Short‐term traffic speed prediction is one of the most critical components of an intelligent transportation system (ITS). The accurate and real‐time prediction of traffic speeds can support travellers’ route choices and traffic guidance/control. In this article, a support vector machine model (single‐step prediction model) composed of spatial and temporal parameters is proposed. Furthermore, a short‐term traffic speed prediction model is developed based on the single‐step prediction model. To test the accuracy of the proposed short‐term traffic speed prediction model, its application is illustrated using GPS data from taxis in Foshan city, China. The results indicate that the error of the short‐term traffic speed prediction varies from 3.31% to 15.35%. The support vector machine model with spatial‐temporal parameters exhibits good performance compared with an artificial neural network, a k‐nearest neighbor model, a historical data‐based model, and a moving average data‐based model.

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The vehicle routing problem with multiple fuzzy time windows is investigated in this paper. The dynamic change of traffic flow and the fuzzy time window of customers are considered. A multi fuzzy time window vehicle routing model based on time-varying traffic flow is proposed, and the objective function is to minimize the total cost of distribution and maximize customer satisfaction. According to the basic principle of wolf pack algorithm, in order to promote the exchange of information between the artificial wolves, improve the wolves' grasp of the global information and enhance the exploring ability of wolves, a drift operator and wave operator were introduced into scouting behaviors and summing behaviors. An adaptive dynamic adjustment factor strategy was proposed for beleaguering behaviors, the exploitation ability of the algorithm strengthened constantly. Thus the convergence rate of algorithm was enhanced. We further do simulation on an example, and compare the results obtained by wolf pack algorithm and ant genetic algorithm. The results show that use improved wolf pack algorithm to solve vehicle routing problem with multiple fuzzy time windows has the advantages of small number of iterations and high efficiency, it can converge to the global optimal solution in a short time. The improved wolf pack algorithm is an efficient algorithm for solving vehicle routing problem with multiple fuzzy time windows.

Article Info AbstractKeywords: Taxi Fleet Size Dynamic Demand Waiting Time of Passenger Taxi Driver Income Bi-Level ModelIn this paper, a bi-level model is proposed, which considers the benefits of taxi driver and passenger. The upper-level model is a bi-objective program. The first objective is to minimize the waiting time of passenger in rush hour. And the second objective is to maximize the income of the driver for one day. The lower-level model is a demand function model, calculating the demand of taxi based on given fleet size and fare. The lower-level model can show the influence of fleet size and fare on the potential demand. At last, the taxi current condition of Dalian city in China is chosen to test the proposed method. The results show that the increase of fleet size can attract potential demand. But the degree of attraction mostly depends on the waiting time of passengers and taxi fare. Furthermore, both fleet size and fare of tax in Dalian are on a low level. It is necessary to increase the fleet size and the fare in the meantime.