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Federal Highway Administration Research and Technology
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Publication Number: FHWA-HRT-04-131
Date: September 2004 |
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Adverse weather conditions can have a dramatic impact on the quality of traffic flow. Traffic analysts need adequate traffic analysis tools to design better for the impacts of adverse weather. One available type of analysis tools is microscopic traffic simulation, which allows analysts to model and evaluate complex roadway geometries, traffic control devices, and Intelligent Transportation Systems (ITS).
The objectives of this effort, as captured in the report, Identifying and Assessing Key Weather-Related Parameters and Their Impacts on Traffic Operations Using Simulation, are to identify how adverse weather affects traffic operations, to assess the sensitivity of weather-related traffic parameters in a microscopic traffic simulation package (CORSIM), and to develop guidelines for using the CORSIM simulation model to account for the affects of adverse weather.
The intended audiences for this report are transportation professionals who use traffic analysis tools, in particular microscopic traffic simulation, to plan, evaluate, or design roadway or traffic control improvements and are interested in incorporating the impacts of adverse weather into their analysis.
Toni Wilbur
Director, Office of Operations Research and Development
Notice
This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained in this document. This report does not constitute a standard, specification, or regulation.
The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers' names appear in this report only because they are considered essential to the objective of the document.
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2. General Relationship Between Weather Events and Traffic Operations
Relationship Between Roadway Environment and Weather Event
Relationship Between Roadway Environment and Traffic Parameters
4. Identifying Simulation Parameters Affected by Weather Events
Traffic Control and Management Parameters
Vehicle Performance Parameters
5. CORSIM Sensitivity Analysis
FRESIM Sensitivity Analysis Results
Sensitivity of Car Following Parameters
Sensitivity of Lane Changing Parameters
Sensitivity of Free-Flow Speed Parameters
NETSIM Sensitivity Analysis Results
Sensitivity of Car Following Parameters
Sensitivity of Lane Changing Parameters
Sensitivity of Free-Flow Speed Parameters
Sensitivity of Discharge Headway Parameters
Sensitivity of Startup Delay Parameters
Sensitivity of Turning Speed Parameters
Summary of Sensitivity Analysis
6. Guidelines for Modeling Weather Events in CORSIM
Relationship Between Weather Events and Traffic Operations
Microsimulation Parameters Affected by Weather Events
Guidelines for Modeling Weather Events in CORSIM
Figure 2. Relationship Between Weather Events and Traffic Operations
Figure 3. NETSIM Suburban Intersection Network. PA
Figure 4. NETSIM Urban Intersection Network
Figure 5. Analysis Area Information for Sample Sensitivity Test
Figure 6. Vehicle-Kilometers Traveled Graph for Sample Sensitivity Test
Figure 7. Average Speed Graph for Sample Sensitivity Test
Figure 8. Average Delay Graph for Sample Sensitivity Test
Figure 9. Average Density Graph for Sample Sensitivity Test
Figure 13. Microsimulation Model Development and Application Process
Table 1. Impacts of Weather Events on Roadway Environment
Table 2. Range of Generic Traffic Simulation Parameters
Table 3. Road Geometry Traffic Parameters Impacted by Weather Events
Table 4. Traffic Control and Management Parameters Impacted by Weather Events
Table 5. Vehicle Performance Traffic Parameters Impacted by Weather Events
Table 6. Traffic Demand Traffic Parameters Impacted by Weather Events
Table 7. Driver Behavior Traffic Parameters Impacted by Weather Events
Table 8. FRESIM Sensitivity Analysis Networks
Table 9. Congestion Levels for FRESIM Sensitivity Analysis
Table 10. FRESIM MOEs for Sensitivity Analysis
Table 11. FRESIM MOE Collection Areas
Table 12. Car Following FRESIM Parameters Included in Sensitivity Analysis
Table 13. Lane Changing FRESIM Parameters Included in Sensitivity Analysis
Table 14. Free-Flow Speed FRESIM Parameters Included in Sensitivity Analysis
Table 15. NETSIM Sensitivity Analysis Networks
Table 16. Congestion Levels for NETSIM Sensitivity Analysis
Table 17. NETSIM MOEs for Sensitivity Analysis
Table 18. NETSIM MOE Collection Areas
Table 19. Car Following NETSIM Parameter Included in Sensitivity Analysis
Table 20. Lane Changing NETSIM Parameters Included in Sensitivity Analysis
Table 21. Free-Flow Speed NETSIM Parameters Included in Sensitivity Analysis
Table 22. Discharge Headway NETSIM Parameters Included in Sensitivity Analysis
Table 23. Startup Lost Time NETSIM Parameters Included in Sensitivity Analysis
Table 24. Turning Speed NETSIM Parameters Included in Sensitivity Analysis
Table 25. General Information for Sample Sensitivity Test
Table 26. Vehicle-Kilometers Traveled Table for Sample Sensitivity Test
Table 27. Average Speed Table for Sample Sensitivity Test
Table 28. Average Delay Table for Sample Sensitivity Test
Table 29. Average Density Table for Sample Sensitivity Test
Table 30. CORSIM Parameter Sensitivity Groups
Table 31. General Sensitivity of FRESIM Car Following Parameters
Table 32. General Sensitivity of FRESIM Lane Changing Parameters
Table 33. General Sensitivity of FRESIM Free-Flow Speed Parameters
Table 34. General Sensitivity of NETSIM Car Following Parameters
Table 35. General Sensitivity of NETSIM Lane Changing Parameters
Table 36. General Sensitivity of NETSIM Free-Flow Speed Parameters
Table 37. General Sensitivity of NETSIM Discharge Headway Parameters
Table 38. General Sensitivity of NETSIM Startup Delay Parameters
Table 39. General Sensitivity of NETSIM Turning Speed Parameters
Table 40. Traffic Parameters with No Effect on MOEs
Table 41. Traffic Parameters with Expected and Medium-to-High Effect on MOEs
Table 42. CORSIM Parameters Impacted by Weather Events
Adverse weather conditions can have a dramatic impact on the operations and quality of traffic flow. With the advent of advanced traffic management systems (ATMS), there is an opportunity to develop traffic management strategies that seek to minimize negative weather-related impacts on traffic operations. Although simulation models are used widely in evaluating various traffic management strategies, applying them to evaluate ATMS strategies under adverse weather conditions needs to be explored.
The objectives of this study were to identify how weather events impact traffic operations, assess the sensitivity of weather-related traffic parameters in the CORridor SIMulation (CORSIM) traffic microsimulation model, and develop guidelines for using the CORSIM model to account for the impacts of adverse weather conditions on traffic operations.
This final report summarizes the methodologies, findings, and conclusions for each of these study objectives. A high-level conclusion from this project is that CORSIM can be used adequately to model the impacts of weather events on traffic operations. This conclusion is based on the fact that a majority of the generic weather-related parameters identified are currently available in CORSIM, and that the key weather-related parameters are adequately sensitive in producing model outputs inline with that expected from adverse weather.
This report is organized into seven major sections. A summary of each section is provided below.
Section 1—Introduction
This section presents the background and motivation for completing this project.It also highlights the objectives of the study and work tasks for each phase of the study.
Section 2—General Relationship Between Weather Events and Traffic Operations
Conceptually, it is easy to understand that a major weather event, such as a snowstorm, will lead to lower average speeds and higher delays. However, it is important to know what this relationship is, or in other words, what causes a weather event to degrade traffic operations.
This section shows that a weather event impacts traffic operations through a chain reaction: a weather event causes a change in the roadway environment (e.g., reduced visibility and pavement friction), which causes a reduction in traffic parameters (e.g., lower free-flow speeds and capacities), thereby creating a degradation in traffic flow (e.g., higher delays and lower average speeds).
The qualitative impacts of weather events are seen easily through this relationship, but the quantitative impacts have been historically difficult to measure for a number of reasons. For example, there are many "shades" of the severity of a weather event, and the impacts are different regionally (i.e., a snowstorm in Florida will have more impact than the same storm in Minnesota) and by time of year (i.e., a snowstorm at the beginning of winter will likely have more impact than the same storm near the end of winter after drivers have acclimated to the adverse weather).
Section 3—Literature Review
This section summarizes past research regarding the impact of weather events on traffic parameters, or inputs to a traffic model. Past research has shown a quantitative link between various weather events and reduced free-flow speeds, saturation (discharge) headway, startup lost time, and traffic demand.
Section 4—Identifying Simulation Parameters Affected by Weather Events
This section identifies the range of simulation parameters likely impacted by weather events. First, researchers developed a list of generic microsimulation parameters that are included in most simulation models. Then, parameters that potentially are impacted by weather events through a change in the roadway environment were determined based on the literature review and engineering judgment (e.g., adverse weather generally causes more conservative driver behavior, which means car following behavior is likely impacted by adverse weather).
Section 5—CORSIM Sensitivity Analysis
The purpose of the sensitivity study was to identify the most sensitive weather-related parameters in CORSIM. Each test parameter was modeled on various geometric networks and congestion (volume) levels using the default value and then changing the value to represent incrementally more conservative driver behavior, as would occur under adverse weather. The measures of effectiveness (MOE) produced by the default value then were compared to the MOEs produced with the changed parameter values to determine the level of sensitivity the parameter has on the MOEs.
Due to the large number of roadway networks, congestion levels, and parameters tested, approximately 45,000 individual CORSIM runs were completed. As a result, a largely automated process of creating the CORSIM input files and summarizing the output files was created specifically for this project.
One interesting result of the sensitivity analysis was that a number of parameters tested (19 total) had little or no impact on the MOEs. The majority of these were lane changing parameters. This finding does not mean they have no sensitivity whatsoever, but that they showed no sensitivity to the aggregate-level MOEs used for this study.
A number of weather-related parameters had an expected effect on the MOEs and were categorized as either having a medium or high effect on the MOEs (relative to the other parameters). These parameters are important because they represent the key weather-related parameters that should be altered when trying to model weather events in CORSIM. These parameters included the car following sensitivity multiplier and mean free-flow speed for freeway facilities, and time to react to sudden deceleration of lead vehicle, mean free-flow speed, mean discharge headway, and mean startup delay for arterial streets.
Section 6—Guidelines for Modeling Weather Events in CORSIM
This section provides practical guidelines for modeling weather events in CORSIM. The guidelines are based on Traffic Analysis Toolbox Volume III: Guidelines for Applying Traffic Microsimulation Modeling Software, a Federal Highway Administration (FHWA) guidance document on the proper guidance in this section builds on the more general microsimulation guidance by providing additional considerations when modeling weather events in CORSIM. For example, the type, severity, extent, and time period of the weather event being modeled should be agreed on before coding the model.
This section also details specific CORSIM parameters to consider changing when modeling various weather events. Finally, this section describes an alternate method of calibrating a microscopic simulation model when field data collection during adverse weather is not possible.
Section 7—Conclusions
This section summarizes the findings and results of each phase of the study and also highlights four areas of future research: empirical data collection to improve base understanding of impact of weather events on traffic operations, CORSIM enhancements for modeling adverse weather events, further study of CORSIM parameters which showed no or little sensitivity, and real-world case study of modeling weather events using CORSIM.
