Author ORCID Identifier
0000-0003-1286-0318
Document Type
Dissertation
Date of Award
8-31-2023
Degree Name
Doctor of Philosophy in Transportation - (Ph.D.)
Department
Civil and Environmental Engineering
First Advisor
Branislav Dimitrijevic
Second Advisor
Lazar Spasovic
Third Advisor
I-Jy Steven Chien
Fourth Advisor
Joyoung Lee
Fifth Advisor
Athanassios K. Bladikas
Abstract
The advent of autonomous urban mobility is expected to effect changes in land use patterns and urban development. A new framework for integrated land use and transportation system models that incorporate Autonomous Vehicles (AVs) and Shared Autonomous Vehicles (SAVs) will be needed to investigate the development of transportation and land use plans and policies. This study aims to analyze the impacts of AVs and SAVs on land use changes by defming scenarios that consider different shares of these emerging transportation modes. Also, this study explores the corollary effects of various land use factors by applying them to a case study area in the State of New Jersey. The data uses in the analysis consists of household attributes and zone attributes, including household travel surveys, loaded highway networks, housing unit density, socio-economic factors, home values, school scores, and crime scores. Three methods are employed for land use models, including Multinomial Logistic Regression (MNL), Agent-Based Model (ABM), and k-means clustering method with Principal Component Analysis (PCA) applied to the MNL model. The models are implemented using a household survey dataset containing records of 7,896 households in the State of New Jersey. Using a static traffic assignment, the study applies a four-step travel demand model to find the travel times and travel costs between zones. The resulting impedance matrix is used to measure the zone accessibility, including residential and job accessibilities, which are then used in the land use models. Two scenarios are introduced to test the models: (a) scenario I, which considers only Conventional Vehicles (CVs), and (b) scenario II, which considers market penetration of 50% CVs, 25% AVs, and 25% SAVs. To test the model performance, scenario I is compared to the actual conditions showing the surveyed households' locations. The results reveal that the k-means clustering method with PCA applied on MNL outperforms two other methods since homogeneity and addressing the multicollinearity of data can improve model prediction accuracy, which is 0.86. Additionally, according to the MNL results in scenario I, which only considers CVs, households with high incomes (above $150,000 annually) have a negative inclination to relocate, which may indicate that they currently reside in desirable areas. It is found that in the model, the family structure impacts the chance of relocation; specifically, single workers without children are more likely to desire to move than two or more employees in households with children. As expected, the results indicate that households prefer locations with better transit, less crime, and higher school scores, and those with fewer vehicles have a greater tolerance for relocating. Furthermore, the results from scenario I are compared to scenario II in terms of median and mean distances to the central business district (CBD). The findings show that, in scenario II, the mean and median distances to CBD are increased in all applied methods. This occurs as a result of some households moving away from CBD in search of higher utilities, like more reasonably priced housing. Moreover, the sensitivity analysis of location decisions is conducted using the best-performing land use model (k-means clustering method with PCA applied on MNL), considering the variation in AV and SAV market penetration and the variation in the perceived value of travel time when using the two autonomous vehicle modes, respectively. According to the findings, the increase in the proportion of SAVs will cause more households to move further from the CBD.
Furthermore, with market penetrations of 50% AVs and 50% SAVs, households tend to move to suburbs, with the mean distance of household locations from the CBD exceeding 36 miles. Also, the median and mean distances to the CBD increase with the reduced perceived value of travel time when using AV or SAV. This occurs due to the increased opportunity for households to utilize AVs and SAVs for longer commuting trips while residing in more attractive zones farther from the CBD. The demonstrated models can inform the development of a policy framework considering different aspects of integrated land use and AVs and SAVs implementation in transportation networks. The proposed modeling framework and the resulting plans and policies will help ensure better coordination and collaboration among relevant stakeholders in advancing regional mobility solutions, addressing infrastructure needs, and improving safety and security in anticipation of the advent of AV and SAV.
Recommended Citation
Asadi, Roksana, "Integrated land use and transportation modeling considering the impact of autonomous vehicles" (2023). Dissertations. 1861.
https://digitalcommons.njit.edu/dissertations/1861
