Abstract
Aiming at the problem of intersection signal control, a method of traffic phase combination and signal timing optimization based on the improved K-medoids algorithm is proposed. Firstly, the improvement of the traditional K-medoids algorithm embodies in two aspects, namely, the selection of the initial medoids and the parameter k, which will be applied to the cluster analysis of historical saturation data. The algorithm determines the initial medoids based on a set of probabilities calculated from the distance and determines the number of clusters k based on an exponential function, weight adjustment, and elbow ideas. Secondly, a phase combination model is established based on the saturation and green split data, and the signal timing is optimized through a bilevel programming model. Finally, the algorithm is evaluated over a certain intersection in Hangzhou, and results show that this algorithm can reduce the average vehicle delay and queue length and improve the traffic capacity of the intersection in the peak hour.
Highlights
With the rapid development of urban construction and socioeconomy, traffic congestion, one of China’s urban diseases, brings tremendous pressure to urban traffic management and seriously affects the harmonious development of cities
In [16], the clustering algorithm was applied to Wireless Communications and Mobile Computing process vehicle motion information, which was the basis for subsequent optimization, but only optimized the signal timing, excluding phase combination
To solve the problems above, this paper proposes a traffic phase combination and signal timing optimization method based on the improved K-medoids algorithm
Summary
With the rapid development of urban construction and socioeconomy, traffic congestion, one of China’s urban diseases, brings tremendous pressure to urban traffic management and seriously affects the harmonious development of cities. The authors in [13] studied dynamic programming algorithms to optimize signal timing and phase, thereby, reducing average vehicle latency. In [15], the authors considered a dynamic phase control method based on traffic flow, but it needed real-time detection and calculation of road conditions, resulting in poor practical application effect. In [23], the authors studied the K-means clustering method to optimize the best switching time of time-of-day (TOD) control scheme, but the number of clusters needed to be specified in advance, which largely affected the effectiveness of the method. To solve the problems above, this paper proposes a traffic phase combination and signal timing optimization method based on the improved K-medoids algorithm.
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