Bon-Yeh Lin

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

Using cellular floating vehicle data is a crucial technique for measuring and forecasting real-time traffic information based on anonymously sampling mobile phone positions for intelligent transportation systems (ITSs). However, a high sampling frequency generates a substantial load for ITS servers, and traffic information cannot be provided instantly when the sampling period is long. In this paper, two analytical models are proposed to analyze the optimal sampling period based on communication behaviors, traffic conditions, and two consecutive fingerprint positioning locations from the same call and estimate vehicle speed. The experimental results show that the optimal sampling period is 41.589 seconds when the average call holding time was 60 s, and the average speed error rate was only 2.87%. ITSs can provide accurate and real-time speed information under lighter loads and within the optimal sampling period. Therefore, the optimal sampling period of a fingerprint positioning algorithm is suitable for estimating speed information immediately for ITSs.

A Traffic Information Estimation Model Using Periodic Location Update Events from Cellular Network

In recent years considerable concerns have arisen over building Intelligent Transportation System (ITS) which focuses on efficiently managing the road network. One of the important purposes of ITS is to improve the usability of transportation resources so as extend the durability of vehicle, reduce the fuel consumption and transportation times. Before this goal can be achieved, it is vital to obtain correct and real-time traffic information, so that traffic information services can be provided in a timely and effective manner. Using Mobile Stations (MS) as probe to tracking the vehicle movement is a low cost and immediately solution to obtain the real-time traffic information. In this paper, we propose a model to analyze the relation between the amount of Periodic Location Update (PLU) events and traffic density. Finally, the numerical analysis shows that this model is feasible to estimate the traffic density.

Traffic Information Estimation Methods From Handover Events

Fast growth of the economy and technology upgrades have led to improvements in the quality of traditional transport systems. As such, the use of intelligent transportation systems (ITS) has become more and more popular. The implementation and improvement of real-time traffic information systems are an important parts of ITS. Compared with other traditional methods, traffic information estimations from cellular network data are now readily available, more cost-effective, and easier to deploy and maintain. This study assumed that nonvehicle calls could be filtered out and vehicles could be tracked on road segments. A novel ITS model was proposed to indicate the relationship between call arrival rate and traffic density. Moreover, the vehicle speed and traffic flow were estimated by using cellular floating vehicle data (CFVD) and the proposed novel ITS model. In experiments, this study used a VISSIM traffic simulator and adopted the average call inter-arrival time and call holding time to simulate communication behavior on road segments. The estimated traffic information was compared with the simulated traffic information from stationary vehicle detectors (VD). The results indicated that the average accuracies for vehicle speed estimation, traffic flow estimation, and traffic density estimation in the congested flow case were 97.63, 89.72, and 90.45 %, respectively. Therefore, this approach was feasible to estimate traffic information for ITS improvement.

The Analysis of Communication Behaviors Based on Using Real-Time Traffic Information in Personal Communication Systems

In cellular networks, when a Mobile Station (MS) wants to communicate, it has to request the free channel provided by the specific cell. If the cell has a free channel, the communication will be performed. Otherwise, the communication will fail. Therefore, the channel allocation for handover is an important issue. In this chapter, we propose a model to analyze the adaptable amount of call arrivals, handovers, and call departures in the specific cell according to the real-time traffic information. The amount of communicating calls is derived from the probability of communication behaviors. Through the model, we can obtain the optimization mechanism for channel allocation in personal communication systems.