SWUTC Research Project Description
Title of Project: Network Methods for Project Selection Based on Optimizing Environmental Impact
Project Number: 161026
Principal Investigator:
S. Travis Waller
(512) 471-4539
P.I. Affiliation: University of Texas at Austin
Project Monitor:
Arash Mirzaei
616 Six Flags Drive, P.O. Box 5888
Arlington, TX 76005-5888
(817) 695-9261
Project Status: Active
Date Started: 9/1/09
Estimation Completion Date: 8/31/10
Estimated Cost - Current Fiscal: $39,000
Estimated Cost - Total Planned: $39,000
Project Summary:
Project Abstract:
Air quality is a worsening problem in developing cities worldwide, resulting in copious health problems and respiratory symptoms. Mobile source emissions which increase with the level of traffic congestion have been shown to be a major source of emissions. Improvements in traffic flow conditions have been shown to reduce congestion levels, thus reducing emission levels. The difficulty arises in predicting future traffic flow conditions resulting from multiple transportation projects implemented in parallel under stochastic network conditions. This research therefore aims to develop a cohesive model combining the state of the art emissions models, and cutting-edge large scale stochastic and/or dynamic network models to efficiently predict link flows which can be used to quantify emissions. This model will be capable of predicting region-wide environmental impacts of transportation projects, and suitable to determine the best network modifications to implement. Thus, these models will produce more accurate and robust predictions of congestion levels and resulting emissions than are available using current methods.
Project Objectives:
There are three main objectives regarding this research topic:
Task Descriptions:
Task 1. Review of Past Research on Emissions Modeling
A careful study will be made of existing research on emissions in transportation systems, both of its causes and its effects. This will include examination of both transportation modeling literature, emissions modeling literature, research into traffic operations, and the role of traffic flows on emissions. Simulation models have been suggested as a method to evaluate emission reductions resulting from transportation projects as they are able to explicitly model changes in traffic flow conditions. In particular, simulation models are capable of modeling interactions between drivers and variance of individual vehicle behavior in order to estimate changes in speed, delay, etc. travel time and resulting emissions. A key research issue will be to determine which network model provides the best balance of resolution and computational complexity to support ongoing emission modeling work in the broader field. These will be of use in determining the most appropriate and useful scope for this project.
Task 2. Identify Relevant Sources of Emissions
Guided by the literature identified in Task 1, the factors which most impact emissions will be identified. These factors will be used to quantify the impact of various transportation projects in the modeling methodologies developed and described in the following tasks. Together with the identification of key factors, the scope of the project will be explicitly delineated based on the state-of-the-art in modeling.
Task 3. Develop New Metrics to Quantify Emission Levels under Stochastic Network Conditions
Due to the inherent complexity of transportation systems, it is impossible to predict travel demand and congestion conditions exactly, and simplistic attempts to account for this consistently underestimate true levels of congestion. Traditional metrics used to evaluate transportation systems do not adequately account for stochastic network conditions; therefore there is a need to develop new metrics which will enable us to evaluate and quantify the sustainability of new transportation projects when the travel demand may vary. The research team will develop independent techniques to quantify emissions impacts when the network is subject to demand uncertainty.
Task 4. Develop Efficient Traffic Flow Prediction Models
This task will be to develop new analysis methods employing cutting-edge stochastic and/or dynamic models that efficiently predict expected traffic flow conditions by estimating links flows on the network. The objective is to find link flows on newly added links/facilities through more efficient means thereby permitting more comprehensive system analysis.
Task 5. Integrate Emissions Models into Traffic Flow Prediction Models
This task will result in a comprehensive model which allows for simultaneous consideration of the effects of expected system wide traffic flow conditions on emissions levels. This task focuses on adaptation of models for estimating emission level based on stochastic traffic conditions, which can then be used to indirectly quantify emission levels and the impact of considered network projects. These new modeling capabilities will also allow for the identification of opportunities to improve system conditions, reduce emissions, or other measures of effectiveness.
Task 6. Develop Project Selection Criteria
The focus of this task is to develop a set of criteria or measures of effectiveness (MOEs) which can be used to evaluate and select a subset of proposed transportation projects to implement, with the objective of optimizing expected environmental impacts. The exact selection criteria will be based on the relevant sources of emissions identified in Task 2, and the results from the model developed in Task 5.
Task 7. Simulation of Results
To examine the applicability and transferability of these models, the research team will perform simulation tests of the models developed in Tasks 5. The exact scope of these tests will depend on the models which are developed, but a network representing a major metropolitan area (such as the cities of Austin or El Paso) will be used.
Index Terms: