Weather-responsive Traffic Management Research Articles

Quantifying the Effect of Rainfall and Visibility Conditions on Road Traffic Travel Time Reliability

Abstract This research explores a data-driven methodological framework to quantify the effect of rainfall and visibility on travel time reliability (TTR) by considering selected road segments in North Carolina. The framework includes capturing, processing, and integrating weather-related information and travel time data for the selected road segments. Various TTR indices were computed for the selected road segments under different rainfall and visibility ranges by day of the week (DOW) and time of the day (TOD). The TTR indices were computed for one week before and after (same DOW and TOD) under the normal weather condition and compared with those obtained under different intensities of rainfall and visibility. The variability in travel time patterns due to other events is expected to be marginal when considering the same DOW and TOD for comparison purposes. The results indicate that poor visibility with different rainfall intensities has the maximum adverse effect on the TTR. The outcomes from the data-driven methodological framework help the transportation planners in developing weather-responsive traffic management strategies and assessing their effectiveness using TTR indices. Significance Statement Travel time reliability (TTR) generally refers to the level of consistency or dependability in transportation service. It is considered as a measure of road operational performance. Ensuring higher levels of reliability is critical for efficient transportation system management along with mobility and accessibility needs. However, factors such as weather condition have a negative effect on the TTR. A data-driven methodological framework is proposed by integrating weather information and travel time data to quantify the effect of common weather conditions like rainfall and visibility on the TTR. The results indicated that heavy rain and poor visibility have an adverse effect on the TTR. These results are useful for agencies to better manage the traffic under different weather conditions.

Modelling speed behaviour in rural highways: Safety analysis of driving under adverse road-weather conditions.

This study proposes a methodical approach to model desired speed distributions under different road-weather and traffic conditions followed by identification of road-weather conditions with potentially higher safety risks in rural divided highways located in extremely cold regions. Desired speed distributions encompassing unique combinations of adverse road-weather and traffic conditions are modelled as normal distributions characterized by their means and standard deviations formulated based on two principal statistical theorems and techniques i.e., Central Limit Theorem and Minimum Variance Unbiased Estimation. Combination of the precipitation conditions, road surface conditions, time of the day, temperature, traffic flow and the heavy vehicle percentage at the time of travel were considered in defining the combinations of road-weather and traffic conditions. The findings reveal that simultaneous occurrence of particular precipitation and pavement conditions significantly affect the characteristics of the desired speed distribution and potentially expose drivers to elevated safety risks. Jurisdictions experiencing extreme road-weather conditions may adapt the proposed methodology to assess speed behaviour under different road-weather conditions to establishing and deploying weather-responsive traffic management strategies such as variable speed limit to regulate speeding and improve traffic safety in winter.

Effects of winter weather on traffic operations and optimization of signalized intersections

Abstract Adverse winter weather has always been a cause of traffic congestion and road collisions. To mitigate the negative impacts of winter weather, transportation agencies are under increasing pressure to introduce weather responsive traffic management strategies. Currently, most traffic signal control systems are designed for normal weather conditions and are therefore suboptimal regarding efficiency and safety for controlling traffic during winter snow events due to changes in traffic patterns and driver behaviors. The main objective of this research is to explore how to modify pre-timed traffic signal control parameters under adverse weather conditions to increase traffic efficiency and road safety. This research consists of two main components. First, we examine the impacts of winter weather on three key traffic parameters, i.e., saturation flow rate, start-up lost time, and free flow speed. Secondly, we investigate the potential benefits of implementing weather-specific signal control plans for uncoordinated intersections as well as coordinated corridors. Two case studies are conducted, each with varying levels of traffic demand and winter event severity, to compare the performance of different signal plans. Evaluation results from both Synchro and VISSIM show that implementing such signal plans is most beneficial for intersection with a medium level of traffic demand. It is also found that the benefit of implementing weather-responsive plans was more compelling at a coordinated-corridor level than at an uncoordinated-intersection level.

Evaluation of weather-related freeway car-following behavior using the SHRP2 naturalistic driving study database

Adverse weather conditions negatively impact the safety, mobility, and reliability of the transportation network. Weather-responsive traffic management (WRTM) strategies have been developed to counteract the hazards of adverse weather on the transportation system. In order to secure investments into applications designed to mitigate adverse weather impacts, explicit evidence of the perceived benefits and challenges are required. Microsimulation modeling is a common tool used to anticipate these impacts and can be used in real-time operational strategies. To achieve reliable output results, realistic driving behavior must be represented in the models. The purpose of this paper is to present findings related to intra-driver heterogeneity as a function of weather: that is, the adjustment of drivers’ behavior to compensate for different adverse weather conditions. This study evaluates drivers’ car-following behavior in various adverse weather conditions—rain, snow, and fog—and calibrates the Gipps car-following model to identify the transferability of those behavioral nuances. The results produce conclusive evidence that intra-driver heterogeneity exists in different adverse weather conditions and indicate that this heterogeneity can be captured by calibrated Gipps parameters. This study supports the need for the inclusion of weather considerations in current microsimulation modeling practices and contributes a novel trajectory validation methodology that can be used to compare observed and modeled behaviors when correlation between model parameters exists.

Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management

This paper presents the development, implementation, and evaluation of predictive active transportation and demand management (ATDM) and weather-responsive traffic management (WRTM) strategies to support operations for weather-affected traffic conditions with traffic estimation and prediction system models. First, the problem is defined as a dynamic process of traffic system evolution under the impact of operational conditions and management strategies (interventions). A list of research questions to be addressed is provided. Second, a systematic framework for implementing and evaluating predictive weather-related ATDM strategies is illustrated. The framework consists of an offline model that simulates and evaluates the traffic operations and an online model that predicts traffic conditions and transits information to the offline model to generate or adjust traffic management strategies. Next, the detailed description and the logic design of ATDM and WRTM strategies to be evaluated are proposed. To determine effectiveness, the selection of strategy combination and sensitivity of operational features are assessed with a series of experiments implemented with a locally calibrated network in the Chicago, Illinois, area. The analysis results confirm the models’ ability to replicate observed traffic patterns and to evaluate the system performance across operational conditions. The results confirm the effectiveness of the predictive strategies tested in managing and improving traffic performance under adverse weather conditions. The results also verify that, with the appropriate operational settings and synergistic combination of strategies, weather-related ATDM strategies can generate maximal effectiveness to improve traffic performance.

Effects of Rain on Freeway Traffic in Southern California

A number of studies in the past quantified the effect of rain on traffic parameters but were limited to wet areas. This research expands the literature by studying the effect of rain in a dry area such as Southern California and considering regional differences in the impact. Traffic data (loop detectors) and precipitation data (rain gauges) from the Los Angeles, California, metropolitan area were analyzed to access the effect of rain on traffic stream parameters such as free-flow speed, speed at capacity, and capacity. Rainfall events were categorized as light, medium, and heavy as discussed in the 2010 Highway Capacity Manual. Density plots and fundamental diagrams for rain types proved that free-flow speed, speed at capacity, and capacity were reduced by 5.7%, 6.91%, and 8.65%, respectively, for light rain; 11.71%, 12.34%, and 17.4%, respectively, for medium rain; and 10.22%, 11.85%, and 15.34%, respectively, for heavy rain. The reductions for free-flow speed were lower, whereas for speed at capacity and for capacity, they were higher than those reported in the 2010 manual. Moreover, headway increased during rain; this finding shows cautious driving behavior. Multiplicative weather adjustment factors were computed to compensate for the loss of speed and capacity. Also demonstrated was the spatial and temporal effect of rain on traffic. Downstream traffic was not much affected by a rainfall event, whereas the upstream traffic was negatively affected. This study is expected to support weather-responsive traffic management strategies for dry areas.

The association between meteorological factors and road traffic injuries: a case analysis from Shantou city, China.

Few studies examined the associations of meteorological factors with road traffic injuries (RTIs). The purpose of the present study was to quantify the contributions of meteorological factors to RTI cases treated at a tertiary level hospital in Shantou city, China. A time-series diagram was employed to illustrate the time trends and seasonal variation of RTIs, and correlation analysis and multiple linear regression analysis were conducted to investigate the relationships between meteorological parameters and RTIs. RTIs followed a seasonal pattern as more cases occurred during summer and winter months. RTIs are positively correlated with temperature and sunshine duration, while negatively associated with wind speed. Temperature, sunshine hour and wind speed were included in the final linear model with regression coefficients of 0.65 (t = 2.36, P = 0.019), 2.23 (t = 2.72, P = 0.007) and −27.66 (t = −5.67, P < 0.001), respectively, accounting for 19.93% of the total variation of RTI cases. The findings can help us better understand the associations between meteorological factors and RTIs, and with potential contributions to the development and implementation of regional level evidence-based weather-responsive traffic management system in the future.

Expanding the Road Weather Information System for Avalanche Support

Transportation agencies use road weather information systems to support operations, maintenance, and informed travel decisions. Road weather capabilities have been expanded through the FHWA Weather Responsive Traffic Management’s advisory, control, and treatment strategies to address weather-related roadway impacts. Monitoring conditions that precede avalanches offers a new opportunity to leverage the road weather information system’s infrastructure. Wet avalanches, particularly wet slab avalanches, pose considerable risk. They can occur simultaneously across an extensive area, encompass a greater mass of material, create considerable energy, cause significant road closures, and lead to major injuries and fatalities. Roadside and at-altitude avalanche indicators can be monitored by adding sensors to existing road weather environmental sensor stations, deploying new environmental sensor stations at both roadside and at-altitude locations, and creating new, collaborative partnerships. The traditional sensor suite of the road weather information system can be augmented with new meteorological and snowpack sensors to monitor preavalanche conditions. Calibrated present weather detector sensors and heated precipitation gauges can provide improved estimates of the precipitation’s liquid water equivalent. Thermistor strings and lysimeters can provide snowpack stability evaluations. Adding environmental sensor stations, both at roadside and at altitude, can provide strategic observations of the atmospheric and snowpack conditions. Increased coordination between agencies on site selection, sensor choices, and data distribution can expand the role of road weather information systems to support avalanche forecasting.

Online Implementation and Evaluation of Weather-Responsive Coordinated Signal Timing Operations

This paper presents the development and application of weather-responsive traffic management strategies and tools to support coordinated signal timing operations with traffic estimation and prediction system (TREPS) models. First, a systematic framework for implementing and evaluating traffic signal operations under severe weather conditions was developed, and activities for planning, preparing, and deploying signal operations were identified in real-time traffic management center (TMC) operations. Next, weather-responsive coordinated signal plans were designed and evaluated with the TREPS method and a locally calibrated network. Online implementation and evaluation was conducted in Salt Lake City, Utah—the first documented online application of TREPS to support coordinated signal operation in inclement weather. The analysis results confirm that the deployed TREPS, which is based on DYNASMART-X, is able to help TMC operators test appropriate signal timing plans proactively under different weather forecasts before deployment and is capable of using real-time measurements to improve the quality and accuracy of the system's estimations and future predictions through detectors and roadside sensor coverage.

Implementation and Evaluation of Weather-Responsive Traffic Management Strategies

This study presents the development and application of methodologies to support weather-responsive traffic management (WRTM) strategies by building on traffic estimation and prediction system models. First, a systematic framework for implementing and evaluating WRTM strategies under severe weather conditions is developed. This framework includes activities for planning, preparing, and deploying WRTM strategies in three different time frames: long-term strategic planning, short-term tactical planning, and real-time traffic management center operations. Next, the evaluation of various strategies is demonstrated with locally calibrated network simulation-assignment model capabilities, and special-purpose key performance indicators are introduced. Three types of WRTM strategies [demand management, advisory and control variable message signs (VMSs), and incident management VMSs] are applied to multiple major U.S. areas, namely, Chicago, Illinois; Salt Lake City, Utah; and the Long Island area in New York. The analysis results illustrate the benefits of WRTM under inclement weather conditions and emphasize the importance of incorporating a predictive capability into selecting and deploying WRTM strategies.

Multilevel Assessment of the Impact of Rain on Drivers’ Behavior

For all road managers, inclement weather events are a source of uncertainty that can affect traffic operations and safety. Regarding safety, various studies reveal significant effects of adverse weather conditions on the frequency and severity of crashes. Regarding mobility, because of a lack of data, there are few comprehensive studies, although the quantification of the effects of adverse weather on traffic represents the first step toward the development of weather-responsive traffic management strategies. This study deals with the analysis of the impact of rain on drivers’ behavior and traffic operations. First, a generic methodology for assessing the effect of weather on traffic is proposed through a multilevel approach: from individual traffic data, the rain impact is assessed at a microscopic level (time headways, spacing). Next, the same data were used to extend the study to a mesoscopic and a macroscopic level. The mesoscopic level deals with the effects of rain on platoons, and the macroscopic level resides in the analysis of the impact of rain on the fundamental diagram enabling weather-responsive macroscopic traffic simulation. Second, following this approach, an empirical study is carried out from individual data collected on a French interurban motorway. Weather data were provided by a weather station located near the test site. The results exhibit a significant impact of rain on drivers’ behavior and traffic operations, which increases with the intensity of rainfall.

Research Needs for Weather-Responsive Traffic Management

In weather-responsive traffic management, weather events are viewed as nonrecurring incidents that can be predicted and observed and have effects that can be mitigated. Weather impacts on traffic flow are reported, and an emerging concept of operations for a systemwide approach to traffic management in adverse weather is described. This approach is structured to assess weather impacts and implement operational strategies that improve safety, mobility, and productivity. Finally, research needs to advance the state of the practice in weather-responsive traffic management are enumerated.