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Millimetre‐wave (MMW) radars have been increasingly deployed along the roadside highways to collect vehicle trajectory data, which are valuable for traffic safety analyses and traffic control decisions. Nevertheless, possible errors of the trajectories resulting from roadside MMW radars have not been well documented. This study scrutinizes roadside MMW radar data and the concurrent video ground truth to identify five typical errors of vehicle trajectories, including different vehicles tagged with the same ID, different trajectories produced by the same vehicle, ghost vehicles, erroneous vehicle classification, and vehicle location drift. Data cleaning methods were developed to identify and correct these errors. The performance of the developed methods was evaluated empirically in a case study based on the data of five radars, which were installed at different locations along the highway and had different headings. The thresholds of the developed method were calibrated using one radar data and then applied to the data of the five radars. The results showed that the data qualities of all radars are greatly improved by the developed methods, demonstrating that the developed methods are generally applicable for cleaning vehicle trajectory data from roadside MMW radars. [ABSTRACT FROM AUTHOR]

This paper develops a new approach for tram prioritization integrating an offline traffic signal timing planner with an online tram progression controller. The offline planner optimizes tram progressions by resynchronizing traffic signals to minimize tram running times, taking into account the effect of the resynchronization on other vehicles. The online controller aims at enhancing tram reliability by adopting three control strategies—green extension, vehicle holding, and speed guidance—to instruct the trams to travel within appropriate progressions. The real-world case studies are presented to demonstrate that, comparing with the state-of-the-practice approach, the proposed approach has the potential to improve the service quality by shortening tram running time and passenger waiting time, and to mitigate the negative impact on other vehicles by avoiding triggering unnecessary green extensions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]