Traffic flow modeling and forecasting using cellular automata and neural networks : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand

In This thesis fine grids are adopted in Cellular Automata (CA) models. The fine-grid models are able to describe traffic flow in detail allowing position, speed, acceleration and deceleration of vehicles simulated in a more realistic way. For urban straight roads, two types of traffic flow, free and car-following flow, have been simulated. A novel five-stage speed-changing CA model is developed to describe free flow. The 1.5-second headway, based on field data, is used to simulate car-following processes, which corrects the headway of 1 second used in all previous CA models. Novel and realistic CA models, based on the Normal Acceptable Space (NAS) method, are proposed to systematically simulate driver behaviour and interactions between drivers to enter single-lane Two-Way Stop-Controlled (TWSC) intersections and roundabouts. The NAS method is based on the two following Gaussian distributions. Distribution of space required for all drivers to enter intersections or roundabouts is assumed to follow a Gaussian distribution, which corresponds to heterogeneity of driver behaviour. While distribution of space required for a single driver to enter an intersection or roundabout is assumed to follow another Gaussian distribution, which corresponds to inconsistency of driver behavior. The effects of passing lanes on single-lane highway traffic are investigated using fine grids CA. Vehicles entering, exiting from and changing lanes on passing lane sections are discussed in detail. In addition, a Genetic Algorithm-based Neural Network (GANN) method is proposed to predict Short-term Traffic Flow (STF) in urban networks, which is expected to be helpful for traffic control. Prediction accuracy and generalization ability of NN are improved by optimizing the number of neurons in the hidden layer and connection weights of NN using genetic operations such as selection, crossover and mutation

Towards Developing a Travel Time Forecasting Model for Location-Based Services: a Review

Travel time forecasting models have been studied intensively as a subject of Intelligent Transportation Systems (ITS), particularly in the topics of advanced traffic management systems (ATMS), advanced traveler information systems (ATIS), and commercial vehicle operations (CVO). While the concept of travel time forecasting is relatively simple, it involves a notably complicated task of implementing even a simple model. Thus, existing forecasting models are diverse in their original formulations, including mathematical optimizations, computer simulations, statistics, and artificial intelligence. A comprehensive literature review, therefore, would assist in formulating a more reliable travel time forecasting model. On the other hand, geographic information systems (GIS) technologies primarily provide the capability of spatial and network database management, as well as technology management. Thus, GIS could support travel time forecasting in various ways by providing useful functions to both the managers in transportation management and information centers (TMICs) and the external users. Thus, in developing a travel time forecasting model, GIS could play important roles in the management of real-time and historical traffic data, the integration of multiple subsystems, and the assistance of information management. The purpose of this paper is to review various models and technologies that have been used for developing a travel time forecasting model with geographic information systems (GIS) technologies. Reviewed forecasting models in this paper include historical profile approaches, time series models, nonparametric regression models, traffic simulations, dynamic traffic assignment models, and neural networks. The potential roles and functions of GIS in travel time forecasting are also discussed.

Short-Term Traffic Prediction Based on Genetic Algorithm Improved Neural Network

This paper takes the time series of short-term traffic flow as research object. The delay time and embedding dimension are calculated by C-C algorithm, and the chaotic characteristics of the time series are verified by small data sets method.Then based on the neural network prediction model and the chaotic phase space reconstruction theory, the network topology is determined, and the prediction is conducted by the wavelet neural network and RBF neural network using Lan-Hai expressway experimental data. The results show that the prediction effect of RBF neural network is better. Due to the poor stability of the network caused by the initial parameters randomness, the genetic algorithm is used to optimize the initial parameters. The results show that the prediction error of the optimized wavelet neural network or RBF neural network is reduced by more than 10%, and prediction accuracy of the latter is better

An integrated method for short-term prediction of road traffic conditions for intelligent transportation systems applications

The paper deals with the short-term prediction of road traffic conditions within Intelligent Transportation Systems applications. First, the problem of traffic modeling and the potential of different traffic monitoring technologies are discussed. Then, an integrated method for short-term traffic prediction is presented, which integrates an Artificial Neural Network predictor that forecasts future states in standard conditions, an anomaly detection module that exploits floating car data to individuate possible occurrences of anomalous traffic conditions, and a macroscopic traffic model that predicts speeds and queue progressions in case of anomalies. Results of offline applications on a primary Italian motorway are presented