After describing the current emission modeling regime, the paper identifies and discusses the major problems with the existing emission modeling approaches. The current short-term modeling improvement programs of the US Environmental Protection Agency and the California Air Resources Board are discussed. The paper then outlines the three long-term modeling improvement approaches that are currently being investigated by regulatory agencies: a multiple-cycle method, an engine map approach, and a modal modeling technique. Finally, the vehicle activity and emission rate data needs for each modeling approach (both for model development and implementation) are described.

On November 24, 1993, the US Environmental Protection Agency adopted the transportation conformity rule, pursuant to Section 176(c)(4) of the Clean Air Act. The conformity rule requires that transportation plans, programs and projects funded or approved by the federal government or their agents under Title 23 (Highways) U.S.C. or the Federal Transit Act conform with state or Federal air quality implementation plans. Federal transportation planning regulations contain reciprocal language (40 CFR 450.312(d)), stipulating that the MPO shall not approve any plan or program that does not conform to the SIP as determined in accordance with the conformity rule.

The final transportation/air quality conformity rule (23 CFR Part 450 and 49 CFR Part 613) constrains the development of transportation plans, improvement programs and projects and has increased pressure on statewide metropolitan transportation agencies to ensure that transportation and air quality plans are coordinated. Rigorous requirements for regional and local air quality modeling of transportation systems are included in the final conformity rule, and agencies are working diligently to meet these demands.

The research community and practitioners alike have raised significant questions as to the reasonableness and accuracy for requiring specific modeling procedures -- large uncertainties in the modeling process are prevalent. These inherent uncertainties are likely to lead to erroneous transportation planning decisions, and possible mis-allocation of local, state, and federal moneys.

This paper briefly addresses and identifies some of the more significant uncertainty issues associated with the transportation modeling process in context of the demands set forth by the Conformity Rule. The uncertainties listed are not meant to be comprehensive, but instead aims to shed light on the 'missing communication link' between technical experts and policy makers. Modeling requirements and procedures set forth in the Conformity Rule do not reflect the magnitude and depth of current modeling deficiencies.

Solutions to these modeling uncertainties can be provided with advancements in both transportation activity and emissions modeling procedures. It is important for future modeling revisions and upgrades to explicitly include estimates of uncertainty in computations, so that decisions makers can re-shape existing policies -- making them more sensitive to explicit uncertainties.

Traffic congestion is now widespread on freeways and arterials in most urban areas. The Federal Highway Administration estimates that more than 65 percent of urban freeways are congested during peak periods, creating more than 2 billion vehicle-hr of delay and user costs in excess of $15.9 billion per year (GAO 1991). Congestion levels are also rising, meaning that the delay, energy, and air quality impacts of congestion continue to worsen. Because there has recently been increased regulatory focus on implementing economic incentives in the environmental arena (EPA 1992a; Guensler 1992; Ketcham 1991; Regulatory Flexibility Group 1991; South Coast Air Quality Management District 1991; Hahn and Stavins 1990), it is natural that congestion pricing would be explored as a means to achieve transportation behavioral changes in the urban areas with the worst air quality.

This study applies bootstrap analysis to historic transportation project item cost data to develop improved estimates of item costconfidence bounds for use in transportation project cost uncertainty analysis (a component of lifecycle analysis). Bootstrap regression results of confidence bounds will then be integrated into AASHTOWare Project Cost Estimator so that Monte Carlo procedures can estimate project-level confidence intervals for use in lifecycle project cost analysis and transportation capital planning. Data and functions contained within AASHTOWare (a cost estimation software licensed to the Departments of Transportation for over 40 states and the District of Columbia) are employed in the analyses. Coordinating with the Georgia Department of Transportation to obtain a research license for AASHTOWare took longer than expected, resulting in projectdelays. This report summarizes the work completed to date and describes the remaining steps required to finish the study and for the primary author to publish a final dissertation.

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Intelligent transportation technologies (ITT’s) are being promoted as a means of reducing congestion delay, improving transportation safety, and also as a means of making vehicle travel "...more energy efficient and environmentally benign (USDOT, 1990)." In theory, IVHS technologies will increase the efficiency and capacity of the existing highway and roadway systems to reduce congestion (Saxton and Bridges, 1991; Conroy, 1990; Shladover, 1991; Shladover, 1989). We are not confident, however, that vehicular emissions will be reduced by the full range of proposed ITT’s.

This paper examines the potential air quality impacts of Intelligent Vehicle Highway Systems (IVHS) upon important emission-producing vehicle activities and those parameters that affect emission rates. Important emission relationships are identified, a framework for comparative analysis is developed, and the general relationships between IVHS technology bundle characteristics and vehicle emission impacts are examined. The primary IVHS emission-related impacts that this project addresses are associated with changes in the average speed and operating mode (acceleration, deceleration, cruise, and idle) characteristics of the vehicle fleet. The emission model algorithms associated with the average speed modeling regime are assessed in the second part of this volume.

Planners and engineers need to know how to assess the impacts of proposed cycling infrastructure projects, so that projects that have the greatest potential impact on the actual and perceived cycling safety are selected over those that would be less effective. Planners also need to be able to communicate these impacts to decision-makers and the public. This research addresses these problems using the BikewaySim cycling shortest path model. BikewaySim uses link impedance functions to account for link attributes (e.g., presence of a bike lane, steep gradients, the number of lanes) and find the least impedance path for any origin-destination pair. In this project, BikewaySim was used to assess the impacts of using time-only and time with attribute impedances, as well as two proposed cycling infrastructure projects, on 28,392 potential trips for a study area in Atlanta, Georgia. These impacts were visualized through bikesheds, individual routing, and betweenness centrality. Two metrics, percent detour and change in impedance, were also calculated. Results demonstrate that BikewaySim can effectively visualize potential improvements of cycling infrastructure and has additional applications for trip planning. An expanded study area was also used to demonstrate bike + transit mode routing for four study area locations. Visualizations examine the accessibility to TAZs, travel time, and the utilized transit modes for each location. Compared to the walk + transit mode, the bike + transit mode provided greater access to other TAZs and reached them in a shorter amount of time. The locations near the center of the transit network where many routes converge offered the greatest accessibility for both the bike + transit and walk + transit modes. The difference in accessibility was greatest for locations near fewer transit routes. This research demonstrated how BikewaySim can be used to both examine the current cycling network and show changes in accessibility likely to result from new infrastructure. Both BikewaySim and TransitSim are open-source Python based tools that will be made available for practitioners to use in bicycle network planning. Future research will focus on calibrating link impedance functions with revealed preference data (cycling GPS traces) and survey response data (surveys on user preference for cycling infrastructure).

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Analysts today would like to assess the emissions impacts likely to result from future implementation of intelligent vehicle and highway system (IVHS) concepts. This requires making assumptions about the simultaneous emergence of technologies and policies. Assumptions about emission characteristics of the future vehicle fleet, the penetration of electric vehicle technologies, and the impacts on driver behavior of future policies has profound implications on research findings. This paper first summarizes the likely impacts of three IVHS technologies (Advanced Traffic Management Systems, Advanced Traveler Information Systems, and Advanced Vehicle Control Systems) given characteristics of current vehicle characteristics and driving behavior. Then, each IVHS technology is re-assessed with some change in a future assumption - be it technology or policy based - to determine the expected air quality impacts. It is found that future assumptions profoundly affect the expected air quality impacts of the three IVHS technologies, in most instances improving their outlook. The authors recommend that transportation planners and researchers consider synergism of technologies and policies, since this is the more likely way in which changes in the transportation paradigm will emerge. The authors acknowledge that assumptions about the future can be used to aid in the formulation of effective solutions to transportation problems.

Electric Vehicles (EVs) significantly reduce energy consumption and emissions from on-road operations and help create more sustainable transportation environment by reducing emissions from the entire well-to wheel energy cycle. Differences between hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), and battery electric vehicles (BEV) users is an important element in understanding potential impacts on travel demand and vehicle adoption, the fact that these vehicles may be adopted into households that undertake very different vehicle activities and energy usage patterns has not been a primary focus in the literature. This study differentiates between HEV, PHEV, and BEV users across three factors: owner household socio-demographic attributes, household daily travel patterns, and household energy usage profiles. The analyses examine factors that appear to influence users’ preferences towards specific EV types and how the selection of different EV types potentially relates to household socio demographics and daily travel patterns. The 2019 Puget Sound Regional Council travel survey data set serves as the main analytical dataset. Influential factors identified as significant through statistical approaches are employed as variables for developing a two-phase choice model for determining potential EV-purchasing households and their choice of specific EV type. As EVs continue to capture increasing market share over time, these research findings and the resulting vehicle type choice model are expected to significantly improve future travel demand model development, allowing activity-based travel demand models to assign specific vehicles to specific households and then to individual trips in planning scenario analysis.

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