Abstract

Many cities around the world adopted the Bike Share Systems (BSS). BSS is an innovative low-cost non-motorized transport that is environmentally friendly and a better alternative for individual mobility. There is limited research on the topics of 1) BSS travel time prediction, and 2) evaluation of BSS network performance. This dissertation studies both topics. The first topic investigates the application of Stepwise Multiple Linear Regression (MLR), Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) to predict BSS travel time under various weather conditions. Preliminary modeling results demonstrate that temperature, distance, and fog have a significant impact on predicting trip duration. These new modeling results have potential benefits to users and system operators who want to take precautionary safety measures under adverse weather conditions. The second topic developed an innovative optimization method that uses node importance to maximize BSS locations coverage. The method implements the Maximal Covering Location Problem (MCLP) to maximize the BSS demand coverage within a service distance. Three centrality criteria had been used to optimize node importance using a Multi- Criteria Decision Method (MCDM) which is based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The TOPSIS methodology implemented in this dissertation has compared to other existing methods and outperformed their results. The developed method has been embedded in the computation of the MCLP model using the top ranked important bike stations to locate bike stations while maximizing demand coverage. By applying this optimization methodology, it will help BSS decision makers to determine if there is a need to add a bike station, or to relocate or remove an existing one.

Notes

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Graduation Date

2020

Semester

Fall

Advisor

Al-Deek, Haitham

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental and Construction Engineering

Degree Program

Civil Engineering

Format

application/pdf

Identifier

CFE0008384; DP0023821

URL

https://purls.library.ucf.edu/go/DP0023821

Language

English

Release Date

December 2025

Length of Campus-only Access

5 years

Access Status

Doctoral Dissertation (Campus-only Access)

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