U.S. Department of Transportation
Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC 20590
202-366-4000
Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations
 |
| This report is an archived publication and may contain dated technical, contact, and link information |
|
Publication Number: FHWA-HRT-04-131
Date: September 2004 |
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.
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?).
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.
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.
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.
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:
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:
