In most major cities, levels of traffic congestion are rising along with their associated problems such as travel delays and pollution. While any increase in public transit rider-ship could reduce the level of traffic congestion and related costs, most transit agencies are not able to expand their existing services because of fiscal• and physical constraints. As a result, a growing interest has been developing recently to maximize the transit system efficiency and productivity using new emerging technologies.
Recently, the emergence of new technologies such as automatic vehicle location (AVL) and
global positioning systems (GPS) has facilitated the design of computer-based real-time decision support systems for public transits. These technologies could significantly help transit agencies improve their operations monitoring and control. In the context of public transit systems, operations monitoring refers to real-time service performance measure and problems detection, and control refers to implementing real time control actions to remedy those problems.
This thesis presents a new approach for operations monitoring and control in public transit systems with real-time information. First, an integrated model that combines both headway-based and schedule-based services is presented. To measure the headway or schedule adherence, the model uses predicted arrival times of vehicles at downstream stops. This feature allows the operational managers to avoid major service interruptions by proactively taking necessary corrective actions. Transit agencies have used and continue to use real-time control strategies to improve quality of their services. These strategies are employed by inspectors at various points along a route to remedy the problems as they occur. Practice shows that it is difficult to apply such strategies effectively without real-time information. In the second part of this thesis, a mathematical model for holding control strategy with real-time information is described. The proposed model aims at minimization of the total passengers waiting time and considers both cases of overcrowded and underutilized services. Due to complexity of the holding problem, several metaheuristics are proposed and tested. Among all intelligent search algorithms, a new version of simulated annealing algorithm is proposed to solve the real-time holding control model.
A Robust Optimization Approach to Public Transit Mobile Real-time Information
