Traffic zone division based on big data from mobile phone base stations

Dong, Hui; Wu, Mingchao; Ding, Xiaoqing; Chu, Lianyu; Jia, Limin; Qin, Yong; Zhou, Xuesong

doi:10.1016/j.trc.2015.06.007

Cited by 117 publications

(55 citation statements)

References 26 publications

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“…Oscillation solutions for sightings data are limited (Alexander et al, 2015a; Dong et al, 2015; Palchykov et al, 2014; Yin et al, 2017). Calabrese et al (2011b) and Widhalm et al (2015) addressed two problems (both locational uncertainty and oscillation) simultaneously using clustering methods.…”

Section: Related Workmentioning

confidence: 99%

“…Indeed, a search in the literature using keyword combinations of mobile phone data, mobility, and travel behavior resulted in more than 1000 articles published in journals across different disciplines (Ahas et al, 2010a; Becker et al, 2013; Calabrese et al, 2013; Candia et al, 2008; Chen et al, 2014, 2016; Gao et al, 2013; Song et al, 2010b; Wang et al, 2014). These articles cover a wide range of topics including, for example, estimating mobility patterns (Csáji et al, 2013; González et al, 2008; Song et al, 2010a), inferring OD matrix (Calabrese et al, 2011b; Iqbal et al, 2014), finding anchor locations (Dong et al, 2015; Isaacman et al, 2011), inferring activity types (Jiang et al, 2017; Widhalm et al, 2015) and travel modes (Qu et al, 2015; Wang et al, 2010). …”

Section: Introductionmentioning

confidence: 99%

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On data processing required to derive mobility patterns from passively-generated mobile phone data

Wang

Chen

2018

Transportation Research Part C: Emerging Technologies

164

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Passively-generated mobile phone data is emerging as a potential data source for transportation research and applications. Despite the large amount of studies based on the mobile phone data, only a few have reported the properties of such data, and documented how they have processed the data. In this paper, we describe two types of common mobile phone data: Call Details Record (CDR) data and sightings data, and propose a data processing framework and the associated algorithms to address two key issues associated with the sightings data: locational uncertainty and oscillation. We show the effectiveness of our proposed methods in addressing these two issues compared to the state of art algorithms in the field. We also demonstrate that without proper processing applied to the data, the statistical regularity of human mobility patterns—a key, significant trait identified for human mobility—is over-estimated. We hope this study will stimulate more studies in examining the properties of such data and developing methods to address them. Though not as glamorous as those directly deriving insights on mobility patterns (such as statistical regularity), understanding properties of such data and developing methods to address them is a fundamental research topic on which important insights are derived on mobility patterns.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On data processing required to derive mobility patterns from passively-generated mobile phone data

Wang

Chen

2018

Transportation Research Part C: Emerging Technologies

164

View full text Add to dashboard Cite

show abstract

“…In addition, by combining the data from the Taxi and Limousine Commission with that of local weather, Yazici et al [25] made a decision-making model for whether New York taxis should wait at John F. Kennedy (JFK) Airport or roam the streets to get passengers. Dong et al [5] demonstrated that effective traffic and travel data can be obtained from mobile phones, providing an opportunity to improve the travel pattern analysis. Ferreira et al [7] proposed a visual query model that supports the visual exploration of big data of origin-destination, enabling the study of mobility across the city.…”

Section: Big Data Analysis For Infrastructure Operationsmentioning

confidence: 99%

Forecasting short-term air passenger demand using big data from search engine queries

Kim

Shin

2016

Automation in Construction

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“…Similarly, Alexander et al [36] used CDRs collected in the Boston metropolitan area over a period of two months to estimate OD trips by purposes (e.g., home-based work trips, home-based other trips, and non-home-based trips). Dong et al [37] used CDRs to suggest traffic zone division in urban areas to assist travel demand forecast. Wang et al [38] used mobile phone data collected in San Francisco and Boston area to evaluate urban road usage patterns.…”

Section: Mobile Phone Data For Travel Behavioral Analysismentioning

confidence: 99%

Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach

Shaw

Fang

et al. 2016

IJGI

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Abstract:This study uses a large-scale mobile phone dataset to estimate potential demand of bicycle trips in a city. By identifying two important anchor points (night-time anchor point and day-time anchor point) from individual cellphone trajectories, this study proposes an anchor-point based trajectory segmentation method to partition cellphone trajectories into trip chain segments. By selecting trip chain segments that can potentially be served by bicycles, two indicators (inflow and outflow) are generated at the cellphone tower level to estimate the potential demand of incoming and outgoing bicycle trips at different places in the city and different times of a day. A maximum coverage location-allocation model is used to suggest locations of bike sharing stations based on the total demand generated at each cellphone tower. Two measures are introduced to further understand characteristics of the suggested bike station locations: (1) accessibility; and (2) dynamic relationships between incoming and outgoing trips. The accessibility measure quantifies how well the stations could serve bicycle users to reach other potential activity destinations. The dynamic relationships reflect the asymmetry of human travel patterns at different times of a day. The study indicates the value of mobile phone data to intelligent spatial decision support in public transportation planning.

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Traffic zone division based on big data from mobile phone base stations

Cited by 117 publications

References 26 publications

On data processing required to derive mobility patterns from passively-generated mobile phone data

On data processing required to derive mobility patterns from passively-generated mobile phone data

Forecasting short-term air passenger demand using big data from search engine queries

Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach

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