research report

The Effect of Trucks Dispatch Decisions on Pavement Damage and Other Externalities

Abstract

External costs of freight trucks include air pollution, highway damage, and congestion. While diesel taxes reduce both the pollution and congestion externalities, the research paper shows that they worsen highway damage. The research team investigates the impact of fuel prices on cargo shipments using weight-in-motion data from New York and California. The paper includes sensor readings on over 1.4 billion vehicle events. These data allow us to track daily changes in the weight and number of trucks at specific locations. The researchers explain the average daily weight differential between New York and California as a function of the diesel price differential using unexpected weather as an instrument. The team finds that when fuel prices increase by 10 percent, fuel use by heavy trucks declines by 3.1 percent and average truck weight increases by 3.2 percent. While total truck traffic decreases by around 1 percent, on net there is 19.6 percent more road damage. The dispatch effect changes the welfare comparison of using fuel taxes versus efficiency standards to control carbon emissions. The researchers find that a reduction in per-mile shipping cost from the standard causes freight to be reallocated across more trucks so that schedules are enhanced—that is, the rebound occurs on both a quality and a quantity dimension. In consequence, road damage declines. While there is considerable uncertainty about the cost of external congestion and the safety of trucks, the research team finds that fuel efficiency standards dominate fuel taxes as a policy to reduce carbon emissions for a wide range of parameter estimates.

Phd Dissertation

Routing and Scheduling Problems of Container Trucks in a Shared Resource Environment

Publication Date

May 31, 2017

Author(s)

Abstract

More frequent vehicle movements are required for moving containers in a local area due to low unit volume that a single vehicle can handle compared with vessels and rails involved in the container supply chain. For this reason, truck operations for moving containers significantly affect not only transportation cost itself but also product price. They have inherent operational inefficiencies associated with empty container movements and container processes at facilities such as warehouses, distribution centers and intermodal terminals. One critical issue facing the trucking industry is the pressing need for truck routing plans that reduce such inefficiencies. Hence, this dissertation proposes to apply the concept of sharing resources, which is an emerging economic model, to container truck operations in order to resolve this issue. Two shareable resources – vehicles and containers – are considered.

This study extends the literature on routing and scheduling problems that arise from container movements, and examines the possible benefits of sharing resources across customers. A series of truck container routing and scheduling problems were developed by assuming different levels of resource sharing among; (1) customers of one trucking operator, (2) customers across collaborations of multiple operators, and (3) customers over multi-day operations. To enable a trucking company to operate its fleet under a shared resource environment, two operational strategies – street turning and decoupling operations – together with temporal precedence constraints – in addition to the time constraints that are typically included in the vehicle routing problem with time windows (VRPTW) – were adopted to address the proposed problems.

Two meta-heuristic algorithms based on a variable neighborhood search (VNS) scheme were developed to solve the proposed problems, including temporal precedence constraints – which are computationally more expensive – for real-world applications. To address flexible time windows resulting from temporal precedence constraints, a novel feasibility check algorithm was developed.

Results from a series of numerical experiments confirm that the proposed approach leverages the advantages of resource sharing, and the meta-heuristic algorithms are efficient solution approaches for each problem with the targeted resource sharing. Consequently, this dissertation offers a platform for the development of a decision-support tool for drayage companies by applying three different levels of resource sharing into their operations.

research report

A Unified Framework for Analyzing and Designing for Stationary Arterial Networks

Abstract

This research aims to develop a unified theoretical and simulation framework for analyzing and designing signals for stationary arterial networks. Existing traffic flow models used in design and analysis of signal control strategies are either too simple to be realistic or too detailed to be efficient. In this research we apply the link transmission model to formulate, analyze, and simulate traffic dynamics in a signalized arterial network. We first analytically derive approximate macroscopic fundamental diagrams for stationary traffic patterns with different network topologies, road conditions, driving behaviors, and signal settings. We then analyze congestion mitigation effects of different signal settings, including cycle lengths, green splits, and offsets. We further formulate and solve an optimization problem with the network flow-rate as performance measure to find optimal signal control parameters. We derived simple formulas for the optimal signal cycle length and offset under different traffic conditions to improve arterial network performance.

policy brief

Advanced Low-NOx Compressed Natural Gas Engines in Medium- and Heavy-Duty Vehicles Are Poised to Deliver Air Quality Benefits and Advance California’s Climate Goals

Abstract

Recent commercialization of advanced low-nitrogen oxides (NOx) Compressed Natural Gas (CNG) engines for medium- (MDV) and heavy-duty (HDV) vehicles has garnered significant interest due to the potential air quality benefits. Further, utilizing renewable natural gas (RNG) in advanced CNG engines from sources such as biomass and/ or biogas can achieve reductions in greenhouse gas (GHG) relative to using petroleum fuels and fossil CNG. However, the regional air quality and GHG reduction benefits of large‐scale deployment of advanced CNG trucks are currently unclear. Further, more information is required regarding RNG production potential from California instate biofuel resources, including potential supply volumes and production pathways that provide maximum GHG reductions. The UC Irvine Advanced Power and Energy Program assessed the air quality and GHG implications of transitioning to advanced CNG engines in MDVs and HDVs in California by developing and comparing different future adoption scenarios. The research team also leveraged prior research of biogas and biomass resources in California to consider different options for producing RNG in-state. Key findings from this research are highlighted in the following section.

research report

A Literature Review: Improving How Active Transportation Demand is Modeled and Evaluated

Abstract

Local transportation agencies typically rely on traditional travel demand forecasting models that focus on highway and roadway improvements to optimize vehicular traffic.  These models are not equipped to evaluate active transportation strategies which align with current State of California policies such as reducing vehicle miles traveled to cut greenhouse gas emissions and fostering active transportation modes.  In this context, ITS at UC Irvine (ITS Irvine) was invited by the Orange County Transportation Authority (OCTA) to propose, develop, and apply an approach to better model active transportation. This report represents the first phase of this work, which is a review of the recent literature on how to model demand for active transportation and an examination of OCTAM’s (OCTA’s own regional travel demand model) Active Transportation (AT) modeling tool to evaluate its potential for modification or incorporation into a new active transportation model. The following observations/suggestions are offered in this report: First, OCTAM Active Transportation does not include variables that could impact people’s decision to leave their vehicles at home in favor of transit. Second, a number of conditions need to be jointly met for people to walk or bike. Third, OCTAM Active Transportation does not capture residential self-selection, which could be important here as people who do not plan to walk/bike self-select into car-oriented neighborhoods.

Phd Dissertation

Compact Development and Gender Inequality: Do More Accessible and Walkable Built Environments Promote Gender Equality in Travel and Activity Space Behaviors?

Abstract

Researchers have been concerned that suburban sprawl could reinforce gendered mobility patterns and lead to gendered differences in mobility. Previous studies also argued that the effectiveness of land use policy could be influenced by men and women’s different mobility patterns in response to built environments. To address these concerns, this dissertation uses the 2010-2012 California Household Travel Survey data and directly compares the within-household gendered travel and spatial behaviors for households with paired heads living in Southern California. The study examines whether built environments, including destination accessibility, design and walkability have different impacts on male and female heads’ daily travel and activity space behaviors and whether potential urban design can help improve gendered inequality in daily mobility.

Based on negative binomial, Tobit, and feasible generalized least squares regressions, the results show that that male and female heads respond to built environments with different travel and spatial behaviors. Living in walkable and accessible areas is likely to encourage male heads to walk, reduce their dependence on driving, locate activity center close to home, and have spatially concentrated activities. Female heads tend to respond to walkable and accessible living environments with reducing automobile travel and with centering and confining their activities near residential neighborhoods.

The negative binomial, Tobit, and binary logit regression analyses that investigate the influences of built environments on gendered inequality indicate that high walkability and regional accessibility are likely to reduce the gendered inequality in motorized travel distance and relax female heads’ spatial (and temporal) constraints relative to their husbands.

This dissertation contributes to the policy debates by informing planners and feminist geographers that the effects of built environments can be heterogeneous even for men and women from similar backgrounds and compact design can be the key to gendered equity. Given that compact developments are being rapidly implemented in Southern California, this dissertation study is expected to help shape effective and efficient land use policies in the future.

research report

The Effectiveness of State and Local Incentives on Household Ownership of Alternative Fuel Vehicles - A SEM Analysis

Abstract

California, where transportation accounts for over half of ozone precursors and particulate matter emissions, as well as nearly 40 percent of greenhouse gas emissions, has adopted the ambitious goal of reducing petroleum use in transportation by 50 percent by 2030. One of the proposed strategies to achieve this goal is to increase the number of alternative fuel vehicles (AFVs) on the road. In California, incentives to foster the addition of AFVs include the removal of occupancy requirements to access high occupancy vehicle (HOV) lanes and parking privileges with charging facilities for Plug-in Hybrid Electric and Battery Electric vehicles. Although popular, the effectiveness of these incentives is not well known. In this context,this paper analyzes the 2012 California Household Travel Survey using a generalized structural equation model that accounts for residential self-selection, household demographic characteristics, and a measure of environmentalism. Our findings suggest that increased proximity to HOV lanes without occupancy requirement or to preferred parking/refueling facilities have a statistically significant but quite small impact (with odds ratios of 1.004 and 1.017 respectively). Pro-environmental beliefs reflected in voting behavior for environmental propositions are also statistically significant, but they have a potentially larger impact with an odds ratio of 4.733. This suggests the need to continue educating the public about the environmental impacts of fossil fuels while working with car manufacturers to make their products more attractive compared to conventional vehicles.

Phd Dissertation

Tour Complexity, Variability, and Pattern using Longitudinal GPS Data

Abstract

Trip chaining is a common phenomenon generally known as linking multiple activities and trips in one travel process. A good understanding about trip chaining complexity is important for travel demand model development and for transportation policy design. However, most of the existing studies on trip chaining limit the complexity classification scheme on number of trips chained and neglect other dimensions that also elevate the degree of complexity. The purpose of this study is to develop a new approach, Tour Complexity Index (TCI), that integrates the multi-dimensional nature of trip chaining into the complexity assessment.

The study contains three analysis components. The first component introduces the TCI approach as a trip chaining complexity measure that not only considers number of trips chained but also includes the spatial relationship across destinations, the route arrangement, and the urban environment of the destinations. By comparing descriptive statistics and generalized linear model results from TCI approach with those from traditional approach, we find that the TCI approach offers more information regarding trip chaining and mode choice. The application of TCI is further demonstrated in the following components. The second component investigates the intrapersonal daily and weekly travel variability with travel characterized by TCI and mode choice. The result reinforces an argument in current literature that the common single-day travel survey may produce biased estimation due to the day-to-day variance in travel behavior. Result also finds that proximity to a new transit service from place of residence is connected with a decline in variability. The third component explores a framework for travel pattern recognition where pattern is characterized by TCI as well. The discrepancy analysis which is a generalized analysis of variance (ANOVA) method is applied to associate individual characteristics with travel pattern. In addition, both components use Sequential Alignment Method (SAM) for travel pattern representation. The TCI approach and proposed analysis frameworks are validated using the longitudinal GPS trajectory data collected between 2011 and 2013 at west Los Angeles area for Expo Study.

research report

Experimental Studies of Traffic Incident Management with Pricing, Private Information, and Diverse Subjects