation, the level-of-service estimation, and real-time road safety assessment, e.g., Kyamakya et al (2011), Messelodi et al (2009), Pell et al (2012 and Diaz et al (2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Real-time identification of probe vehicle trajectories in the mixed traffic corridor

Tang

2015

Transportation Research Part C: Emerging Technologies

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Generating Origin–Destination Matrices from Mobile Phone Trajectories

Cited by 43 publications

References 6 publications

Discovering urban activity patterns in cell phone data

Discovering urban activity patterns in cell phone data

Incorporating spatial interaction patterns in classifying and understanding urban land use

Real-time identification of probe vehicle trajectories in the mixed traffic corridor

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