Abstract
N/A
research report
Reducing Degradation in High-Occupancy Lanes
Publication Date
November 1, 2019
Archives: Research Products
policy brief
New Tool from UC Irvine Could Save the State Millions while Providing Better Data on Truck Activity in California
Publication Date
October 1, 2019
Associated Project
Author(s)
Areas of Expertise
Abstract
The U.S. population is expected to increase to 389 million by 2045 compared to 321 million in 2015, with economic growth doubling in size. Consequently, freight movements are expected to increase by approximately 42 percent by 2040. Among all freight modes, trucks show the largest expected increase in flows by 2040. However, the ability for transportation agencies to understand and adequately plan for increased truck movement and related impacts is extremely limited due to a lack of data on truck travel patterns.The main sources of truck data are truck surveys and truck counts collected by infrastructure-based detectors. Surveys provide detailed information (i.e., truck type, Origin-Destination, weight, and vehicle miles traveled) useful for understanding truck activity pattern by industry or associating freight commodities with specific truck types, but because of low response rates, surveys cannot be utilized to provide the actual quantification of truck activity at the geographical level. In-pavement sensor technologies, such as Weigh-in-Motion (WIM) or Automated Vehicle Classifiers (AVCs), provide point observations, such as truck volumes. These existing data sources are used to model and generate truck path flows (i.e., travel routes) and/or travel time estimations.
Related Publications
research report |
February 03, 2025
New Methods for Monitoring Spatial Truck Travel Patterns in California Using Existing Detector Infrastructure
policy brief
New Tool from UC Irvine Could Save the State Millions while Providing Better Data on Truck Activity in California
Publication Date
October 1, 2019
Associated Project
Author(s)
Areas of Expertise
Abstract
The U.S. population is expected to increase to 389 million by 2045 compared to 321 million in 2015, with economic growth doubling in size. Consequently, freight movements are expected to increase by approximately 42 percent by 2040. Among all freight modes, trucks show the largest expected increase in flows by 2040. However, the ability for transportation agencies to understand and adequately plan for increased truck movement and related impacts is extremely limited due to a lack of data on truck travel patterns.The main sources of truck data are truck surveys and truck counts collected by infrastructure-based detectors. Surveys provide detailed information (i.e., truck type, Origin-Destination, weight, and vehicle miles traveled) useful for understanding truck activity pattern by industry or associating freight commodities with specific truck types, but because of low response rates, surveys cannot be utilized to provide the actual quantification of truck activity at the geographical level. In-pavement sensor technologies, such as Weigh-in-Motion (WIM) or Automated Vehicle Classifiers (AVCs), provide point observations, such as truck volumes. These existing data sources are used to model and generate truck path flows (i.e., travel routes) and/or travel time estimations.
Related Publications
research report |
February 03, 2025
New Methods for Monitoring Spatial Truck Travel Patterns in California Using Exisiting Dectector Infrastructure
Phd Dissertation
Environmental Impacts of Various Heavy-Duty Natural Gas Vehicles Incentivized in California
Publication Date
September 14, 2019
Associated Project
Author(s)
Areas of Expertise
Abstract
Society has an interest in reducing pollutants emitted from the vehicles used for transporting people and goods. The main goal of heavy-duty natural gas vehicle (NGV) incentive projects is to offer upfront monetary incentives to reduce greenhouse gas emissions and the production of regulated pollutants in the state. However, these incentives are often based on vehicle weight and do not account for environmental impacts. In addition, although heavy-duty NGVs are being used in a variety of vocation types, conventional emission models only support a limited number of these vocation types. Because of this, it is challenging to assess the precise impacts of the heavy-duty NGV (HD NGV) adoption and predict the specific environmental benefits per given operational conditions and vocation type. If government agencies realize the environmental benefits of alternative fuel vehicles (AFVs), like NGVs, with respect to vocation type and operating characteristics, it would be beneficial to design cost-effective incentive structures and implementation plans. This study primarily focused on the operational characteristics and environmental impacts of the HD NGVs incentivized in California. This study conducted pattern clustering and classification analyses to obtain drive mode compositions (DMC) over duty cycles and showed the heterogeneity of operational and emission characteristics of the vocational HD NGVs. The vocational impact analysis computed the adoption impact of 40 NGVs operating in California across ten different vocation types. The proposed evaluation framework included life-cycle nitrogen oxides (NOx) and carbon dioxide (CO2) emissions of natural gas, renewable natural gas and diesel fuel pathways and compared the lifetime NOx emission reduction potential of the considered vocation type vehicles. The resulting emission benefits of the fuel pathways were used to determine the most incentive-effective vocation types among the considered NGV applications. The multi-criteria decision-making analysis prioritized the fuel pathways based on multiple criteria which are related to an incentive effectiveness index as well as life cycle emissions. Refuse truck and transit bus pathways are likely to achieve the highest return for the total incentive granted when the vehicles are renewable natural gas (RNG)-powered. For compressed natural gas (CNG) fuel pathways, school and transit buses take the highest ranks over the various analysis scenarios. Each vocation type showed different incentive effects and emission reduction potential, which means that some vocational vehicles can play a critical role in the state’s funding and emission reduction plans. The suggested decision-making tool and assessment framework can provide useful reference data to improve the performance of future alternative fuel vehicle incentive programs.
Related Publications
research report |
February 03, 2025
Natural Gas Vehicle Incentive Program
Phd Dissertation |
February 03, 2025
Impacts of electric highways for heavy-duty trucks
Phd Dissertation
Alternative Light- and Heavy-Duty Vehicle Fuel Pathway and Powertrain Optimization
Publication Date
September 29, 2019
Author(s)
Areas of Expertise
Abstract
An increasing number of alternative vehicle fuel and powertrain options are evolving for both light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) to combat climate change and degraded air quality. Electricity, hydrogen, substitute natural gas, renewable gasoline, and renewable diesel are examples of alternative fuels, while internal combustion engines, fuel cell engines, plug-in battery engines, hybrids, plug-in hybrids, and electrical drivetrains are examples of components comprising powertrains. With such a diverse set of options for LDVs and HDVs, a systematic evaluation of the options that meet environmental goals at a minimum cost is required.
Using linear programming with fuel pathway and vehicle costs, emission constraints, realistic growth scenarios for travel and technology, and fuel feedstock availability, a methodology is developed (“Transportation Rollout Affecting Cost and Emissions, TRACE”) to assess combinations of fuel and vehicle pathways. Each pathway has an associated efficiency, cost, and emission of greenhouse gases (GHGs) and criteria air pollutants (CAPs). Techno-economic data from the literature and Wright’s Law project the cost of infrastructure to produce, distribute, and dispense fuel, and to produce vehicles through 2050.
The results from a Reference Case, comprised of business-as-usual fossil fuel and internal combustion vehicles (ICVs), projects costs of $1.43 trillion. For current LDV regulations in California, the optimization suggests adoption of ICVs fueled by renewable gasoline in the early years with many plug-in hybrid electric vehicles, a large population of zero-emission battery electric vehicles starting in 2030, and significant plug-in fuel cell electric vehicle (PFCEV) adoption in 2050. For all modeled HDV vocations (linehaul, drayage, refuse, and construction), TRACE projects ICVs fueled by renewable diesel until 2045, after which hybrids and PFCEVs are adopted for all vocations except refuse. This LDV and HDV rollout is projected to cost $1.28 trillion by 2050, 10% less than the Reference Case. Significant factors affecting results include battery costs, change in vehicle miles traveled, and zero-emissions vehicles (ZEV) constraints. For cases with proactive ZEV inducements, plug-in FCEVs displace ICVs while satisfying the long range and short fueling attributes provided today by ICVs, reducing GHGs an additional 18% and CAPs up to an additional 40%.
Phd Dissertation
Environmental and health benefits of airport congestion pricing - The case of Los Angeles International Airport
Publication Date
August 25, 2019
Author(s)
Abstract
Airports are a source of greenhouse gases (GHG) and air pollutants such as fine particulate matter with an aerodynamic diameter under 2.5 μm (PM2.5), which adversely affect the climate and human health. This pollution is worsening with increasing aircraft congestion. Even though aviation is the second largest source of GHG emissions in the transportation sector, it was excluded from the recent COP21 Paris Agreement. Little is known about the climate change and adverse health impacts from increasing airports congestion. The purpose of this study is to start filling this gap.
In this dissertation, I estimate congestion, health, and climate benefits from airport congestion pricing for Los Angeles International Airport (LAX), the fourth busiest airport in the world by passenger numbers in 2018. I first derive the optimal congestion fee for airports like LAX that primarily serve local and regional markets. To quantify the impacts of airport congestion pricing, I analyze one year of airport operations (2014), which corresponds to 593,547 flights (both inbound and outbound). My simulation results suggest that hourly congestion pricing would on average reduce waiting time by 2.9 minutes per flight and annual PM2.5 emissions by 11.4 percent, thus decreasing the environmental impacts from aircraft landing and takeoff operations (LTO), which extend as far as 19 km downwind from the airport.
An analysis of the health gains from implementing a congestion fee that accounts for air pollution cost shows that it would annually reduce premature mortality from PM2.5 exposure by 4.6 cases, avoided hospital admissions for cardiovascular diseases by 167 cases, and avoid 8,539 lost work days. The corresponding monetary value of these health gains are $45.8 million, $21.9 million, and $1.4 million respectively (all in 2014 dollars).
For my climate change analysis, I consider both the country-level social cost of carbon (CSCC; $36 per tonne) and the global social cost of carbon (GSCC; $417 per tonne). While pricing GHG emissions with the CSCC only has a minor impact, using the GSCC helps further reduce aircraft congestion and its associated health impacts. Indeed, an aircraft congestion fee with GHG based on the GSCC would reduce premature mortality by 6 cases each year, avoided hospital admissions by 221 cases, and avoid 11,528 lost work days (95 % CI: 4,995, 18,060). The corresponding monetary value of these health gains are $60.7 million, $27.7 million, and $1.9 million respectively.
The methodology presented in this study is widely applicable. It provides engineers, planners, and policymakers a tool for reducing airport congestion and for quantifying the resulting health and climate benefits.
Phd Dissertation
Modelling and Optimization of Smart Mobility Systems with Agent Envy as a Paradigm for Fairness and Behavior
Publication Date
September 4, 2019
Author(s)
Abstract
Smart Urban Mobility in the future demands a paradigm shift. Transportation supply needs to be designed to incorporate individual-level preferences in an era of readily-available information about other users and network performance. It is, therefore, reasonable to expect that an individual would have information to compare his/her transportation allocation with other users. For individuals having the same goal (e.g., the shortest path to the destination from the same departure location and time), the peer to peer comparison may induce ‘envy’ if the user perceives his/her assigned travel option to be worse than that of his/her peers. In turn, a user may adjust his/her travel options until he/she does not feel envy. This concept is an extension of the well-known travel behavior assumption called “User Equilibrium”. Existing behavior models, however, do not allow users to compare their allocations with others on an individual basis. Furthermore, it is assumed that users have perfect information about their own alternative and all users are homogeneous. A smart mobility system of the future may also include users who are not human but machines such as logistics, an autonomous vehicle that may have programmed behavior, and thus they too can be considered “agents” in our analysis. This dissertation is dedicated to modeling a smart mobility system which accounts for individual level of allocation. Mobility systems that include connected, autonomous, and subscribed components to various extents will all qualify as smart systems in this context. More specifically, we focus on the optimization of the allocation problem to achieve both system-wide efficiency and minimum envy among individuals. We consider envy to be an important allocation aspect in the transportation system. Maximizing the efficiency of a system necessarily brings about some level of unfairness where some users (or agents) are allocated to inferior alternatives. When agents having superior alternatives can compensate the envy of groups having inferior alternatives, an envy-free state can be achieved-which can be shown to be Pareto efficient state. Using a combination of pricing and incentives, we propose an optimization model to arrive at this new equilibrium. This research has significant contributions in that the proposed model provides a framework to combine system-wide objectives with individual users’ utility objectives. Furthermore, we consider user heterogeneity, which has not been researched in the general area of transportation assignment. The proposed optimization model can be applied to pricing strategies both for commercial and public agencies, who have real-time information about customer characteristics and system performance. Numerical results from running our optimization on both illustrative and real networks show that the proposed model converges to both envy-free and system optimum states with appropriate allocation and pricing schemes. Our findings show that the proposed smart mobility system technically works efficiently without governmental subsidy since the budget-balance mechanism trades off credits among users. In addition, the level of user heterogeneity affects the amount of credits charged or disbursed.
Phd Dissertation
Time-varying networks measurement, modeling, and computation
Publication Date
June 14, 2019
Author(s)
Abstract
Time-varying networks and techniques developed to study them have been used to analyze dynamic systems in social, computational, biological, and other contexts. Significant progress has been made in this area in recent years, resulting from a combination of statistical advances and improved computational resources, giving rise to a range of new research questions. This thesis addresses problems related to three lines of inquiry involving dynamic networks: data collection designs; the conditions needed for structural stability of an evolving network; and the computational scalability of statistical models for network dynamics. The first contribution involves a commonly neglected problem concerning data collection protocols for dynamic network data: the impact of in-design missingness. A systematic formalization is offered for the widely used class of retrospective life history designs, and it is shown that design parameters have nontrivial effects on both the quantity of missingness and the impact of such missingness on network modeling and reconstruction. Using a simulation study, we also show how the consequences of design parameters for inference vary as a function of look-back time relative to the time of measurement. The second contribution of this thesis is related to a fundamental question of network dynamics: when or where are changes in a network most likely to occur? A novel approach is taken to this question, by exploring its complement — what factors stabilize a network (or subgraphs thereof) and make it resistant to change? For networks whose behavior can be parameterized in exponential family form, a formal characterization of the graph-stabilizing region of the parameter space is shown to correspond to a convex polytope in the parameter space. A related construction can be used to find subgraphs that are or are not stable with respect to a given parameter vector, and to identify edge variables that are most vulnerable to perturbation. Finally, the third contribution of this thesis is to scalable parameter estimation for a class of temporal exponential family random graph models (TERGM) from sampled data. An algorithm is proposed that allows accurate approximation of maximum likelihood estimates for certain classes of TERGMs from egocentrically sampled retrospective life history data, without requiring simulation of the underlying network (a major bottleneck when the network size is large). Estimation time for this algorithm scales with the data size, and not with the size of the network, allowing it to be employed on very large populations.
Phd Dissertation
Essays on Transportation Externalities
Publication Date
June 29, 2019
Author(s)
Areas of Expertise
Abstract
This dissertation concerns the measurement and regulation of externalities with a focus on the numerous and interrelated external costs in the transportation sector. The research touches on pollution, congestion, and collisions as well as various modes of transportation. The results have important implications for public policy and the regulation of externalities both within and outside of transportation. Chapter 1 investigates the effectiveness of traffic laws which require drivers to provide a minimum amount of distance between their vehicle and cyclists when passing them on roadways in improving cyclist safety. Many believe these laws are ineffective in reducing the number of bicyclist fatalities because they are difficult for police to enforce, contain loopholes, and the minimum distance required is inadequate. This chapter tests this claim empirically using data on 18,534 bicyclist fatalities from the Fatality Analysis Reporting System and a differences-in-differences approach, in a negative binomial model, to identify the effect of minimum distance passing laws on bicyclist fatalities. The analysis fails to find a significant effect of enacting a minimum distance passing law on the number of cyclist fatalities after controlling for differences in weather, demographics, bicycling commuter rates, state level traffic, and time variation. Chapter 2 examines the effects of freight truck weight and miles traveled on both the quantity and severity of truck-involved collisions using a unique panel data set covering the universe of truck-involved collisions and 3.5 billion truck-weight observations. Estimates reveal that, though both measures of trucking activity can increase collisions, increases in truck weight skew the distribution of collisions towards more severe outcomes involving either injury or death. The results are applied to a welfare analysis of diesel fuel taxes, which have been shown to both reduce truck miles traveled and increase truck cargo weight. Though diesel taxes are shown to slightly reduce the total number of collisions, the remaining collisions become more severe. Societal gains from pollution, congestion, and collision reductions are entirely offset by the increased fatal collision costs, reducing total welfare by $39.9 billion annually. The final chapter examines the regulation of heterogeneous externalities. When demand for or damages from an externality producing good vary, uniform policy instruments are an inefficient tool for correcting market failures. This chapter examines how a policy that reflects this heterogeneity can improve efficiency. County travel demand elasticities and congestion damages are estimated to compare the efficiency of a uniform fuel tax to county-specific fuel taxes. Because elasticities, congestion damages, and pollution damages exhibit heterogeneity across regions, county-specific fuel taxes, levied in a subset of major metropolitan areas, provide welfare gains between $7-$26 per capita annually in addition to equity gains relative to a revenue neutral uniform fuel tax.
Phd Dissertation
Commute Mode Choice, Parking Policies, and Social Influence
Publication Date
May 31, 2019
Author(s)
Areas of Expertise
Abstract
This dissertation examines the impact of parking policies and social influence on commute mode choice using discrete choice analysis. A key feature of the dissertation is overcoming the problem of insufficient data by using unique datasets, building unique datasets, or exploring appropriate estimation strategies and assumptions.
Chapter 1 studies the impact of parking prices on the decision to drive to work using the California Household Travel Survey. The chapter tackles estimation challenges posed by insufficient parking information. The first challenge is the estimation of parking prices for those who do not drive, which is addressed by using a sample selection model. The second challenge is to understand the effect of the extent of the prevalence of Employer-Paid parking coupled with incentive programs offered in-lieu of parking. To address this challenge, two extreme scenarios are examined, and a range for the marginal effects of parking prices is estimated; one scenario assumes everyone receives Employer-Paid parking coupled with in-lieu of parking incentives, and the second assumes that no one is offered such incentives. The results suggest that higher parking prices reduce driving, regardless of the followed approach. It is estimated that a 10% increase in parking prices leads to a 1 – 2 percentage point decline in the probability of driving to work. Moreover, there seems to be no evidence of sample selection bias. The evidence suggests that parking pricing can indeed be an effective transportation demand management tool.
Chapter 2 extends the analysis of Chapter 1 to simultaneously estimate the impact of parking pricing, parking availability, and urban form on commute mode choice. The joint role of these three factors is examined using a dataset that is constructed by merging three major different data sources. The California Household Travel Survey data are matched to two unique datasets on parking for Los Angeles County; one for prices and the other availability. Chapter 2 first examines how these three factors affect the binary decision of whether to drive, while controlling for a rich set of covariates. The analysis then becomes more specific and examines how these factors affect particular commute modes in a multinomial context. The results indicate that parking prices have a significant negative impact on the decision to drive to work, where a 10% increase in parking prices is associated with a 1.1% drop in the probability of driving to work. Both on-street and off-street parking availability at home, as well as urban form measures of the workplace tract, are found to significantly affect commute mode choices. These findings have important policy implications in terms of minimum parking requirements, maximum parking standards, employer-paid parking, and parking pricing policies.
Chapter 3, on the other hand, examines the impact of a number of fundamental determinants of commute mode choice on transit use, and introduces the role of social influence. The determinants explored cover socioeconomic characteristics, built environment and neighborhood characteristics, transit accessibility, and trip characteristics. Social interactions have been found to affect many of the decisions of economic agents, and are likely to play a role in the decision to use transit. A unique dataset is built to conduct this analysis across a number of major US cities and examine the effects in both the residence and workplace neighborhoods, where a neighborhood is defined as a census tract. Social influence is explored along three different dimensions: space (neighborhood), income, and race. A novel instrumental variable is constructed in order to identify spatial social influence, and an alternative identification strategy is devised to identify income-group and racial social influence. The evidence suggests that spatial social influence exists among both coworkers and residential neighbors, and that peer effects among coworkers are larger than those among residential neighbors. Moreover, income-group social influence, among both coworkers and residential neighbors, plays a significant role in the rich commuter’s decision to use transit. However, racial social influence does not affect a commuter’s decision to use transit, regardless of race.