Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper adopts the multiagent system approach to develop distributed unsupervised traffic responsive signal control models, where each agent in the system is a local traffic signal controller for one intersection in the traffic network. The first multiagent system is developed using hybrid computational intelligent techniques. Each agent employs a multistage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multiagent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN). The problem of real-time traffic signal control is especially challenging if the agents are used for an infinite horizon problem, where online learning has to take place continuously once the agent-based traffic signal controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District of Singapore has been developed using PARAMICS microscopic simulation program. Simulation results show that the hybrid multiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases. Using the hybrid NN-based multiagent system, the mean delay of each vehicle was reduced by 78% and the mean stoppage time, by 85% compared to the existing traffic signal control algorithm. The promising results demonstrate the efficacy of the hybrid NN-based multiagent system in solving large-scale traffic signal control problems in a distributed manner

The existing taxi dispatch system that taxi operators in Singapore use to handle current bookings was studied. This dispatch system adopts the Global Positioning System and is based on the nearest-coordinate method: the taxi assigned for each booking is the one with the shortest, direct, straight-line distance to the customer location. However, the taxi assigned under this system often is not capable of reaching the customer in the shortest time possible. An alternative dispatch system is proposed, whereby the dispatch of taxis is determined by real-time traffic conditions. In the proposed system, the taxi assigned the booking job is the one with the shortest time path, reaching the customer in the shortest time. This dispatch ensures that customers are served within the shortest period of time and increases customer satisfaction. The effectiveness of both the existing and the proposed dispatch systems is investigated through computer simulations. The results from a simulation model of the Singapore central business district network are presented and analyzed. Data from the simulations show that the proposed dispatch system is capable of being more efficient in dispatching taxis more quickly and leads to more than 50% reductions in passenger pickup times and average travel distances. A more efficient dispatch system would result in higher standards of customer service and a more organized taxi fleet to meet customer demands better.