| Summary: | Travel time prediction is an essential part of intelligent transportation system applications. However, the existing travel time prediction methods mainly focus on the freeway due to its simplicity and the high coverage of sensors and few researches have been conducted for the urban arterial road. Consequently, a travel time prediction algorithm based on particle filter is proposed in this paper to predict short-term travel time of the arterial traffic with historical floating car data and the concept of speed matrix is developed to illustrate the spatiotemporal properties of the arterial traffic. Unlike previous travel time prediction methods, the proposed algorithm uses particles with corresponding weights to model the traffic trend in the historical data instead of state-transition function and the weight for each particle are calculated with similarities between the speed matrix of the particle and the current traffic pattern. Moreover, a resampling process is developed to solve the degeneracy problem of the particles by replacing the low-weight particles with historical data. A real floating car dataset of 10357 taxis over a period of 3 months within Beijing is utilized to evaluate the performances of the algorithms. The proposed algorithm has the least errors by comparing with other three algorithms.
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