Development of a Methodology to Evaluate Projects Using Dynamic Traffic Assignment Models
ABSTRACT
The identification and selection of performance measures play an important role in any decision making process. Additionally, millions of dollars are spent on appropriate planning and identification of prospective projects for improvements. As a result, current practitioners spend a lot of time and money in prioritizing their limited resources. This research proposes two tasks: 1) estimation of performance measures using a simulation based on dynamic traffic assignment model, and 2) development of a methodology to evaluate multiple projects based on benefit-cost analysis. The model, DynusT, is used for the Las Vegas roadway network during the morning peak time period. A comparative analysis of the results from proposed methodology with existing California Benefit-Cost (Cal-B/C) models is presented. The results indicate that the new methodology provides an accurate benefit-cost ratio of the projects. In addition, it signifies that the existing Cal-B/C models underestimate the benefits associated with the prospective project improvements. The major contribution of this research is the simultaneous estimation of the performance measures and development of a methodology to evaluate multiple projects. This is helpful to decision makers to rank and prioritize future projects in a cost-effective manner. Planning and operational policies for the transportation systems can be developed based on the gained insights from this study.
The identification and selection of performance measures play an important role in any decision making process. Additionally, millions of dollars are spent on appropriate planning and identification of prospective projects for improvements. As a result, current practitioners spend a lot of time and money in prioritizing their limited resources. This research proposes two tasks: 1) estimation of performance measures using a simulation based on dynamic traffic assignment model, and 2) development of a methodology to evaluate multiple projects based on benefit-cost analysis. The model, DynusT, is used for the Las Vegas roadway network during the morning peak time period. A comparative analysis of the results from proposed methodology with existing California Benefit-Cost (Cal-B/C) models is presented. The results indicate that the new methodology provides an accurate benefit-cost ratio of the projects. In addition, it signifies that the existing Cal-B/C models underestimate the benefits associated with the prospective project improvements. The major contribution of this research is the simultaneous estimation of the performance measures and development of a methodology to evaluate multiple projects. This is helpful to decision makers to rank and prioritize future projects in a cost-effective manner. Planning and operational policies for the transportation systems can be developed based on the gained insights from this study.
KEYWORDS
Performance Measures, Travel Demand Models, Dynamic Traffic Assignment, Benefit-Cost Analysis, California Benefit-Cost Models
Performance Measures, Travel Demand Models, Dynamic Traffic Assignment, Benefit-Cost Analysis, California Benefit-Cost Models
Cite this paper
Maheshwari, P. and Paz, A. (2015) Development of a Methodology to Evaluate Projects Using Dynamic Traffic Assignment Models. Open Journal of Applied Sciences, 5, 50-61. doi: 10.4236/ojapps.2015.52006.
Maheshwari, P. and Paz, A. (2015) Development of a Methodology to Evaluate Projects Using Dynamic Traffic Assignment Models. Open Journal of Applied Sciences, 5, 50-61. doi: 10.4236/ojapps.2015.52006.
References
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http://dx.doi.org/ 10.1080/15568310902940209 [2] Litman, T. (2007) Developing Indicators for Comprehensive and Sustainable Transport Planning.
Transportation Research Record: Journal of the Transportation Research Board, 2017, 10-15.
http://dx.doi.org/ 10.3141/2017-02 [3] Paz, A., Maheshwari, P., Kachroo, P. and Ahmad, S. (2013) Estimation of Performance Indices for
the Planning of Sustainable Transportation Systems. Advances in Fuzzy Systems, 2013, Article ID:601468.
http://dx.doi.org/10.1155/2013/601468 [4] Maheshwari, P., Khaddar, R., Kachroo, P. and Paz, A. (2014) Dynamic Modeling of Performance
Indices for the Planning of Sustainable Transportation Systems. Networks and Spatial Economics.
http://dx.doi.org/10.1007/s11067-014-9238-6 [5] Lord, D. (2002) Issues Related to the Application of Accident Prediction Models for the Computation of Accident Risk on Transportation Networks. Transportation Research Record: Journal of the Transportation Research Board, 1784, 17-26. [6] Lord, D. (2002) Application of Accident Prediction Models for Computation of Accident Risk on
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http://dx/doi.org/10.1016/S0001-4575(03)00069-1 [10] Lord, D., Manar, A. and Vizioli, A. (2005) Modeling Crash-Flow-Density and Crash-Flow-V/C Ratio
Relationships for Rural and Urban Freeway Segments. Accident Analysis and Prevention, 37, 185-199. [11] Ahn, K. and Rakha, H. (2002) Estimating Vehicle Fuel Consumption and Emissions Based on
Instantaneous Speed and Acceleration Levels. Journal of Transportation Engineering, 128, 182-190.
http://dx.doi.org/10.1061/(ASCE)0733-947X(2002)128:2(182) [12] Rakha, H., Ahn, K. and Trani, A. (2004) Development of VT-Micro Model for Estimating Hot Stabilized Light Duty Vehicle and Truck Emissions. Transportation Research Part D: Transport and Environment, 9, 49-74.
http://dx.doi.org/10.1016/S1361-9209(03)00054-3 [13] Rakha, H. and Ahn, K. (2004) Integration Modeling Framework for Estimating Mobile Source Emissions. Journal of Transportation Engineering, 130, 183-193.
http://dx.doi.org/10.1061/(ASCE)0733-947X(2004)130:2(183) [14] Coelho, M.C., Farias, T.L. and Rouphial, N.M. (2006) Effect of Roundabout Operations on Pollutant Emissions. Transportation Research Part D: Transport and Environment, 11, 333-343.
http://dx.doi.org/10.1016/j.trd.2006.06.005 [15] Mensink, C. and Cosemans, G. (2008) From Traffic Flow Simulations to Pollutant Concentrations in Street Canyons and Backyards. Environmental Modeling & Software, 23, 288-295.
http://dx.doi.org/10.1016/j.envsoft.2007.06.005 [16] Paz, A., Veeramisti, N. and Maheshwari, P. (2011) Life-Cycle Benefit-Cost Analysis of Alternatives for Accommodating Heavy Truck Traffic in the Las Vegas Roadway Network. 13th TRB National Transportation Planning Applications Conference, Reno, May 8-12. [17] Bai, S., Chiu, Y.C. and Niemeier, D.A. (2007) A Comparative Analysis of Using Trip-Based versus Link-Based Traffic Data for Regional Mobile Source Emissions Estimation. Atmospheric Environment, 41, 7512-7523.
http://dx.doi.org/10.1016/j.atmosenv.2007.05.051 [18] Lin, J., Chiu, Y.C., Vallamsundar, S. and Bai, S. (2011) Integration of MOVES and Dynamic Traffic
Assignment Models for Fine-Grained Transportation and Air Quality Analyses. Proceedings of the
IEEE Integrated and Sustainable Transportation System, Vienna, June 29-July 1, 176-181. [19] Patriksson, M. (2003) Algorithms for Computing Traffic Equilibria. Networks and Spatial Economics, 4, 23-38.
http://dx.doi.org/10.1023/B:NETS.0000015654.56554.31 [20] Kockelman, K., Fagnant, D., Nichols, B. and Boyles, S. (2012) User’s Guide for PET: Project Evaluation Toolkit. A Sketch-Planning Toolkit for Evaluating Highway Transportation Projects. University of Texas at Austin, Austin. [21] Iida, Y. (1999) Basic Concepts and Future Directions of Road Network Reliability Analysis.
Journal of Advanced Transportation, 33, 125-134. http://dx.doi.org/10.1002/atr.5670330203 [22] Jha, M., Moore, K. and Pashaie, B. (2004) Emergency Evacuation Planning with Microscopic Traffic Simulation. Transportation Research Record, 1886, 40-48. [23] Chiu, Y-C., Nava, E., Zheng, H. and Bustillos, B. (2010) DynusT User’s Manual. [24] FHWA (2004) Traffic Analysis Toolbox Volume III: Guidelines for Applying Traffic Microsimulation Modeling Software. FHWA-HRT-04-040. US Department of Transportation, Washington DC. [25] Cambridge Systematics Inc (2014) Intelligent Deployment Analysis Systems. Appendix B:
IDAS Default Values. http://idas.camsys.com/documentation.htm [26] NDOT (2010) Nevada Traffic Crash Report. Nevada Department of Transportation.
http://www.nevadadot.com/uploadedFiles/NDOT/About_NDOT/NDOT_Divisions/Planning/Safety_
Engineering/2010_Nevada_Traffic_Crashes.pdf [27] NHTSA (2010) Fatality Analysis Reporting System. National Highway Traffic Safety Administration.
http://www-fars.nhtsa.dot.gov/Main/index.aspx [28] CALTRANS (2012) California Life-Cycle Benefit/Cost Analysis Model (Cal-B/C). California Department of Transportation. Cal-B/C Technical Supplement to User’s Guide, Vol. 3.
http://www.dot.ca.gov/hq/tpp/offices/eab/LCBC_Analysis_Model.html [29] CARB (2013) California Air Resources Board. EMFAC 2011. www.arb.ca.gov/msei/emfac2011-ldv-users-guide-final.pdf [30] Sinha, K.C. and Labi, S. (2007) Transportation Decision Making: Principles of Project Evaluation and Programming. John Wiley and Sons, Inc., Hoboken.
http://dx.doi.org/10.1002/9780470168073 [31] US Department of Transportation, Federal Highway Administration (2013) Economic Analysis Primer. http://www.fhwa.dot.gov/infrastructure/asstmgmt/primer05.cfm [32] Office of Management and Budget (OMB) (1992) Circular A-94—Guidelines and Discount Rates for Benefit-Cost Analysis of Federal Programs.
[1] Jeon, C.M., Amekudzi, A.A. and Guensler, R.L. (2010) Evaluating Plan Alternatives for Transportation System Sustainability: Atlanta Metropolitan Region. International Journal of Sustainable Transportation,4, 227-247.
http://dx.doi.org/ 10.1080/15568310902940209 [2] Litman, T. (2007) Developing Indicators for Comprehensive and Sustainable Transport Planning.
Transportation Research Record: Journal of the Transportation Research Board, 2017, 10-15.
http://dx.doi.org/ 10.3141/2017-02 [3] Paz, A., Maheshwari, P., Kachroo, P. and Ahmad, S. (2013) Estimation of Performance Indices for
the Planning of Sustainable Transportation Systems. Advances in Fuzzy Systems, 2013, Article ID:601468.
http://dx.doi.org/10.1155/2013/601468 [4] Maheshwari, P., Khaddar, R., Kachroo, P. and Paz, A. (2014) Dynamic Modeling of Performance
Indices for the Planning of Sustainable Transportation Systems. Networks and Spatial Economics.
http://dx.doi.org/10.1007/s11067-014-9238-6 [5] Lord, D. (2002) Issues Related to the Application of Accident Prediction Models for the Computation of Accident Risk on Transportation Networks. Transportation Research Record: Journal of the Transportation Research Board, 1784, 17-26. [6] Lord, D. (2002) Application of Accident Prediction Models for Computation of Accident Risk on
Transportation Networks. Transportation Research Record, 1784, 17-26.
http://dx.doi.org/10.3141/1784-03 [7] Sawalha, Z. and Sayed, T. (2005) Transferability of Accident Prediction Models. Safety Science,
44, 209-219. http://dx.doi.org/10.1016/j.ssci.2005.09.001 [8] Greibe, P. (2003) Accident Prediction Models for Urban Roads. Accident Analysis & Prevention,
35, 273-285. http://dx.doi.org/10.1016/S0001-4575(02)00005-2 [9] Lord, D. and Persaud, B.N. (2004) Estimating the Safety Performance of Urban Road Transportation Networks. Accident Analysis and Prevention, 36, 609-620.
http://dx/doi.org/10.1016/S0001-4575(03)00069-1 [10] Lord, D., Manar, A. and Vizioli, A. (2005) Modeling Crash-Flow-Density and Crash-Flow-V/C Ratio
Relationships for Rural and Urban Freeway Segments. Accident Analysis and Prevention, 37, 185-199. [11] Ahn, K. and Rakha, H. (2002) Estimating Vehicle Fuel Consumption and Emissions Based on
Instantaneous Speed and Acceleration Levels. Journal of Transportation Engineering, 128, 182-190.
http://dx.doi.org/10.1061/(ASCE)0733-947X(2002)128:2(182) [12] Rakha, H., Ahn, K. and Trani, A. (2004) Development of VT-Micro Model for Estimating Hot Stabilized Light Duty Vehicle and Truck Emissions. Transportation Research Part D: Transport and Environment, 9, 49-74.
http://dx.doi.org/10.1016/S1361-9209(03)00054-3 [13] Rakha, H. and Ahn, K. (2004) Integration Modeling Framework for Estimating Mobile Source Emissions. Journal of Transportation Engineering, 130, 183-193.
http://dx.doi.org/10.1061/(ASCE)0733-947X(2004)130:2(183) [14] Coelho, M.C., Farias, T.L. and Rouphial, N.M. (2006) Effect of Roundabout Operations on Pollutant Emissions. Transportation Research Part D: Transport and Environment, 11, 333-343.
http://dx.doi.org/10.1016/j.trd.2006.06.005 [15] Mensink, C. and Cosemans, G. (2008) From Traffic Flow Simulations to Pollutant Concentrations in Street Canyons and Backyards. Environmental Modeling & Software, 23, 288-295.
http://dx.doi.org/10.1016/j.envsoft.2007.06.005 [16] Paz, A., Veeramisti, N. and Maheshwari, P. (2011) Life-Cycle Benefit-Cost Analysis of Alternatives for Accommodating Heavy Truck Traffic in the Las Vegas Roadway Network. 13th TRB National Transportation Planning Applications Conference, Reno, May 8-12. [17] Bai, S., Chiu, Y.C. and Niemeier, D.A. (2007) A Comparative Analysis of Using Trip-Based versus Link-Based Traffic Data for Regional Mobile Source Emissions Estimation. Atmospheric Environment, 41, 7512-7523.
http://dx.doi.org/10.1016/j.atmosenv.2007.05.051 [18] Lin, J., Chiu, Y.C., Vallamsundar, S. and Bai, S. (2011) Integration of MOVES and Dynamic Traffic
Assignment Models for Fine-Grained Transportation and Air Quality Analyses. Proceedings of the
IEEE Integrated and Sustainable Transportation System, Vienna, June 29-July 1, 176-181. [19] Patriksson, M. (2003) Algorithms for Computing Traffic Equilibria. Networks and Spatial Economics, 4, 23-38.
http://dx.doi.org/10.1023/B:NETS.0000015654.56554.31 [20] Kockelman, K., Fagnant, D., Nichols, B. and Boyles, S. (2012) User’s Guide for PET: Project Evaluation Toolkit. A Sketch-Planning Toolkit for Evaluating Highway Transportation Projects. University of Texas at Austin, Austin. [21] Iida, Y. (1999) Basic Concepts and Future Directions of Road Network Reliability Analysis.
Journal of Advanced Transportation, 33, 125-134. http://dx.doi.org/10.1002/atr.5670330203 [22] Jha, M., Moore, K. and Pashaie, B. (2004) Emergency Evacuation Planning with Microscopic Traffic Simulation. Transportation Research Record, 1886, 40-48. [23] Chiu, Y-C., Nava, E., Zheng, H. and Bustillos, B. (2010) DynusT User’s Manual. [24] FHWA (2004) Traffic Analysis Toolbox Volume III: Guidelines for Applying Traffic Microsimulation Modeling Software. FHWA-HRT-04-040. US Department of Transportation, Washington DC. [25] Cambridge Systematics Inc (2014) Intelligent Deployment Analysis Systems. Appendix B:
IDAS Default Values. http://idas.camsys.com/documentation.htm [26] NDOT (2010) Nevada Traffic Crash Report. Nevada Department of Transportation.
http://www.nevadadot.com/uploadedFiles/NDOT/About_NDOT/NDOT_Divisions/Planning/Safety_
Engineering/2010_Nevada_Traffic_Crashes.pdf [27] NHTSA (2010) Fatality Analysis Reporting System. National Highway Traffic Safety Administration.
http://www-fars.nhtsa.dot.gov/Main/index.aspx [28] CALTRANS (2012) California Life-Cycle Benefit/Cost Analysis Model (Cal-B/C). California Department of Transportation. Cal-B/C Technical Supplement to User’s Guide, Vol. 3.
http://www.dot.ca.gov/hq/tpp/offices/eab/LCBC_Analysis_Model.html [29] CARB (2013) California Air Resources Board. EMFAC 2011. www.arb.ca.gov/msei/emfac2011-ldv-users-guide-final.pdf [30] Sinha, K.C. and Labi, S. (2007) Transportation Decision Making: Principles of Project Evaluation and Programming. John Wiley and Sons, Inc., Hoboken.
http://dx.doi.org/10.1002/9780470168073 [31] US Department of Transportation, Federal Highway Administration (2013) Economic Analysis Primer. http://www.fhwa.dot.gov/infrastructure/asstmgmt/primer05.cfm [32] Office of Management and Budget (OMB) (1992) Circular A-94—Guidelines and Discount Rates for Benefit-Cost Analysis of Federal Programs.