Large-scale GPS data contain hidden information and provide us with the opportunity to discover knowledge that may be useful for transportation systems using advanced data mining techniques. In major metropolitan cities, many taxicabs are equipped with GPS devices. Because taxies operate continuously for nearly 24 hours per day, they can be used as reliable sensors for the perceived traffic state. In this paper, the entire city was divided into subregions by roads, and taxi GPS data were transformed into traffic flow data to build a traffic flow matrix. In addition, a highly efficient anomaly detection method was proposed based on wavelet transform and PCA (principal component analysis) for detecting anomalous traffic events in urban regions. The traffic anomaly is considered to occur in a subregion when the values of the corresponding indicators deviate significantly from the expected values. This method was evaluated using a GPS dataset that was generated by more than 15,000 taxies over a period of half a year in Harbin, China. The results show that this detection method is effective and efficient.
This research proposed a feeder bus dispatching tool that reduces rides' effort to reach a feeder bus. The dispatching tool takes in real-time user specific request information and optimizes total cost accordingly (passenger access time cost and transit operation cost) by choosing the best pick-up locations and feeder buses' routes. The pick-up locations are then transmitted back to passengers along with GPS guidance. The tool fits well with the Advanced Traveler Information Services (ATIS) which is one of the six highpriority dynamic mobility application bundles currently being promoted by the United State Department of Transportation. The problem is formulated into a Mixed Integer Programming (MIP) model. For small networks, out-of-the-shelf commercial solvers could be used for finding the optimal solution. For large networks, this research developed a GA-based metaheuristic solver which generates reasonably good solutions in a much shorter time. The proposed tool is evaluated on a real-world network in the vicinity of Jiandingpo metro station in Chongqing, China. The results demonstrated that the proposed ATIS tool reduces both buses operation cost and passenger walking distance. It is also able to significantly bring down computation time from more than 1 hour to about 1 min without sacrificing too much on solution optimality.
Abstract:This research proposed an eco-approach and departure system for left-turn vehicles at a fixed-time signalized intersection. This system gives higher priority to enhancing traffic safety than improving mobility and fuel efficiency, and optimizes the entire traffic consisted of connected and automated vehicles (CAVs) and conventional human-driven vehicles by providing ecological speed trajectories for left-turn CAVs. All the ecological speed trajectories are offline optimized before the implementation of system. The speed trajectory optimization is constructed in Pontryagin's Minimum Principle structure. The before and after evaluation of the proposed system shows the percentage of vehicles that drive pass the intersection at safe speed increases by 2.14% to 45.65%, fuel consumption benefits range 0.53% to 18.44%, emission benefits range from 0.57% to 15.69%, no significant throughput benefits is observed. The proposed system significantly enhances the traffic safety and improves the fuel efficiency and emission reduction of left-turn vehicles with no adverse effect on mobility, and has a good robustness against the randomness of traffic. The investigation also indicates that the computation time of proposed system is greatly reduced compared to previous eco-driving system with online speed optimization. The computation time is up to 0.01 s. The proposed system is ready for real-time application.
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