working paper

A Dynamic Forecasting System for Vehicle Markets with Clean-Fuel Vehicles

Abstract

This research deals with demand for automobiles and light-duty and medium-duty trucks. Planners concerned with energy consumption, air quality and the provision of transportation facilities must have dependable forecasts of vehicle ownership and use from both the residential (personal-use vehicle) sectors and the fleet (commercial and governmental) sectors. As long as vehicles evolved slowly, it was possible to base such forecasts on extrapolations of observed demand. However, in an era of increasing environmental awareness, mandated in part by the U.S. Clean Air Act Amendments (US EPA, 1990), government agencies are now concerned with promoting clean-fuel vehicles; vehicle manufacturers are faced with designing and marketing clean-fuel vehicles; and suppliers of fuels other than gasoline must plan infrastructure and pricing policies. 

In California, and potentially also in a number of Northeast States, stringent vehicle emission standards have been adopted or proposed and specific zero-emissions and ultra-low-emissions vehicle mandates are in place. The California Air Resources Board (CARB) requires that new cars sold in the state emit 80 percent less hydrocarbons by the year 2000, and 50 to 75 percent less carbon monoxide and nitrogen oxide. CARB has also mandated the production and sale of zero-emission (presumably electric) vehicles, beginning with 2 percent of annual car sales in 1998 and increasing to 10 percent in 2003. Elsewhere in the United States, clean-air and fuel management legislation (U.S. DOE, 1994) specifically targets fleets as markets for clean-fuel vehicles. Research is needed to establish the extent to which there is demand for clean-fuel vehicles. In reaction to this need, the Southern California Edison Company and the California Energy Commission is sponsoring a project to develop a dynamic demand forecasting model for clean-fuel vehicles in California. In this paper we briefly describe the forecasting system being developed and summarize some preliminary results.

working paper

Public Finance and Transit-Oriented Planning: New Evidence from Southern California

Abstract

Local governments seem to be continually strapped for funds. While the role of their planners in generating revenue is often discussed, it is also rarely investigated in any detail. We address this research gap by considering the fiscal nature of land use policy vis-a-vis a specific planning opportunity, namely “transit-oriented development.”

A massive and influential literature has explored the potential for leveraging rail system investments by locating high density residential developments near commuter rail stations. The feasibility and focus of these strategies have been question, however, in the face of evidence that local government support for these projects is mixed at best. To explain this behavior, we examine the role basic fiscal conditions play in the decision to zone land near all existing and proposed commuter rail stations in Southern California. The analysis indicates that station-area zoning depends significantly on community public finances. The importance of sales taxation in financing local services is consistently important in explaining the concentration of commercial activity in each city, associated revenue mix and tax base trends, and many other features differentiating communities. The results underscore how the practice of transit-oriented development must account not only for travel behavior and the broader goals of any given urban design, but also for the parochial and self-interested nature of municipal planning.

working paper

A Vehicle Usage Forecasting Model Based on Revealed and Stated Vehicle Type Choice and Utilization Data

Abstract

This research describes a new model of household vehicle usage behavior by type of vehicle. Forecasts of future vehicle emissions, including potential gains that might be attributed to introductions of alternative-fuel (clean-fuel) vehicles, critically depend upon the ability to forecast vehicle miles traveled by the fuel type, body style and size, and vintage of the vehicle.

research report

Socio-Economic Attributes and Impacts of Travel Reliability: A Stated Preference Approach

Abstract

This research examines the behavioral reactions to the impact of changes in the probability of non-recurrent incident and how this effects the expected costs of a commute trip. This basic approach combines the estimation of a travel demand model (estimated with data collected from a stated preference survey) with a supply side model of a congested highway. We also examine the impact of various socio-economic variables, including a detailed classification of occupational groupings. Our demand model is based on a theoretical model developed to explain how unreliability in travel times affects expected travel costs. We find that expected schedule delay (early and late), lateness probability, and expected travel time influence the expected costs of travel. Our parameter estimates confirm the anticipated values of these parameters: lateness probability has a high disutility, while expected schedule delay early is preferable to expected schedule delay late, and the disutility of expected travel time is between these two. We do not find a high level of significance for planning costs, as expressed by the variance in travel times. Our simulation model shows that schedule costs and lateness probability represent a large fraction of the total cost to the commuter; these are generally not affected by capacity increases but can be reduced by decreasing the probability of a non-recurrent incident.

working paper

The Economic Effects of Highway Congestion

Publication Date

September 30, 1995

Author(s)

Abstract

This paper examines the link between highway congestion, labor productivity, and output in a sample of California counties for the years 1977 through 1987. A county production function is modified to include both the value of each county’s highway capital stock and a measure of the congestion on each county’s highway network. This allows a comparison of two distinct policies — expanding the highway stock versus reducing congestion on the existing stock. The productive effects of congestion reduction are significantly positive in five of six regression specifications. The effects of expanding the highway stock are more suspect, and are insignificant in what are arguably the preferred specifications. Overall, the results provide evidence that efficiently using the existing highway network is more likely to yield economic benefits than expanding the highway stock.

working paper

Highways and Economic Productivity: Interpreting Recent Evidence

Publication Date

September 30, 1995

Author(s)

Abstract

This paper reviews the recent literature on public infrastructure and economic productivity, with special attention to the particular case of highway infrastructure. Recent evidence suggests that, at the margin, highway infrastructure contributes little to state or national productivity. This is consistent with studies that show relatively small land use impacts from modern highways. Yet the idea that highways enhance economic health is common in the policy and planning communities. Two explanations can help reconcile this divergence between academic research and popular perception. First, some of the economic development observed near highways might not actually be caused by the highway. Second, some of the economic development near highways might be a shift of economic activity away from other areas. Either explanation suggests the need for reforms in highway project analysis and funding. Appropriate policy reforms and directions for future research are suggested.

working paper

Travel and Activity Participation as Influenced by Car Availability and Use

Publication Date

July 31, 1995

Abstract

The objective of the research described in this paper is to determine how the use of specific modes of travel affects the relationships between out-of-home activity duration and the travel required for such activities. We proceed by constructing a model that interrelates classes of out-of-home activities and the travel required to participate in these activities, all as a function of population sociodemographic characteristics and the modes of travel used by the population. 

working paper

Economic Impacts of the Northridge Earthquake's Transportation Damage: Results from a Survey of Firms

Publication Date

August 31, 1995

Abstract

This report summarizes the results of a study into the economic impacts of the transportation damage caused by the January 17, 1994 Northridge Earthquake. The Northridge Earthquake damaged four major freeways in the Los Angeles metropolitan area. Even though state and local agencies responded quickly to reroute traffic and rebuild collapsed bridges and overpasses, major interstates were closed for several weeks and, in some cases, months. This research focuses on the effects of those transportation disruptions on business activity.

working paper

Twenty Seconds that Shook the Agenda: An Assessment of Transportation Issues in the Mass Media Following the Northridge Earthquake

Publication Date

June 30, 1995

Author(s)

Abstract

This study investigates how transportation issues were conveyed by the mass media, following the January 17, 1994 Northridge earthquake in Los Angeles. It is shown that the media are a vital tool for transportation planners, when a disaster causes damage to major arterials. The media are the primary means through which the public assesses damage to the transportation system, and learns which roadways and detours to use. They are also studied by out-of-town officials, who must appraise the damage. The media also expose people to new travel alternatives, like bus, carpooling, and rail service. 

A content-analysis is used to study trends in major mass media, including radio, local and national television, and newspapers. This technique was used to quantify the volume of transportation news, as well as changes in emphasis over time. A number of related issues were investigated through content analysis including change in the overall level of traffic news; change in information about alternative modes, like train service and buses; and change in news about freeway recovery and rebuilding efforts. It is observed that stories about transportation were a regular, and continuous component of earthquake reports.

Phd Dissertation

Modular neural network architecture for detection of operational problems on urban arterials

Abstract

A major concern in Advanced Transportation Management Systems (ATMS), one of the principal thrusts of the national program on Intelligent Transportation Systems (ITS), is providing decision support to effectively detect, verify and develop response strategies for incidents that disrupt the flow of traffic. A key element of providing such support is automating the process of detecting operational problems on large area networks. Successful detection of operational problems in their early stages is vital for formulating response strategies such as modifying surface street signal timing plans and activating or updating traveler information systems, including changeable message signs, in-vehicle navigation systems and highway advisory radio, altering emergency services, amongst others. Reliable surface street incident detection is also necessary for the development of integrated freeway-arterial control systems. Incident detection has been the subject of research for the past two decades. But the focus has been on detecting capacity reducing non-recurring congestion on freeways. Only recently has attention begun to focus on developing a methodology for surface street networks. The main focus of this research was to develop a methodology to detect different types of operational problems relevant to the operations of surface street networks. In this research, a modular architecture of neural network has been proposed to develop a comprehensive system to detect different types of operational problems, based on detector data from an urban traffic control system. The modularity of the classifier proposed decomposed the task of detecting different types of problems and produced an overall system of models that individually outperformed a single multi-layer feed-forward neural network model for lane-blocking incidents, special event conditions and detector malfunction, and also a statistically-based discriminant function model. The neural network-based models and the statistical models were developed and tested with simulated and field data from two test study areas in Anaheim and Los Angeles, California, USA. The higher detection rates and lower false alarm rates of the modular neural network model compared to other techniques demonstrated its potential of detecting different types of traffic operational problems on urban arterials.