Optimization of signalized traffic network using swarm intelligence

Traffic lights are the signaling devices located at a road intersection for granting right-of-way movement to road users. Thus, optimization of traffic signalization is essential to improve road service as it is the cost-effective way. Commonly, the signal optimization aims to minimize the average travel delay by manipulating the green signal timing. Besides to optimize the signal timing, the local intersection controller needs to collaborate with neighboring intersection controllers for minimizing the average delay for whole network as the congestion will be propagated to the downstream intersection. However, the current fixed-time signal controller is inadequate to manage the high growing demands of traffic as it is tuned offline using the nominal traffic flow data. Thus, this work aims to explore the potential of using Particle Swarm Optimization (PSO) to optimize the traffic signal timing for the traffic network. The proposed algorithm is texted using a benchmarked 1x2 traffic model and its performances are compared with the classical Genetic Algorithm (GA). The simulated results show the proposed PSO has improved the performances in minimizing average travel delay by 3.94 %

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