Oscillation Resolution for Mobile Phone Cellular Tower Data to Enable Mobility Modelling

Wu, Wei; Wang, Yue; Gomes, João Bártolo; Antonatos, Spiros; Xue, Minhui; Yang, Peng; Yap, Ghim Eng; Li, Xiaoli; Krishnaswamy, Shonali; Decraene, James; Nash, Amy Shi

doi:10.1109/mdm.2014.46

Cited by 39 publications

(31 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are efforts to improve spatial accuracy, for example, via interpolation methods (Järv et al, 2017; Louail et al, 2014). The second uncertainty related issue that exists in both types of data is the oscillation phenomenon: due to dynamic factors including varying transmission conditions that influence received signal strength and load balancing policies, instead of connecting to the nearest cell tower, a mobile phone switches its connection between different towers (typically within a short period of time) even though the device itself is not moving (Calabrese et al, 2011b; Wu et al, 2014). The oscillation phenomenon generates a considerable number of records that do not reflect devices' actual movements (Lee and Hou, 2006).…”

Section: Related Workmentioning

confidence: 99%

“…However, the results can still be biased if traces generated from oscillations are not removed (Qi et al, 2016; Wu et al, 2014). When oscillation occurs, some devices may be observed switching between two (or more) far-away locations with high frequencies.…”

Section: Related Workmentioning

confidence: 99%

“…A few methods have been designed to improve the detection precision by utilizing the temporal information available in the data (Iovan et al, 2013; Qi et al, 2016; Widhalm et al, 2015; Wu et al, 2014). In an oscillation sequence, the frequent switches between distinct locations give the illusion that people travel at an abnormally high speed.…”

Section: Related Workmentioning

confidence: 99%

“…Wu et al elaborated several heuristic rules to detect oscillation traces (Wu et al, 2014). Working on CDR data, they begin with finding suspicious sequences that contain at least three traces from multiple cellular towers within a short time period (e.g., one minute).…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

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

Wang

Chen

2018

Transportation Research Part C: Emerging Technologies

166

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

Wang

Chen

2018

Transportation Research Part C: Emerging Technologies

166

View full text Add to dashboard Cite

show abstract

“…However, these methods are limited by their sample size and face di culty scaling up. Approaches that do scale up make use of cell phone call detail records (CDRs), which are recorded by telcos for billing purposes and include the location of calls and SMS, to spot trips of all cell phone subscribers in a mobile network [17,9,10,13,14,23,11].…”

Section: Introductionmentioning

confidence: 99%

Traffic Measurement and Route Recommendation System for Mass Rapid Transit (MRT)

Holleczek¹,

Yin²,

Jin³

et al. 2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Self Cite

View full text Add to dashboard Cite

Understanding how people use public transport is important for the operation and future planning of the underlying transport networks. We have therefore developed and deployed a tra c measurement system for a key player in the transportation industry to gain insights into crowd behavior for planning purposes. The system has been in operation for several months and reports, at hourly intervals, (1) the crowdedness of subway stations, (2) the flows of people inside interchange stations, and (3) the expected travel time for each possible route in the subway network of Singapore. The core of our system is an e cient algorithm which detects individual subway trips from anonymized real-time data generated by the location based system of Singtel, the country's largest telecommunications company. To assess the accuracy of our system, we engaged an independent market research company to conduct a field study-a manual count of the number of passengers boarding and disembarking at a selected station on three separate days. A strong correlation between the calculations of our algorithm and the manual counts was found. One of our key findings is that travelers do not always choose the route with the shortest travel time in the subway network of Singapore. We have therefore also been developing a mobile app which allows users to plan their trips based on the average travel time between stations.

show abstract

In-stream mobility and speed estimation of mobile devices from mobile network data

Scholler,

Alaoui-Ismaïli,

Renaud

et al. 2024

Transportation

View full text Add to dashboard Cite

Oscillation Resolution for Mobile Phone Cellular Tower Data to Enable Mobility Modelling

Cited by 39 publications

References 15 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

Traffic Measurement and Route Recommendation System for Mass Rapid Transit (MRT)

In-stream mobility and speed estimation of mobile devices from mobile network data

Contact Info

Product

Resources

About