Extracting origin destination information from mobile phone data

White, Joanna; Wells, Ivan

doi:10.1049/cp:20020200

Cited by 86 publications

(51 citation statements)

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“…In the remainder of the paper we will call these events network connections. These events represent a superset of the ones contained in the Call Details Records, previously considered in [10], [16]. In this research we have been able to analyze 829 million mobile location data for 1 million device collected by AirSage 1 .…”

Section: Mobile Phone Datasetmentioning

confidence: 99%

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Estimating Origin-Destination Flows Using Mobile Phone Location Data

Calabrese

Lorenzo

Liu

et al. 2011

IEEE Pervasive Comput.

432

267

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In this paper, we present an algorithm for the analysis of opportunistically collected mobile phone location data to estimate a population's travel demand in terms of origins and destinations of individual trips. Aggregating the trips from millions individual mobile phone users in the Boston Metropolitan area, we show that the estimated Origin-Destination flows correlate well with the US Census estimates at both the county and census tract levels. Moreover, compared to traditional census survey data, our estimations allow capturing weekday and weekend patterns as well as seasonal variations. These features could make methods for Origin-Destination flow estimation based on opportunistically collected mobile phone location data a critical component for transportation management and emergency response.

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Section: Mobile Phone Datasetmentioning

confidence: 99%

“…Initial work was done by [10] using billing data, consisting of cell phone tower information every time a phone received or made a call. In [11] the authors used mobile phone positions every two hours to infer trips.…”

Section: Introductionmentioning

confidence: 99%

Estimating Origin-Destination Flows Using Mobile Phone Location Data

Calabrese

Lorenzo

Liu

et al. 2011

IEEE Pervasive Comput.

432

267

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show abstract

“…Claiming to be the first attempt in this field, this study analyzes the use of location information to estimate on-line traffic conditions of important roads and highways by exploiting the presence of mobile phones on board of vehicles. The presence of a cellular terminal could be (White and Wells 2002), where the Transport Research Laboratory (TRL) developed a system to generate journey times and traffic speeds from OD matrices based on billing data from the telecom network. This study only uses a subset of all monitored phones, resulting in a very small sample size.…”

Section: European Effortsmentioning

confidence: 99%

Mobile phone data from GSM networks for traffic parameter and urban spatial pattern assessment: a review of applications and opportunities

et al. 2011

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The use of wireless location technology and mobile phone data appears to offer a broad range of new opportunities for sophisticated applications in traffic management and monitoring, particularly in the field of incident management. Indeed, due to the high market penetration of mobile phones, it allows the use of very detailed spatial data at lower costs than traditional data collection techniques. Albeit recent, the literature in the field is wide-ranging, although not adequately structured. The aim of this paper is to provide a systematic overview of the main studies and projects addressing the use of data derived from mobile phone networks to obtain location and traffic estimations of individuals, as a starting point for further research on incident and traffic management. The advantages and limitations of the process of retrieving location information and transportation parameters from cellular phones are also highlighted. The issues are presented by providing a description of the current background and data types retrievable from the GSM network. In addition to a literature review, the main findings on the so-called Current City project are presented. This is a test system in Amsterdam (The Netherlands) for the extraction of mobile phone data and for the analysis of the spatial network activity patterns. The main purpose of this project is to provide a full picture of the mobility and area consequences of an incident in near real time to create situation awareness. The first results from this project on how telecom data can be utilized for understanding individual presence and mobility in regular situations and during nonrecurrent events where regular flows of people are disrupted by an incident are presented. Furthermore, various interesting studies and projects carried out so far in the field are analyzed, leading to the identification of important research issues related to the use of mobile phone data in transportation applications. Relevant issues concern, on the one hand, factors that influence accuracy, reliability, data quality and techniques used for validation, and on the other hand, the specific role of private mobile companies and transportation agencies.

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“…In the remaining of the paper we will call these events network connections. These events represent a superset of the ones contained in the Call Details Records, previously considered in [9], [15]. Moreover, not only the id of the cell the mobile phone is connected to is available, but also an estimation of its position within the cell is generated through triangulation by means of the Airsage's Wireless Signal Extraction technology.…”

Section: A Datasets 1) Mobile Phones Locationmentioning

confidence: 99%

Human mobility prediction based on individual and collective geographical preferences

Calabrese

Lorenzo

Ratti

2010

13th International IEEE Conference on Intelligent Transportation Systems

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Abstract-Understanding and predicting human mobility is a crucial component of transportation planning and management. In this paper we propose a new model to predict the location of a person over time based on individual and collective behaviors. The model is based on the person's past trajectory and the geographical features of the area where the collectivity moves, both in terms of land use, points of interests and distance of trips. The effectiveness of the proposed prediction model is tested using a massive mobile phone location dataset available for the Boston metropolitan area. Experimental results show good levels of accuracy in terms of prediction error and prove the advantage of using the collective behavior in the prediction model.

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Extracting origin destination information from mobile phone data

Cited by 86 publications

References 0 publications

Estimating Origin-Destination Flows Using Mobile Phone Location Data

Estimating Origin-Destination Flows Using Mobile Phone Location Data

Mobile phone data from GSM networks for traffic parameter and urban spatial pattern assessment: a review of applications and opportunities

Human mobility prediction based on individual and collective geographical preferences

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