Assessment of Data Quality Needs for Use in Transportation Applications

The objective of this project is to investigate data quality measures and how they are applied to travel time prediction. This project showcases a short‐term travel time prediction method that takes into account the data needs of real‐time applications. The objective of this research is to prepare and disseminate accurate short‐term (up to 15 minutes ahead) travel time predictions on major highway corridors in the state of Maryland using real‐time and archived Bluetooth travel time samples, probe-based INRIX data, and stationary sensor data pooled together in Regional Integrated Transportation Information System (RITIS). In addition the research effort also develops a medium-term travel time prediction algorithm using pattern recognition techniques. The algorithm is used to predict travel times between Richmond and Virginia Beach in the state of Virginia. Unlike previous studies that use travel time as the variable, the traffic state spatiotemporal evolution is used to predict traffic patterns. The approach uses traffic state data for the current day matched with a historical data set to identify similar traffic patterns and predict travel times into the future. The tasks of this study start from data collection and analysis. The raw INRIX data, including data from I-64 and I-264 between Richmond to Virginia Beach for the past three years, are used in this study. Several problems with the raw data are analyzed, including geographically inconsistent sections, irregular time intervals of data collection, and missing data. Subsequently, a travel database is constructed to obtain daily spatiotemporal traffic states in which traffic state information and dynamic travel times are included. A travel time prediction algorithm is developed using speed measurements and which fully utilizes the relationship between traffic state and travel time. INRIX data for the selected 37-mile freeway stretch (Newport News to Virginia Beach) are used to test the proposed algorithm. The testing results indicate that the proposed algorithm outperforms the other three methods including using instantaneous measurements, using a Kalman filter, and using the k-nearest-neighbor method. Moreover, the case study on the entire 95-mile freeway stretch from Richmond to Virginia Beach demonstrates the superiority of the proposed algorithm over the instantaneous approach that is currently used by Virginia Department of Transportation (VDOT). The proposed prediction method reduces the prediction error by approximately 50% compared to the current instantaneous method, especially at the shoulders of the peak periods.

[1]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[2]  Markos Papageorgiou,et al.  Real-time freeway traffic state estimation based on extended Kalman filter: a general approach , 2005 .

[3]  Alexandre M. Bayen,et al.  Lax–Hopf Based Incorporation of Internal Boundary Conditions Into Hamilton–Jacobi Equation. Part I: Theory , 2010, IEEE Transactions on Automatic Control.

[4]  Gang-Len Chang,et al.  A Hybrid Model for Reliable Travel Time Estimation on a Freeway with Sparsely Distributed Detectors , 2007 .

[5]  Chung-Cheng Lu,et al.  A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction , 2011 .

[6]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[7]  Michael J. Cassidy,et al.  METHODOLOGY FOR ASSESSING DYNAMICS OF FREEWAY TRAFFIC FLOW , 1995 .

[8]  Huizhao Tu,et al.  Monitoring Travel Time Reliability on Freeways , 2008 .

[9]  A. Bayen,et al.  Guaranteed bounds for traffic flow parameters estimation using mixed Lagrangian-Eulerian sensing , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[10]  Tschangho John Kim,et al.  Development and evaluation of a hybrid travel time forecasting model , 2000 .

[11]  Donald R. Drew,et al.  Analyzing freeway traffic under congestion: Traffic dynamics approach , 1998 .

[12]  B D Greenshields,et al.  A study of traffic capacity , 1935 .

[13]  Hao Chen,et al.  Real-time freeway traffic state prediction: A particle filter approach , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[14]  Jianwei Wang,et al.  Application of Advanced Traffic Information Systems , 2009 .

[15]  Panos G. Michalopoulos,et al.  An application of the finite element method in traffic signal analysis , 1984 .

[16]  J. Nédélec,et al.  First order quasilinear equations with boundary conditions , 1979 .

[17]  Malachy Carey,et al.  A Whole-Link Travel-Time Model with Desirable Properties , 2003, Transp. Sci..

[18]  Hong Li,et al.  The discontinuous finite element method for red-and-green light models for the traffic flow , 2001 .

[19]  A. R. Cook,et al.  ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES , 1979 .

[20]  Donald J Dailey,et al.  Travel-time estimation using cross-correlation techniques , 1993 .

[21]  Dongjoo Park,et al.  Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks , 1998 .

[22]  Hesham Rakha,et al.  Comparison of Greenshields, Pipes, and Van Aerde Car-Following and Traffic Stream Models , 2002 .

[23]  Baibing Li,et al.  A Non-Gaussian Kalman Filter With Application to the Estimation of Vehicular Speed , 2009, Technometrics.

[24]  G. F. Newell.  A simplified theory of kinematic waves in highway traffic, part I: General theory , 1993 .

[25]  Sul.I. Bajwa,et al.  Performance evaluation of an adaptive travel time prediction model , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[26]  J. A. Sethian,et al.  Fast Marching Methods , 1999, SIAM Rev..

[27]  Jorge Ramos,et al.  Driver response to variable message signs-based traffic information , 2006 .

[28]  H. Mahmassani,et al.  Travel time estimation based on piecewise truncated quadratic speed trajectory , 2008 .

[29]  Pei-Wei Lin,et al.  Developments and Applications of Simulation-Based Online Travel Time Prediction System: Traveling to Ocean City, Maryland , 2006 .

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

[31]  Malachy Carey,et al.  Retaining desirable properties in discretising a travel-time model , 2007 .

[32]  J. Tsitsiklis.  Efficient algorithms for globally optimal trajectories , 1995, IEEE Trans. Autom. Control..

[33]  Partha Chakroborty,et al.  Using Bus Travel Time Data to Estimate Travel Times on Urban Corridors , 2004 .

[34]  Pat Langley,et al.  Learning to Predict the Duration of an Automobile Trip , 1998, KDD.

[35]  Laurence R. Rilett,et al.  System-Wide Data Quality Control of Inductance Loop Data using Nonlinear Optimization , 2006 .

[36]  G. Hoffmann,et al.  Travel times as a basic part of the LISB guidance strategy , 1990 .

[37]  Steven I-Jy Chien,et al.  Dynamic Freeway Travel-Time Prediction with Probe Vehicle Data: Link Based Versus Path Based , 2001 .

[38]  John W. Polak,et al.  Modeling Urban Link Travel Time with Inductive Loop Detector Data by Using the k-NN Method , 2005 .

[39]  Bin Ran,et al.  Traffic Estimation Based on Particle Filtering with Stochastic State Reconstruction Using Mobile Network Data , 2006 .

[40]  Samir A. Ahmed,et al.  APPLICATION OF TIME-SERIES ANALYSIS TECHNIQUES TO FREEWAY INCIDENT DETECTION , 1982 .

[41]  L. Vanajakshi,et al.  Support Vector Machine Technique for the Short Term Prediction of Travel Time , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[42]  L. Mihaylova,et al.  A particle filter for freeway traffic estimation , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[43]  Eleni I. Vlahogianni,et al.  Short‐term traffic forecasting: Overview of objectives and methods , 2004 .

[44]  Philip J Tarnoff,et al.  Data Collection of Freeway Travel Time Ground Truth with Bluetooth Sensors , 2010 .

[45]  R. LeVeque.  Numerical methods for conservation laws , 1990 .

[46]  Vittorio Astarita,et al.  A CONTINUOUS TIME LINK MODEL FOR DYNAMIC NETWORK LOADING BASED ON TRAVEL TIME FUNCTION , 1996 .

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

[48]  Panos G Michalopoulos,et al.  Analysis of traffic flow by the finite element method , 1985 .

[49]  Malachy Carey.  Link Travel Times I: Desirable Properties , 2004 .

[50]  Alexandre M. Bayen,et al.  Incorporation of Lagrangian measurements in freeway traffic state estimation , 2010 .

[51]  J. Strikwerda.  Finite Difference Schemes and Partial Differential Equations , 1989 .

[52]  Benjamin Coifman.  Identifying the Onset of Congestion Rapidly with Existing Traffic Detectors , 1999 .

[53]  Lili Du,et al.  An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks , 2012 .

[54]  Donald R. Drew,et al.  Automatic measurement of traffic variables for intelligent transportation systems applications , 1999 .

[55]  Markos Papageorgiou,et al.  Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing , 2008 .

[56]  Piet H. L. Bovy,et al.  Evaluation of Online Travel Time Estimators and Predictors , 2000 .

[57]  Shing Chung Josh Wong,et al.  A wavelet-Galerkin method for the kinematic wave model of traffic flow , 2000 .

[58]  C. Daganzo.  A variational formulation of kinematic waves: Solution methods , 2005 .

[59]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[60]  Benjamin Coifman,et al.  Estimating travel times and vehicle trajectories on freeways using dual loop detectors , 2002 .

[61]  Katalin György,et al.  Unscented Kalman Filters and Particle Filter Methods for Nonlinear State Estimation , 2014 .

[62]  Yi-Chang Chiu,et al.  Real-Time Freeway-Experienced Travel Time Prediction Using N-Curve and k Nearest Neighbor Methods , 2011 .

[63]  Alexandre M. Bayen,et al.  Guaranteed bounds on highway travel times using probe and fixed data , 2009 .

[64]  Yadong Lu,et al.  The Entropy Solutions for the Lighthill-Whitham-Richards Traffic Flow Model with a Discontinuous Flow-Density Relationship , 2009, Transp. Sci..

[65]  Donald J Dailey,et al.  A statistical algorithm for estimating speed from single loop volume and occupancy measurements , 1999 .

[66]  Andreas Hegyi,et al.  Freeway traffic estimation within particle filtering framework , 2007, Autom..

[67]  Nour-Eddin El Faouzi,et al.  Particle filter-based real-time estimation and prediction of traffic conditions , 2007 .

[68]  Hironori Suzuki,et al.  Application of Probe-Vehicle Data for Real-Time Traffic-State Estimation and Short-Term Travel-Time Prediction on a Freeway , 2003 .

[69]  H. J. Van Zuylen,et al.  Bayesian Neural Networks for the Prediction of Stochastic Travel Times in Urban Networks , 2011 .

[70]  Joseph J. LaViola,et al.  On Kalman Filtering With Nonlinear Equality Constraints , 2007, IEEE Transactions on Signal Processing.

[71]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[72]  Dongjoo Park,et al.  Direct Forecasting of Freeway Corridor Travel Times Using Spectral Basis Neural Networks , 2001 .

[73]  R. Horowitz,et al.  Mixture Kalman filter based highway congestion mode and vehicle density estimator and its application , 2004, Proceedings of the 2004 American Control Conference.

[74]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[75]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[76]  Haitham Al-Deek,et al.  Travel-Time Prediction for Freeway Corridors , 1999 .

[77]  Jiann-Shiou Yang.  Travel time prediction using the GPS test vehicle and Kalman filtering techniques , 2005, Proceedings of the 2005, American Control Conference, 2005..

[78]  Martin L. Hazelton.  Estimating Vehicle Speed from Traffic Count and Occupancy Data , 2004 .

[79]  Mei Chen,et al.  Dynamic Freeway Corridor Travel Time Prediction Using Single Inductive Loop Detector Data , 2009 .

[80]  Zhirui Ye,et al.  Unscented Kalman Filter Method for Speed Estimation Using Single Loop Detector Data , 2006 .

[81]  Stanley B. Gershwin,et al.  Estimation of Roadway Traffic Density on Freeways Using Presence Detector Data , 1980 .

[82]  Pini Gurfil,et al.  Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms , 2010, IEEE Transactions on Signal Processing.

[83]  Donald R. Drew,et al.  TRAFFIC DYNAMICS: METHOD FOR ESTIMATING FREEWAY TRAVEL TIMES IN REAL TIME FROM FLOW MEASUREMENTS , 1996 .

[84]  Peter J. Bickel,et al.  Day-to-Day Travel-Time Trends and Travel-Time Prediction from Loop-Detector Data , 2000 .

[85]  Singiresu S Rao,et al.  Applied numerical methods for engineers and scientists , 2001 .

[86]  Francois Dion,et al.  Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates , 2006 .

[87]  Jan-Ming Ho,et al.  Travel time prediction with support vector regression , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[88]  Chi-Wang Shu,et al.  Explicit construction of entropy solutions for the Lighthill-Whitham-Richards traffic flow model with a piecewise quadratic flow-density relationship , 2008 .

[89]  Masao Kuwahara,et al.  Implementing kinematic wave theory to reconstruct vehicle trajectories from fixed and probe sensor data , 2011 .

[90]  C. Daganzo.  THE CELL TRANSMISSION MODEL.. , 1994 .

[91]  Bhagwant Persaud,et al.  EVALUATION OF SPEED ESTIMATES MADE WITH SINGLE-DETECTOR DATA FROM FREEWAY TRAFFIC MANAGEMENT SYSTEMS , 1989 .

[92]  Alexandre M. Bayen,et al.  An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices , 2008, 2008 47th IEEE Conference on Decision and Control.

[93]  H. W. Ho,et al.  Variation Based Online Travel Time Prediction Using Clustered Neural Networks , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[94]  Sherif Ishak,et al.  Performance evaluation of short-term time-series traffic prediction model , 2002 .

[95]  Khaled Hamad,et al.  Near‐Term Travel Speed Prediction Utilizing Hilbert–Huang Transform , 2009, Comput. Aided Civ. Infrastructure Eng..

[96]  Alexandre M. Bayen,et al.  Trade-offs Between Inductive Loops and GPS Probe Vehicles for Travel Time Estimation: Mobile Century Case Study , 2012 .

[97]  Hao Chen,et al.  Prediction of dynamic freeway travel times based on vehicle trajectory construction , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[98]  Edgar Ergueta,et al.  IMPROVED VEHICLE REIDENTIFICATION AND TRAVEL TIME MEASUREMENT ON CONGESTED FREEWAYS , 2002 .

[99]  L. Chu.  Adaptive Kalman Filter Based Freeway Travel time Estimation , 2004 .

[100]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[101]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[102]  S. Travis Waller,et al.  Short Term Travel Time Prediction on Freeways in Conjunction with Detector Coverage Analysis , 2007 .

[103]  William W. Symes,et al.  Upwind finite-difference calculation of traveltimes , 1991 .

[104]  Chang-Ho Park,et al.  Travel Time Prediction Using k Nearest Neighbor Method with Combined Data from Vehicle Detector System and Automatic Toll Collection System , 2011 .

[105]  Stephen D. Clark,et al.  Traffic Prediction Using Multivariate Nonparametric Regression , 2003 .

[106]  Wenxin Qiao,et al.  Short-Term Travel Time Prediction considering the Effects of Weather , 2012 .

[107]  Baibing Li,et al.  On the recursive estimation of vehicular speed using data from a single inductance loop detector: A Bayesian approach , 2009 .

[108]  A. Haghani,et al.  Real-Time Solution of Velocity-Based First-Order Continuum Traffic Model with Finite Element Method , 2011 .

[109]  Yiguang Liu,et al.  The Reliability of Travel Time Forecasting , 2010, IEEE Transactions on Intelligent Transportation Systems.

[110]  H. J. Van Zuylen,et al.  Accurate freeway travel time prediction with state-space neural networks under missing data , 2005 .

[111]  Carlos Carmona,et al.  Travel Time Forecasting and Dynamic Origin-Destination Estimation for Freeways Based on Bluetooth Traffic Monitoring , 2010 .

[112]  Alexandre M. Bayen,et al.  Lax–Hopf Based Incorporation of Internal Boundary Conditions Into Hamilton-Jacobi Equation. Part II: Computational Methods , 2010, IEEE Transactions on Automatic Control.

[113]  C. Daganzo.  A variational formulation of kinematic waves: basic theory and complex boundary conditions , 2005 .

[114]  Ying Liu,et al.  Estimation of Freeway Travel Time Based on Sparsely Distributed Detectors , 2006 .

[115]  Hesham Rakha,et al.  Particle Filter Approach for Real-Yime Freeway Traffic State Prediction , 2012 .

[116]  P. I. Richards.  Shock Waves on the Highway , 1956 .

[117]  Panos G Michalopoulos,et al.  Finite element analysis of freeway dynamics , 1986 .

[118]  Xiaoyan Zhang,et al.  Short-term travel time prediction , 2003 .

[119]  Mei Chen,et al.  A Multistep Corridor Travel-Time Prediction Method Using Presence-Type Vehicle Detector Data , 2011, J. Intell. Transp. Syst..

[120]  Laurence R. Rilett,et al.  Improved Flow-Based Travel Time Estimation Method from Point Detector Data for Freeways , 2009 .

[121]  Markos Papageorgiou,et al.  Real-Time Freeway Traffic State Estimation Based on Extended Kalman Filter: A Case Study , 2007, Transp. Sci..

[122]  Daiheng Ni,et al.  Trajectory Reconstruction for Travel Time Estimation , 2008, J. Intell. Transp. Syst..

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

[124]  J. W. C. van Lint,et al.  Bayesian Combination of Travel Time Prediction Models , 2008 .