The Optimal Sampling Period of a Fingerprint Positioning Algorithm for Vehicle Speed Estimation

Cheng, Ding Yuan; Chen, Chi‐Hua; Hsiang, Chia Hung; Lo, Chun-Chih; Lin, Hui; Lin, Bon Yeh

doi:10.1155/2013/306783

Cited by 12 publications

(20 citation statements)

References 13 publications

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“…These methods can be classified into three categories: a cell-ID-based method [19], a handover-based method [20][21][22], and a fingerprint positioning method [23][24][25][26][27][28]. However, the lengths of cells and handoff zones are approximately 2 km and 200 m, respectively, and the location error depends on the cell size [12,19,29].…”

Section: Database Lookup Methodsmentioning

confidence: 99%

A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data

Chen

Lin

Kuan

et al. 2016

IJGI

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Abstract:Commercial vehicle operation (CVO) has been a popular application of intelligent transportation systems. Location determination and route tracing of an on-board unit (OBU) in a vehicle is an important capability for CVO. However, large location errors from global positioning system (GPS) receivers may occur in cities that shield GPS signals. Therefore, a highly efficient mobile positioning method is proposed based on the collection and analysis of the cellular network signals of CVO data. Parallel-and cloud-computing techniques are designed into the proposed method to quickly determine the location of an OBU for CVO. Furthermore, this study proposes analytical models to analyze the availability of the proposed mobile positioning method with various outlier filtering criteria. Experimentally, a CVO system was designed and implemented to collect CVO data from Chunghwa Telecom vehicles and to analyze the cellular network signals of CVO data for location determination. A case study found that the average errors of location determination using the proposed method vs. using the traditional cell-ID-based location method were 163.7 m and 521.2 m, respectively. Furthermore, the practical results show that the average location error and availability of using the proposed method are better than using GPS or the cell-ID-based location method for each road type, particularly urban roads. Therefore, this approach is feasible to determine OBU locations for improving CVO.

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Section: Database Lookup Methodsmentioning

confidence: 99%

A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data

Chen

Lin

Kuan

et al. 2016

IJGI

Self Cite

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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%

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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“…For MSR-based location methods, each MS can send periodically MSR messages (e.g., received signal strength indication (RSSI), round-trip delay and relative delay) to core networks in cellular networks (e.g., Global System Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS) and Long-Term Evolution (LTE)) [9]. Some studies analyzed the MSR messages to determine the locations of MSs, and these locations can be used to estimate traffic information.…”

Section: Location Servicesmentioning

confidence: 99%

“…Some studies analyzed the MSR messages to determine the locations of MSs, and these locations can be used to estimate traffic information. For instance, Cheng et al [9] proposed a fingerprint-positioning algorithm (FPA) based on the k-nearest neighbors (kNN) algorithm to analyze the RSSIs of MSR messages for generating the locations of MS in car, and the average vehicle speed and travel time between two locations of the same MS can be estimated by FPA. Because the interfaces of A-bis and Iub in radio networks should be monitored to retrieve RSSIs for FPA, it is required to have above 10,000 servers to deploy to proceed with recording and analysis by Taiwanese environment.…”

Section: Location Servicesmentioning

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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The Optimal Sampling Period of a Fingerprint Positioning Algorithm for Vehicle Speed Estimation

Cited by 12 publications

References 13 publications

A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data

A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data

A Channel Allocation Mechanism for Cellular Networks

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

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