Traffic Flow Estimation Models Using Cellular Phone Data (original) (raw)
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Motorway traffic parameter estimation from mobile phone counts
European Journal of Operational Research, 2006
In this paper a new method for real time estimation of vehicular flows and densities on motorways is proposed. This method is based on fusing traffic counts with mobile phone counts. The procedure used for the estimation of traffic flow parameters is based on the hypothesis that "instrumented" vehicles can be counted on specific motorway sections and traffic flow can be measured on entrance and exit ramps. The motorway is subdivided into cells, assuming that mobile phones entering and exiting every cell can be counted during the observation period. An estimate of "instrumented" vehicle concentration is obtained and propagated on the network in time and space. This allows one to estimate traffic flow parameters by sampling "instrumented" traffic flow parameters using a "concentration" (the ratio of the densities of instrumented vehicles to the density of overall traffic) propagation mechanism.
The Cellular Network as a Sensor: From Mobile Phone Data to Real-Time Road Traffic Monitoring
IEEE Transactions on Intelligent Transportation Systems, 2015
Mobile cellular networks can serve as ubiquitous sensors for physical mobility. We propose a method to infer vehicle travel times on highways and to detect road congestion in real-time, based solely on anonymized signaling data collected from a mobile cellular network. Most previous studies have considered data generated from mobile devices active in calls, namely Call Detail Records (CDR), an approach that limits the number of observable devices to a small fraction of the whole population. Our approach overcomes this drawback by exploiting the whole set of signaling events generated by both idle and active devices. While idle devices contribute with a large volume of spatially coarse-grained mobility data, active devices provide finer-grained spatial accuracy for a limited subset of devices. The combined use of data from idle and active devices improves congestion detection performance in terms of coverage, accuracy, and timeliness. We apply our method to real mobile signaling data obtained from an operational network during a one-month period on a sample highway segment in the proximity of a European city, and present an extensive validation study based on groundtruth obtained from a rich set of reference datasources -road sensor data, toll data, taxi floating car data, and radio broadcast messages.
Review of traffic data estimations extracted from cellular networks
IET Intelligent Transport Systems, 2008
One of the main characteristics of modern society is the never-ending increase in mobility. This leads to a series of problems such as congestion and increased pollution. To resolve these problems, it is imperative to have a good road network management and planning. To efficiently identify the characteristics of traffic in the road network, it would be necessary to perform a permanent monitorisation of all roadway links. This would involve an excessive cost of installation and maintenance of road infrastructure. Hence, new alternatives are required which can characterise traffic in a real time with good accuracy at an acceptable price. Mobile telephone systems are considered as a promising technology for the traffic data collection system. Its extensive use in converting its subscribers in a broad sample to draw information from phones becomes anonymous probes to monitor traffic. It is reviewed how to obtain parameters related to traffic from cellular-network-based data, describing methods used in existing simulation works as well as field tests in the academic and industrial field. 2 Mobility management in mobile phone networks The mobility required today is modifying the life style both at an individual and collective level. The result is the need for
Cellular data meet vehicular traffic theory
Proceedings of the 2012 ACM Conference on Ubiquitous Computing - UbiComp '12, 2012
Road traffic can be monitored by means of static sensors and derived from floating car data, i.e., reports from a sub-set of vehicles. These approaches suffer from a number of technical and economical limitations. Alternatively, we propose to leverage the mobile cellular network as a ubiquitous mobility sensor. We show how vehicle travel times and road congestion can be inferred from anonymized signaling data collected from a cellular mobile network. While other previous studies have considered data only from active devices, e.g., engaged in voice calls, our approach exploits also data from idle users resulting in an enormous gain in coverage and estimation accuracy. By validating our approach against four different traffic monitoring datasets collected on a sample highway over one month, we show that our method can detect congestions very accurately and in a timely manner.
Geojournal, 2000
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 non-recurrent 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.
Mobile Phone Location Area Based Traffic Flow Estimation in Urban Road Traffic
Signaling data of cellular phones can be used as valuable information for state-of-the-art traffic applications especially in urban areas. The traces of mobiles like handovers and location area updates may be efficiently utilized in the field of road traffic measurement and forecasting or even traffic control. By detecting and processing the locomotion of anonymous mobile phones, origin-destination flows and trip distribution can be inferred. This information may serve as important basis for transportation planning, and even for realtime applications, such as traffic control or route guidance. The proposed method apply only passive signaling events generated by the user and captured by the cellular system operator. Therefore, it does not require additional infrastructure on the operator-side or the use of any active application on the client-side such as GPS. Moreover, it can be applied in 2G as well as 3G cellular systems.
Exploiting Cellular Networks for Road Traffic Estimation: A Survey and a Research Roadmap
VTC Spring 2009 - IEEE 69th Vehicular Technology Conference, 2009
In this contribution we address the problem of using cellular network signaling for inferring real-time road traffic information. We survey and categorize the approaches that have been proposed in the literature for a cellular-based road monitoring system and identify advantages and limitations. We outline a unified framework that encompasses UMTS and GPRS data collection in addition to GSM, and prospectively combines passive and active monitoring techniques. We identify the main research challenges that must be faced in designing and implementing such an intelligent road traffic estimation system via third-generation cellular networks.
Detection and Estimation of Road Congestion Using Cellular Phones
2007 7th International Conference on ITS Telecommunications, 2007
This research proposes the methodology of detection and estimation of road congestion using cellular phones. We survey the road that is well-known as one of the most congested roads in Bangkok, Thailand. In our survey, we collect two kinds of information, Cell Dwell Time (CDT) using a cellular phone and velocity and position using Global Position System (GPS). To categorize CDT according to Cell Coverage Area (CCA), we have also collected Local Area Code (LAC) and Cell ID. We collect data into "Green" survey and "Red" survey, and analyze the data using two models: Junction model and Link model. The results have shown that there is significant difference between the two surveys. However, the accuracy of traffic flow estimation is tentatively decreased as the CCA size is decreased.
Estimating Dynamic Origin-Destination Data and Travel Demand Using Cell Phone Network Data
International Journal of Intelligent Transportation Systems Research, 2013
This study develops cell phone location tracking algorithms from a large cell phone network database to estimate the dynamic origin-destination (O-D) traffic flow and travel demand data as well as commuting traffic. A case study was conducted in the Kansas Metro Corridor to analyze the feasibility of using cell phone data to track crossregion (cities) traffic activities, and to derive the O-D traffic, travel demand by time-of-day and commuting traffic data along the traffic corridor based on a 6 week observation period. The results found that the available cell phone network data detected about 17.6% of the daily traffic data compared to the AADT data along the Kansas Metro Corridor. Approximately 58% of the total traffic was determined to be O-D traffic through the study corridor. This indicates that most of the traffic is from three major regions (the Kansas City metropolitan area, the City of Topeka, KS and the City of Lawrence, KS) and the estimated dynamic travel demand can be used for public transportation system planning and schedule arrangements. Due to the low location resolution using the network-based cell phone network, the use of cell phone network in collecting traffic data would be more feasible for long distance or inter-city trips. A longer observation period is also needed to increase the cell phone sample size and could be useful to obtain stable cell phone traffic, reducing the bias of the data. be used in both vehicle and traveler tracking. Previous studies [1-4] have evaluated the application of cell phones in travel speed and travel time estimation, as well as identifying congested sections. Several researchers have examined different types of cell phone network data in deriving O-D matrices. White and Wells [5] proposed a pilot study which investigated the feasibility of using phone billing data to obtain O-D information in the Kent area of the United Kingdom. The billing data included the location of signal towers indicating where and when the calls were made. It demonstrated that it is possible to obtain O-D information from cell phone data. Caceres et al. [6] also assessed the feasibility of using cell phone location databases to infer O-D matrices via cell phones that were switched on all the time. A cell phone communication network simulator was used to simulate and extract the O-D matrix from the phone network. Zhang et al.