Review of traffic data estimations extracted from cellular networks

Cáceres, Noelia; Wideberg, Johan; Benítez, Francisco G.

doi:10.1049/iet-its:20080003

Cited by 110 publications

(69 citation statements)

References 2 publications

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“…For instance, Caceres et al [22] collected and counted the handover events, which were triggered when the communicating MSs entered a new cell for traffic flow estimation. Furthermore, a virtual traffic counter at inter-cell boundaries was proposed to monitor call events in different cells and to estimate traffic flow [23].…”

Section: Signal Statisticsmentioning

confidence: 99%

Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

2016

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Traffic information estimation and forecasting methods based on cellular floating vehicle data (CFVD) are proposed to analyze the signals (e.g., handovers (HOs), call arrivals (CAs), normal location updates (NLUs) and periodic location updates (PLUs)) from cellular networks. For traffic information estimation, analytic models are proposed to estimate the traffic flow in accordance with the amounts of HOs and NLUs and to estimate the traffic density in accordance with the amounts of CAs and PLUs. Then, the vehicle speeds can be estimated in accordance with the estimated traffic flows and estimated traffic densities. For vehicle speed forecasting, a back-propagation neural network algorithm is considered to predict the future vehicle speed in accordance with the current traffic information (i.e., the estimated vehicle speeds from CFVD). In the experimental environment, this study adopted the practical traffic information (i.e., traffic flow and vehicle speed) from Taiwan Area National Freeway Bureau as the input characteristics of the traffic simulation program and referred to the mobile station (MS) communication behaviors from Chunghwa Telecom to simulate the traffic information and communication records. The experimental results illustrated that the average accuracy of the vehicle speed forecasting method is 95.72%. Therefore, the proposed methods based on CFVD are suitable for an intelligent transportation system.

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Section: Signal Statisticsmentioning

confidence: 99%

Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

2016

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“…For instance, the signals of HOs from GSM and UMTS could be used to analyze the cell dwell time in cells and to estimate vehicle speed and travel time [8,11,12,16,25,26,28]. Figure 1 shows a case study of CFVD for highway and urban roads.…”

Section: Cfvdmentioning

confidence: 99%

“…These two HO signals can be analyzed to obtain the cell dwell time of Cell 2. Then the vehicle speed and travel time of Highway 1 can be estimated in accordance with the cell dwell time [8,11,12,16,25,26,28].…”

Section: Cfvdmentioning

confidence: 99%

“…ITS can estimate and obtain traffic information (e.g., traffic flow, traffic density, and vehicle speed) to road users and managers for the improvement of service levels of the road network. The traffic information can be collected and estimated by three approaches, which include: (1) vehicle detection (VD) [1][2][3]; (2) global positioning system (GPS)-equipped probe car reporting [4][5][6][7]; and (3) cellular floating vehicle data (CFVD) [8]. However, vehicle data (VD) has high establishment and maintenance costs.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Positioning and Speed Estimation Based on Cellular Network Signals for Urban Roads

Lai

Kuo

2016

IJGI

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Abstract:In recent years, cellular floating vehicle data (CFVD) has been a popular traffic information estimation technique to analyze cellular network data and to provide real-time traffic information with higher coverage and lower cost. Therefore, this study proposes vehicle positioning and speed estimation methods to capture CFVD and to track mobile stations (MS) for intelligent transportation systems (ITS). Three features of CFVD, which include the IDs, sequence, and cell dwell time of connected cells from the signals of MS communication, are extracted and analyzed. The feature of sequence can be used to judge urban road direction, and the feature of cell dwell time can be applied to discriminate proximal urban roads. The experiment results show the accuracy of the proposed vehicle positioning method, which is 100% better than other popular machine learning methods (e.g., naive Bayes classification, decision tree, support vector machine, and back-propagation neural network). Furthermore, the accuracy of the proposed method with all features (i.e., the IDs, sequence, and cell dwell time of connected cells) is 83.81% for speed estimation. Therefore, the proposed methods based on CFVD are suitable for detecting the status of urban road traffic.

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“…As stated in [39], since the outliers' influence is severe, especially for dense urban areas, they should be identified and filtered out. In our system, the validity of traffic probes is not a big issue any more.…”

Section:  mentioning

confidence: 99%

Real-Time Urban Traffic State Estimation with A-GPS Mobile Phones as Probes

Tao¹,

Manolopoulos²,

Rodriguez³

et al. 2012

JTTs

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This paper presents a microscopic traffic simulation-based method for urban traffic state estimation using Assisted Global Positioning System (A-GPS) mobile phones. In this approach, real-time location data are collected by A-GPS mobile phones to track vehicles traveling on urban roads. In addition, tracking data obtained from individual mobile probes are aggregated to provide estimations of average road link speeds along rolling time periods. Moreover, the estimated average speeds are classified to different traffic condition levels, which are prepared for displaying a real-time traffic map on mobile phones. Simulation results demonstrate the effectiveness of the proposed method, which are fundamental for the subsequent development of a system demonstrator.

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Review of traffic data estimations extracted from cellular networks

Cited by 110 publications

References 2 publications

Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data

Vehicle Positioning and Speed Estimation Based on Cellular Network Signals for Urban Roads

Real-Time Urban Traffic State Estimation with A-GPS Mobile Phones as Probes

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