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

Abstract

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.

A Channel Allocation Mechanism for Cellular Networks

“…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 .…”

“…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.…”

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

“…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.…”

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

“…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.…”