MS Thesis

Feedback with Alternate Trip Assignment Approaches in the Four-Step Model

Publication Date

March 30, 2014

Author(s)

Abstract

The traditional Four-Step Model is widely used in the Transportation Planning and Forecasting Process. However, the model itself has a structural defect: it is only equilibrium in terms of Trip Assignment. Thus it is often viewed as an inadequate, partial equilibrium model. To achieve an overall equilibrium in the Transportation Planning and Forecasting Process, a feedback process can be introduced into the Four-Step Model. The objective of this research is to incorporate different feedback methods into the traditional Four-Step Modeling process to improve model performance. The specific approach herein is to examine the relative performance of direct and averaging feedback methods, and then to investigate the convergence of this two approaches by replacing User Equilibrium trip assignment with All-or-Nothing trip assignment during each feedback loop, but not in the original four step model application. An evaluation and comparison of these methods is presented. Two measures of effectiveness, the Root-Mean-Square Error (RMSE) and Total Vehicle Hours Traveled (VHT), are employed to test the convergence and evaluate the methods.

MS Thesis

Comparison of alternate feedback methods for the four-step model

Publication Date

March 30, 2014

Author(s)

Abstract

The purpose of this thesis is to study the feasibility and effectiveness of different feedback methods as applied to Miasma Beach Project. There are two methods of feedback, direct and averaging link volumes via MSA, each with and without averaging of OD volumes, totally is four methods, are introduced to the Four-Step Model based on Miasma Beach Project. Also, the RMSE of link flow and OD matrix, and VHT value are in use to measure the result of each feedback solution. After compared the result and confirmed the “true solution” of the model, a short conclusion of is drawn. It’s very clear that any approach to feedback makes significant corrections to the FSM result, the model consistency is much improved

Phd Dissertation

ReMuLAA - A new algorithm for the route choice problem

Publication Date

March 15, 2014

Author(s)

Abstract

A new framework for analyzing the choice set formation for route choice models in transportation networks is presented and an algorithm is proposed. The algorithm is tested against a sample of GPS data for heavy trucks for the State of California. The results are presented in detail along with an analysis of both their qualitative and quantitative merits. A new algorithm for the route choice problem is also presented and its results analyzed against the state of the practice and state of the art. This new algorithm, ReMuLAA, is also the first known closed solution algorithm for the route choice problem using the Multinomial Logit Model (MNL) for an entire class of networks (Directed Acyclic Networks) without explicit route enumeration. A correction for the MNL model to account for route overlapping is also presented and the results are compared with other state-of-the-art route choice algorithms. The results of the application of ReMuLAA in a real world model are also presented and its advantages discussed.

research report

Quantifying the Effect of Local Government Actions on VMT

Publication Date

February 14, 2014

Author(s)

Deborah Salon, Marlon Boarnet, Patricia (Pat) Mokhtarian

Abstract

This research uses empirical analysis of travel survey data to quantify how much Californians will change the amount that they drive in response to changes in land use and transport system variables. The study improves upon past research in three key ways. First, a dataset comprising merged information from five California-based household travel surveys was assembled. Second, a novel approach to control for residential self-selection was developed. Third, understanding heterogeneity in effects of variables on vehicle miles of travel (VMT) across two important dimensions – neighborhood type and trip type — was a focus. The effects of some land use and transport system characteristics do depend on neighborhood type, in ways that are intuitive but had not previously been empirically verified. Results of this research are embedded in the VMT Impact spreadsheet tool, which allows users to easily see the implications of this work for any census tract, city, or region in California.

published journal article

A vehicle ownership and utilization choice model with endogenous residential density

Abstract

This paper explores the impact of residential density on households’ vehicle type and usage choices using the 2001 National Household Travel Survey (NHTS). Attempts to quantify the effect of urban form on households’ vehicle choice and utilization often encounter the problem of sample selectivity. Household characteristics that are unobservable to the researchers might determine simultaneously where to live, what vehicles to choose, and how much to drive them. Unless this simultaneity is modeled, any relationship between residential density and vehicle choice may be biased. This paper extends the Bayesian multivariate ordered probit and tobit model developed in Fang (2008) to treat local residential density as endogenous. The model includes equations for vehicle ownership and usage in terms of number of cars, number of trucks (vans, sports utility vehicles, and pickup trucks), miles traveled by cars, and miles traveled by trucks. We carry out policy simulations that show that an increase in residential density has a negligible effect on car choice and utilization, but slightly reduces truck choice and utilization. The largest impact we find is a -.4 arc elasticity of truck fuel use with respect to density. We also perform an out-of-sample forecast using a holdout sample to test the robustness of the model.

Accessibility, Affordability, and the Allocation of Housing Targets to California’s Local Governments

Status

Complete

Project Timeline

October 4, 2019 - October 3, 2020

Principal Investigator

Department(s)

Urban Planning and Public Policy

Project Summary

California’s Housing Element law requires the allocation of housing targets to local governments. These targets should align with long-range regional strategies to concentrate growth in transit rich areas, but little evidence exists about the effectiveness of housing allocation schemes for achieving accessibility and affordability. Indeed, there is some evidence that – to date – the law has not served these goals effectively. In 2018 California Senate Bill (SB) 828 significantly amended the Housing Element law, conferring additional authority on the California Department of Housing & Community Development (HCD) to determine housing targets. Moreover, SB 50 (introduced in 2019) proposes to require HCD to identify “jobs-rich” areas, in which local governments would be required to allow relatively dense residential development. SB 50 proposes to also raise the minimum allowable density for residential development in areas close to transit stops. State legislators and administrators have very little information to evaluate the current housing target allocation process or its interaction with the regulatory scheme contemplated by SB 50. This project would fill that gap, providing guidance for linking housing and transportation policy. This project will provide decision support for state legislators and administrators by: 1) comparing California’s housing target allocation and implementation methods to methods currently used in other states, as well as methods described in the scholarly literature; 2) comparing the possible effects of different allocation and implementation methods on job accessibility at different levels of housing affordability; and 3) identifying ways that future legislation and implementation could promote the goals of the Housing Element law related to accessibility.

Understanding the Impact of Housing Costs in California on Commute Length in Terms of Travel Time and Distance

Status

Complete

Project Timeline

October 6, 2019 - October 5, 2020

Principal Investigator

Department(s)

Civil and Environmental Engineering

Project Summary

Concerns about the environmental impacts of transportation have turned reducing vehicle-miles traveled (VMT) into a policy priority. One way to decrease VMT is to reduce the length of commuting trips. Unfortunately, the average U.S. commute keeps getting longer. Prior research has investigated the determinants of commuting, but few have analyzed the linkage between housing costs and the length of commuting. This problem is especially salient in California given the state’s perennial housing shortage and the high costs of housing, which have forced many lower- and middle-class households to move inland in search of more affordable housing at the cost of longer commutes. Most of those commuting trips are by car. This project investigates these linkages using Generalized Structural Equation Modeling and analyzing 2012 CHTS data for Los Angeles County – the most populous county in the U.S. The model, which jointly explains commuting distance and time, accounts for residential self-selection and car use endogeneity, while controlling for household characteristics and land use around residences and workplaces. Better understanding the determinants of commuting is critical to inform housing and transportation policy, improve the health of commuters, reduce air pollution, and achieve climate goals.