Mapping to Cells: A Simple Method to Extract Traffic Dynamics from Probe Vehicle Data

In the era of big data, mining data instead of collecting data are a new challenge for researchers and engineers. In the field of transportation, extracting traffic dynamics from widely existing probe vehicle data is meaningful both in theory and practice. Therefore, this article proposes a simple mapping-to-cells method to construct a spatiotemporal traffic diagram for a freeway network. The method partitions a network region into small square cells and represents a real network inside the region by using the cells. After determining the traffic flow direction pertaining to each cell, the spatiotemporal traffic diagram colored according to traffic speed can be well constructed. By taking the urban freeway in Beijing, China, as a case study, the mapping-to-cells method is validated, and the advantages of the method are demonstrated. The method is simple because it is completely based on the data themselves and without the aid of any additional tool such as Geographic Information System software or a digital map. The method is efficient because it is based on discrete space-space and time-space homogeneous cells that allow us to match the probe data through basic operations of arithmetic. The method helps us understand more about traffic congestion from the probe data, and then aids in carrying out various transportation researches and applications.

[1]  I. Iervolino,et al.  Computer Aided Civil and Infrastructure Engineering , 2009 .

[2]  Gennady L. Andrienko,et al.  Spatio-temporal aggregation for visual analysis of movements , 2008, 2008 IEEE Symposium on Visual Analytics Science and Technology.

[3]  Hubert Rehborn,et al.  Recognition and tracking of spatial–temporal congested traffic patterns on freeways , 2004 .

[4]  Ya Tian,et al.  Large-scale taxi O/D visual analytics for understanding metropolitan human movement patterns , 2015, J. Vis..

[5]  Dirk Helbing,et al.  Reconstructing the spatio-temporal traffic dynamics from stationary detector data , 2002 .

[6]  Alexandre M. Bayen,et al.  The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data , 2011, IEEE Transactions on Intelligent Transportation Systems.

[7]  Serge P. Hoogendoorn,et al.  A Robust and Efficient Method for Fusing Heterogeneous Data from Traffic Sensors on Freeways , 2010, Comput. Aided Civ. Infrastructure Eng..

[8]  Soyoung Ahn,et al.  Evolution of Oscillations in Congested Traffic , 2009 .

[9]  Carlos F. Daganzo,et al.  Fundamentals of Transportation and Traffic Operations , 1997 .

[10]  Xuegang Ban,et al.  Bottleneck Identification and Calibration for Corridor Management Planning , 2007 .

[11]  Nikolaos Geroliminis,et al.  Experienced travel time prediction for congested freeways , 2013 .

[12]  Ludovic Leclercq,et al.  A mechanism to describe the formation and propagation of stop-and-go waves in congested freeway traffic , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[13]  Boris S. Kerner,et al.  Introduction to Modern Traffic Flow Theory and Control: The Long Road to Three-Phase Traffic Theory , 2009 .

[14]  Liang Zheng,et al.  A simple nonparametric car-following model driven by field data , 2015 .

[15]  Soyoung Ahn,et al.  Passing Rates to Measure Relaxation and Impact of Lane‐Changing in Congestion , 2011, Comput. Aided Civ. Infrastructure Eng..

[16]  Martin Treiber,et al.  Traffic Flow Dynamics: Data, Models and Simulation , 2012 .

[17]  Xuesong Zhou,et al.  Traffic zone division based on big data from mobile phone base stations , 2015 .

[18]  Bin Jiang,et al.  Characterizing the human mobility pattern in a large street network. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Lei Yu,et al.  Floating Car Data-Based Method for Detecting Flooding Incident under Grade Separation Bridges in Beijing , 2015 .

[20]  Haris N. Koutsopoulos,et al.  Probe vehicle data sampled by time or space: Consistent travel time allocation and estimation , 2015 .

[21]  Cláudio T. Silva,et al.  Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips , 2013, IEEE Transactions on Visualization and Computer Graphics.

[22]  L. Craig Davis,et al.  Introduction to Modern Traffic Flow Theory and Control: The Long Road to Three-Phase Traffic Theory , 2009 .

[23]  Alexandre M. Bayen,et al.  Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment , 2009 .

[24]  Nikolas Geroliminis,et al.  Queue Profile Estimation in Congested Urban Networks with Probe Data , 2015, Comput. Aided Civ. Infrastructure Eng..

[25]  Martin Treiber,et al.  Reconstructing the Traffic State by Fusion of Heterogeneous Data , 2009, Comput. Aided Civ. Infrastructure Eng..

[26]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[27]  Simon Washington,et al.  Shortest path and vehicle trajectory aided map-matching for low frequency GPS data , 2015 .

[28]  Robert L. Bertini,et al.  Techniques for Validating an Automatic Bottleneck Detection Tool Using Archived Freeway Sensor Data , 2010 .

[29]  Xiaoru Yuan,et al.  Visual Traffic Jam Analysis Based on Trajectory Data , 2013, IEEE Transactions on Visualization and Computer Graphics.

[30]  Martin Treiber,et al.  Traffic Flow Dynamics , 2013 .

[31]  Hubert Rehborn,et al.  Traffic dynamics in empirical probe vehicle data studied with three-phase theory: Spatiotemporal reconstruction of traffic phases and generation of jam warning messages , 2013 .

[32]  Robert B. Noland,et al.  Current map-matching algorithms for transport applications: State-of-the art and future research directions , 2007 .

[33]  Reginald R. Souleyrette,et al.  A Generic Approach to Estimate Freeway Traffic Time Using Vehicle ID‐Matching Technologies , 2016, Comput. Aided Civ. Infrastructure Eng..

[34]  Fei-Yue Wang Scanning the Issue and Beyond: Toward ITS Knowledge Automation , 2014, IEEE Trans. Intell. Transp. Syst..

[35]  James R. Montgomery,et al.  A Generic Approach , 1989 .

[36]  Xiaoru Yuan,et al.  Visual Exploration of Sparse Traffic Trajectory Data , 2014, IEEE Transactions on Visualization and Computer Graphics.

[37]  Heidrun Schumann,et al.  Stacking-Based Visualization of Trajectory Attribute Data , 2012, IEEE Transactions on Visualization and Computer Graphics.

[38]  Alexander Skabardonis,et al.  Systematic Identification of Freeway Bottlenecks , 2004 .

[39]  Zonglian He,et al.  A figure-eight hysteresis pattern in macroscopic fundamental diagrams and its microscopic causes , 2015 .

[40]  Lei Yu,et al.  Analysis of Traffic Flow Characteristics on Ring Road Expressways in Beijing , 2009 .

[41]  R. E. Wilson,et al.  Mechanisms for spatio-temporal pattern formation in highway traffic models , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.