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Intersection delay estimation from floating car data via principal curves: a case study on Beijing’s road network

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Abstract

It is a pressing task to estimate the real-time travel time on road networks reliably in big cities, even though floating car data has been widely used to reflect the real traffic. Currently floating car data are mainly used to estimate the real-time traffic conditions on road segments, and has done little for turn delay estimation. However, turn delays on road intersections contribute significantly to the overall travel time on road networks in modern cities. In this paper, we present a technical framework to calculate the turn delays on road networks with float car data. First, the original floating car data 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 trajectory data set was distributed among 96 time intervals (from 0: 00 to 23: 59). All of the intersections where the trajectories passed were connected with the trajectory segments, and constituted an experiment sample, while the intersections on arterial streets were specially selected to form another experiment sample. Thirdly, a principal curve-based algorithm was presented to estimate the turn delays at the given intersections. The algorithm argued is not only statistically fitted the real traffic conditions, but also is insensitive to data sparseness and missing data problems, which currently are almost inevitable with the widely used floating car data collecting technology. We adopted the floating car data collected from March to June in Beijing city in 2011, which contains more than 2.6 million trajectories generated from about 20000 GPS-equipped taxicabs and accounts for about 600 GB in data volume. The result shows the principal curve based algorithm we presented takes precedence over traditional methods, such as mean and median based approaches, and holds a higher estimation accuracy (about 10%–15% higher in RMSE), as well as reflecting the changing trend of traffic congestion. With the estimation result for the travel delay at intersections, we analyzed the spatio-temporal distribution of turn delays in three time scenarios (0: 00–0: 15, 8: 15–8: 30 and 12: 00–12: 15). It indicates that during one’s single trip in Beijing, average 60% of the travel time on the road networks is wasted on the intersections, and this situation is even worse in daytime. Although the 400 main intersections take only 2.7% of all the intersections, they occupy about 18% travel time.

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Authors and Affiliations

  1. State Key Laboratory of Resources and Environmental Information system, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Xiliang Liu, Feng Lu, Hengcai Zhang & Peiyuan Qiu

Authors
  1. Xiliang Liu
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  2. Feng Lu
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  3. Hengcai Zhang
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  4. Peiyuan Qiu
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Corresponding author

Correspondence to Hengcai Zhang.

Additional information

Xiliang Liu obtained his B.S. degree in land resources management in Tongji University, China, 2007 and M.S. degree in geographic information engineering in China University of Mining & Technology (Beijing), China, 2011. Now he is a Ph.D. candidate in geographic information system in the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. His current research interests mainly focus on trajectory data mining and intersection delay estimation.

Dr. Feng Lu is a Professor of Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. He received his B.S. degree from Wuhan University and Ph.D. degree from Institute of Remote Sensing Applications, Chinese Academy of Sciences. He is the Deputy Director of State Key Laboratory of Resources and Environmental Information System, a guest Ph.D. Advisor for the Fuzhou University, a member of the Information Technology Committee of Chinese Transportation Association, a member of the Theory and Methodology Committee of the Chinese Association of GIS and a member of the New Technology Applications Committee of Chinese City Planning Association. Dr. Lu’s research interests cover spatial data modeling, spatial DBMS, trajectory data mining, GIS for transportation and urban GIS application. During the past years, he has published over 120 referred journal and conference papers.

Hengcai Zhang received his B.S. degree in geographic science from Shandong Normal University and M.S. degree in cartography and geographic information system from Capital Normal University. Now he is a Ph.D. student from Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. His interests focus on moving objects database and spatial-temporal data mining.

Peiyuan Qiu received the B.S. degree in geographic science from Qingdao University, China in 2009 and the M. E. degree in cartography and geographic information engineering from Beijing University of Civil Engineering and Architecture, China in 2012. He is currently a Ph.D. candidate in Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. His current research interests focus on geographic semantics and trajectory mining.

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Liu, X., Lu, F., Zhang, H. et al. Intersection delay estimation from floating car data via principal curves: a case study on Beijing’s road network. Front. Earth Sci. 7, 206–216 (2013). https://doi.org/10.1007/s11707-012-0350-y

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