Title:
Designing optimal demand-responsive transportation feeder systems and comparing performance in heterogeneous environments
Designing optimal demand-responsive transportation feeder systems and comparing performance in heterogeneous environments
Author(s)
Edwards, Derek L.
Advisor(s)
Dickerson, Stephen L.
Vachtsevanos, George J.
Vachtsevanos, George J.
Editor(s)
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Abstract
The goal of this research is to develop a method of objectively comparing and optimizing the performance of demand-responsive transportation systems in heterogeneous environments. Demand-responsive transportation refers to modes of transportation that do not follow fixed routes or schedules, including taxis, paratransit, deviated-route services, ride sharing as well as other modes. Heterogeneous environments are transportation environments in which streets do not follow regular patterns, passenger behavior is difficult to model, and transit schedules and layouts are non-uniform. An example of a typical heterogeneous environment is a modern suburb with non-linear streets, low pedestrian activity, and infrequent or sparse transit service. The motivation for this research is to determine if demand-responsive transportation can be used to improve customer satisfaction and reduce operating costs in suburban and low-density urban areas where fixed-route transportation may be inefficient.
This research extends existing comparison and optimization techniques that are designed to work in homogeneous environments. Homogeneous environments refer to transportation systems where the streets follow regular and repeating patterns, passengers are evenly distributed throughout the system, and the transit system is easily modeled. The performance of systems with these characteristics can be approximated with closed-form analytical expressions representing passenger travel times, vehicle distances traveled, and other performance indicators. However, in the low-density urban areas studied in this research, the street patterns and transit schedules are irregular and passenger behavior is difficult to model. In these areas, analytical solutions cannot be found. Instead, this research develops a simulation-based approach to compare and optimize performance in these heterogeneous environments. Using widely-available route-planning tools, open-source transit schedules, and detailed passenger data, it is possible to simulate the behavior of transit vehicles and passengers to such an exacting degree that analytical solutions are not needed.
A major technical contribution of this research is the development of a demand-responsive transportation simulator to analyze performance of demand-responsive systems in heterogeneous environments. The simulator combines several open-source tools for route planning with a custom-built demand-responsive vehicle and passenger-itinerary optimizer to simulate individual vehicles and passengers within a large system. With knowledge of the street network, the transit schedule, passenger locations, and trip request times, the simulator will output exact passenger transit times, passenger travel distances, vehicle travel distance, and other performance indicators for a particular transportation setup in a given area.
The simulator is used to develop a method of comparing various demand-responsive and fixed-route systems. By predefining a set of performance indicators, such as passenger travel time and operating cost, the simulator can be used to ascertain the performance of a wide array of transportation systems. Comparing the weighted cost of each type of system permits a transportation engineer or planner to determine what type of system will provide the best results in a given area.
The simulator is extended to assist in optimization of the demand-responsive transportation system layout. A key problem that needs to be solved when implementing a demand-responsive system is to determine the size, shape, and location of the demand-responsive coverage areas, i.e., the areas in which passengers are eligible for demand-responsive transportation. Using a particle swarm optimization algorithm and the simulation-based comparison technique, the optimal size and shape for a demand-responsive coverage area can be determined.
The efficacy of the comparison and optimization techniques is demonstrated within the city of Atlanta, GA. It is shown that for certain areas of the city of Atlanta, demand-responsive transportation is more efficient than the currently implemented fixed-route system. Depending on the objective of the transportation planner, passenger satisfaction as well as operating costs can be improved by implementing a demand-responsive system in certain low-density areas.
The techniques introduced in this research, and the simulation tool developed to implement those techniques, provide a repeatable, accurate, and objective method with which to optimize and compare demand-responsive transportation systems in heterogeneous environments.
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Date Issued
2014-05-14
Extent
Resource Type
Text
Resource Subtype
Dissertation