Analysis, Modeling, and Simulation for Traffic Incident Management Applications

Evaluation of TIM AMS Methods

This subsection provides a qualitative assessment of incident modeling methods based on selected criteria.

Qualitative Assessment Based on Selected Criteria (Review Matrix)

Table 2 shows a comprehensive assessment of incident modeling methods.

Table 2. TIM Review Matrix

Category	Application															Data Requirements	Ease of Use	Amount of Applications in Practice	Validation Efforts	Consistency with Traffic Flow Theory	Known Shortcomings	Qualitative Assessment of Validity of Results	Document Used/Reference
	Development and Evaluation of TIM Plans	Analysis and Valuation of TIM Strategies	Decision Support Systems (On-line/Off-line)	Incident Prediction and Detection	Incident Duration Prediction	TIM Performance Measures	Relationship between TIM and Overall Congestion/Travel Time Reliability	Benefit-Cost Analysis of TIM Programs/ Strategies	Safety Analysis Applications	Real-Time ATIS	Integrated Corridor Management	Appropriate for Long-Range Planning	Appropriate for Corridor Planning	Appropriate for Deployment Planning	Appropriate for Benefit/Cost Analysis
Measuring Impacts of Incidents on Traffic Flow
Data Collection and Archiving (Incident and Travel Time Data)
Roadway Sensors/Detectors	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Extensive	Easy	Many	Unknown	N/A	Detector health Spot measurement (not continuous)	Good for Empirical-Based Statistical Analysis	Decomposition of Travel Time Reliability into Various Sources: Incidents, Weather, Work Zones, Special Events, and Base Capacity, Kwon, J. et al., TRB Annual Meeting, January 2011. Freeway Travel Time Forecasting Under Incident, Xia, J. et al., Transportation Research Record: Journal of the Transportation Research Board, Issue 2178, 2010. Decision Support Tools to Support the Operations of Traffic Management Centers (TMC), Hadi, M. et al., January 2011. Modeling Travel Time Variability on Urban Links in London, Hasan, S. et al., European Transport Conference, 2009. A Cellular Automata Approach to Estimate Incident-Related Travel Time on Interstate 66 in Near Real Time, Wang, Z. et al., Virginia Transportation Research Council, 2010. Modeling Incident-Related Traffic and Estimating Travel Time with a Cellular Automaton Model, Murray-Tuite, P, Transportation Research Board 89th Annual Meeting, 2010.
Freeway Service Patrol (FSP)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Extensive	Moderate	Many	Unknown	N/A	Not all incidents are included as FSP does not respond to all incidents	Good for Empirical-Based Statistical Analysis	iMiT: A Tool for Dynamically Predicting Incident Durations, Secondary Incident Occurrence, and Incident Delays, Khattak, A. et al., TRB Annual Meeting, January 2011. Benefit-Cost Analysis of Freeway Service Patrol Programs: Methodology and Case Study, Chou, C. et al.
Accident Logs	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Extensive	Moderate	Many	Unknown	N/A	Not all incidents are included in accident logs and some of the records are not accurate	Good for Empirical-Based Statistical Analysis	Structure Learning for the Estimation of Non-Parametric Incident Duration Prediction, Demiroluk, S. et al., Transportation Research Board 90th Annual Meeting. Development of a Hybrid Model for Freeway Incident Duration: A Case Study in Maryland, Kim, W. et al., 17th ITS World Congress, Busan, 2010. Are Incident Durations and Secondary Incidents Interdependent, Khattak, A. et al., Transportation Research Record: Journal of the Transportation Research Board Issue Number: 2099, 2009. Identifying Secondary Crashes and Their Contributing Factors, Zhan, C. et al., Transportation Research Record: Journal of the Transportation Research Board Issue Number: 2102, 2009. Analysis of Freeway Incident Duration for ATIS Applications, Kim, W. et al., 15th World Congress on Intelligent Transport Systems and ITS America’s Annual Meeting, 2008. Dynamic Incident Progression Curve for Classifying Secondary Traffic Crashes, Journal of Transportation Engineering, December 2010.
TMC Data	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Extensive	Moderate	Many	Unknown	N/A	May contain incomplete information	Good for Empirical-Based Statistical Analysis	Incident Duration Prediction for In-Vehicle Navigation System, Hu, J. et al., Transportation Research Board 90th Annual Meeting, 2011. What Is the Role of Multiple Secondary Incidents in Traffic Operations, Zhang, H. et al., Journal of Transportation Engineering Volume: 136, 2010. Decision Support Tools to Support the Operations of Traffic Management Centers (TMC), Hadi, M. et al., January 2011.
Simulation	Yes	Yes	Yes	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Extensive	Difficult	Many	Unknown	N/A	Resource intensive (cost, expertise, analysis time)	Good if the simulation model is developed and calibrated well	Use of Simulation-Based Forecast for Real Time Traffic Management Decision Support: The Case of the Madrid Traffic Centre, Torday, A. et al., European Transport Conference, 2008. Measurement of Uncertainty Costs with Dynamic Traffic Simulations, Marchal, F. et al., Transportation Research Record: Journal of the Transportation Research Board, 2008. On-line Microscopic Traffic Simulation to Support Real Time Traffic Management Strategies, Barcelo, J. et al. Benefit-Cost Analysis of Freeway Service Patrol Programs: Methodology and Case Study, Chou, C. et al. Estimation of Nonrecurring Post-incident Traffic Recovery Time for Different Flow Regimes: Comparing Shock Wave Theory and Simulation Modeling, Jeihani, M. et al., Transportation Research Board 90th Annual Meeting, 2011. Regional Emergency Action Coordination Team (REACT) Evaluation, by Battelle, July 2002.
Automatic Number Plate Reader (ANPR)	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Extensive	Difficult	Rare	Unknown	N/A	Resource intensive (cost and analysis time)	Good for Empirical-Based Statistical Analysis	Modeling Travel Time Variability on Urban Links in London, Hasan, S. et al., European Transport Conference, 2009.
Web-based Data Collection and Archiving System	Yes	Yes	Yes	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Extensive	Easy	Some	Unknown	N/A	Unknown	Good if the quality of data feeding into the system is good	Freeway and Arterial System of Transportation (FAST) Dashboard (Regional Transportation Commission (RTC) of Southern Nevada).
Analytical Methods
Coordinated Highways Action Response Team (CHART, University of Maryland, Model-Based Stochastic Approach)	Yes	Yes	Yes	No	No	No	No	Yes	No	No	No	Yes	Yes	Yes	Yes	Minimal	Easy	One	Unknown	N/A; Statistical-based	Only includes a volume term – should include a V/C term instead	Will overestimate delay at low volumes	Performance Evaluation and Benefit Analysis for CHART in Year 2009, Chang, G. et al.
Quantile Regression (Empirical-Based Statistical Method)	Yes	Yes	Yes	No	No	Yes	Yes	Yes	No	No	No	Yes	Yes	Yes	Yes	Extensive	Easy	One	Unknown	N/A; Statistical-based	It is site specific and hard to make generalization to other facilities	Results are valid as long as data input is reasonably accurate	Decomposition of Travel Time Reliability into Various Sources: Incidents, Weather, Work Zones, Special Events, and Base Capacity, Kwon, J. et al., TRB Annual Meeting, January 2011.
Simulation	Yes	Yes	Yes	No	No	Yes	Yes	Yes	No	No	No	Yes	Yes	Yes	Yes	Extensive	Difficult	One	Unknown	N/A; Simulation-based	Unknown	Moderate	Measurement of Uncertainty Costs with Dynamic Traffic Simulations, Marchal, F. et al., Transportation Research Record: Journal of the Transportation Research Board, 2008. On-line Microscopic Traffic Simulation to Support Real Time Traffic Management Strategies, Barcelo, J. et al. Regional Emergency Action Coordination Team (REACT) Evaluation, by Battelle, July 2002. Estimation of Incident Delays on Arterial Streets, Yang, S. et al., Transportation Research Board 87th Annual Meeting, 2008.
Predicting Impacts of Incidents on Traffic Flow
Regression Model	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Extensive	Easy	Two	Unknown	N/A; Statistical-based	N/A	Good	Estimation of Nonrecurring Postincident Traffic Recovery Time for Different Flow Regimes: Comparing Shock Wave Theory and Simulation Modeling, Jeihani, M. et al., Transportation Research Board 90th Annual Meeting, 2011. Estimation of Incident Delays on Arterial Streets, Yang, S. et al., Transportation Research Board 87thAnnual Meeting, 2008.
Shock Wave Model	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Moderate	Moderate	One	Unknown	Yes	Tend to report shorter recovery time as it only calculates queue dissipation time which does not necessarily equate with the time to return to pre-incident normal traffic flow condition	Moderate	Estimation of Nonrecurring Postincident Traffic Recovery Time for Different Flow Regimes: Comparing Shock Wave Theory and Simulation Modeling, Jeihani, M. et al., Transportation Research Board 90th Annual Meeting, 2011.
Queuing Analysis	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Easy	Three	Moderate validation efforts	Yes	Unknown	Moderate	Primary and Secondary Incident Management: Predicting Durations in Real Time, Khattak, A. et al., Final Report VCTIR 11-R11, Virginal Center for Transportation Innovation and Research, April 2011. Decision Support Tools to Support the Operations of Traffic Management Centers (TMC), Hadi, M. et al., January 2011. Comprehensive Analysis of Important Questions Related to Incident Durations Based on Past Studies and Recent Empirical Data, Yazici, A. et al., TRB 89th Annual Meeting, January 2010.
Adjustment Method Based on Queuing Analysis	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Extensive	Moderate	One	Moderate validation efforts	Yes	Unknown	Moderate	Freeway Travel Time Forecasting Under Incident, Xia, J. et al., Transportation Research Record: Journal of the Transportation Research Board, Issue 2178, 2010.
Difference-in-Travel-Time Method	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Moderate	One	Moderate validate efforts using a case study	N/A; Statistical-based	Unknown	Moderate	Empirical Method for Estimating Traffic Incident Recovery Time, Zeng, X. et al., Transportation Research Record: Journal of the Transportation Research Board, Issue 2178, 2010.
Marginal Incident Computation (MIC) Model	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Difficult	One	Unknown	N/A; Statistical-based	Model is complicated Should be refined to consider other causes of variable travel times, such as demand fluctuations and capacity fluctuations	Moderate	Stochastic Dynamic Network Loading for Travel Time Variability Due to Incidents, Corthout, R. et al., New Developments in Transport Planning: Advances in Dynamic Transport Assignment, 2010.
IDAS	Yes	Yes	Yes	No	Yes	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Moderate	N/A	Unknown	Partly (Combined analytical model and empirical data)	Do not consider spatial characteristics of incident delay	Moderate	N/A
Genetic Neural Network (GNN)	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Difficult	One	Unknown	N/A; Statistical-based	Difficult to understand the model parameters (blackbox)	Moderate	Prediction of Freeway Travel Time in Incident Management Evaluation Based on Genetic Neural Network, He, D. et al., Seventh International Conference on Traffic and Transportation Studies, 2010.
Cellular Automata	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Difficult	Two	Moderate validation efforts	Yes	Computation resource intensive May give poor results if input detector data is not accurate	Moderate	A Cellular Automata Approach to Estimate Incident-Related Travel Time on Interstate 66 in Near Real Time, Wang, Z. et al., Virginia Transportation Research Council, 2010. Modeling Incident-Related Traffic and Estimating Travel Time with a Cellular Automaton Model, Murray-Tuite, P, Transportation Research Board 89th Annual Meeting, 2010.
Simulation	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Extensive	Difficult	Five	Unknown	N/A; Simulation-based	Resource intensive (data, cost, expertise, analysis time)	Good	Estimation of Non-recurring Post-incident Traffic Recovery Time for Different Flow Regimes: Comparing Shock Wave Theory and Simulation Modeling, Jeihani, M. et al., Transportation Research Board 90th Annual Meeting, 2011. Estimation of Traffic Recovery Time for Different Flow Regimes on Freeways, Saka, A. et al., Maryland State Highway Administration, Report No. MD-09-SP708B4L, July 2008. Use of simulation-based forecast for real time traffic management decision support: the case of the Madrid traffic centre, Torday, A. et al,. European Transport Conference, 2008. On-line Microscopic Traffic Simulation to Support Real Time Traffic Management Strategies, Barcelo, J. et al. Non-Recurrent Congestion Simulation And Application, Jiang, Z. et al., 15th World Congress on Intelligent Transport Systems and ITS America’s Annual Meeting, 2008. Development of a Traffic Simulator for the Baltimore Beltway for Traffic Operations and Incident Management (MD-10-SP808B4M). Management and Analysis of Michigan Intelligent Transportation Systems Center Data with Application to the Detroit Area I-75 Corridor, Grand Valley State University and Wayne State University, Detroit, Michigan, Report No: MIOH UTC TS21p1-2 2011.
Predicting Incident Characteristics (e.g., Duration)
Regression Model	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Easy	Three	Moderate validation efforts	N/A; Statistical-based	The explanatory power of the model may be poor	Moderate	A Comparative Study of Models for the Incident Duration Prediction, Valenti, G. et al., European Transport Research Review, 2010. Are Incident Durations and Secondary Incidents Interdependent, Khattak, A. et al., Transportation Research Record: Journal of the Transportation Research Board Issue Number: 2099, 2009. Primary and Secondary Incident Management: Predicting Durations in Real Time, Khattak, A. et al, Final Report VCTIR 11-R11, Virginal Center for Transportation Innovation and Research, April 2011.
Log-Logistic (Accelerated Failure Time, or AFT) Model	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Moderate	One	Out-performed other naïve predictors	N/A; Statistical-based	Unknown	Moderate	Incident Duration Prediction for In-vehicle Navigation System, Hu, J. et al., Transportation Research Board 90th Annual Meeting, 2011.
iMiT – Incident Management Integration Tool (On-line Tool Based on Statistical Regression)	Yes	Yes	Yes (on-line)	No	No	Yes	No	Yes	No	Yes	Yes (Predict secondary incident occurrence)	Yes	Yes	Yes	Yes	Moderate	Easy	One	Empirically validated by comparing the model’s predicted incident durations in year 2007 against the observed incident durations	N/A; Statistical-based	The model was based on Safety Service Patrol (SSP) data, but SSP did not respond to all incidents; therefore, the data used for the model may be biased	Good (Considering it was able to predict incident duration with root mean squared error (RMSE) within 16.4%	iMiT: A Tool for Dynamically Predicting Incident Durations, Secondary Incident Occurrence, and Incident Delays, Khattak, A. et al., TRB Annual Meeting, January 2011.
Shock Wave Model	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Moderate	One	N/A	Yes	Unknown	Moderate	Stochastic Incident Duration: Impact on Delay, Knoop, V. et al., Transportation Research Board 89th Annual Meeting, 2010.
Hazard-Based Duration Regression Model	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Moderate	One	N/A	N/A; Statistical-based	Unknown	Moderate	An Information-Based Time Sequential Approach to On-line Incident Duration Prediction, Qi, Y. et al., Journal of Intelligent Transportation Systems Volume: December 2008.
Prediction/Decision Tree (DT)	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Moderate	One	Moderate validation efforts	N/A; Statistical-based	Unknown	Moderate	A Comparative Study of Models for the Incident Duration Prediction, Valenti, G. et al., European Transport Research Review, 2010.
Rule-Based Tree Model (RBTM)	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Moderate	One	N/A	N/A; Statistical-based	May need to use together with supplemental models for more accurate prediction	Moderate	Analysis of Freeway Incident Duration for ATIS Applications, Kim, W., S. Natarajan, and G. Chang, 15th World Congress on Intelligent Transport Systems and ITS America’s Annual Meeting, 2008. An Integrated Knowledge Based System for Real-Time Estimation of Incident Durations and Nonrecurrent Congestion Delay for Freeway Networks (MD-09-SP708B4C).
Support/Relevance Vector Machine (RVM) Model	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Difficult	One	Moderate validation efforts	N/A; Statistical-based	Tend to underestimate the prediction values for the long duration incident cases	Moderate	A Comparative Study of Models for the Incident Duration Prediction, Valenti, G. et al., European Transport Research Review, 2010.
Hybrid Model (Rule-Based Tree Model (RBTM), Multinomial Logit Model (MNL), and Naïve Bayesian Classifier (NBC))	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Difficult	One	N/A	N/A; Statistical-based	Model is complicated	Moderate	Development of a Hybrid Model for Freeway Incident Duration: A Case Study in Maryland, Kim, W. et al., 17th ITS World Congress, Busan, 2010.
K-Nearest-Neighbor (KNN)	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Difficult	Two	Moderate validation efforts	N/A; Statistical-based	Tend to overestimate the prediction values for the short duration incident cases	Moderate	A Comparative Study of Models for the Incident Duration Prediction, Valenti, G. et al., European Transport Research Review, 2010. Decision Support Tools to Support the Operations of Traffic Management Centers (TMC), Hadi, M. et al., January 2011.
Artificial Neural Network (ANN)	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Difficult	One	Moderate validation efforts	N/A; Statistical-based	Tend to overestimate the prediction values for the short duration incident cases	Moderate	A Comparative Study of Models for the Incident Duration Prediction, Valenti, G. et al., European Transport Research Review, 2010.
Bayesian Network	Yes	Yes	Yes	No	No	Yes	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Moderate	Moderate	Two	Unknown	N/A; Statistical-based	It is site specific and hard to make generalization to other facilities	Good	Structure Learning for the Estimation of Non-Parametric Incident Duration Prediction, Demiroluk, S. et al., Transportation Research Board 90th Annual Meeting. Traffic Incident Duration Prediction Based on the Bayesian Decision Tree Method, Yang, B. et al., The First International Symposium on Transportation and Development – Innovative Best Practices, 2008.
Quantifying Occurrence and Characteristics of Secondary Crashes
Regression Model	No	No	No	No	No	No	No	No	Yes	No	No	Yes	Yes	Yes	Yes	Moderate	Moderate	One	The model was validated against 640 sample data set and the result showed that the methodology reduced Type I error by 24.38% and Type II by 3.13%	N/A; Statistical-based	Unknown	Moderate	Dynamic Incident Progression Curve for Classifying Secondary Traffic Crashes, Journal of Transportation Engineering, December 2010.
Ordered Logit Model and Heckman Model	Yes	Yes	Yes	No	No	No	No	Yes	Yes	No	No	Yes	Yes	Yes	Yes	Moderate	Moderate	One	N/A	N/A; Statistical-based	Model has limited goodness of fit due to the complexity and randomness of secondary incident occurrence	Moderate	What Is the Role of Multiple Secondary Incidents in Traffic Operations, Zhang, H. et al., Journal of Transportation Engineering Volume: 136, 2010.
Simulation-Based Secondary Incident Filtering (SBSIF) Method	Yes	Yes	Yes	No	No	No	No	Yes	Yes	No	No	Yes	Yes	Yes	Yes	Moderate	Difficult	One	Validated using 6-month data along a segment of I-287 in the New York State	N/A; Simulation-based	Model needs to be recalibrated for use for other locations Additional factors, such as weather, could be considered	Moderate	Simulation-Based Secondary Incident Filtering Method, Chou, C. et al., Journal of Transportation Engineering Volume: 136, 2010.
Probit Model	Yes	Yes	Yes	No	No	Yes	No	Yes	Yes	No	No	Yes	Yes	Yes	Yes	Moderate	Moderate	Two	N/A	N/A; Statistical-based	Model has limited goodness of fit due to the complexity and randomness of secondary incident occurrence	Moderate	Are Incident Durations and Secondary Incidents Interdependent, Khattak, A. et al., Transportation Research Record: Journal of the Transportation Research Board Issue Number: 2099, 2009. Primary and Secondary Incident Management: Predicting Durations in Real Time, Khattak, A. et al, Final Report VCTIR 11-R11, Virginal Center for Transportation Innovation and Research, April 2011.
Logistic Regression Model	Yes	Yes	Yes	No	No	Yes	No	Yes	Yes	No	No	Yes	Yes	Yes	Yes	Moderate	Moderate	Two	Unknown	N/A; Statistical-based	Unknown	Moderate	Identifying Secondary Crashes and Their Contributing Factors, Zhan, C. et al., Transportation Research Record: Journal of the Transportation Research Board Issue Number: 2102, 2009 Decision Support Tools to Support the Operations of Traffic Management Centers (TMC), Hadi, M. et al., January 2011.
Bayesian Network	Yes	Yes	Yes	No	No	Yes	No	Yes	Yes	No	No	Yes	Yes	Yes	Yes	Moderate	Moderate	One	Moderate validation efforts	N/A; Statistical-based	Unknown	Moderate	Freeway Operations, Spatiotemporal-Incident Characteristics and Secondary-Crash Occurrence, Vlahogianni, E. et al., Transportation Research Record: Journal of the Transportation Research Board Issue Number: 2178, 2010.

Notes

A: Development and evaluation of TIM plans.

B: Analysis and valuation of TIM strategies such as use of service patrols.

C: Decision support systems (on-line/off-line).

D: Incident prediction and detection.

E: Incident duration prediction.

F: TIM Performance Measures.

G: Relationship between TIM and overall congestion/travel time reliability.

H: Benefit-cost analysis of TIM programs/strategies.

I: Safety analysis applications such as secondary crash analysis.

J: Real-time ATIS.

K: Integrated Corridor Management.

L: Most appropriate uses of the method (e.g., long-range planning, corridor planning, deployment planning, benefit/cost analysis).