Phd Dissertation

Deployment of Fuel Cell Electric Buses in Transit Agencies : Hydrogen Demand Allocation and Preferable Hydrogen Infrastructure Rollout Scenarios

Publication Date

December 31, 2018

Author(s)

Analy Munoz

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation Zero-Emission Vehicles & Low-Carbon Fuels

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Abstract

Aiming to reduce criteria air pollutant and greenhouse gas emissions, several initiatives have been announced throughout the world to incorporate zero emission buses into public transit agencies within the next 15 years. One example is the California Air Resources Board “Innovative Clean Transit Regulation” with the goal to transform the statewide transit bus fleet by 2040 with zero emission buses. In response, transit authorities face decisions between multiple bus technologies, each with different strengths and weaknesses as well as infrastructure requirements. Furthermore, because the performance of new bus technologies depends on the operating conditions of each transit agency, the results from demonstration projects are not typically applicable to another district.

This dissertation addresses the use of Life Cycle Assessment (LCA) to compare different zero-emission bus (ZEB) technologies for transit districts in the State of California. For LCAs conducted to date, the focus has been on one-on-one bus technology comparisons rather than a combination of bus technologies integrated into bus fleets (mixed fleet). This dissertation extends the traditional LCA approach by using Multi-Objective Linear Programming (MOLP) to identify the optimal ZEB technology mix.

The novelty of this extended LCA is the use of a consistent framework across multiple powertrain types with the same operating conditions. The fleet optimization incorporates essential aspects of a fleet operation such as operational constraints, route length, required infrastructure, and cost. Additionally, a Multi-Criteria Decision Analysis (MCDA) is incorporated to evaluate parameter weighting in the optimization problem, thereby creating an optimization solution that considers real constraints and priorities from stakeholders, users, and regulatory agencies.

The combination of these capabilities (LCA, MOLP, and MCDA) provides a comprehensive tool, including a variety of energy supply chains, which can inform transit agencies in the design of an electric bus fleet comprised by a mix of available and emerging ZEB technologies.

published journal article

Use of Ride-Hailing Services among Older Adults in the United States

Publication Date

March 13, 2019

Associated Project

Mobility of Older Adults in California: In the Era of Shared Mobility

Author(s)

Suman Mitra, Youngeun Bae, Stephen Ritchie

Areas of Expertise

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

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Abstract

This paper presents an analysis of data from the 2017 National Household Travel Survey to examine the factors influencing the adoption and the frequency of use of on-demand ride-hailing services such as Uber and Lyft among older adults. Using a zero-inflated negative binomial model (ZINB), the results indicate that the determinants of adoption of on-demand ride-hailing services (users versus non-users) are different from the determinants of the frequency of use of these services among older adult users. Seniors who are younger, living alone, in urban dwellings, more highly educated, more affluent, or male with a medical condition that results in asking others for rides are more likely to be adopters of ride-hailing services. However, seniors who are middle elderly, less educated, or are carless older adults, are more likely to be frequent users of on-demand ride-hailing services as long as they adopt these services. In addition, smartphone possession plays an important role in the adoption behavior of on-demand ride-hailing services among older adults. Results of bivariate analysis showed that older adult ride-hailing users make more transit trips than their non-user counterparts, suggesting that ride-hailing services have the potential to serve as a complementary form of public transportation for older adults. The findings of this research will help ride-hailing operators in identifying potential market segments of their services and in developing campaign strategies for potential adopters.

Related Publications

published journal article | February 03, 2025

Gender differences in elderly mobility in the United States

research report

Transportation Plans: Their Informational Content and Use Patterns in Southern California

Publication Date

November 1, 2018

Associated Project

Transportation Plans: Their Informational Content and Use Patterns in Southern California

Author(s)

Jae Hong Kim, Tanjeeb Ahmed, Victor Paitimusa

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

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Abstract

While a large amount of effort has been devoted to making and updating local transportation plans, little is known about the informational contents of these plans and their use patterns. This project attempted to identify key informational contents of Californian cities’ transportation plans and to investigate how various stakeholders can use the plan contents through (i) a plan content analysis of a sample of general plans (recently adopted by eight municipalities in Orange County, California) and (ii) a plan use survey and follow-up analysis of survey responses. All plans that were analyzed were found to convey a variety of information about their visions, goals, policies, and implementation strategies, but the plan content analysis revealed substantial variation in the way cities composed their general plans and integrated them with other plans/players. Compared to land use elements, circulation elements tended to focus more on their connections with other agencies (external consistency) than on internal consistency. The plan use survey yielded a low response rate which may indicate limited use of plans in the field. However, a majority of the survey responses were positive about the usefulness and usability of general plans. In particular, the survey participants reported that they found the plans comprehensive, visionary, and well-organized, while relatively lower scores were obtained for two evaluation criteria: ‘[the plan] clearly explains what actions will be taken and when’ and ‘[the plan] is relevant to my everyday life and/or work’. Furthermore, some respondents reported that they used general plans not for their professional duties but for other (non-conventional) purposes, suggesting that plan contents could be used for a variety of decision-making processes.

Related Publications

policy brief | February 06, 2025

Transportation Plans: Their Informational Content and Use Patterns in Southern California

policy brief | February 03, 2025

General Plan Content Related to Transportation and Land Use Varies Significantly Across Cities in Orange County

Phd Dissertation

Electrification, Connectivity, & Active Demand Management: Addressing the traffic, health, and EJ impacts of drayage trucks in Southern California

Publication Date

October 13, 2018

Associated Project

Simulation of Zero-Emissions Self-Driving Drayage Trucks in a Busy Freight Corridor

Author(s)

Monica Ramirez-Ibarra

Areas of Expertise

Freight, Logistics, & Supply Chain Zero-Emission Vehicles & Low-Carbon Fuels

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Abstract

Trucking electrification combined with connected and automated technologies promises to cut the cost of freight transportation, reduce its environmental footprint, and make roads safer. If electric trucks are powerful enough to cease behaving as moving bottlenecks, they could also increase road capacity and reduce the demand for new infrastructure, a consequence that has so far been overlooked by the literature. In this dissertation, I study the traffic and infrastructure demand impacts of electrifying and connecting (via cooperative adaptive cruise control, CACC) heavy-duty drayage trucks (HDDTs) that serve the San Pedro Bay Ports (SPBP; the ports of Los Angeles and Long Beach, which is the largest port complex in the U.S), quantify the resulting health, environmental, and Environmental Justice impacts, and explore how to maximize the benefits of connected vehicles with active demand management.In Chapter 2, I explore the potential traffic and infrastructure implications of replacing conventional HDDTs that serve the SPBP with electric and/or connected HDDTs. I rely on microscopic simulation on a freeway and arterial network centered on I-710, the country’s most important economic artery. My results show that 1,000-hp electric/hydrogen trucks can be a substitute for additional road capacity. Accounting for the traffic impacts of new vehicle technologies is critical in infrastructure planning, and my results suggest shifting funding from building new capacity to financing zero-emission (ZE) 1,000 hp HDDTs until the market for these vehicles has matured. In Chapter 3, I quantify the health and GHG reduction benefits of replacing the HDDTs serving the SPBP with ZE-HDDTs. I simulate ZE-HDDTs on a regional freeway network to analyze their PM2.5 and CO2 emissions in 2012 and 2035 using MOVES3 emission factors. I then estimate their contribution to PM2.5 concentrations with InMAP and health impacts with BenMAP. I find that despite technology improvements and air quality regulations, SPBP HDDTs would still cause 106 premature deaths (valued at $1.3 billion in $2022) and 2,142 asthma attacks (over two thirds of which would accrue to disadvantaged communities) in 2035 due to population and drayage traffic growth, not to mention at least $220 million in climate costs. With ZE-HDDTs becoming attractive in the next few years from a total cost of ownership point-of-view, the main cost of achieving ZE road drayage is a scrappage program for non-ZE-HDDTs. My results justify implementing this program by 2035.In Chapter 4, I study the performance impacts of lane management strategies implemented on I-710 to support the deployment of CACC-enabled vehicles and their potential to absorb the 2035 projected growth in cargo demand at the SPBP. I find that a designated lane for CACC-enabled vehicles can decrease congestion by creating more platooning opportunities, thus maximizing CACC benefits.

Related Publications

policy brief | February 21, 2025

What are the Public Health and Environmental Implications of Drayage Truck Electrification Targets in California?

published journal article | February 03, 2025

Health and equity impacts from electrifying drayage trucks

Phd Dissertation

Automated Identification of Near-Stationary Traffic States and Calibration of Unifiable Multi-Lane Multi-Class Fundamental Diagrams

Publication Date

September 14, 2018

Author(s)

Qinglong (Louis) Yan

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience Intelligent Transportation Systems, Emerging Technologies, & Big Data

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Abstract

Experience of daily commuters shows that stationary traffic patterns can be observed during peak periods in urban freeway networks. Such stationary states play an important role in various traffic flow studies. Conceptually, studies on the impact of capacity drop and design of traffic control strategies have been built on the assumption of stationarity. Mathematically, the existence and stability of stationary states in general road networks have been proved. Empirically, near-stationary states have been utilized for calibration of fundamental diagrams and investigation of traffic features at freeway bottlenecks. Therefore, an imperative need for real-world near-stationary data has been realized to better understand, investigate, and explore such above studies. However, there lacks an efficient method to identify near-stationary states.

To fill the gap, in this research, an automated method has been developed to efficiently identify near-stationary states from large amounts of inductive loop-detector data. The method consists of four steps: first, a data pre-processing technique is performed to select healthy datasets, fill in missing values, and normalize vehicle counts and occupancies; second, a PELT changepoint detection method is adopted to detect changes in means and partition time series into candidate intervals; third, informative characteristics of each candidate, including duration and gap, are defined and calculated; finally, near-stationary states are selected from candidates through duration and gap criteria.

A game theory approach is further designed to directly calibrate parameters of the above method. First, a multi-objective optimization problem is formulated to consider the quantity and quality of near-stationary states as the objective functions. Then the problem is converted into a non-cooperative game with at least one Nash equilibrium. To solve the game and obtain a unique solution, an alternated hill-climbing search algorithm is developed.

Furthermore, two calibration schemes for multi-lane and multi-class fundamental diagrams are respectively designed by utilizing near-stationary states. Such multi-commodity fundamental diagrams possess unifiable and non-FIFO properties and can capture interaction among different commodities. Calibration and validation results show that both the calibrated unifiable multi-lane and multi-class fundamental diagrams are well-fitted, physically meaningful, and have robust performance on the estimation of commodity flow-rates.

MS Thesis

A Direct Demand Model for Commuter Rail Ridership in the San Francisco Bay Area

Publication Date

August 30, 2018

Author(s)

Jennifer Kwong

Areas of Expertise

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

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Abstract

This thesis documents the development of a direct travel demand model for commuter rail in the San Francisco Bay Area. A direct demand model simultaneously estimates trip generation and attraction, which for this thesis would be trips between an origin-destination pair of stations. In the model, the number of trips assigned to an origin-destination pair of stations is dependent on land use characteristics at the origin and destination stations in combination with travel time on the network during congested peak periods and via transit. The model uses a multiplicative direct demand model to estimate ordinary least square regression parameters for the origin-destination trips. From the model form, the resultant estimated regression parameters are elasticities, and as such, can be used to postulate the effects of the selected land use characteristics and network travel times upon the number of trips made. At both the origin and destination, the location of the station within the central business districts of the San Francisco Bay region had the largest effect on trip generation and attraction. Higher employment density at the destination and a larger number of workers per household at the origin had a positive effect on trips, while the total number of industrial workers at the destination and an increased number of two car households had a negative effect on trips. Longer travel times on transit appeared to have a positive effect on trips, yet longer travel times in congested peak periods appeared to have a negative effect on trips.

Phd Dissertation

Transportation network companies’ (tnc) impacts and potential on airport access

Publication Date

August 9, 2018

Author(s)

Karina Hermawan

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation

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Abstract

When Transportation Network Company (TNC) services first emerged, there was extensive discussion in the popular press and among academics about the benefits that these “shared” services would bring. TNC as a form of ground transportation to and from the airport in contrast, is less often studied or permitted. TNC operations at airports are highly controversial. At Los Angeles International Airport for example, Uber and Lyft could not conduct pickups until about seven years after they were founded. Still, research on both airports and TNCs rarely intersect. This dissertation aims to fill the gap in the literature and address such questions as: which and how many airports have various types of TNC service (standard, pooled)? How do they impact other modes, vehicle-occupancy, congestion, and access at airports? Can their service be modified (i.e. through pricing or service improvement) to encourage higher uses of shared modes? Using Uber and Lyft websites, it documents all airports in the U.S. and internationally that permit TNC service and the type of services available. It analyzes airport passenger surveys to evaluate how much TNC replaces and complements transit and the net effects at several airports. Also using the passenger survey, Google Maps Directions API, and other sources, it estimates travel time and costs of the different modes to the airport, builds a discrete choice model of the access mode choices, and simulates various scenarios; some of the scenarios are a TNC price increase (to match the cost of taxis) or a price cut and travel time increase (to mimic Uber Pool and Lyft line which are carpool versions of TNCs). Finally, it assesses how a pooled TNC service to the airport would operate. We apply the pick-up and delivery problem to airport access requests (formed based on the airport passenger survey) and measure the number of private trips that would be eliminated when passengers are pooled. The motivation for understanding the consequences of making private TNCs more expensive, or pooled TNCs less expensive and more efficient (with shorter detours or travel time) is to identify effective tools to encourage modal shifts to vehicles with higher occupancy.

Phd Dissertation

Impacts of Capacity Drop on Freeway Control

Publication Date

July 25, 2018

Author(s)

Felipe De Souza

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience

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Abstract

An unfortunate feature of freeway traffic flow at merge bottlenecks is the capacity drop (CD) phenomenon. It refers to a drop in the bottleneck outflow when a queue forms upstream to that bottleneck compared to the outflow observed before the formation of the queue. While its causes and exact mechanism are still open questions, this research concerns in the impacts of CD and how to mitigate them.

The distinct features of CD in a freeway corridor are assessed based on the behavior of equilibrium states in a model capable of replicating CD. The impacts are unveiled by comparing the system properties with and without the CD. The main finding is that the highest outflow occurs under uncongested equilibrium; however, it may not be reachable depending on the demands and initial conditions.

The local ramp metering control is investigated into more details. CD imposes a hysteresis on the system response with respect to the demand level. Also, we analyze the system in closed loop considering ALINEA, a well-known control algorithm. We establish the stability range with respect to parameters which is a necessary requirement for the controller to be effective. Further, we propose an extension of ALINEA to enlarge the stability range mitigating a performance loss that occurs when the on-ramp and the bottleneck are far apart.

Essential aspects of ramp metering are better captured with microscopic models; however, there were few evidences that such models can replicates CD. To that end, we propose a parameter calibration procedure that ensures the underlying model properly captures CD. The approach is tested with loop detector data from a merge bottleneck in which the CD is consistently observed.

All results with different approaches point to the direction that the existence of CD imposes additional challenges on the system control. Fortunately, in most cases the effects of CD can be mitigated with a properly designed control strategy, such as the ones tested and proposed in this research.

MS Thesis

Analysis of health impacts resulting from truck and rail emissions reductions attained through the san pedro bay ports clean air action plan programs

Publication Date

June 29, 2018

Author(s)

Tammie Kuo

Areas of Expertise

Freight, Logistics, & Supply Chain Safety, Public Health, & Mobility Justice

View MS Thesis

Abstract

Various policies have been implemented to deal with the air pollution generated by freight operations at the Ports of Los Angeles and Long Beach (also known as the San Pedro Bay Ports, or SPBP), including mandating cleaner vehicles and cleaner fuels, shifting container transport from trucks to trains for long distance travel, or shifting freight deliveries from peak to off-peak hours. The purpose of this thesis is to analyze the co-benefits of some of these policies on the reduction of regional pollutants, and on human health. Port-related emissions of nitrogen oxides (NOx) and particulate matter (PM2.5) are dispersed throughout the surrounding area using CALPUFF, and the resulting pollutant concentrations are compared between milestone years for the policies and a baseline year, 2005, using EPA’s BenMAP health analysis model. Results show that within the SPBP boundaries, the Clean Trucks program has cut heavy duty vehicle NOx emissions by 65-80% between 2005 and 2012, and PM2.5 levels have been reduced by over 95%. This has resulted in net annual savings related to cardiovascular and respiratory impacts of over $9 million. The Rail Line-Haul and Switcher Fleet Modernization program has achieved lower pollutant reductions, around 50% for NOx and 45% for PM2.5, but the broader range of this program’s impacts has resulted in even higher net savings of over $100 million between 2005 and 2012. These examples indicate that the Clean Air Action Plan has made a positive impact on quality of life for residents in the Los Angeles area.

Phd Dissertation

Driver Response to Variable Message Signs in a 2D Multiplayer Real-time Driving Simulator

Publication Date

June 29, 2018

Author(s)

Si-Yuan Kong

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience Travel Behavior, Land Use, & the Built Environment

View Phd Dissertation

Abstract

This research seeks to understand how information displayed by variable message signs (VMS) can affect driver route-choice and be better used for active traffic incident management. I study the effect of various VMS messaging strategies using a money incentivized behavioral experiment with a novel 2D real-time driving simulator that supports dozens of subjects driving on a shared virtual roadway where traffic incidents unpredictably occur. Drivers are shown a VMS display before choosing between two congestible routes. I conducted this experiment with students at the UCI Experimental Social Science Laboratory (ESSL) and with a more diverse sample of online subjects crowdsourced from the Amazon Mechanical Turk (MTurk) marketplace.

Chapter 1 will present the research motivation and methodology, the design and implementation of the experiment platform, and the results with student subjects. I find that subjects learned to efficiently operate the driving simulator, all tested VMS messaging strategies improved aggregate outcomes compared to the No VMS baseline, displaying messages didn’t cause highly volatile diversion rates, and subject gender exhibited consistent correlations with route choice.

Chapter 2 will discuss the reasons for replicating on MTurk, the methodological modifications necessary to conduct the experiment online, and how the MTurk results compare to the student results. I find that it’s viable but challenging to conduct real-time multiplayer experiments on MTurk, there are significant differences in individual characteristics between the MTurk and student subjects, and there are limited behavioral differences between the two groups.

Chapter 3 will introduce a framework using long short-term memory (LSTM) neural networks to predict driver route choice using real-time contextual data. I use varyingly limited vectors of data from my driving simulator experiments as the neural network’s input to predict driver route choice at the decision point between the two available routes. I find that the best performing model configuration can predict individual route choice with 74.0% average accuracy with in-sample cross validation and 72.2% average accuracy with out-of-sample validation.