First published in 1998, this volume enters the debate on human behaviour in the form of neural networks in a spatial context. As most transportation research techniques had been developed in the 1960s and 1970s, these authors sought to bring that research into the modern era. Featuring 17 articles from 37 contributors, it begins with an overview and proceeds to examine aspects of travel behaviour, traffic flow and traffic management.
Table of Contents
Part A. Overview. 1. Computational Neural Networks: An Attractive Class of Mathematical Models for Transportation Research. M.M. Fischer. 2. Neural Network: An Overview and Applications in the Space Economy. A. Reggiani, R. Romanelli, T. Tritapepe and P. Nijkamp. Part B. Travel Behaviour. 3. Analysis of Performance of Backpropagation ANN with Different Training Parameters. A. Faghri and A. Sandeep. 4. Daily Travelling Viewed by Self-Organizing Maps. V. Himanen, T. Järvi-Nykänen and J. Raitio. 5. Neural Network and Logit Models Applied to Commuters’ Mobility in The Metropolitan Area of Milan. A. Reggiani and T. Tritapepe. 6. Neural Networks as Adaptive Logit Models. L.A. Schintler and O. Olurotimi. 7. Neural Network Analysis of Travel Behaviour. D. Shmueli, I. Salomon and D. Shefer. 8. A Methodology for Modelling Driver Behaviour in Signalized Urban Intersections Using Artificial Neural Networks. L. Mussone, G. Reitani and S. Rinelli. Part C. Traffic Flow. 9. A New Traffic Light Single Junction Control System Implemented by a Symbolic Neural Network. E. Burattini, M. de Gragorio and G. Improta. 10. Exploring Traffic Systems by Elasticity Analysis of Neural Networks. M. Dougherty. 11. Factors Influencing the Performance of a Neural Network Driver Decision Model: A Case Study Using Simulated Data. G.D. Lyons, J.G. Hunt and S.Y. Yousif. 12. Neural Network Models Applied to Traffic Flow Problems. T. Nakatsuji and S. Shibuya. 13. Two Dimensional Estimation of Speed Flow Relationships with Backpropagation Neural Networks. M. Pursula. Part D. Traffic Management. 14. The Application of Fuzzy Multiobjective and Artificial Neural Networks on Urban Public Transport Equilibrium. Y. Chang and C.C. Shen. 15. The Impact of Data Quantity on the Performance of Neural Network Freeway Incident Detection Models. D. Hussein and G. Rose. 16. Predicting Parking Characteristics: the Use of Neural Networks to Support Parking Management. M. Kontaratos, T. Tillis and K. Kleanthous. 17. Travel Time Prediction for Freeway Traffic Information by Neural Network Driven Fuzzy Reasoning. H. Matsui and M. Fujita.