City-scale traffic animation using statistical learning and metamodel-based optimization

Rapid urbanization and increasing traffic have caused severe social, economic, and environmental problems in metropolitan areas worldwide. Traffic reconstruction and visualization using existing traffic data can provide novel tools for vehicle navigation and routing, congestion analysis, and traffic management. While traditional data collection methods are becoming increasingly common (e.g. using in-road sensors), GPS devices are also becoming ubiquitous. In this paper, we address the problem of traffic reconstruction, visualization, and animation using mobile vehicle data (i.e. GPS traces). We first conduct city-scale traffic reconstruction using statistical learning on mobile vehicle data for traffic animation and visualization, and then dynamically complete missing data using metamodel-based simulation optimization in areas of insufficient data coverage. We evaluate our approach quantitatively and qualitatively, and demonstrate our results with 2D visualization of citywide traffic, as well as 2D and 3D animation of reconstructed traffic in virtual environments.

[1]  Ming C. Lin,et al.  Continuum Traffic Simulation , 2010, Comput. Graph. Forum.

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

[3]  Harvey J. Miller,et al.  Estimating the most likely space–time paths, dwell times and path uncertainties from vehicle trajectory data: A time geographic method , 2016 .

[4]  A. Bayen,et al.  A traffic model for velocity data assimilation , 2010 .

[5]  Ming C. Lin,et al.  Flow reconstruction for data-driven traffic animation , 2013, ACM Trans. Graph..

[6]  Jiannong Cao,et al.  Mining Traffic Congestion Correlation between Road Segments on GPS Trajectories , 2016, 2016 IEEE International Conference on Smart Computing (SMARTCOMP).

[7]  Michel Bierlaire,et al.  A Simulation-Based Optimization Framework for Urban Transportation Problems , 2013, Oper. Res..

[8]  Matthias Grossglauser,et al.  CRAWDAD dataset epfl/mobility (v.2009-02-24) , 2009 .

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

[10]  Daniel G. Aliaga,et al.  Designing large‐scale interactive traffic animations for urban modeling , 2014, Comput. Graph. Forum.

[11]  Dinesh Manocha,et al.  Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatio-Temporal Data , 2009, 2009 IEEE Virtual Reality Conference.

[12]  Sai-Keung Wong,et al.  Generating Believable Mixed-Traffic Animation , 2016, IEEE Transactions on Intelligent Transportation Systems.

[13]  Daqing Zhang,et al.  Urban Traffic Modelling and Prediction Using Large Scale Taxi GPS Traces , 2012, Pervasive.

[14]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[15]  Chao Wei,et al.  Efficient Traffic State Estimation for Large-Scale Urban Road Networks , 2013, IEEE Transactions on Intelligent Transportation Systems.

[16]  Zhigang Deng,et al.  Realistic Data-Driven Traffic Flow Animation Using Texture Synthesis , 2018, IEEE Transactions on Visualization and Computer Graphics.

[17]  Alexandre M. Bayen,et al.  Estimating arterial traffic conditions using sparse probe data , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[18]  Peter Willemsen,et al.  Ribbon networks for modeling navigable paths of autonomous agents in virtual environments , 2006, IEEE Transactions on Visualization and Computer Graphics.

[19]  Yang Lu,et al.  Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts , 2017 .

[20]  Ming C. Lin,et al.  Transforming GIS Data into Functional Road Models for Large-Scale Traffic Simulation , 2012, IEEE Transactions on Visualization and Computer Graphics.

[21]  Peter Wonka,et al.  Manhattan-World Urban Reconstruction from Point Clouds , 2016, ECCV.

[22]  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.

[23]  Erik Jenelius,et al.  Non-parametric estimation of route travel time distributions from low-frequency floating car data , 2015 .

[24]  J. Wardrop.  ROAD PAPER. SOME THEORETICAL ASPECTS OF ROAD TRAFFIC RESEARCH. , 1952 .

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

[26]  S. Bera,et al.  Estimation of origin-destination matrix from traffic counts: the state of the art , 2011 .

[27]  Daniel Krajzewicz,et al.  Recent Development and Applications of SUMO - Simulation of Urban MObility , 2012 .

[28]  Dani Lischinski,et al.  Crowds by Example , 2007, Comput. Graph. Forum.

[29]  Alexandre M. Bayen,et al.  Cellpath: Fusion of Cellular and Traffic Sensor Data for Route Flow Estimation via Convex Optimization , 2015 .

[30]  Stéphane Donikian,et al.  Modelling virtual cities dedicated to behavioural animation , 2000, Comput. Graph. Forum.

[31]  Xing Xie,et al.  An Interactive-Voting Based Map Matching Algorithm , 2010, 2010 Eleventh International Conference on Mobile Data Management.

[32]  R. Tibshirani,et al.  Sparsity and smoothness via the fused lasso , 2005 .

[33]  Daniel G. Aliaga,et al.  Fast Weather Simulation for Inverse Procedural Design of 3D Urban Models , 2017, ACM Trans. Graph..

[34]  Liping Fu,et al.  Decomposing Travel Times Measured by Probe-based Traffic Monitoring Systems to Individual Road Segments , 2008 .

[35]  Zhigang Deng,et al.  A personality model for animating heterogeneous traffic behaviors , 2014, Comput. Animat. Virtual Worlds.

[36]  S. Shankar Sastry,et al.  Travel Time Dynamics for Intelligent Transportation Systems: Theory and Applications , 2016, IEEE Transactions on Intelligent Transportation Systems.

[37]  Sang Nguyen,et al.  A unified framework for estimating or updating origin/destination matrices from traffic counts , 1988 .

[38]  Zhigang Deng,et al.  A data-driven model for lane-changing in traffic simulation , 2016, Symposium on Computer Animation.

[39]  Yiorgos Chrysanthou,et al.  The PAG Crowd: A Graph Based Approach for Efficient Data‐Driven Crowd Simulation , 2014, Comput. Graph. Forum.

[40]  Marcos Fernández,et al.  Virtual reality for driving simulation , 1996, CACM.

[41]  D. Schrank,et al.  2015 Urban Mobility Scorecard , 2015 .

[42]  Xing Xie,et al.  Urban computing with taxicabs , 2011, UbiComp '11.

[43]  Carolina Osorio Pizano.  Mitigating Network Congestion: Analytical Models, Optimization Methods and their Applications , 2010 .

[44]  Hua Wang,et al.  An efficient lane model for complex traffic simulation , 2015, Comput. Animat. Virtual Worlds.

[45]  Jukka Riekki,et al.  Urban traffic analysis through multi-modal sensing , 2015, Personal and Ubiquitous Computing.

[46]  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.

[47]  Ming C. Lin,et al.  Citywide Estimation of Traffic Dynamics via Sparse GPS Traces , 2017, IEEE Intelligent Transportation Systems Magazine.

[48]  Peter Willemsen,et al.  Steering behaviors for autonomous vehicles in virtual environments , 2005, IEEE Proceedings. VR 2005. Virtual Reality, 2005..

[49]  Stephen Shaoyi Liao,et al.  Aggregating and Sampling Methods for Processing GPS Data Streams for Traffic State Estimation , 2013, IEEE Transactions on Intelligent Transportation Systems.

[50]  Yiannis E. Papelis,et al.  The Iowa Driving Simulator: An Immersive Research Environment , 1995, Computer.

[51]  J. G. Wardrop,et al.  Some Theoretical Aspects of Road Traffic Research , 1952 .

[52]  Dinesh Manocha,et al.  Self-Aware Traffic Route Planning , 2011, AAAI.

[53]  Ming C. Lin,et al.  Participatory route planning , 2014, SIGSPATIAL/GIS.

[54]  A. Bayen,et al.  Probability Distributions of Travel Times on Arterial Networks: Traffic Flow and Horizontal Queuing Theory Approach , 2012 .

[55]  Ming C. Lin,et al.  Virtualized Traffic at Metropolitan Scales , 2015, Front. Robot. AI.

[56]  Daniel G. Aliaga,et al.  A Survey of Urban Reconstruction , 2013, Comput. Graph. Forum.

[57]  Ming C. Lin,et al.  Interactive hybrid simulation of large-scale traffic , 2011, ACM Trans. Graph..

[58]  M. Taniguchi,et al.  Incorporating an information acquisition process into a route choice model with multiple information sources , 1999 .

[59]  Gennady L. Andrienko,et al.  Exploratory analysis of spatial and temporal data - a systematic approach , 2005 .

[60]  Hai Yang,et al.  Estimation of origin-destination matrices from link traffic counts on congested networks , 1992 .

[61]  Xiaogang Jin,et al.  Detailed traffic animation for urban road networks , 2012, Graph. Model..

[62]  Lei Zhang,et al.  Multimodel Ensemble for Freeway Traffic State Estimations , 2014, IEEE Transactions on Intelligent Transportation Systems.

[63]  Gunnar Flötteröd,et al.  A Metamodel Simulation-based Optimization Approach for the Efficient Calibration of Stochastic Traffic Simulators , 2015 .

[64]  Jehee Lee,et al.  Morphable crowds , 2010, SIGGRAPH 2010.

[65]  Xiaogang Jin,et al.  Video-based personalized traffic learning , 2013, Graph. Model..

[66]  Timothy Jason Hunter,et al.  Large-Scale, Low-Latency State Estimation Of Cyberphysical Systems With An Application To Traffic Estimation , 2014 .

[67]  Katya Scheinberg,et al.  Introduction to derivative-free optimization , 2010, Math. Comput..

[68]  Dimitris N. Metaxas,et al.  Eurographics/ Acm Siggraph Symposium on Computer Animation (2007) Group Behavior from Video: a Data-driven Approach to Crowd Simulation , 2022 .