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This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW.

With the background of rapid development of intelligent city, intelligent traffic is also getting more and more attention and bus arrival time prediction has become one hotspot to the researchers in recent years. Accurate and real-time prediction of bus state cannot only help travelers to choose a better trip mode, but also provide some scientific advices for the traffic department to manage scientifically and make a reasonable scheduling. Considering the most study focus on the current reliability evaluation and few references about reliability prediction were written, this paper aims to firstly use a reliability evaluation method to get the reliability of bus line. Based on this, this paper proposes a reliability prediction method of further bus service using the random forest. Finally, the model of reliability prediction proposed in this paper is tested with the data of the bus line 23 in Dalian city of China. The result shows that the random forest with the reasonable parameters can predict the reliability of bus service accurately. Furthermore, the random forest method performs better than artificial neural network and support vector machine. It is feasible to predict the reliability of bus service.