Understanding the distribution characteristics of bus speed based on geocoded data
mode - bus, organisation - management, infrastructure - bus/tram priority, technology - geographic information systems, operations - traffic, operations - performance
Geocoded data, Bus operation, Speed distribution, Finite mixture model, Cluster analysis
Data-driven traffic management and control has attracted much attention recently. This paper conducts a series of coherent analyses based on geocoded data to understand the distribution characteristics of bus operational speed and to explore the potential applications of speed distributions. First, an original bipartite model is adopted for capturing instantaneous speed where the suspended and moving states are considered separately and a two-component mixed Weibull distribution is used to model the speed distribution in moving states. The mixed Gaussian distribution with variable components is found to be capable of expressing the speed distribution patterns of different road sections. Second, elaborate analyses on the basis of speed distribution modelling are conducted: (i) regression analyses are conducted to explore the correlations between parameters of instantaneous speed distributions and traffic related factors; (ii) a powerful clustering method using Kullback-Leibler divergence as the distance measure is proposed to grade the road sections of a bus route. These results can be utilized in fields such as bus operations management, bus priority signal control and infrastructure transformation aiming to improve the efficiency of bus operations systems.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Du, Y., Deng, F., Liao, F., & Ji, Y. (2017). Understanding the distribution characteristics of bus speed based on geocoded data. Transportation Research Part C: Emerging Technologies, Vol. 82, pp. 337-357.