Efficient and reliable public transport systems are fundamental in promoting green growth developments in metropolitan areas. A large range of Advanced Public Transport Systems (APTS) facilitates the design of real-time operations and demand management. The analysis of transit performance requires a dynamic tool that will enable to emulate the dynamic loading of travelers and their interaction with the transit system. BusMezzo, a dynamic transit operations and assignment model was developed to enable the analysis and evaluation of transit performance and level of service under various system conditions and APTS. The model represents the interactions between traffic dynamics, transit operations and traveler decisions. The model was implemented within a mesoscopic traffic simulation model. The different sources of transit operations uncertainty including traffic conditions, vehicle capacities, dwell times, vehicle schedules and service disruptions are modeled explicitly. The dynamic path choice model in BusMezzo considers each traveler as an adaptive decision maker. Travelers’ progress in the transit system consists of successive decisions that are defined by the need to choose the next path element. The evaluations are based on the respective path alternatives and their anticipated downstream attributes. Travel decisions are modeled within the framework of discrete random utility models. A non-compensatory choice-set generation model and the path utility function were estimated based on a web-based survey. BusMezzo enables the analysis and evaluation of proactive control strategies and the impacts of real-time information provision. Several experiments were conducted to analyze transit performance from travelers, operator and drivers perspectives under various holding strategies. This analysis has facilitated the design of a field trial of the most promising strategy. Furthermore, a case study on real-time traveler information systems regarding the next vehicle arrival time investigated the impacts of various levels of coverage and comprehensiveness. As passengers are more informed, passenger loads are subject to more fluctuation due to the traveler adaptations. / QC 20111201

This dissertation studies three public transit operations control strategies with automatic vehicle location (AVL) data available. Specifically, holding control, stop-skipping control and vehicle dispatching with swapping are investigated. Moreover, AVL data from Tucson, Arizona are employed to investigate the methodologies for deriving vehicle operating parameters.The problem of holding vehicles at multiple holding stations can be modeled as a convex mathematical programming problem which can be solved to near optimality by a proposed heuristic. A simulation study on the holding problem suggests that holding control based on the proposed problem formulation can effectively reduce the total passenger cost. Also, multiple holding stations may offer more opportunities to regularize vehicle headways so that holding vehicles at multiple stations can further reduce the passenger cost compared to holding vehicles only at a single station.Stop-skipping is investigated to respond more rapidly to vehicle disruptions occurring in the middle of a route. Based on a preliminary analysis of the basic stop-skipping policy, a policy alternative is constructed. The stop-skipping strategy is formulated separately for both policies as a nonlinear integer programming problem. The problem solution relies on an exhaustive search method. Another simulation study is conducted to examine how the performance of the two policies change with the passenger distribution pattern, the vehicle disruption location and length, and the vehicle travel time variability. The simulation result suggests selective superiority of the two policies.The vehicle dispatching problem investigates the potential of integrating real-time swapping into the vehicle dispatching strategies at a transit transfer terminal. With a hypothetical study design, simulation is employed again to evaluate the significance of real-time swapping by comparing the performance of a swapping-holding combined strategy with the holding-only strategy. A sensitivity analysis is also employed to compare these two strategies among key transit operating factors.Finally, using three different understandings (assumptions) of vehicle operating behavior, regression methods are proposed for using AVL data to derive the vehicle running speeds and passenger boarding rates, which serve as inputs to the operations control models. The regression results show that the day-specific operating behavior may not be appropriate, and that operating behavior combining both trip-specific and day-specific effects seems to be slightly superior to the trip-specific behavior overall.

This is a report of a professional project intended to act as an informational tool for the evaluation of a dynamic dispatch system at Embarcadero Station for the San Francisco Municipal Transportation Agency (SFMTA). Systems that operate dispatch algorithms do so in response to on-time performance and reliability. The optimization problem is documented in many transportation contexts including airline networks, bus dispatch, and freight routing. According to the research, optimizing available options and re-routing based on available options can create a more efficient system that would minimize operating costs and improving service reliability for customers. The methodology presented here uses current headway information to dynamically change dispatch assignment between J, L, and M lines. The reassignment program was applied to randomly selected weekdays in March of 2012. After analysis it was determined that the application has the potential to decrease mean operating headways by up to 3% (or approximately 15 seconds).