Traffic speed estimation based on normal location updates and call arrivals from cellular networks

Chen, Chi‐Hua; Chang, Hsu Chia; Su, Chun Yun; Lo, Chun-Chih; Lin, Hui

doi:10.1016/j.simpat.2013.02.005

Cited by 21 publications

(20 citation statements)

References 6 publications

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“…This study assumes that the call inter-arrival time (t) is exponentially distributed with the mean 1/λ [26][27][28][29][30][31][32][33] to generate the Call Arrival. Because the distance of the cell coverage is l i and the speed of the car is V i , the time that the car moves from one side to the other side is l i /V i .…”

Section: Channel Allocation Mechanism In Cellular Networkmentioning

confidence: 99%

“…For estimating the number of Handover In (Ii), the call holding time (τ) is assumed to be exponentially distributed with the mean 1/μ [26][27][28][29][30][31][32][33]. In this study, the car flow and vehicle speed are assumed as Fi and Vi.…”

Section: Callmentioning

confidence: 99%

“…Figure 11. The timing diagram for Call Departure in the specific Celli derived from Call Arrival [27][28][29][30][31][32][33]. …”

Section: The Number Of Call Departures (D I )mentioning

confidence: 99%

“…The conceptual development and limited available data can be considered by the Wiedemann model to generate traffic steam data for highways and freeways [27,28]. For MS communication trace generation, this study assumed that the distribution of call holding time is exponential with the mean 1/µ and the distribution of call inter-arrival time is exponential with the mean 1/λ [26][27][28][29][30][31][32][33]. The communications of each MS were simulated according to the assumptions.…”

Section: Simulation Analysesmentioning

confidence: 99%

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A Channel Allocation Mechanism for Cellular Networks

et al. 2017

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Abstract:In cellular networks, call blocking causes lower customer satisfaction and economic loss. Therefore, the channel allocation for call block avoidance is an important issue. This study proposes a mechanism that considers the real-time traffic information (e.g., traffic flow and vehicle speed) and the user behavior (e.g., call inter-arrival time and call holding time) to analyze the adaptable number of communication calls in the specific cell for channel allocation. In experiments about call block probabilities (CBP), this study simulated two cases that are the situations of the whole day and traffic accident. The simulation results show that all CBPs in the scenario of whole day are less than 21.5% by using the proposed mechanism, which is better than using the static channel allocation (SCA) mechanism. Moreover, all CBPs in the scenario of traffic accidents are less than 16.5% by using the proposed mechanism, which is better than using the SCA mechanism. Therefore, the proposed mechanism can decrease the number of CBPs effectively.

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Section: Channel Allocation Mechanism In Cellular Networkmentioning

confidence: 99%

Section: Callmentioning

confidence: 99%

“…Figure 11. The timing diagram for Call Departure in the specific Celli derived from Call Arrival [27][28][29][30][31][32][33]. …”

Section: The Number Of Call Departures (D I )mentioning

confidence: 99%

Section: Simulation Analysesmentioning

confidence: 99%

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A Channel Allocation Mechanism for Cellular Networks

et al. 2017

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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%

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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Traffic speed mapping with cellular network signaling data by FOSS4G

Wang

Zhu

et al. 2021

Spat. Inf. Res.

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Traffic speed estimation based on normal location updates and call arrivals from cellular networks

Cited by 21 publications

References 6 publications

A Channel Allocation Mechanism for Cellular Networks

A Channel Allocation Mechanism for Cellular Networks

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

Traffic speed mapping with cellular network signaling data by FOSS4G

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