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

doi:10.1007/s11707-012-0350-y

Front Earth Sci

2013, Vol. 7

Issue (2) : 206-216 https://doi.org/10.1007/s11707-012-0350-y

RESEARCH ARTICLE

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

Xiliang LIU, Feng LU, Hengcai ZHANG(

), Peiyuan QIU

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

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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.

Keywords intersection delay float car data trajectory principal curves

Corresponding Author(s): ZHANG Hengcai,Email:zhanghc@lreis.ac.cn

Issue Date: 05 June 2013

Cite this article:

Xiliang LIU,Feng LU,Hengcai ZHANG, et al. Intersection delay estimation from floating car data via principal curves: a case study on Beijing’s road network[J]. Front Earth Sci, 2013, 7(2): 206-216.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-012-0350-y
https://academic.hep.com.cn/fesci/EN/Y2013/V7/I2/206

Fig.1 An example of principal curves. The artificial red points represent the original data set with noise, and the blue curve stands for the fitting result of the principal curves, which represents the ‘ of the data cloud

Fig.2 Study area and main roads with 400 intersections. The blue lines represent the ring roads, the yellow lines stand for the expressways, arterial streets and collector streets, and the purple points are the selected intersections. Some significant landmarks are also labeled with their names, using cyan filled blocks

Fig.3 Demonstration of the boundary of an intersection. In this figure, the circle’s center is the junction of the two central lines of the crossing roads, with a radius of 100 m

Tab.1 The turn delay records extraction algorithm

Fig.4 The demonstration of the fitting ability of principal curves

Tab.2 The turn delay table in Beijing (part)

Fig.5 The spatial distribution of turning left at three given time intervals

Fig.6 The spatial distribution of turning right at three given time intervals

Fig.7 The spatial distribution of going straight at three given time intervals

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