Abstract
Traffic congestion is one of the most serious problems that impact urban transportation efficiency, especially in big cities. Identifying traffic congestion locations and occurring patterns is a prerequisite for urban transportation managers in order to take proper countermeasures for mitigating traffic congestion. In this study, the historical GPS sensing data of about 12,000 taxi floating cars in Beijing were used for pattern analyses of recurrent traffic congestion based on the grid mapping method. Through the use of ArcGIS software, 2D and 3D maps of the road network congestion were generated for traffic congestion pattern visualization. The study results showed that three types of traffic congestion patterns were identified, namely: point type, stemming from insufficient capacities at the nodes of the road network; line type, caused by high traffic demand or bottleneck issues in the road segments; and region type, resulting from multiple high-demand expressways merging and connecting to each other. The study illustrated that the proposed method would be effective for discovering traffic congestion locations and patterns and helpful for decision makers to take corresponding traffic engineering countermeasures in order to relieve the urban traffic congestion issues.
Highlights
As contemporary GPS sensor technology enables us to track vehicle trajectories in a traffic network, it provides an alternative way to monitor traffic operation performance in a large traffic network with low cost but high efficiency
Taxi floating car data (FCD) collected from installed GPS equipment presents an opportunity for the governments and scholars to detect and describe traffic congestion occurrence locations and patterns in the whole traffic network, which were previously difficult to identify due to the lack of traffic data [1]
More than half of the taxis have been equipped with GPS data recorders, and the majority of them work for the whole day
Summary
As contemporary GPS sensor technology enables us to track vehicle trajectories in a traffic network, it provides an alternative way to monitor traffic operation performance in a large traffic network with low cost but high efficiency. FCD analyses gives substantial knowledge about traffic operation patterns of urban road networks [22].
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