Generating Road Traffic Information from Cellular Networks - New Possibilities in UMTS

Gundlegård, David; Karlsson, Johan M.

doi:10.1109/itst.2006.288805

Cited by 8 publications

(9 citation statements)

References 13 publications

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“…[7,8]. A survey and a detailed d escription about the d ifferences in location d ata for generating road traffic inform ation from the GSM and UMTS netw orks can be found in [9]. Recent extensive surveys of the area can be found in [10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Handover location accuracy for travel time estimation in GSM and UMTS

Gundlegård

Karlsson

2009

IET Intell. Transp. Syst.

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In this paper, field m easurem ents from the GSM and UMTS netw orks are analysed in a road traffic inform ation context. The m easurem ents ind icate a p otentially large im provem ent using UMTS signalling d ata com pared to GSM regard ing hand over location accuracy. These im provem ents can be used to generate real-tim e traffic inform ation w ith higher quality and extend the geographic usage area for cellular based travel tim e estim ation system s. The r esults confirm previous reports ind icating that he technology has a large p otential in GSM and they also show that the potential m ight be even larger u sing UMTS. Assum ing that non vehicle term inals can be filtered out, that veh icles are tracked to the correct route and that hand overs can be pred icted co rrectly, a conclusion from the experim ents is that the hand ov er location accuracy in both GSM and UMTS w ill be sufficient to estim ate useful travel tim es, also in urban environm ents. If there is a scalable w ay of d oing this filtering, tracking and pred iction in urban environm ents is not clear to the authors t od ay.58

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Section: Introductionmentioning

confidence: 99%

Handover location accuracy for travel time estimation in GSM and UMTS

Gundlegård

Karlsson

2009

IET Intell. Transp. Syst.

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“…The sampling strategy strongly depends on which type of cellular network and which interface that is monitored, see e.g. [46] for details.…”

Section: Cellular Network Datamentioning

confidence: 99%

Traffic Management for Smart Cities

Allström

Barceló

Ekström

et al. 2016

Designing, Developing, and Facilitating Smart Cities

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Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management. This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction and control.

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“…GPS-equipped probe car reporting has a low accuracy rate when the penetration rate of GPS-equipped probe cars is too low. The CFVD can be obtained from mobile phones, which have high penetration in many countries [9], and some studies pointed that CFVD could be used to estimate traffic status with high accuracy [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Collecting traffic information using CFVD is economic and low cost.…”

Section: Introductionmentioning

confidence: 99%

“…These studies illustrated that higher accuracies of traffic information estimation were performed by using CFVD for highways [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. However, these studies assumed that vehicles can be tracked to the correct route, but the determination of the correct route driven by the user of a mobile station (MS) is difficult and has not been investigated, especially for urban roads.…”

Section: Introductionmentioning

confidence: 99%

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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Generating Road Traffic Information from Cellular Networks - New Possibilities in UMTS

Cited by 8 publications

References 13 publications

Handover location accuracy for travel time estimation in GSM and UMTS

Handover location accuracy for travel time estimation in GSM and UMTS

Traffic Management for Smart Cities

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

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