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Publication Number: FHWA-HRT-04-131
Date: September 2004

Identifying and Assessing Key Weather-Related Parameters and Their Impacts on Traffic Operations Using Simulation

7. Conclusions

The objectives of this study were to identify how weather events impact traffic operations, assess the sensitivity of weather-related parameters in the 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 section highlights the major findings and conclusions reached from the analysis documented in this report. Also, future research needs were identified as part of this project and are summarized below.

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 understand what this relationship is, or in other words, what causes a weather event to degrade traffic operations.

A weather event impacts traffic operations through a chain reaction: the 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 degrading 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).

Numerous past research studies have shown a quantitative link between various weather events and reduced free-flow speeds, saturation (discharge) headway, startup lost time, and traffic demand. This research shows a link between weather events and changes in traffic parameters; however, very little research has focused on the role of roadway environment impacts in causing the degradation in traffic parameters. This lack of information probably is due to the difficulty in understanding why motorists respond to a weather event (i.e., is a reduction in free-flow speed really due to a reduction in pavement friction or reduction in visibility?).

Microsimulation Parameters Affected by Weather Events

Past research has documented a number of traffic parameters that are impacted by weather events. However, many microsimulation parameters have not been measured empirically to behave differently during adverse weather.

Tables 3 through 6 show the traffic simulation parameters that are likely impacted by weather events (through a change in the roadway environment). The selection of these parameters was based on the range of simulation parameters identified in table 2, the literature review, and engineering judgment based on the concept that driver behavior becomes more conservative during adverse weather conditions. Unfortunately, there is currently no empirical research supporting the latter concept. Therefore, the table only lists the range of potential, not proven, simulation parameters that may be used to model adverse weather conditions in a simulation model. These simulation parameters may be used as a guide for traffic analysts when considering which parameters to adjust when modeling adverse weather.

CORSIM Sensitivity Analysis

The purpose of the sensitivity analysis 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, then changing the value to represent incrementally more conservative driver behavior, as would occur under adverse weather. The MOEs produced by the default value were then 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; rather, the finding shows no sensitivity to the aggregate-level MOEs used for this study. It is likely that more sensitivity would have been measured by using more disaggregate MOEs, or by evaluating trajectories of individual vehicles.

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 include:

Table 41 provides a more detailed list of the key weather-related parameters identified during the sensitivity analysis.

Guidelines for Modeling Weather Events in CORSIM

A set of practical guidelines for modeling weather events in CORSIM was developed as a result of the CORSIM sensitivity analysis. The guidelines are based on Traffic Analysis Toolbox Volume III—Guidelines for Applying Traffic Microsimulation Modeling Software, an FHWA guidance document on the proper development and application of microsimulation models.(1) The guidance 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 before coding the model.

The guidelines list CORSIM parameters to consider changing when modeling various weather events (see table 42). Also, an alternate method of calibrating a weather event model is presented when field data collection during adverse weather is not possible.

Future Research Needs

Based on the findings of this study, a number of areas of future research were identified that would improve the ability of CORSIM to model weather events and improve the base understanding of the relationship between weather events and traffic operations. The four identified areas of future research are:

  1. Empirical data collection to improve base understanding of impact of weather events on traffic operations.
  2. CORSIM enhancements for modeling adverse weather events.
  3. Further study of "insensitive" CORSIM parameters.
  4. Real-world case study of modeling weather events using CORSIM.

The first area, improving the base understanding of the weather event-to-traffic operations link, is the most important area of future research, because it will help analysts in all applications of traffic analysis models better understand the impacts of adverse weather on traffic operations. This study showed the most sensitive weather-related parameters in CORSIM and, as such, these parameters should be of high importance to collect during adverse weather. While the empirical data collection will help determine the true impact of adverse weather on traffic operations, it will not shed light on the impacts not measurable by video or roadway detectors, namely, the reason why drivers change their driving behaviors. For this reason, supplemental human factors studies should be initiated that would help explain the impact of the roadway environment on traffic operations (i.e., did a driver slow down because of a reduction in visibility, reduction in pavement friction, or a combination of both?).

The second area of future research deals with improving the ability of CORSIM to model the impacts of weather events. Some basic improvements to CORSIM that would help analysts improve how they model the impacts of weather events include:

References

  1. Volume IIIGuidelines for Applying Traffic Microsimulation Modeling Software. FHWA-HRT-04-040 Federal Highway Administration, Washington, DC, 2003.
  2. Highway Capacity Manual. Transportation Research Board, National Research Council, Washington, DC, 2000.
  3. Pisano, P. and L.C. Goodwin. "Surface Transportation Weather Applications." Institute for Transportation Engineers 2002 Annual Meeting and Exhibit Compendium of Papers, Philadelphia, PA, August 2002.
  4. Kyte, M., Z. Khatib, P. Shannon, and F. Kitchener. "Effect of Weather on Free-Flow Speed." In Transportation Research Record 1776, Transportation Research Board, National Research Council, Washington, DC, 2001, pp. 60-68.
  5. May, A.D. Capacity and Level of Service for Freeway Systems. Third Interim Report, Phase C, Tasks C1 to C10. National Cooperative Highway Research Program, Washington, DC, 1998.
  6. Lamm, R., E.M. Choueiri, and T. Mailaender. "Comparison of Operating Speeds on Dry and Wet Pavements of Two-Lane Rural Highways." In Transportation Research Record 1280, Transportation Research Board, National Research Council, Washington, DC, 1990, pp. 199-207.
  7. Ibrahim, A.T. and F.L. Hall. "Effect of Adverse Weather Conditions on Speed-Flow-Occupancy Relationships." In Transportation Research Record 1457, Transportation Research Board, National Research Council, Washington, DC, 1994, pp. 184-191.
  8. Perrin, J., P.T. Martin, and B.G. Hansen. "Modifying Signal Timing During Inclement Weather." Institute for Transportation Engineers 2002 Annual Meeting and Exhibit Compendium of Papers, Philadelphia, PA, August 2002.
  9. Maki, P.J. "Adverse Weather Traffic Signal Timing." Institute for Transportation Engineers 1999 Annual Meeting and Exhibit Compendium of Papers, Las Vegas, NV, August 1999.
  10. 1977 Economic Impact of the Highway Snow and Ice Control. Final Report. FHWA-RD-77-95, Federal Highway Administration, Washington, DC, 1977.
  11. Botha, J.L. and T.R. Kruse. "Flow Rates at Signalized Intersections Under Cold Winter Conditions." In Journal of Transportation Engineering, American Society of Civil Engineers, Reston, VA, 1992, Volume 118, Number 3, pp. 439-450.
  12. ITT Industries, Inc., Systems Division. TSIS Version 5.1 User's GuideVolumes 1-3. Federal Highway Administration Contract No. DTFH61-01-C-00005.
  13. Halati, A., H. Lieu, and S. Walker. "CORSIM—Corridor Traffic Simulation Model." 1997 Annual Transportation Research Board Meeting Compendium of Papers, Washington, DC, 1997.

Bibliography

  1. Al Hassan, Y. and D.J. Barker. "The Impact of Unseasonable or Extreme Weather on Traffic Activity Within Lothian Region, Scotland." In Journal of Transport Geography, Elsevier Science Ltd., 1999, pp. 209-213.
  2. Chen, S., T.B. Sheridan, H. Kusunoki, and N. Komoda. "Car-Following Measurements, Simulations, and a Proposed Procedure for Evaluating Safety." In IFAC Man-Machine Systems, Cambridge, MA, 1995, pp. 529-534.
  3. Law, A. and Kelton, W. D., Simulation Modeling and Analysis, 2nd edition, McGraw Hill Publishing, 1991.
  4. Peltola, H. "Effects of Seasonally Changing Speed Limits on Speeds and Accidents." Transportation Research Board 79th Annual Meeting Preprint, Washington, DC, January 2000.
  5. Policy on Geometric Design of Highways and Streets. American Association of State Highway and Transportation Officials, 2001.

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