policy brief
research report |
Dec 2023
book/book chapter
This circular summarizes input on the design of the next National Household Travel Survey (NHTS) from the databaseâ??s user community. In Phase One of its charter, the Task Force on Understanding New Directions for the NHTS focused on the needs and experiences of technical users of the NHTS. This includes those who use the data for research, policy, and travel demand modeling, among others. The task forceâ??s main outreach activities have included committee presentations at the 91st and 92nd Annual Meeting of the Transportation Research Board, user forums at related events, and a user survey to identify user needs for the future NHTS. The task force also established a listserv to communicate NHTS updates to the user community and listserv members were invited to participate in a survey about their use of the NHTS. The task force identified six key areas to focus on user input. These included usage of the NHTS and how well the data meets the userâ??s needs, additional data desired for the next NHTS, sampling needs (including underrepresented groups), and the frequency of data collection. Observations for each key area are summarized in this circular.
book/book chapter
With growing auto traffic and scarce land in most Chinese cities, parking guidance systems (PGS) are becoming increasingly valuable for reducing congestion and making efficient use of existing parking facilities. This paper contributes to the growing parking choice literature by proposing a two-phase parking model for pre-trip PGS with an application based on a parking behavior survey conducted in Nanjing, China. Our two-phase parking choice model simulates parking choice behavior in real-world situations; it searches all available parking options based on maximum acceptable walking distance to the driver’s destinations, and selects optimal routes based on five criteria including distance, safety, convenience, cost, and accessibility. The authors’ modeling approach can help drivers select optimal parking spaces with the benefit of detailed pre-trip route information.
published journal article
conference paper
working paper
The goal of this dissertation is to develop an activity-based trip generation model which addresses shortcomings of the conventional trip-based approach. Problems with conventional generation models resulted from a fundamental incapability to address the temporal and spatial characteristics of activities and the trips which they generated. The sequencing and scheduling of trips and activities, and interactions between household members, are ignored in the standard model. The proposed activity-based generation model was developed to estimate trip production from the analysis of complete travel/activity patterns. This approach classifies travel patterns with respect to activity, spatial, and temporal characteristics; standard trip rates can be also estimated from these representative activity patterns. In addition to a standard category production model, a stochastic logit-based pattern choice model and a deterministic discriminant analysis model were developed to stimulate activity pattern choice and the associated trip production level. A variety of variables describing the socioeconomic and demographic attributes at the household or personal level comprise the utility functions for choice prediction. Temporal stability of activity patterns was evident in similar life cycle groups in the 1985 and 1994 Portland test data, supporting the conclusion that patterns are a viable structure on which to base future forecasts.
working paper |
Jan 1999
working paper |
Dec 1994
working paper |
Sep 1994
working paper |
Oct 1993
policy brief
California’s legislature has attempted to address the state’s housing affordability crisis in recent years by adopting numerous laws encouraging new development in transit-accessible and/or jobs-rich areas, but the evidence concerning the impacts of these laws on housing development remains largely anecdotal. In particular, policymakers lack adequate information concerning: (1) the types of neighborhoods where developers are more likely to build; and (2) the causes of delays in approvals for proposed projects in jobs-rich and transit-accessible areas. In new research, scholars from UC Irvine and UC Berkeley address this problem by drawing on a unique project-level dataset, the Comprehensive Assessment of Land Use Entitlements (CALES), to analyze development projects including five or more residential units that were approved for development from 2014 through 2017 in six cities: Inglewood, Long Beach, Los Angeles, Pasadena, Redondo Beach, and Santa Monica.
research report
Phd Dissertation
Mobility systems are undergoing a major transformation due to emerging autonomous and shared mobility technologies. A primary aspect of such technologies is improving the mobility system inefficiencies via a reduction in the number of vehicles needed to fulfill the transportation needs. This would impact the use of vehicles and their expected lifetime. This dissertation is focused on the importance of the increased usage of vehicles and how the system can benefit from an optimization with a vehicle point of view. The improvements come from mainly two aspects of shared mobility – carsharing and ridesharing – which are both implemented in the modeling and optimization framework. An analysis of the current vehicle ownership and trip distributions is presented. A vehicle usage cost function is designed to incorporate the changed relative importance of fixed and usage-based variable costs. It presents a framework that analyzes the interactions between all the elements, including a pricing scheme for benefit-cost analysis and optimizations from a service provider perspective. With shared mobility, ownership paradigms can also change to subscription-based use of vehicles from fleet service providers, as included implicitly in the interaction framework. Modeling is carried out for idealized networks, as well as a real-world network of a reasonable size from the city of Irvine, CA. The results capture the increased use of shared and/or autonomous vehicles and the benefits of optimizing the system with properly updated costs. Results and conclusions are provided on the viability of service provider plans as well as on system benefits in terms of the replacement ratio indicating how many personal vehicles can be removed using autonomous fleets.
Preprint Journal Article
Recent advancements in Vision-Language Models (VLMs) have sparked interest in their use for autonomous driving, particularly in generating interpretable driving decisions through natural language. However, the assumption that VLMs inherently provide visually grounded, reliable, and interpretable explanations for driving remains largely unexamined. To address this gap, we introduce DriveBench, a benchmark dataset designed to evaluate VLM reliability across 17 settings (clean, corrupted, and text-only inputs), encompassing 19,200 frames, 20,498 question-answer pairs, three question types, four mainstream driving tasks, and a total of 12 popular VLMs. Our findings reveal that VLMs often generate plausible responses derived from general knowledge or textual cues rather than true visual grounding, especially under degraded or missing visual inputs. This behavior, concealed by dataset imbalances and insufficient evaluation metrics, poses significant risks in safety-critical scenarios like autonomous driving. We further observe that VLMs struggle with multi-modal reasoning and display heightened sensitivity to input corruptions, leading to inconsistencies in performance. To address these challenges, we propose refined evaluation metrics that prioritize robust visual grounding and multi-modal understanding. Additionally, we highlight the potential of leveraging VLMs’ awareness of corruptions to enhance their reliability, offering a roadmap for developing more trustworthy and interpretable decision-making systems in real-world autonomous driving contexts. The benchmark toolkit is publicly accessible.
