research report

Do Compact, Accessible, and Walkable Communities Promote Gender Equality?

Publication Date

March 1, 2018

Associated Project

Do Compact, Accessible, and Walkable Communities Promote Gender Equality?

Author(s)

Wan-Tzu (Ashley) Lo, Doug Houston

Areas of Expertise

Safety, Public Health, & Mobility Justice Travel Behavior, Land Use, & the Built Environment

View research report

Abstract

Directing growth towards denser communities with mixed-use, accessible, and walkable neighborhood design has become an important strategy for promoting sustainability, but few studies have examined whether compact development strategies could help reduce within-household gender disparities in spatial behavior by increasing accessibility. The research team analyzed the spatial behavior of heterosexual married couples in Southern California based on the 2012 California Household Travel Survey and found that households living in areas with greater regional accessibility and neighborhood walkability have smaller, more centered, and more compact activity spaces overall compared to households in less compact areas, and that married pairs living in more accessible areas have greater equality in the size and centeredness of their activity spaces. Results support the hypothesis that compact development provides married couples greater flexibility in how they divide household out-of-home activities by making destinations more convenient. Future research and planning efforts should carefully consider which aspects of compact, accessible development are most effective for a given local context.

Related Publications

policy brief | June 1, 2019

Compact, Accessible, and Walkable Communities Help Support Gender Equality

research report

Designing a Transit-Feeder System Using Bikesharing and Peer-to-Peer Ridesharing

Publication Date

January 31, 2018

Associated Project

Designing a Transit-Feeder System Using Bikesharing and Peer-to-Peer Ridesharing

Author(s)

R. (Jay) Jayakrishnan, Michael McNally, Jiangbo (Gabe) Yu, Daisik (Danny) Nam, Dingtong Yang, Sunghi (Sunny) An

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation

View research report

Abstract

Peer-to-peer (P2P) ridesharing is a relatively new concept that aims at providing a sustainable method for transportation in urban areas. This research is on the second phase of a sequence of projects that follows the previously funded UCConnect project titled “Promoting Peer-to-Peer Ridesharing Services as Transit System Feeders”. In this phase, the study constructs a multimodal network, which includes P2P ridesharing, transit and city bike-sharing. The research develops schemes to provide travel alternatives, routes and information across multiple modes in the network. In addition, the authors develop a mobile application that demonstrates the research in the context of Los Angeles, CA, by using a combination of subway transit lines, proposed P2P ridesharing, and bikesharing to provide multi-modal itineraries to users. The Los Angeles Metro’s Red and Gold line subway rail and the downtown bike-share system are included in the network for a case study. The study includes a simulation of the operation of the combined system that provides travel alternatives during morning peak hours for multiple riders. The results indicate that a multi-modal network would expand the coverage of public transit. Rideshaiing and bike-sharing could both act as transit feeders when properly.

policy brief

Analysis of Comprehensive Multi-modal Shared Travel Systems with Transit, Rideshare, Carshare and Bikeshare Options

Publication Date

February 27, 2018

Associated Project

Analysis of Comprehensive Multi-modal Shared Travel Systems with Transit, Rideshare, Carshare and Bikeshare Options.

Author(s)

R. (Jay) Jayakrishnan

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation

View policy brief

Abstract

A primary goal of the study is to develop insights on efficiencies to be gained through the use of various shared mode travels. Further goals are to develop a mobile application that can providetrip plans across multiple modes that include several options such as shared cars, rides, bikes, and bus/rail transit, and to understand user response through limited field surveys.

Phd Dissertation

Commodity Based Freight Demand Modeling Framework using Structural Regression Model

Publication Date

January 1, 2018

Associated Project

Modeling Freight Corridor Movements Using a Mass Balance Approach

Author(s)

Yue (Ethan) Sun

Areas of Expertise

Freight, Logistics, & Supply Chain

View Phd Dissertation

Abstract

Among the main freight modeling approaches, commodity-based models stand out in their ability to incorporate all travel modes and capture the economic mechanisms driving freight movements. However, challenges still exist on the effective use of public freight data and the ability to accurately reflect the supply chain relationships between commodities. In this research, a commodity-based framework for freight demand forecasting using a Structural Regression Model (SRM) is explored, and applied to the original California Statewide Freight Forecasting Model (CSFFM) using the Freight Analysis Framework Version 4 (FAF4) data.

The framework developed in this study contains four innovative components: (1) mathematical approach for determining freight economic centroids; (2) the aggregation of commodities using the Fuzzy C-means clustering algorithm; (3) employing weighted travel distance by commodity group (CG) instead of highway skim to provide a more representative travel distance across multiple modes; and (4) the forecasting of freight demand using SRM method to comprehensively consider the direct effect, indirect effect and latent variables. The SRM is adopted in both the total generation model and domestic direct demand model. The application results are further compared with the original CSFFM forecasts in 2012 to illustrate the advantages of the proposed framework.

Phd Dissertation

Unraveling the Effects of Land Use Planning and Energy Policy on Travel Behavior

Publication Date

September 14, 2017

Author(s)

Harya Dillon

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

View Phd Dissertation

Abstract

This three-essay dissertation focuses on understanding linkages between urban form, travel behavior, ownership of alternative fuel vehicles, active commuting, congestion, fuel consumption, and air pollution (including greenhouse gas emissions). These essays estimated different specifications of Generalized Structural Equation Models (GSEM) to explicitly account for residential self-selection and vehicle choice endogeneities.

The first essay analyzes the influence of land use policies and gasoline prices on driving patterns. I estimated a Generalized Structural Equation Model (GSEM) with a Tobit-link specification on a Southern California subsample of the 2009 National Household Travel Survey (NHTS). These data haves a quasi-experimental nature thanks to large exogenous variation in gasoline price during the survey period. I analyzed separately home-based work trips and non-work trips under the hypothesis that households have more flexibility to adjust their non-work trips when gasoline prices change, whereas most of the literature does not take trip purpose into account. To measure urban form, which is treated as a latent construct, I used fine-grained geospatial information including population density, land use mix, employment density, distance to employment centers and transit availability. I found that, in the short run, households drive 0.171% less for non-work trips when gasoline prices increase by 1%, while work trips are not responsive to gasoline price changes. This suggests that, in the short term, higher fuel prices reduce discretionary driving such as shopping and recreational trips, but they do not affect non-discretionary driving such as commuting trips. My results also suggest that policies that seek to increase transit service and housing opportunities near employment centers will reduce driving.

The second essay investigates the impact of government incentives such as access exemption to High Occupancy Vehicle (HOV) lanes and parking privileges on household ownership of Alternative Fuel Vehicles (AFVs) using Generalized Structural Equation Models (GSEM), and accounts for residential self-selection, household demographics and ambient political-environmentalism. I analyzed geocoded travel diary data from the 2012 California Household Travel Survey (CHTS), linked with fueling station data from the US Department of Energy Alternative Fuels Data Center and precinct level election data from the UC Berkeley Statewide Database. My findings suggest that, on average, households with alternative fuel vehicles drive approximately 10 miles more on weekdays and about 0.5 miles more on non-discretionary trips than otherwise similar households. In addition, households who live closer to a freeway with HOV lanes, work closer to an AFV charging facility (that provides free parking), and are likely supportive of pro-environmental measures are more likely to own alternative fuel vehicles.

The third essay examines the influence of urban form on transit use and non-motorized travel (NMT, including biking and walking) for households (with at least one employed adult) in Los Angeles and Orange Counties in California based on 2009 National Household Travel Survey (NHTS) data. The objectives of the research are (1) to assess several methods for measuring urban form features in the near-residence and near-workplace environments and (2) to assess the importance of these urban form features on transit use and NMT after accounting for the influence of these features on household vehicle ownership and residential selection. Results provide insights into the relative influence of several specifications of population density, transit access and walkability measures on transit use and NMT for commute and non-work trips. Reduced form models suggest that the dominant determinant of discretionary travel is household socio-demographic status. In terms of residential selection, lower income, younger, and smaller households are more likely to choose a dense, pedestrian friendly, and transit rich neighborhood. In terms of vehicle ownership, households living in high density, pedestrian friendly, and transit rich neighborhoods are less likely to own vehicles. After accounting for the influence of urban form on vehicle ownership and residential selection, workplace transit accessibility has greater influence on transit commuting than transit access near a household’s residence. Results vary by how urban form is specified and by the source of travel data. Finally, there is some evidence that population density affects active travel for discretionary purposes.

Phd Dissertation

Peer-to-peer and Collaborative Consumption of Supply in Transportation Systems

Publication Date

August 14, 2017

Author(s)

Roger Lloret-Batlle

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation

View Phd Dissertation

Abstract

Transportation systems have been traditionally operated on a First-Come-First-Served (FCFS) fashion. FCFS consumption of supply occurs because it is accepted as a natural paradigm when the operators have no individual-specific information that allows consideration of any other serving order, and when users are assumed not to communicate among themselves. Thus, FCFS behaves as a status quo policy that is generally considered as fair, since it is presumed that all users are treated equally. We know though, that there exists heterogeneity in users’ valuation of time and delay savings, and that the values may be different in different situations even for the same user. Taking advantage of smartphones and connected vehicle environments, it is now possible to include this user heterogeneity into operations in order to increase overall system efficiency and fairness, where efficiency refers to satisfaction of users. There are then possibilities of accomplishing this through exchanges among users with appropriate pricing, which can be determined by the users themselves to their satisfaction, so as to determine the order and extent of the utilization of supply. This new operational paradigm leads to collaborative consumption of supply.

This dissertation explores the idea of violating FCFS by allowing users to trade in real-time the part of supply that they effectively “own” while they are in a transportation system. This de-facto ownership originates from the space-time region which each user rightfully controls, either due to their physical presence or due to reservations such as after purchasing a future trip from an operator. Attempting to answer the question of what pricing scheme would be fair and acceptable, leads this dissertation to introduce for the first time in transportation literature, the fundamental economic concept of envy-freeness. It can be taken as a pricing scheme as well as a user-behavior model. A resource allocation is said to be envy-free, when no agent feels any other agent’s allocation to be better than their own, at the current price. An extension called dynamic envy-freeness is then developed for use in the domain of dynamic problems that the transportation field invariable pose, and a new family of envy-minimizing criteria are developed, namely the Constant Elasticity of Substitution Envy Intensity (CESEI) criteria, which strongly fits into the existing axiomatic body of Welfare Economics.

Several applications of collaborative consumption that breaks FCFS ordering are explored in this dissertation. First, the dissertation develops PEXIC, Priced EXchanges in Intersection Control, in which users can pay other users to reduce their waiting delays in a fair manner. This system is shown to be Pareto-efficient, envy minimizing and financially self-sustainable. Second, it studies new operational policies in highway control: parallel queue routing policies for bottleneck situations where the vehicles’ lane-queue selections are the results of trades, and queue-jumping operations for exit lanes where vehicles can take forward spots in a queue by paying the overtaken vehicles in a fair fashion that achieves queue stability. Third, it proposes Peer-to-peer (P2P) ride exchange in ridesharing systems, in which trip property rights are transferred to users in such a way that they can trade their rides between each other. Finally, the dissertation models a P2P ridesharing system as a dual role market exchange economy, introducing a truthful pricing scheme which includes High-Occupancy-Vehicle (HOV) lane savings and uses a novel min-cost max flow formulation that guarantees users a ride-back, a complementarity in preferences never explored before.

The research does not attempt any elaborate examination of the social equity implications of such exchange-based systems with non-FCFS operations, but identifies some of such key issues and presents pointers for further study. It does not purport to take an advocacy position on transforming the transportation system operations to the newer paradigms, nor does it examine all the regulatory complications. The research does, however, demonstrate through modeling and analysis results from a variety of applications, that better system efficiency and user satisfaction can be achieved with the use of the proposed paradigms.

Phd Dissertation

Essays in Transportation and Environmental Economics

Publication Date

September 14, 2017

Author(s)

Debrapriya Chakraborty

Areas of Expertise

Transportation Economics, Funding, & Finance

View Phd Dissertation

Abstract

This thesis uses the tools of applied econometrics to study the impact of economic incentives on household welfare and decision-making and the environmental outcome of urban transportation policies in the U.S. and in developing countries.

Transportation is an essential component of day-to-day life. An extensive transportation system offers mobility, expanding individuals’ access to employment opportunities, agglomeration benefits to firms and employees, reduced trade costs, and an overall increase in productivity. The positive effects of an efficient transportation network in an economy are often accompanied by rising motorization rates. This, in turn, can lead to air pollution, road congestion, and increasing dependence on fossil fuels. In the past few decades, climate change concerns have made policymakers and governments agencies in both developed and developing countries incentivize improvement in fuel economy of vehicles as well as promote alternative fuel vehicles.

Alternative fuel vehicles currently arriving in the market offer better driving performance compared to their predecessors, and their market penetration is higher than before. However, most people still do not consider these alternative fuel vehicles as a substitute of traditional gasoline cars. Incentives offered to consumers to promote adoption have achieved varied results. The first chapter of the dissertation studies the stated vehicle transaction decisions of 3,154 survey respondents located in the state of California. While the effectiveness of policy incentives like tax credits and rebates is found to be more universal, the effect of High Occupancy Vehicle (HOV) lane permit or free parking benefit on adoption decision depends on the likelihood of the household being able to use the benefits. In addition, familiarity with an alternative fuel technology is found to be positively correlated with the preference for electric battery or hydrogen fuel cell vehicles. Prior ownership of a hybrid vehicle made the household more likely to purchase an alternative fuel vehicle in the future. This persistence in choice behavior can be attributed to heterogeneity among vehicle purchasers or considered as a sign of positive experience. Experience can reduce skepticism about alternative fuel vehicles and induce future adoption. Accounting for the number of years of ownership of alternative fuel vehicles, the results show that more experience has a positive effect on the probability of repurchase of the same or a newer technology vehicle. This result contributes towards a long standing debate of whether the incentives work only as a marketing mechanism or does it have any long term benefits. The positive correlation in preference pattern and the willingness to pay measures indicate that even if the price-based incentives work as a marketing mechanism they play an important role in initiating potential state dependence in purchase behavior to improve adoption in the long run.

In recent years, emerging economies like India and China have been experiencing the externalities related to increased motorization. Urbanization accompanied with increasing per capita income has led to a rise in private automobile demand. Historically, the infrastructure of major metropolitans in these emerging economies was not designed to support a sudden rise in the use of automobiles. As a result, a majority of these metropolitans suffer from congestion and pollution from greenhouse gas (GHG) emissions. The local government and policymakers in these economies are considering a variety of policies like to scrap old polluting vehicles, impose fuel standards, cordon tolls, and driving restrictions to address these issues. Driving restrictions has been implemented in several metropolitan cities in emerging economies like Beijing, China, Santiago, Chile, Mexico City, Mexico, S~{a}o Paulo, Brazil, Bogot

research report

New Methods for Monitoring Spatial Truck Travel Patterns in California Using Existing Detector Infrastructure

Publication Date

August 1, 2017

Associated Project

Network-wide Truck Tracking and Weight Estimation

Author(s)

Andre (Yeow Chern) Tok, Kyung (Kate) Hyun, Stephen Ritchie

Areas of Expertise

Freight, Logistics, & Supply Chain

View research report

Abstract

This study developed a methodology to accurately estimate network-wide truck flows by leveraging existing point detection infrastructure, namely inductive loop detectors. The tracking model identifies individual trucks at detector locations using advanced inductive signatures and matches vehicle pairs at detector locations, using an extended form of the Bayesian classification model to estimate matching and non-matching probabilities of the vehicle pairs Several vehicle feature selection and weighting methods including Self Organizing Map and K-means clustering were applied to better identify individual vehicles from signature data. It was shown that the proposed extensive feature processing enhanced vehicle identification performance even among vehicle pools sharing similar physical configurations. The developed model was tested along an approximately 5.5-mile freeway segment on I-5 and CA-78 in San Diego, California where only 67 percent of the total trucks were observed at both up- and down-stream detector sites. Results showed balanced performances in exactness and completeness of matching with 91 percent of correct outcomes for multi-unit trucks

Related Publications

policy brief | October 1, 2019

New Tool from UC Irvine Could Save the State Millions while Providing Better Data on Truck Activity in California

research report

New Methods for Monitoring Spatial Truck Travel Patterns in California Using Exisiting Dectector Infrastructure

Publication Date

August 1, 2017

Associated Project

New Methods for Monitoring Spatial Truck Travel Patterns in California Using Existing Detector Infrastructure

Author(s)

Stephen Ritchie, Kyung (Kate) Hyun, Andre (Yeow Chern) Tok

Areas of Expertise

Freight, Logistics, & Supply Chain

View research report

Abstract

This study developed a methodology to accurately estimate network-wide truck flows by leveraging existing point detection infrastructure, namely inductive loop detectors. The tracking model identifies individual trucks at detector locations using advanced inductive signatures and matches vehicle pairs at detector locations, using an extended form of the Bayesian classification model to estimate matching and non-matching probabilities of the vehicle pairs Several vehicle feature selection and weighting methods including Self Organizing Map and K-means clustering were applied to better identify individual vehicles from signature data. It was shown that the proposed extensive feature processing enhanced vehicle identification performance even among vehicle pools sharing similar physical configurations. The developed model was tested along an approximately 5.5-mile freeway segment on I-5 and CA-78 in San Diego, California where only 67 percent of the total trucks were observed at both up- and down-stream detector sites. Results showed balanced performances in exactness and completeness of matching with 91 percent of correct outcomes for multi-unit trucks

Related Publications

policy brief | October 1, 2019

New Tool from UC Irvine Could Save the State Millions while Providing Better Data on Truck Activity in California

MS Thesis

Supply-demand forecasting for a ride-hailing system

Publication Date

June 29, 2017

Author(s)

Runyi Wang

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation Travel Behavior, Land Use, & the Built Environment

View MS Thesis

Abstract

Ride-hailing or Transportation Network Companies (TNCs) such as Uber, Lyft and Didi Chuxing are gaining increasing market share and importance in many transportation markets. To estimate the efficiency of these systems and to help them meet the needs of riders, big data technologies and algorithms should be used to process the massive amounts of data available to improve service reliability. The model developed predicts the gap between rider demands and driver supply in a given time period and specific geographic area using data from Didi Chuxing, the dominant ride-hailing company in China. The data provided includes car sharing orders, point of interest (POI), traffic, and weather information. A passenger calls a ride (makes a request) by entering the place of origin and destination and clicking “Request Pickup” on the Didi phone based application. A driver answers the request by taking the order. Our training data set contains three consecutive weeks of data in 2016, for large Chinese city which is referred to as City M. Though the training set is relatively small when compared to the whole of Didi’s ride sharing market, it is large enough so that patterns can be discovered and generalized. These data were made available to researchers and entrepreneurs by Didi after removal of some identifying information.