Technological advances that impact on microsimulation modelling

This review covers technological advances that are beginning to impact on the state-of-the-art of road traffic microsimulation models. Three main areas are covered, reflecting the conventional division between software, hardware and data. The emphasis is on developments in modelling techniques, the increasing richness of data made available from intelligent transport systems and the rapid fall in the cost of computing hardware. These areas interact closely because more sophisticated tools are needed to cope with the huge data sets now available. Applications are also increasingly expected to run in realtime rather than off-line, with consequent increases in demand for computing power and functionality. To the non-specialist user, this increasing diversity can cause difficulties. The literature is often full of jargon or mathematics and it can be hard to decide which developments are important for different enduser applications. This paper, therefore, aims to explain in simple terms what these technologies are and how they affect modelling practice. It is hoped this will help end users in several ways; to choose appropriate tools, to better understand the models they use and to be aware of likely data sources that will improve modelling accuracy.

[1]  U. R. Hanebutte,et al.  Traffic simulations on parallel computers using domain decomposition techniques , 1995 .

[2]  Tsutomu Sugimoto,et al.  TRAFFIC PREDICTION AND QUALITATIVE REASONING , 1992 .

[3]  Dean Bubley Virtual reality : video game or business tool? , 1994 .

[4]  Mohamed Abdel-Aty,et al.  Exploring route choice behavior using geographic information system-based alternative routes and hypothetical travel time information input , 1995 .

[5]  C Young VANS NAVIGATION: FORD NAVIGATION SYSTEM , 1996 .

[6]  Walid Najjar,et al.  Parallel Discrete-Event Simulation , 1987, IEEE Design & Test of Computers.

[7]  Lyle H. Ungar,et al.  Using radial basis functions to approximate a function and its error bounds , 1992, IEEE Trans. Neural Networks.

[8]  Yi-Bing Lin,et al.  Asynchronous parallel discrete event simulation , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[9]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[10]  P Rieth,et al.  SUMMARY OF EXPERIENCES WITH AUTONOMOUS INTELLIGENT CRUISE CONTROL (AICC). PART 2: RESULTS AND CONCLUSIONS , 1994 .

[11]  Ronald J. Brachman,et al.  The Process of Knowledge Discovery in Databases , 1996, Advances in Knowledge Discovery and Data Mining.

[12]  Marcel Holsheimer,et al.  Data Surveyor: Searching the Nuggets in Parallel , 1996, Advances in Knowledge Discovery and Data Mining.

[13]  Oliver Carsten VRU-TOO : An ATT project for vulnerable road user safety , 1992 .

[14]  John C Sutton Role of Geographic Information Systems in Regional Transportation Planning , 1995 .

[15]  S. Dunstan,et al.  Idris enhancement techniques for incident detection , 1997 .

[16]  W. J. Gillam,et al.  The 'Instrumented City': data provision for traffic management and research , 1994 .

[17]  B Chambers CONTACTLESS SMARTCARDS: NEW AUTOMATIC FARE COLLECTION SYSTEM FOR HONG KONG'S PUBLIC TRANSPORT , 1996 .

[18]  Johan de Kleer,et al.  A Qualitative Physics Based on Confluences , 1984, Artif. Intell..

[19]  Gordon Duncan SIMULATION AT THE MICROSCOPIC LEVEL , 1996 .