The Cellular Network as a Sensor: From Mobile Phone Data to Real-Time Road Traffic Monitoring

Janecek, Andreas; Valerio, Danilo; Hummel, Karin Anna; Ricciato, Fabio; Hlavacs, Helmut

doi:10.1109/tits.2015.2413215

Cited by 94 publications

(55 citation statements)

References 33 publications

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“…The active phones in cellular network generate CDRs (Call Data Record) where as the idle phones do not generate CDR [2]. The paper studied the data set obtained over a period of month to arrive at congestion pattern.…”

Section: R S Parmar Et Al Journal Of Transportation Technologiesmentioning

confidence: 99%

A Model with Traffic Routers, Dynamically Managing Signal Phases to Address Traffic Congestion in Real Time

Parmar¹,

Trivedi²,

Stevanović³

2018

JTTs

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On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3 2N * vis-à-vis computation cost of N 2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.

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Section: R S Parmar Et Al Journal Of Transportation Technologiesmentioning

confidence: 99%

A Model with Traffic Routers, Dynamically Managing Signal Phases to Address Traffic Congestion in Real Time

Parmar¹,

Trivedi²,

Stevanović³

2018

JTTs

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show abstract

“…Currently, the most popular wearable sensor platform is the mobile phone. By analysing the data obtained from mobiles phones, many different applications can be applied, such as health monitoring [10], road traffic monitoring [11], activity recognition [12], identification of patterns and outliers [13,14], mobile e-learning [15], and so on. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymised datasets.…”

Section: Smartphone-based Approachesmentioning

confidence: 99%

Real-Time Recognition of Calling Pattern and Behaviour of Mobile Phone Users through Anomaly Detection and Dynamically-Evolving Clustering

et al. 2017

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Abstract:In the competitive telecommunications market, the information that the mobile telecom operators can obtain by regularly analysing their massive stored call logs, is of great interest. Although the data that can be extracted nowadays from mobile phones have been enriched with much information, the data solely from the call logs can give us vital information about the customers. This information is usually related with the calling behaviour of their customers and it can be used to manage them. However, the analysis of these data is normally very complex because of the vast data stream to analyse. Thus, efficient data mining techniques need to be used for this purpose. In this paper, a novel approach to analyse call detail records (CDR) is proposed, with the main goal to extract and cluster different calling patterns or behaviours, and to detect outliers. The main novelty of this approach is that it works in real-time using an evolving and recursive framework.

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“…Also, other studies reported real-time road traffic information extracted from the CDR data [30], [31], [32]. In [33], Janecek et al proposed a novel approach combining signaling data ('idle' device information) together with CDR (SigCDR) to obtain canonical information of the mobile users in an area. They showed through vehicle tracking that there is a strong correlation between vehicles on the road and CDR with signaling.…”

Section: Introductionmentioning

confidence: 99%

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Kaiser

Lwin

Mahmud

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g. mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This article mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, a possible crowd event detection framework is discussed which fuses data from all the available pervasive technology sources. In addition, available open-access crowd datasets for urban event detection are provided along with relevant Application Programming Interfaces, and finally, some open challenges and promising research directions are outlined.Index Terms-Urban sensing, pervasive technology, crowd mobility and management, information fusion, decision support system, benchmark datasets.

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The Cellular Network as a Sensor: From Mobile Phone Data to Real-Time Road Traffic Monitoring

Cited by 94 publications

References 33 publications

A Model with Traffic Routers, Dynamically Managing Signal Phases to Address Traffic Congestion in Real Time

A Model with Traffic Routers, Dynamically Managing Signal Phases to Address Traffic Congestion in Real Time

Real-Time Recognition of Calling Pattern and Behaviour of Mobile Phone Users through Anomaly Detection and Dynamically-Evolving Clustering

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

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