MongoDB: The Definitive Guide
Hadoop: The Definitive Guide
Computing turn delay in city road network with GPS collected trajectories
Proceedings of the 2011 international workshop on Trajectory data mining and analysis
Computing with Spatial Trajectories
Computing with Spatial Trajectories
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In this paper, we presented a technical framework to calculate the turn delays on road network with floating car data (FCD). Firstly the original FCD collected with GPS equipped taxies was cleaned and matched to a street map with a distributed system based on Hadoop and MongoDB. Secondly the refined dataset was distributed and matched to the specific intersections among 96 time intervals (from 0:00 to 23:59 per 15 minutes). Thirdly a principal curve-based algorithm was presented to estimate the turn delays at the given intersections. The algorithm is argued not only statistically fitted the real traffic conditions but also is insensitive to data sparseness and data missing problems. We adopted the floating car data collected from March to June in Beijing in 2011, which contains more than 2.6 million trajectories generated from about 20,000 GPS-equipped taxicabs and accounts for about 600 GB in data volume. The result shows the presented algorithm takes precedence of traditional methods, such as mean and median based approaches, and holds a higher estimation accuracy (about 10%~15% higher in RMSE), as well as reflects the changing trend of traffic congestion. With the estimation result, the turn delay ratios both on the whole network and on the 400 main intersections are calculated. It indicates that average 60% of the travel time on the road network, especially in daytime, is cost on intersections, and the 400 main intersections, which only take 2.7% of all the intersections, yet cost about 18% travel time in Beijing.