Center Identification Number: 77809
Project Title: Developing a Technique that Predicts the Impacts of TDM on a Transportation System
Nevine Labib Georggi, Senior
Chanyoung Lee, Ph.D., Senior Research Associate
Edward Hillsman, Ph.D., Senior Research Associate
Center for Urban Transportation Research
External Project Contact:
I. Start and End Dates
Start Date: October 2008 Expected End Date: January 2010
I. Project Objective/Problem Statement
State DOTs are increasingly interested in managing demand for vehicle trips as a way to mitigate congestion and improve overall performance of the highway system. This requires an ability to predict the magnitude and geographic distribution of potential changes in travel behavior resulting from steps taken at specific locations to manage transportation demand. It also requires an ability to build on these predictions to estimate the impacts of implementing transportation demand management (TDM) strategies on travel demand on a specific corridor. Some estimation is possible via modeling, but this work has been done retrospectively (what happened) rather than predicatively (what if). Multimodal, operational, and construction mitigation planning all have tools that estimate the effects of various options for improving transportation system performance. Because TDM planning lacks such a quantitative tool, it is often overlooked as an option when searching for solutions to mitigate traffic congestion, construction, or air quality. If available, such a tool/technique will facilitate consideration of TDM as a viable option to provide the same level of benefit as other approaches.
The proposed research seeks to develop a technique that estimates the effect of implementing a mix of employer-based demand management strategies on the performance of a transportation system during peak commuting periods. The proposed research will use the following resources to estimate changes in mode split and numbers of commute vehicle trips between home and workplace:
If feasible, a sketch-planning tool that interfaces with travel demand models would facilitate the estimation of the impacts of trips reduced due to deployed TDM strategies on delay, speed, and travel time through specific corridors. An example of such a tool is the ITS Deployment Analysis System (IDAS) software that has been successfully used with the TRANPLAN, EMME/2, MINUTP, and other modeling packages, Currently, IDAS is used to compare ITS deployments by estimating benefit cost (B/C) ratios for various alternatives. An easy-to-use interface in IDAS uses the outputs from the travel demand model, maintained and validated by the local agency, as inputs into the software. Often IDAS imports these outputs in an ASCII format file from the model. IDAS offers the capability for a systematic assessment of ITS with one analysis tool and is used for determining the benefits and costs of various ITS deployments. IDAS was designed to assist public agencies and consultants in integrating ITS in the transportation planning process.
The capability to incorporate TDM strategies in software such as IDAS would allow state, regional, and local transportation planners and decision-makers to actively consider TDM strategies as a congestion mitigation tool, just as they do with ITS deployments that are designed to increase the efficiency of managing traffic capacity.
The proposed research seeks to develop a technique for estimating how the implementation of a particular employer-based TDM strategy or mix of strategies affects the travel time, delay, and speed in specific corridors. The nature of this technique will be dictated by the research as a methodology, model, a computer program, a systematic process, or algorithm.
The proposed research will primarily use employer and employee data collected by TDM programs implemented across different states to estimate future impacts of TDM strategies. This information will be combined with Census data, or data on infrastructure for alternative modes collected by state DOTs or transit agencies, to estimate changes in trip generation and mode split between home and workplace. Based on the results of the research, a methodology could be developed for incorporating this application in IDAS software.
Task 1: Conduct Literature Review and Collect Data
CUTR will review previous approaches used for estimating the impacts and B/C of TDM on a road network. With assistance from State DOTs, CUTR will obtain data from a variety of sources including transit agencies. It may be necessary to purchase databases that contain employer and employee surveys as part of this effort.
CUTR will review the IDAS software documentation and identify the data required for incorporating TDM strategies in this software. CUTR will reach out to FHWA and its contractor responsible for maintaining and improving IDAS to identify specific requirements, specifications, and processes for consideration for incorporating this projectís end product into IDAS. CUTR will also collect data on the cost of implementing various TDM strategies.
Task 2: Model the Impact/Benefit of TDM programs
A model will be developed to assess the impact of implementing various TDM strategies in a region. This proposed model should allow transportation engineers, local planners, developers, employers, and TDM professionals to input bundles of various TDM strategies, incentives, disincentives, and worksite characteristics to obtain predictions impacts of TDM on a transportation system. This model will accept existing person trips and mode split as inputs and conduct analysis to determine the impact of proposed TDM strategies. This analysis will use existing models such as Environmental Protection Agency (EPA) COMMUTER Model, Trip Reduction Impacts of Mobility Management Strategies (TRIMMS) models and others to assess the impact of a particular or mix of strategies. As an output, it will generate a modified mode split which planning models can then e use to determine the impact on a particular corridor. A methodology for integrating this model or using its output with IDAS software will be explored.
Subtask 2.1: Assessment of Impact of TDM Strategies on Travel Demand
Analysis of travel demand data maintained by various agencies is required to determine the impact of TDM strategies over time. With many years of employee and employer data, the research team will also place specific attention to the longitudinal impacts of TDM programs. This analysis would provide the model with the ability to predict impact of a specific or mix of strategies over time. Also, data from other states will be reviewed and will be included in the analysis.
A technique will be developed for selecting a model for forecasting the impacts of TDM programs in a region. Several models such as EPA COMMUTER Model, TRIMMS models and other will be considered. Since most planning models conduct analysis on a Traffic Analysis Zone (TAZ) level, a methodology will be developed to transform the results from these models to a TAZ level.
Subtask 2.2: Extraction of Existing Mode Share and Person Trips from the Travel Demand Model
The process of developing this technique will focus on two steps of the traditional transportation planning four-step process: trip generation and mode split. In the trip generation step, for example, transportation planners calculate the number of trip productions (i.e., the number of person trips going out of each TAZ) and trip attractions (i.e., number of Person Trips going into each TAZ). The inputs for the trip generation component depend on the following:
From the above inputs, the number of produced person trips will be estimated for each TAZ by trip purpose (trip productions): home-based work, home-based shopping, home-based social/recreational and home-based other. The number of attracted person trips will also be estimated for each TAZ by trips purpose (Trip attractions).
Subtask 2.3: Estimation of Reduction in Person Trips
TDM strategies such as compressed work week and telecommuting are expected to reduce the number of person trips generated. There are two potential ways for incorporating the impact of compressed work weeks, flex work schedules, and telecommuting into trip generation. One method is to use the results of the MPO-run original trip generation model as input. CUTR would estimate the number of person trips reduced due to alternative work schedules separately and subtract the TDM reduced person trips from original model inputs (i.e., Home Based Work trips (HBW)) to create a revised trip generation table. An alternative is to adjust the trip rates for Home Based Work trips for the TAZs where the affected employees work or live, then provide the revised trip generation table. CUTR will investigate each approach and pursue the best.
Subtask 2.4: Application of Available Models in Estimation of Modified Mode Shares
CUTR will examine alternative approaches to estimating TDMís effect on the mode split. One approach would be to person trips to vehicle trips, transit trips and others by including new explanatory variables in the traffic planning modelís utility function. Normally, the variables in these functions include characteristics of the connections between TAZs, such as distance, travel time by modes, number of times of transferring for transit, time spent per transfer, travel cost, etc. In addition, the model would include social-economic and demographic characteristics of the TAZs, such as average household income, average household size, average household car ownership, transit accessibility, population density, etc. CUTR will examine incorporating variables describing employer support strategies, alternative mode subsidies, and parking management directly into the utility functions of the mode split model. The alternative would be for CUTR to change the mode shares on a zonal basis.
Based on the methodology developed in Task 2.1 for forecasting impact of TDM programs at a TAZ level for a future year, a modified mode share will be computed. This modified mode share can be used by other planning models such as IDAS to estimate the changes in travel demand throughout the system or on a corridor. This would allow for estimation in changes in travel time, delay and speed on a specific corridor.
Task 3: Cost and Benefit Estimates for Various TDM strategies
The mode share changes from Task 2 will be processed through the selected travel demand model software to determine the transportation system benefits such as reduction in travel delay.
Costs associated with each type of TDM strategy will be collected from different agencies. Using the costs and benefits, limited B/C ratios could be calculated such as cost per travel time reduction. In order to get a comprehensive assessment of the range of benefits, including travel time, travel time reliability and accidents (fatality, injury, property damage), and to do so inexpensively, transportation professionals need to use a sketch-planning tool such as IDAS.
Task 4: Introducing the TDM Components for Potential Integration into IDAS
The purpose of this task is to support the development of a sketch-planning analysis capability for analyzing alternative TDM strategies in IDAS. The CUTR research team will review the documentation of the IDAS software to obtain an understanding of the software inputs, outputs, strengths and weaknesses. This will allow for development of a methodology for including TDM strategies in IDAS, which assists agencies in integrating TDM into the planning process. The data specifications for identifying cost and benefit information for each strategy will be identified. CUTR team will work closely with FHWA and its IDAS contractor for the development of this methodology.
Task 5: Final Report
The final products will be a model that state and local planning officials can use to estimate TDMís impacts on travel demand, and a methodology for performing cost-benefit analysis of these impacts in IDAS. CUTR will produce the final report and hold a streaming media presentation on the project.
Work not included in this scope of service is not to be performed and will not be subject to compensation by the Department.
Progress Reports The University will submit quarterly progress reports to the Research Center. The first report will cover the activity that occurred in the 90 days following the issuance of the Task Work Order.
Reports should be submitted within 30 days of the end of the reporting period. Reports are due even if little or no progress has occurred (in which case, the report should explain delays and/or lack of progress). Progress reports should be sent in MS Word to Sandra Bell, firstname.lastname@example.org .
Progress reports must include the following information:
1. Contract Number, Task Work Order Number, and Title
2. Work performed during the period being reported
3. Work to be performed in the following period
4. Anticipated modifications (i.e., to funding, schedule, or scope). This section is for reporting/informational purposes, not for officially requesting an amendment. Note: To request an amendment to a contract, the contractor must provide the project manager with the appropriate information (i.e., what is being requested with justification) in the required format. If the project manager concurs with the request, he/she shall forward it with his/her approval and commentary, as appropriate, to the Research Center for administrative review and processing (pending available funds, etc.)
5. A Progress Schedule updated to reflect activities for the period being reported.
Failure to submit progress reports in a timely manner may result in termination of the work order.
Draft Final Reports The draft final report will be submitted to Sandra Bell, email@example.com. It should be edited for technical accuracy, grammar, clarity, organization, and format prior to submission to the Department for technical approval. The Research Center expects contractors to be able to provide well-written, high-quality reports that address the objectives defined by the scope of service.
Final Reports Once the draft final report has been approved, the university shall prepare the final report. The university will deliver a minimum eight (8) copies of the final report in MS Word on CD and one (1) unbound original, no later than the end date of the task work order, to
The Florida Department of Transportation
Research Center, MS 30
605 Suwannee Street
Tallahassee, FL 32399-0450
Each copy will be provided on a CD or DVD (i.e., for a total of eight disks). If the project manager requires additional copies, such provision must be indicated in the scope.
The project manager will review the final report to insure that all issues identified for correction in the draft final report have been addressed.
Note: All materials printed under the contract will have the FDOT, USF, NCTRCUTR logos
Project Certification The Sponsored Research office or appropriate authority will submit as a final deliverable a project certification prepared according to university compliance standards.
Project Kickoff Meeting
A kickoff meeting shall be scheduled to occur before any work begins. As a minimum, the project manager and the principal investigator will attend. The Research Center staff must be advised of the meeting and given the option to attend. Other parties may be invited, as appropriate. The subject of the meeting will be to review and discuss the projectís tasks, schedule, milestones, deliverables, reporting requirements, and deployment plan.
IV. Project Schedule
V. Project BudgetSalaries and Fringe 89,159.09
Fixed Price Sub Total 89,159.09
Indirect Cost (fixed price subtotal x 10%) 8,915.91
Total Fixed Price Amount 98,075.00
Total Lump Sum Amount (Salaries and Benefits) 98,075.00
Expenses (Cost Reimbursable) 1,750.00
Cost Reimbursable (Subtotal) 1,750.00
Indirect Costs (cost reimbursable subtotal x 10%) 175.00
Total Project Cost 100,000.00
VI. Use of Graduate Student(s) and Other Research Assistants
The proposed budget includes funds for a graduate student to assist with all project tasks.
No equipment will be needed for this project.
Travel may be necessary for project kickoff meeting as requested by FDOT.
National Center for Transit Research ∑ at the Center For Urban Transportation Research ∑ University of South Florida ∑ 4202 E. Fowler Ave., CUT100 ∑ Tampa, FL 33620-5375 ∑ (813) 974-3120 ∑ (813) 974-5168 ∑ www.nctr.usf.edu ∑ Comments: firstname.lastname@example.org