Abstract
With the development of connected vehicle (CV) and Vehicle to X (V2X) communication, more traffic data is being collected from the road network. In order to predict future traffic condition from connected vehicles’ data in real-time, we present an online traffic condition evaluation model utilizing V2X communication. This model employs the Analytic Hierarchy Process (AHP) and the multilevel fuzzy set theory to fuse multiple sources of information for prediction. First, the contemporary vehicle data from the On Board Diagnostic (OBD) is fused with the static road data in the Road Side Unit (RSU). Then, the real-time traffic evaluation scores are calculated using the variable membership model. The real data collected by OBU in field test demonstrates the feasibility of the evaluation model. Compared with traditional evaluation systems, the proposed model can handle more types of data but demands less data transfer.
Highlights
Nowadays, traffic congestion is a serious issue due to the growing number of vehicles moving on the urban road networks
Connected Vehicle (CV) technology enhances the ability of traffic information collection and management through Vehicle to X communication (including Vehicle-ToInfrastructure (V2I) and Vehicle-To-Vehicle (V2V) communication), which presents one of the best ways to mitigate urban traffic congestion, improve traffic safety, and reduce fuel consumption [1]
Through the multilayer numerical calculation based on the evaluation criteria and weights, we will determine the results of the evaluation object [20, 21]
Summary
Traffic congestion is a serious issue due to the growing number of vehicles moving on the urban road networks. Huang proposed a data fusion method to optimize urban traffic flow based on neural network and fuzzy reasoning, which collected the traffic data from varied detectors on the urban road [4]. Thomas and Dia presented a neural network algorithm based on traffic data fusion and tested it with simulated data It analyzed various influence factors on data collection, such as positions of detectors, numbers of floating cars, length of the urban road, and severities of traffic accidents. Guo et al proved the urban road traffic conditions can be analyzed with traffic data of coil detector by improved fuzzy clustering method [14] He et al improved a fusion method with new data collected from mobile phone and microwave sensors, providing enough data for traffic analysis [15].
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