A Vehicle Speed Estimation Mechanism Using Handovers and Call Arrivals of Cellular Networks

Abstract

Information and communication technologies have improved the quality of Intelligent Transportation Systems (ITS). The real-time traffic information has traditionally been collected via stationary vehicle detectors or GPS-based probe cars. Compared to the traditional ways, estimating traffic information from Cellular Floating Vehicle Data (CFVD) is more cost-effective, and easier to acquire. In this paper, this study proposes a novel approach to evaluate the relation of call arrival, handover, traffic flow, and traffic density. Moreover, the traffic speed is estimated by the proposed approach according to CFVD. Through the analytical analysis, this study analyzes the effects of traffic information (e.g. traffic flow and vehicle speed) and communication behaviors (e.g. call arrival rate and call holding time) on handovers and call arrivals. In the simulation, this study compares the estimated traffic information with the real traffic information. The experiment results show that the accuracy of traffic speed estimation is 89.75%. Therefore, the proposed approach can be used to estimate traffic speed from CFVD for ITS.

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

“…A method was proposed to retrieve the NLU events, which were triggered when MSs entered a new location area, and to estimate traffic flow in accordance with the amount of NLU events [24]. For traffic density estimation, a method was proposed to monitor the CA events and to analyze the relationship between traffic density and the amount of CA events [25]. These studies indicated that their proposed methods can estimate traffic information precisely with anonymous MSs.…”

“…The process is called a handover from Celli−1 to Celli. This study assumes that the call holding time (t) is exponentially distributed with the mean 1/ μ to generate handovers [25]. The average speed of cars is Ui, and the traffic flow is Qi.…”

“…Thereby, the amount of handover (hi) on the road segment covered by Celli can be expressed as Equation (2), and this study can estimate traffic flow ( h i q , ) by using Equation (3), which is the traffic flow multiplied by the number of HO events. This study assumes that the call holding time (t) is exponentially distributed with the mean 1{µ to generate handovers [25]. The average speed of cars is U i , and the traffic flow is Q i .…”

“…The MS in a car moving along the road performs the first call at time t 0 (in Figure 3a,b) and enters the coverage area of Cell i at time t 1 (in Figure 3a,b). The MS performs another call at time t 2 (in Figure 3a,b) before leaving Cell i (time t 3 in Figure 3a, This study assumes that the call inter-arrival time ( τ ) is exponentially distributed with the mean 1/ λ to generate call arrivals [25]. The length of road segment covered by Celli is li, and the average speed of cars is Ui.…”

“…Traffic Density Estimation by Using PLU Events This study assumes that the call inter-arrival time (τ) is exponentially distributed with the mean 1{λ to generate call arrivals [25]. The length of road segment covered by Celli is l i , and the average speed of cars is U i .…”