It is a new attempt to use an adaptive neuro-fuzzy inference system (ANFIS) as an adaptive traffic signal control method for an isolated intersection under mixed traffic conditions in Hanoi City, the capital of Vietnam. The proposed method using ANFIS can work more effectively as it gives full play to both an artificial neural network and a fuzzy logic system, hence can intelligently control the green time for each phase of the traffic signal lights according to the fluctuating traffic volume under mixed traffic conditions to improve the vehicular throughput and reduce delays. Taking a typical signalized intersection in Hanoi City as a case study is to evaluate the performance of the proposed method through a microscopic traffic simulator with an interface between VISSIM and MATLAB. Simulation results of the proposed method using ANFIS indicate better performances and adaptability compared with the fixed-time method and the fuzzy logic method.
Safety evaluation of traffic conflict is a very important and challenging issue in evaluating intersection safety under incomplete traffic accident data conditions and is also one of the main safety surrogate measures of analyzing accident data recently. It helps to analyze and solve intersection problems comprehensively and deeply. From there, it helps to improve traffic safety as well as reduce the risk of traffic accidents at intersections. Various evaluation methods based on traffic conflict have been proposed to make conflict safety levels at intersections more consistent and objective. However, a major concern is that many existing measurements are still subjective and are not easy to obtain uniformly. This study aimed to develop a model for safety evaluation at intersections in a comprehensive way that may be expected to directly link to the severity of the accident from different evaluation indicators. First, the three factors, including time to collision (TTC), conflicting speed (CS), and deceleration rate (DR) to avoid a crash, are introduced into safety evaluation of conflicts as the indicators. And then, as regards the fuzziness and randomness of the evaluation indicators, the qualitative concept has to be converted into a quantitative one utilizing cloud model, which implements the natural transformation between the qualitative concept of the safety level of traffic conflict and the membership degree of the evaluation indicators corresponding to the different safety levels. Finally, an indicator weight model is built based on the information entropy and the AHP method to determine the safety level. We illustrate the practical implementation of the proposed method using actual data of a typical signalized intersection from Hanoi City of Vietnam. The results indicate that traffic conflict analyzed by the proposed method was appropriate with actual state of the intersection, and the proposed method is simple, effective, and feasible, so it has a certain application value.
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