conference paper
policy brief
There is growing international interest in electrolytic hydrogen produced from renewable energy (often referred to as green hydrogen) as a potential zero-emission alternative to gasoline and diesel in a variety of on-road and off-road transportation applications. Currently, gasoline and diesel are priced around $4 per gallon at the pump and a gallon of either fuel is roughly the equivalent of one kilogram of hydrogen based on energy content. Although hydrogen vehicles are generally more efficient than those fueled by petroleum, transporting and dispensing hydrogen is more expensive than for conventional fuel, so hydrogen must reach a cost substantially below $4/kg, possibly as low as $2/kg, to be a cost competitive option. Is this achievable? In short, this depends on the extent to which green hydrogen markets scale up globally. Projections of future green hydrogen production costs are generally in the range of $2–$4/kg by 20301 ; however, some expect faster and deeper declines reaching as low as $1.5/kg by 20302 and even $1/kg by 2030 under ideal conditions.3 This brief examines the evidence in support of green hydrogen production achieving a cost at or below $2/kg starting from its current level of between $5 and $6/kg,4 and assesses the time point at which this cost benchmark could be achieved.
policy brief |
Sep 2022
policy brief
California’s Housing Element law requires all local governments to adequately plan to meet the state’s existing and future housing needs. The law establishes processes for determining regional housing needs and requires regional councils of governments (COGs) with allocating these housing needs to cities and counties in the form of numerical targets. Local governments must update the housing element of their general plans and adopt policies to accommodate the housing targets. The California Department of Housing and Community Development (HCD) reviews all local housing elements and determines whether the elements comply with state law.
conference paper
Preprint Journal Article
Despite the deployment of alternative fuel vehicles (AFVs) being one of the promising measures to reduce air pollutants and greenhouse gas emissions, AFVs still represent a very small share in the heavy-duty vehicle (HDV) sector. Understanding HDV fleet operator perspectives on alternative fuels is critical to developing effective demand-side strategies to facilitate wider and more rapid adoption of heavy-duty AFVs. This study explored California HDV fleet operator perspectives on viable alternative fuel options in the next 10 to 20 years, along with motivators for, and barriers to, such adoption. Eighteen in-depth qualitative interviews were conducted, after which thematic analysis was employed to analyze the interview data. Electric, hydrogen, compressed natural gas (CNG), and hybrid options were commonly perceived as viable in the 2030s by the participating organizations. Various optimistic aspects were addressed, including advanced technologies and emission reduction benefits (electric/hydrogen), continued fuel commitments due to their fleet or infrastructure investments already made (CNG), and lower complexity in fleet routing along with favorable driver acceptance (hybrid options). However, many concerns and uncertainties were also reported, including functional unsuitability (electric), uncompetitive upfront costs (hydrogen), unready infrastructure, perceived unavailability of vehicles, uncertain return on investment (electric/hydrogen), and unpromising support from state government (CNG). The study findings help fill a key knowledge gap in AFV fleet adoption research regarding HDV fleet operator perspectives, and contribute to developing demand-side strategies to aid the success of AFV diffusion throughout the HDV market.
Phd Dissertation |
Jan 2019
published journal article |
Jul 2023
Phd Dissertation |
Jan 2020
other |
Aug 2023
research report |
May 2022
working paper
This report presents evaluation results of the Smart Card Phase III FieldDemonstration. Its purpose is to demonstrate the feasibility of using Smart Cards and othertechnology to provide an integrated fare medium across several transit operators. TheDemonstration took place from May 1995 through June 1997, and it was located in VenturaCounty, California. Participants included seven transit agencies, with the Ventura CountyTransportation Commission acting as the lead local agency. The technology deployed is anintegrated fare transaction and vehicle management/monitoring system, termed FareTransVMS. Major system elements include smart passenger fare cards, fare transactionhardware and software, automatic passenger counters, a geo-positioning system, andassociated communications hardware and software.The evaluation addressed technical performance, user response, and institutionalissues. Many problems were encountered in installing and deploying the hardware andsoftware, and equipment failures continued throughout the demonstration. Communicationsproblems prevented complete deployment of the system and implementation of all plannedsupport functions, though most technical elements were ultimately put into revenue servicein the field. Technical problems were often the outcome of institutional issues. The systemwas deployed before adequate testing could take place. Problems at specific sites were dueto inadequate training, lack of maintenance and trouble-shooting procedures, etc. AlthoughSmart Card users were very satisfied with the new fare medium, few transit users boughtand used the cards. Transit patrons in Ventura County have very low incomes, and manyare not English speakers. Smart Cards are more attractive to higher income, discretionaryriders. Overall, the demonstration suffered from the absence of clear roles andresponsibilities, as well as a lack of understanding of the complexity and demands of thetechnology. Despite deployment problems, however, participating agencies were generallyenthusiastic about the FareTrans VMS, and ultimately it became a permanent part of transitoperations in the county.
working paper |
Nov 1995
research report |
Sep 2001
research report |
Sep 1993
research report |
Sep 1997
working paper
The purpose of this paper is to introduce variables ·which may yield explanations of travel behavior which go beyond the economic and transportation-related explanations of existing models. This analysis explores whether improvements can be made in the understanding of individual travel behavior and in the predictive power of travel demand models. This applied emphasis extends the author’s previous work which demonstrated how attitudinal and behavioral information can be used to structure the development and marketing of transportation improvements (Fielding, 1972; Fielding, et.al., 1976).
book/book chapter
conference paper
The objective of this paper is to develop an on-line traffic control scheme with real time distributed vehicle data. Though most existing signal systems include some optimization schemes, their performance suffers from inaccurate route travel prediction due to the limitations of data. The commonly used data collection devices like Inductive Loop Detector (ILD) are unable to collect the path-based variables needed for network-level traffic control. In the Persistent Traffic Cookies (PTC) system proposed in UC Irvine recently, those variables can be obtained from the authenticated historical trip tables and current movement information stored in individual vehicles, as written in by wireless hardware at the intersections for vehicles equipped for it. From the inference of each individual vehicleâ??s historical movements in a network using PTC data, the path-based variables including path flow and path travel time can be predicted. In this study, the improved traffic control schemes with the path-based variables are introduced not only for a single intersection but also for a group of intersections in a sub-network. For a single intersection, the dynamic arrival flow of every movement to an intersection is predicted from the PTC data. Then an optimal signal plan can be determined based on the predicted arrival patterns with the objective of minimizing total delay during a given time horizon. For network-level control, the path flows are estimated from the inference of individual vehicle movement to capture a movement along several intersections. The future path flow can be predicted based on the current path flow and historical data. Based on it, we present a conceptual scheme to group a series of intersections as a sub-network for signal optimization. First, the interaction between any two intersections in a network is estimated by the path flow between them. After choosing a Critical Intersection in a network, a group of intersections having a certain interaction with it is selected and formed a sub-network. The signal optimization in a sub-network is accomplished by a Mixed Integer Linear Problem with the objective to minimize the total delay and a set of constraints. The solution schemes of a problem are discussed base on the results of previous studies.
published journal article