Rail transit operations analysis : framework and applications

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2001.Includes bibliographical references (leaves 126-136).by Adam B. Rahbee.S.M

Farecard Passenger Flow Model at Chicago Transit Authority, Illinois

A system using farecard data at the Chicago Transit Authority in Chicago, Illinois, to estimate rail passenger flows within the rail rapid transit network has been validated and used for several planning and analysis purposes. The system, which derives destinations for entry-only rail farecard users, is an enhanced version of the New York City Transit farecard origin–destination model. Enhancements include algorithmic changes to improve destination inference, greater time period granularity, enhanced sampling techniques, and path assignment to individual scheduled trips. Applications of the model outputs, including its use in lieu of passenger counts, are illustrated

OPTIMAL BUS STOP SPACING THROUGH DYNAMIC PROGRAMMING AND GEOGRAPHIC MODELING

A discrete approach was used to model the impacts of changing bus-stop spacing on a bus route. Among the impacts were delays to through riders, increased operating cost because of stopping delays, and shorter walking times perpendicular to the route. Every intersection along the route was treated as a candidate stop location. A simple geographic model was used to distribute the demand observed at existing stops to cross-streets and parallel streets in the route service area, resulting in a demand distribution that included concentrated and distributed demands. An efficient, dynamic programming algorithm was used to determine the optimal bus-stop locations. The model was compared with the continuum approach used in previous studies. A bus route in Boston was modeled, in which the optimal solution was an average stop spacing of 400 m (4 stops/mi), in sharp contrast to the existing average spacing of 200 m (8 stops/mi). The model may also be used to evaluate the impacts of adding, removing, or relocating selected stops

Decision Factors in Service Control on High-Frequency Metro Line, Importance in Service Delivery

Service control—the task of implementing the timetable in daily operations on a metro line—plays a key role in service delivery, because it influences the quality of the service provided to passengers. Shortfalls of previous research on the role and importance of service control have been noted. A framework intended to remedy some of these shortfalls is proposed. An important element of this framework is the description of the full decision environment in which service control takes place. On the basis of insights gained from extended visits to a control center, the reliability of the system is found to depend on many endogenous factors. These factors were not previously recognized in a comprehensive manner by either researchers or practitioners. Aside from the objectives of maintaining adequate levels of service from an operations perspective and minimizing the impact of schedule deviations on passengers, the management of crew and rolling stock, safety, and infrastructure capacity are major considerations in service control decisions. Given the uncertain environment in which service control operates, a strong preference was observed among controllers for manageable and robust control strategies. An example is discussed in which service controllers react to two similar disruptions with different recovery strategies, mainly because of crew management considerations. This research demonstrates the importance of a comprehensive understanding of the objectives and constraints faced by service controllers in daily operations