Traffic congestion has become a very serious urban problem, which increases travel costs and fuel consumption and reduces the efficiency of the transportation system. However, most of the existing traffic congestion identification efforts rely on high-precision electronic maps and consider traffic congestion as an instantaneous state, which ignores the cumulative effect of vehicle speed fluctuations on traffic state transition. To address the above challenges, this paper proposes a map-independent method for urban traffic congestion detection, which consists of three parts: meter-scale cellbased urban road network reconstruction, cell congestion modeling, and cellular congestion identification. Considering map-related issues, we divided the study area into meter-scale cells and then used floating cab data (FTD) to reduce the road network. The results show that customized congestion metrics are able to monitor changes in traffic speeds within cells and quantify traffic congestion. In addition, the methodology may inform specialized map inferences.
Active traffic management has become the mainstream means of road traffic control under the environment of vehicle-road coordination and gradually develops towards the direction of refined control. This study focuses on the occurrence of multi-lane failure on highways and how to divide the traffic flow upstream of the failure zone into lanes to limit the speed. It explores the lane-level speed limit control problems under different lane failure scenarios. To improve road access efficiency and safety and alleviate the traffic congestion caused by road failure. First, a multi-lane variable speed limit METANET model is established, and the lane-changing decision-making behaviors of networked vehicles and manual vehicles are integrated to obtain a cooperative control model for mixed traffic flow scenarios. Multiple speed limit strategies are delineated to compare the effect of control strategies under different lane failure scenarios. Results show that: The 90-80km/h speed limit strategy improves the congestion recovery time and exceeds the positive impacts of other speed limit scenarios. Comparative analysis of temporal and spatial speed maps exposes the reasons for this effect.
Bike-sharing not only provides more options for urban transportation trips but also has an important impact on the transportation system. Bike sharing plays an important role in making up for other public transport. Studies have shown that bike sharing expands the coverage of subway stations. In this paper, a time series clustering algorithm based on the K-means algorithm and DTW distance is proposed to cluster the time series of shared bicycles that transfer to subway stations. The shared bicycles transferred to subway stations are identified by building a buffer zone at the entrance and exit of a subway station. The results show that the temporal patterns of bike-sharing in different metro stations can be classified into five major categories. The temporal patterns of bicycle sharing are related to the land use characteristics near the metro stations, and for residential and commercial metro stations, the trips are more and the peak duration is longer. The travel volume is decreasing from the city center to the surrounding area. The spatio-temporal patterns of the transferred shared bicycle can provide feasible suggestions for the scheduling and allocation of shared bicycles, and provide help for optimizing urban transportation.
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