Estimation of urban traffic conditions using an Automatic Vehicle Location (AVL) System

The aim of this paper is to develop an Information Extension Model (IEM) which uses location data of bus fleets (AVL data) to estimate road traffic conditions and provide input for implementing control strategies. The IEM consists of three sub-models: the Link Traffic Condition Model (LTCM), the AVL Adaptation Model (AVLAM) and the Network Traffic Condition Model (NTCM). The first provides road traffic conditions as a function of mass-transit traffic conditions in the case of shared lanes, the second provides mass-transit traffic conditions as a function of AVL data, and the last provides road traffic conditions over the whole road network as a function of mass-transit traffic conditions. The IEM (and its sub-models) were developed and calibrated in the case of real dimension networks and some tests were performed on a trial network. Numerical results show the effectiveness of the proposed method since it allows a reduction in travel demand estimation errors.

[1]  Licinio da Silva Portugal,et al.  INTELLIGENT TRANSPORTATION SYSTEMS AND PARKING MANAGEMENT: IMPLEMENTATION POTENTIAL IN A BRAZILIAN CITY , 2004 .

[2]  Haris N. Koutsopoulos,et al.  Simulation Laboratory for Evaluating Dynamic Traffic Management Systems , 1997 .

[3]  Hai Yang,et al.  Modeling user adoption of advanced traveler information systems: dynamic evolution and stationary equilibrium , 2001 .

[4]  K B Davidson,et al.  THE THEORETICAL BASIS OF A FLOW-TRAVEL TIME RELATIONSHIP FOR USE IN TRANSPORTATION PLANNING , 1978 .

[5]  Ennio Cascetta,et al.  Estimation of Travel Demand Using Traffic Counts and Other Data Sources , 2002 .

[6]  R. Bertini,et al.  Transit Buses as Traffic Probes: Use of Geolocation Data for Empirical Evaluation , 2004 .

[7]  Hussein Dia,et al.  An agent-based approach to modelling driver route choice behaviour under the influence of real-time information , 2002 .

[8]  Giulio Erberto Cantarella,et al.  A General Fixed-Point Approach to Multimode Multi-User Equilibrium Assignment with Elastic Demand , 1997, Transp. Sci..

[9]  Donald J Dailey,et al.  A PRESCRIPTION FOR TRANSIT ARRIVAL/DEPARTURE PREDICTION USING AUTOMATIC VEHICLE LOCATION DATA , 2003 .

[10]  Jaume Barceló,et al.  Microscopic traffic simulation: A tool for the design, analysis and evaluation of intelligent transport systems , 2005, J. Intell. Robotic Syst..

[11]  J. R. Wootton,et al.  Intelligent transportation systems: A global perspective , 1995 .

[12]  Lawrence A Klein,et al.  SUMMARY OF VEHICLE DETECTION AND SURVEILLANCE TECHNOLOGIES USED IN INTELLIGENT TRANSPORTATION SYSTEMS , 2000 .

[13]  Kalidas Ashok,et al.  DYNAMIC ORIGIN-DESTINATION MATRIX ESTIMATION AND PREDICTION FOR REAL- TIME TRAFFIC MANAGEMENT SYSTEMS , 1993 .

[14]  W. Y. Szeto,et al.  A methodology for sustainable traveler information services , 2002 .

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

[16]  Peter Loukopoulos,et al.  Mapping the potential consequences of car-use reduction in urban areas , 2004 .

[17]  P Larima.  VERDI - FROM FIELD TRIAL TO DEPLOYMENT , 1997 .

[18]  Randolph W. Hall,et al.  Buses as a Traffic Probe: Demonstration Project , 2000 .

[19]  Gilbert Laporte,et al.  Operations Research and Decision Aid Methodologies in Traffic and Transportation Management , 1998, NATO ASI Series.

[20]  Antoneta X Horbury.  Using non-real-time Automatic Vehicle Location data to improve bus services , 1999 .

[21]  Ennio Cascetta,et al.  Transportation Systems Engineering: Theory and Methods , 2001 .

[22]  Qing Shen,et al.  Urban transportation in Shanghai, China: problems and planning implications , 1997 .

[23]  C A Brebbia,et al.  Urban Transport IX: Urban Transport and the Environment in the 21st Century , 1998 .

[24]  Carlos F. Daganzo,et al.  TRANSPORTATION AND TRAFFIC THEORY , 1993 .

[25]  Consultant,et al.  TRAVEL TIME ESTIMATION USING MOBILE DATA , 2005 .

[26]  J Gorys,et al.  MEASURING CONGESTION: THE GTA TRIP TRAVEL-TIME STUDY, A METHODOLOGICAL DISCUSSION , 1999 .

[27]  Song Gao,et al.  Optimal routing policy problems in stochastic time-dependent networks , 2006 .

[28]  Mohamed Abdel-Aty,et al.  INVESTIGATING EFFECT OF TRAVEL TIME VARIABILITY ON ROUTE CHOICE USING REPEATED-MEASUREMENT STATED PREFERENCE DATA , 1995 .

[29]  W Konig,et al.  THE OPERATION OF A DYNAMIC ROUTE GUIDANCE SYSTEM - EXPERIENCES FROM THE DYNAMIC TRAFFIC GUIDANCE SYSTEM, BERLIN , 1997 .

[30]  Markos Papageorgiou,et al.  Automatic Control Methods in Traffic and Transportation , 1998 .

[31]  Datta N. Godbole,et al.  Automated Highway Systems , 1996 .

[32]  Daniel J. Dailey,et al.  Transit Vehicles as Traffic Probe Sensors , 2002 .

[33]  Ennio Cascetta,et al.  Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts , 1993, Transp. Sci..

[34]  T. Abrahamsson,et al.  IR-98-021 / May Estimation of Origin-Destination Matrices Using Traffic Counts – A Literature Survey , 1998 .

[35]  Linda K. Nozick,et al.  Evaluation of travel demand measures and programs: a data envelopment analysis approach , 1998 .

[36]  Karthik K. Srinivasan,et al.  Determination of Number of Probe Vehicles Required for Reliable Travel Time Measurement in Urban Network , 1996 .

[37]  Moshe E. Ben-Akiva,et al.  Estimation and Prediction of Time-Dependent Origin-Destination Flows with a Stochastic Mapping to Path Flows and Link Flows , 2002, Transp. Sci..

[38]  Panos G Michalopoulos,et al.  Evaluation of Ramp Control Effectiveness in Two Twin Cities Freeways , 2002 .

[39]  Vincenzo Punzo,et al.  Travel Time Cost Functions For Urban Roads:A Case Study In Italy , 2007 .

[40]  Randolph W. Hall,et al.  Handbook of transportation science , 1999 .

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

[42]  Aj De Hoog Jochem,et al.  FLOATING CAR DATA IN THE NETHERLANDS , 1998 .