Demand Data Modelling for Microscopic Traffic Simulation

Abstract The current paper aim is to present the technique of demand data modelling for microscopic simulation of the traffic flows. Traffic microscopic simulation is a powerful decision supporting tool, which could be applied for a wide range of tasks. In a past microscopic traffic simulation was used to test local changes in transport infrastructure, but the growth of computers performance allows now to simulate wide-scale fragments of the traffic network and to apply more advanced traffic flow simulation approaches, like an example dynamic assignment (DA). The results, obtained in the frame of this research are part of the project completed for one of the shopping malls (Riga, Latvia). The goal of the project was to evaluate different development scenarios of the transport network to raise the accessibility of the shopping mall. The number of practical issues in the frame of this project pushed to develop a new technique to model the demand data for the simulation model. As a traffic flow simulation tool, the PTV VISSIM simulation software was applied. The developed model was based on dynamic assignment approach. To complete the simulation the demand data was represented in two forms: 1) OD matrix for regular traffic in the transport network; 2) trip-chain file for a description of the pass-by and targeted trips.

[1]  M. Bell THE ESTIMATION OF ORIGIN-DESTINATION MATRICES BY CONSTRAINED GENERALISED LEAST SQUARES , 1991 .

[2]  M. Maher INFERENCES ON TRIP MATRICES FROM OBSERVATIONS ON LINK VOLUMES: A BAYESIAN STATISTICAL APPROACH , 1983 .

[3]  Yupo Chan,et al.  ESTIMATING AN ORIGIN-DESTINATION MATRIX WITH FUZZY WEIGHTS. PART I: METHODOLOGY , 1993 .

[4]  Soumya Sekhar Dey,et al.  Bayesian updating of trip generation data: Combining national trip generation rates with local data , 1994 .

[5]  Martin L. Hazelton,et al.  Estimation of origin-destination matrices from link flows on uncongested networks , 2000 .

[6]  M J Maher,et al.  THE ANALYSIS OF PARTIAL REGISTRATION-PLATE DATA , 1985 .

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

[8]  R. Legget,et al.  Performance Concept in Building , 1967 .

[9]  Per Högberg,et al.  Estimation of parameters in models for traffic prediction: A non-linear regression approach , 1976 .

[10]  Luis G. Willumsen,et al.  ESTIMATION OF AN O-D MATRIX FROM TRAFFIC COUNTS - A REVIEW , 1978 .

[11]  Irina Pticina,et al.  Methodology of OD Matrix Estimation Based on Video Recordings and Traffic Counts , 2017 .

[12]  H. Spiess A MAXIMUM LIKELIHOOD MODEL FOR ESTIMATING ORIGIN-DESTINATION MATRICES , 1987 .

[13]  Pierre N. Robillard,et al.  Estimating the O-D matrix from observed link volumes , 1975 .

[14]  Yupo Chan,et al.  Estimating an origin‐destination matrix with fuzzy weights: Part II: Case studies , 1993 .

[15]  P Kryger,et al.  CAN THE LICENSE-PLATE METHOD BE USED FOR TRAFFIC SURVEYS? , 1956 .

[16]  Zhejun Gong Estimating the urban OD matrix: A neural network approach , 1998, Eur. J. Oper. Res..

[17]  M. Bierlaire MEUSE: an origin-destination matrix estimator that exploits structure , 1995 .

[18]  Yasuo Asakura,et al.  Origin-destination matrices estimation model using automatic vehicle identification data and its application to the Han-Shin expressway network , 2000 .