Document Type

Dissertation

Date of Award

Summer 8-31-2012

Degree Name

Doctor of Philosophy in Transportation - (Ph.D.)

Department

Civil and Environmental Engineering

First Advisor

RongFang Liu

Second Advisor

Athanassios K. Bladikas

Third Advisor

I-Jy Steven Chien

Fourth Advisor

Janice Rhoda Daniel

Fifth Advisor

Jian Yang

Abstract

The methodology introduced in this dissertation is to optimally find a feeder bus network in a suburban area for an existing rail system that connects the suburban area with the Central Business District (CBD). The objective is to minimize the total cost, including user and supplier costs. Three major access modes (walk, feeder bus, and auto) for the rail station are considered and the cost for all modes makes up the user cost. The supplier cost comes from the operating cost of the feeder bus network. The decision variables include the structure of the feeder bus network, service frequencies, and bus stop locations.

The developed methodology consists of four components, including a Preparation Procedure (PP), Initial Solution Generation Procedure (ISGP), Network Features Determination Procedure (NFDP) and Solution Search Procedure (SSP). PP is used to perform a preliminary processing on the input data set. An initial solution that will be used in SSP is found in ISGP. The NFDP is a module to determine the network related features such as service frequency, mode split, stop selections and locations. A logit-based Multinomial Logit-Proportional Model (MNL-PM) model is proposed to estimate the mode shares of walk, bus and auto. A metaheuristic Tabu Search (TS) method is developed to find the optimal solution for the methodology. In the computational experiments, an Exhaustive Search (ES) method is designed and tested to validate the effectiveness of the proposed methodology. The results of networks of different sizes are presented and sensitivity analyses are performed to investigate the impacts of various model parameters (e.g., fleet size, parking fee, bus fare, etc.).

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.