Mark Bradley

Demand for clean-fuel vehicles in California: A discrete-choice stated preference pilot project

A study was conducted to determine how demand for clean-fuel vehicles and their fuel is likely to vary as a function of attributes that distinguish these vehicles from conventional gasoline vehicles. For the purposes of the study, clean-fuel vehicles are defined to encompass both electric vehicles and unspecified (methanol, ethanol, compressed natural gas or propane) liquid and gaseous fuel vehicles, in both dedicated or multiple-fuel versions. The attributes include vehicle purchase price, fuel operating cost, vehicle range between refueling, availability of fuel, dedicated versus multiple-fuel capability and the level of reduction in emissions (compared to current vehicles). In a mail-back stated preference survey, approximately 700 respondents in the California South Coast Air Basin gave their choices among sets of hypothetical future vehicles, as well as their choices between alternative fuel versus gasoline for hypothetical multiple-fuel vehicles. Estimates of attribute importance and segment differences are made using discrete-choice nested multinomial logit models for vehicle choice and binomial logit models for fuel choice. These estimates can be used to modify present vehicle-type choice and utilization models to accomodate clean-fuel vehicles; they can also be used to evaluate scenarios for alternative clean-fuel vehicle and fuel supply configurations. Results indicate that range between refueling is an important attribute, particularly if range for an alternative fuel is substantially less than that for gasoline. For fuel choice, the most important attributes are range and fuel cost, but the predicted probability of choosing alternative fuel is also affected by emissions levels, which can compensate for differences in fuel prices.

SACSIM: An applied activity-based model system with fine-level spatial and temporal resolution

Abstract This paper presents the regional travel forecasting model system (SACSIM) being used by the Sacramento (California) Area Council of Governments (SACOG). Within SACSIM an integrated activity-based disaggregate econometric model (DaySim) simulates each residents full-day activity and travel schedule. Sensitivity to neighborhood scale is enhanced through disaggregation of the modeled outcomes in three key dimensions: purpose, time, and space. Each activity episode is associated with one of seven specific purposes, and with a particular parcel location at which it occurs. The beginning and ending times of all activity and travel episodes are identified within a specific 30-minute time period. Within SACSIM, DaySim equilibrates iteratively with traditional traffic assignment models. SACSIM was calibrated and tested for a base year of 2000 and for forecasts to the years 2005 and 2035, and was subjected to a formal peer-review. It was used to provide forecasts for the Regional Transportation Plan (RTP) and continues to be used for various policy analyses. The paper explains the model system structure and components, the integration with the traffic assignment model, calibration and validation, sensitivity tests, model application and Federal peer review results. We conclude that it is possible to create and apply a regional demand model system using parcel-level geography and half-hour time of day periods. Experiences thus far have pointed to major benefits of using detailed land use variables and urban design variables, but also to new challenges in providing parcel-level land use inputs for future years.

Advanced Activity-Based Models in Context of Planning Decisions

Travel demand modeling today is undergoing a transition from the conventional four-step models to a new generation of advanced activity-based models. The new generation of travel models is characterized by such distinctive features as the use of tours instead of trips as the base unit of travel, the generation of travel in the framework of daily activity agendas of individuals, and the use of fully disaggregate microsimulation techniques instead of the aggregate zonal calculations. Although the theoretical advantages of activity-based models—in particular, behavioral realism and consistency across all travel dimensions—are well known, the practical advantages in the context of planning decisions have rarely been discussed and documented. Experiences to date are summarized for application of activity-based models for various planning purposes in metropolitan regions of New York City; Columbus, Ohio; Atlanta, Georgia; San Francisco, California; and Montreal, Canada. The focus is on the practical planning qu...

Hybrid Discrete Choice Departure-Time and Duration Model for Scheduling Travel Tours

A new model for scheduling travel tours is described. The model is essentially a discrete choice construct that operates with tour departure-from-home and arrival-back-home time combinations as alternatives. The proposed utility structure, based on continuous-shift variables, represents an analytical hybrid that combines the advantages of a discrete choice structure (flexible and easy to estimate and apply) with advantages of a duration model (parsimonious structure with a few parameters that support any level of temporal resolution including continuous time). The hybrid model currently has a temporal resolution of 1 h, which is expressed in 190 hour-by-hour departure- and arrival-time alternatives. The model is applied sequentially for all tours in the individual daily activity-travel pattern according to a predetermined priority of each activity type. The enhanced temporal resolution allows for applying direct availability rules for each subsequently scheduled tour to be placed in the residual time window left after the tours of higher priority are scheduled. This feature ensures a full consistency for the whole individual daily schedule. The model has been estimated and applied as a part of the new regional travel demand model developed recently for the Mid-Ohio Regional Planning Commission.

SYSTEMATIC INVESTIGATION OF VARIABILITY DUE TO RANDOM SIMULATION ERROR IN AN ACTIVITY-BASED MICROSIMULATION FORECASTING MODEL

A key difference between stochastic microsimulation models and more traditional forms of travel demand forecasting models is that micro-simulation-based forecasts change each time the sequence of random numbers used to simulate choices is varied. To address practitioners’ concerns about this variation, a common approach is to run the microsimulation model several times and average the results. The question then becomes: What is the minimum number of runs required to reach a true average state for a given set of model results? This issue was investigated by means of a systematic experiment with the San Francisco model, a microsimulation model system used in actual planning applications since 2000. The system contains models of vehicle availability, day pattern choice, tour time-of-day choice, destination choice, and mode choice. To investigate the variability of the forecasts of this system due to random simulation error, the model system was run 100 times, each time changing only the sequence of random numbers used to simulate individual choices from the logit model probabilities. The extent of random variability in the model results is reported as a function of two factors: (a) the type of model (vehicle availability, tour generation, destination choice, or mode choice); and (b) the level of geographic detail—transit at the analysis zone level, neighborhood level, or countywide level. For each combination of these factors, it is shown graphically how quickly the mean values of key output variables converge toward a stable value as the number of simulation runs increases.

Predicting the market penetration of electric and clean-fuel vehicles

Abstract Air quality in Southern California and elsewhere could be substantially improved if some gasoline-powered personal vehicles were replaced by vehicles powered by electricity or alternative fuels, such as methanol, ethanol, propane, or compressed natural gas. Quantitative market research information about how consumers are likely to respond to alternative-fuel vehicles is critical to the development of policies aimed at encouraging such technological change. In 1991, a three-phase stated preference (SP) survey was implemented in the South Coast Air Basin of California to predict the effect on personal vehicle purchases of attributes that potentially differentiate clean-fuel vehicles from conventional gasoline (or diesel) vehicles. These attributes included: limited availability of refueling stations, limited range between refueling or recharging, vehicle prices, fuel operating costs, emissions levels, multiple-fuel capability and performance. Respondents were asked to choose one vehicle from each of five sets of hypothetical clean-fuel and conventional gasoline vehicles, each vehicle defined in terms of attributes manipulated according to a specific experimental design. Discrete choice models, such as the multinominal logit model, are then used to estimate how the values of the attribute levels influence purchase decisions. The SP survey choice sets were customized to each respondents situation, as determined in the preceding phase of the survey. The final phase of the survey involved fuel-choice SP tasks for multi-fuel vehicles that can run on either clean fuels or gasoline. Preliminary results from a pilot sample indicate that the survey responses are plausible and will indeed be useful for forecasting.

California Statewide Model for High-Speed Rail

The California High Speed Rail Authority and the Metropolitan Transportation Commission are developing an innovative statewide model to support evaluation of high-speed rail alternatives in the State of California. This statewide model will also support future planning activities of the California Department of Transportation. The approach to this statewide model explicitly recognizes the unique characteristics of intraregional travel demand and interregional travel demand. As a result, interregional travel models capture behavior important to longer distance travel, such as induced trips, business and commute decisions, recreational travel, attributes of destinations, reliability of travel, party size, and access and egress modal options. Intraregional travel models rely on local highway and transit characteristics and behavior associated with shorter distance trips (such as commuting and shopping).

Activity-Based Travel Demand Models: A Primer

This publication is a guide for practitioners that describes activity-based travel demand model concepts and the practical considerations associated with implementing them. Activity-based travel demand models portray how people plan and schedule their daily travel. This type of model more closely replicates actual traveler decisions than traditional travel demand models and thus may provide better forecasts of future travel patterns. The guide is composed of two parts. Part 1 is intended to help managers, planners, and hands-on practitioners and modelers make informed decisions about activity-based model development and application. Part 2 examines the practical issues that transportation agencies face in migrating from traditional to “advanced” travel demand models, in which activity-based models are linked with regional-scale dynamic network assignments.

Activity-Based Travel Forecasting Models in the United States

This paper focuses on the use of activity based models in practice for urban and regional planning in the United States (U.S.). In addition to projects that have been carried out, also recognized is work done in a variety of research settings, both inside and outside the U.S. that will assist in the choice of activity based models in the future. Regional planning in the U.S. is at a critical stage with the adoption of activity based models accelerating. The papers goal is to provide an opportunity to review the types of modeling developments that have been successfully implemented. Also discussed are factors that remain as hindrances to the acceptance of activity based models for planning by U.S. government agencies.

Attitudes and Willingness to Pay for Tolled Facilities: A Panel Survey Evaluation

This paper presents the results of an evaluation study of the behavioral impacts of a high-occupancy toll (HOT) lane project in Minnesota. The I-394 MnPASS Express Lane Project is the fifth HOT lane project implemented in the United States. HOT lanes remain a new enough concept that there is little empirical information on methods for evaluating them or on their impacts on travel behavior for transportation planners and policy makers to use when making decisions about future facilities. The MnPASS evaluation study is significant not only because it uses a panel design but also because it involved multiple waves of stated preference (SP) experiments. These waves were conducted before and after project implementation. This paper uses information from the evaluation study to examine two significant issues: (a) How applicable is a panel design to evaluating road pricing projects? and (b) How does willingness to pay vary on the basis of before-and-after iterations of the SP experiments?