The goal of this study is to assess and quantify the potential employment accessibility benefits of Shared Autonomous Mobility Service (SAMS) commute modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study employs a welfare-based (i.e. logsum-based) measure of accessibility, obtained via estimating a hierarchical work destination-commute mode choice model. The employment accessibility logsum measure incorporates the spatial distribution of worker residences and employment opportunities, the attributes of the available commute modes, and the characteristics of individual workers. This research further captures heterogeneity of workers using latent class analysis (LCA). The LCA model inputs include the socio-demographic characteristics of workers to subsequently account for different worker clusters valuing different types of employment opportunities differently. The accessibility analysis results indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high- and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode; and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits.
Numerical simulations have shown that the network fundamental diagram (NFD) of a signalized network is significantly affected by the green ratio. An analytical approximation of the NFD has been derived from the link transmission model. However, the consistency between these approaches has not been established, and the impacts of other factors are still unrevealed. This research evalutes the impacts of start-up and clearance behaviors in a signalized network from a network fundamental diagram approach. Microscopic simulations based on Newell’s car-following model are used for testing the bounded acceleration (start-up) and aggressiveness (clearance) effects on the shape of the NFD in a signalized ring road. This new approach is shown to be consistent with theoretical results from the link transmission model, when the acceleration is unbounded and vehicles have the most aggressive clearance behaviors. This consistency validates both approaches; but the link transmission model cannot be easily extended to incorporate more realistic start-up or clearance behaviors. With the new approach, this project demonstrates that both bounded acceleration and different aggressiveness lead to distinct network capacities and fundamental diagrams. In particular, they lead to start-up and clearance lost times of several seconds; and these lost times are additive. Therefore, the important role that these behaviors play in the NFD shape is studied to reach a better understanding of how the NFD responds to changes. This will help with designing better start-up and clearance behaviors for connected and autonomous vehicles.
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 the plan contents can be used by various stakeholders 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 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.
In spite of their substantial number in the U.S., our understanding of the travel behavior of households who do not own motor vehicles (labeled “carless” herein) is sketchy. The goal of this paper is to start filling this gap for California. We perform parametric and non-parametric tests to analyze trip data from the 2012 California Household Travel Survey (CHTS) after classifying carless households as voluntarily carless, involuntarily carless, or unclassifiable based on a CHTS question that inquires why a carless household does not own any motor vehicle. We find substantial differences between our different categories of carless households. Compared to their voluntarily carless peers, involuntarily carless households travel less frequently, their trips are longer and they take more time, partly because their environment is not as well adapted to their needs. They also walk/bike less, depend more on transit, and when they travel by motor vehicle, occupancy is typically higher. Their median travel time is longer, but remarkably, it is similar for voluntarily carless and motorized households. Overall, involuntarily carless households are less mobile, which may contribute to a more isolated lifestyle with a lower degree of well-being. Compared to motorized households, carless households rely a lot less on motor vehicles and much more on transit, walking, and biking. They also take less than half as many trips and their median trip distance is less than half as short. This study is a first step toward better understanding the transportation patterns of carless households.
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
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 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, that OCTAM AT 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 AT 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.
External costs of freight trucks include air pollution, highway damage, and congestion. While diesel taxes reduce both the pollution and congestion externalities, we show that they worsen highway damage. We investigate the impact of fuel prices on cargo shipments using weight-in-motion data from New York and California. We obtained 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. We 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. We find that when fuel prices increase 10 percent, fuel use by heavy trucks declines 3.1 percent and average truck weight increases 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. WE 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 safety of trucks, we find that fuel efficiency standards dominate fuel taxes as a policy to reduce carbon emissions for a wide range of parameter estimates.
Vehicle travel has reduced substantially across all demographics in the 2000’s, but millennials or young adults born from 1985-2000 stand out as the group that has reduced vehicle travel the most. This reduction of travel among millennials is known as the millennial effect. This policy and literature review discusses insights from recent policy reports and literature regarding the millennial effect and identifies the prominent themes and gaps in our knowledge. The first section reviews existing research on the millennial effect on vehicle miles traveled (VMT). The second section discusses the influence of the built environment on the travel and activities of the millennial generation. The third section highlights scenarios describing the millennials effect’s potential magnitude and identifies topics for consideration in future scenario planning efforts. The final section discusses the uncertainty that exists regarding the future behavior of millennials and their influence on VMT and greenhouse gas emissions.