The Road Weather System of the Future
High-quality weather information about the roadway environment, including both current observations and forecasts, communicated in a timely and effective manner can help drivers to make better, safer decisions regarding travel plans and to react properly when faced with potentially compromised conditions. Providing improved road weather information to those who construct, operate, and maintain the nation’s roadways will enable them to operate the roads more efficiently and respond more quickly and appropriately to weather problems. Both drivers and transportation professionals will benefit from new technologies for vehicles, roadway infrastructure, and communications. In a more global sense, an exploration of road weather issues will help position the nation’s road transportation system to respond appropriately to changes in demographics, technology, and the environment (see Chapter 6).
Recent and anticipated advances position the field of road weather for significant improvements in understanding and capability. For example, the meteorological community has made important advances in understanding smaller-scale meteorology. Great strides in observational capabilities have been made, making it possible to obtain a more comprehensive picture of current weather and roadway conditions. Numerical modeling has become more sophisticated and capable of resolving important phenomena on small space scales and short time scales. Transportation personnel have implemented dynamic message signs and have begun to take advantage of cellular technology to communicate with drivers while en route. Vehicle manufacturers have demonstrated the feasibility of a wide variety of onboard computing and telecommunications tools and have begun to move some of these to the market place. Accompanying these advances in meteorology and transportation are improvements in communications, computational capabilities, and geographic information systems, technologies that have applications to the road weather problem. It is clear that road weather is a
problem that can be significantly mitigated by appropriate action, not a situation that simply has to be endured.
The idea of smart vehicles in constant communication with weather information providers and traffic control centers, commercial fleets constantly adjusting their routing to avoid anticipated storms, and road maintenance personnel being guided continuously by telemetered in-road sensors no longer needs to be limited to the realm of science fiction. Rather, over the course of the next 15 years a focused road weather research program could deliver this as a reality to the nation saving thousands of lives and billions of dollars. The committee sees the road weather system of 2020 as including a robust observation and communication infostructure,1 models to support decisions, smart vehicles, enhanced roadway maintenance, and enhanced traffic and emergency management.
On her way to pick up her children from band practice Karen notes the dark clouds of a thunderstorm rapidly approaching. Meanwhile, computers at a private meteorological service are using data from the National Weather Service and the Kansas Department of Transportation to produce a highly specific “pathcast” for the storm, indicating which areas are expected to be affected. This pathcast takes advantage of a sophisticated four-dimensional data assimilation system that integrates local data from lightning sensors, radars, surface-observing stations, wind profilers, and stream gauges, to name but a few. Karen’s in-vehicle communication system beeps three times and relays a message that the storm, which will be accompanied by lightning, gusty winds, and heavy rain with the possibility of flash flooding, will reach her location within 30 minutes. As the rain begins to fall heavily she worries about flooding of the nearby creek that already is high due to heavy rain during the past couple of days. Karen tells the hands-free communication system her destination and requests that it identify an alternate route to circumvent the nearby creek. The system integrates both weather and traffic information to send Karen on a safer route that will avoid flooded streets but remain uncongested.
ROBUST OBSERVATION AND COMMUNICATION INFOSTRUCTURE
As illustrated by Karen’s story the roadway system of the future will make a diverse collection of observations readily available to users when and where needed. Included in the observational network will be
meteorological data from national and mesoscale in situ networks, satellite platforms, weather radars, and other remote-sensing instruments;
an instrumented corridor for characterization of the road surface (for example using sensors embedded in the pavement) and the near road environment;
vehicles that continuously measure conditions with which they come in contact, such as surface temperature, chemistry, and friction, air temperature, or visibility; and
observations of land cover, stream flow, sea level, snow pack, and other environmental features that can affect the roadway.
These observations will be taken and then communicated to regional and national collection facilities, where they will be assimilated to provide continuously updated, four-dimensional fields of data. Periodically and on demand these fields of data will be used to initialize numerical prediction models, with the resulting forecasts stored in similar four-dimensional data fields. From these fields of observed and forecast data, interpolation techniques will be used to provide information at the location and time of interest to a roadway user. The interface to this vast quantity of observational and forecast data will be seamless. A robust “infostructure” will be in place that brings together all the desired data and disseminates the information to users, taking advantage of advances in communication sensor technology, information technology, global positioning system data, data management, computing, and geographical information systems, among others.
MODEL-BASED TOOLS TO SUPPORT DECISIONS
The road weather system of the future will employ sophisticated models to support enhanced decision making by the driving public, as in the case of Lou, and by those who build, maintain, and operate the nation’s roads. The models will be “end-to-end,” meaning they will integrate real-time observations of current weather, traffic, and road conditions; numerical weather predictions; models of traffic flow given these observed conditions; and rules of practice, to better serve a diverse customer base with decision
Lou is driving his semi-trailer tractor north on Interstate-35 across Iowa headed to Duluth, Minnesota. A major winter storm is heading east, with a foot of snow and strong winds forecast for much of northern Iowa and southern Minnesota. He consults his dispatcher using his “always-on” communication system to ask whether he should continue driving through the storm, stop and wait for the storm to pass, or head east and detour around the storm. The dispatcher must consider Lou’s safety and the possibility that he’ll be marooned for a few days if he continues, additional fuel and other vehicle costs, and any change in the time of delivery to the customer. Based on a continuously updated forecast that the storm is going to stay west of I-35, the dispatcher decides that Lou should continue on his original route. After a couple hours, however, the forecast now indicates the snow will spread farther east, causing patchy snow and ice accumulation on the road. Several vehicles about a mile ahead detect the icy conditions and report this through the highway’s intelligent network. The main communication computer on Lou’s truck picks up these reports, alerts Lou with a yellow light in the corner of his head-up display, and automatically reconfigures for better traction. Lou’s dispatcher also monitors and relays to Lou information about the status of plowing and chemical treatments, automated spray of chemicals to anti-ice bridges, and traffic and visibility conditions, as well as the progress of the storm.
support, such as for snow and ice treatment or for pouring concrete. These models will be used by roadway maintenance officials, operational transportation managers, emergency managers, and those who build roads, and even incorporated into automated systems that provide alternate routes to drivers on the road. High-quality weather forecasts that provide information of the greatest accuracy possible at the necessary scale for the roadway environment are necessary to ensure the value of these modeling tools.
The cars and trucks of the future will be able to detect and respond to road weather conditions with ease. Not only will the vehicles be outfitted with instruments to measure road and atmospheric conditions but they will
Curt, the winter weather maintenance official responsible for western Montana, is awakened at 1:00 a.m. by his beeper; the forecast service has sent out an alert that snow expected to arrive in his region of responsibility is still on track and will begin shortly. He uses his personal information management system to access a suite of decision support tools, including highly localized weather forecasts, a sophisticated road temperature model, and an application that helps him choose treatment options. Curt is most concerned about two mountain passes that typically ice up first due to their higher elevation. He finds that three pavement sensors in a mountain pass confirm the previous evening’s forecast that pavement temperatures would fall to below freezing just in that area. Curt quickly posts the weather radar display on his screen and the precipitation extrapolation shows that snow indeed is due at the mountain pass in a little over two hours. Based on this information, Curt quickly concludes that an anti-icing treatment would be necessary to ensure sufficient friction in the mountain pass in time for people to drive to work in the morning. A few verbal commands bring up his newly installed winter road maintenance decision aid system. It already has ingested all of the current weather and transportation information and confirms Curt’s preventative maintenance strategy. It also recommends the precise amount of brine to apply for the current and forecast pavement temperatures and snow amount as well as the length of the roadway, based on road elevation, to treat on either side of the pass. Based on this information, Curt decides that an anti-icing treatment is warranted. He contacts the several employees he had placed on call the night before based on the weather forecasts he received, and by 2:30 a.m. they begin to apply liquid brine at the recommended rates to the road segments identified. The trucks are equipped with sensors for road temperature, air temperature and humidity, precipitation, and surface chemistry and friction along with a computer system that determines how much additional chemical treatment is necessary to address the conditions.
also have sophisticated positioning and communication capabilities. These “smart vehicles” could stay in constant communication with weather information providers and traffic control centers. Drivers will be informed immediately of suboptimum road conditions, such as whether a driver several minutes ahead encountered an icy road, or whether an accident occurred along the driver’s route. Likewise, drivers will be able to call on route-finding tools to determine optimum routes, which will be of particularly great value for navigating roads affected by weather or congestion. The invehicle communication and route-planning tools will smoothly interface with World Wide Web tools available for pretrip planning, allowing planned routes to be transferred to the vehicle. For example, onboard navigation systems could be designed to show only the evacuation routes or to color code roads on the map to reflect up-to-the-minute driving conditions.
These smart vehicles will also use highly honed communication methods that take careful account of the human factors involved in receiving and processing information. They will be designed to provide the driver with an appropriate amount of information about, for example, quickly approaching hazardous conditions so that a good decision can be made. Innovative ways to communicate such information will be incorporated into the vehicle design, taking advantage of visual, auditory, and tactile methods. Some vehicles, as was the case for Lou’s truck in the story above, will be able to automatically optimize its handling for specific road conditions.
ENHANCED ROADWAY MAINTENANCE
Efforts to remove snow and ice from the roadways in the past have combined regional scale coarse weather information, various rules of practice, lessons learned from experience, and a significant degree of guesswork. The road weather system of the future will be able to provide highly targeted weather information and decision support models that codify the best of past practices and use cost-benefit analyses to help maintenance officials make more informed decisions. Indeed, prototypes of this sort of decision support tool are already being developed.
Plowing equipment will also be much improved in the road weather system of the future. As was the case in the story about Curt, the plows will be outfitted with sensors that ascertain road surface temperature, friction, and even the amount of chemical already on the surface, along with software that automatically adjusts chemical treatment for the specific needs of small portions of roadway. In addition, equipment operators, who often struggle to see the road during heavy snow conditions, will be continuously
guided by telemetered in-road sensors, magnetized lane tape on roads, or in-vehicle navigational systems.
The Sarno family attentively watches the television weather report in their south Miami home. A hurricane that developed north of the Lesser Antilles five days ago has intensified to a strong Category 1 with maximum sustained winds of 95 mph, and it is heading west-northwest toward Miami. Following the weather report an emergency management official again explains that evacuation is voluntary and that routes I-95 and I-4 have been designated as evacuation routes on which all traffic will be directed away from the storm. The Sarnos, who initially had decided to ride out the storm, change their mind. They quickly pack their hybrid SUV and prepare their home for the storm before beginning to drive to Orlando, where they plan to stay with relatives. After driving about an hour on I-95, an announcement comes over their in-vehicle routing system that the hurricane has accelerated and has taken a more northerly route than anticipated. Gale- and hurricane-force winds now are forecast to affect the coast and 20 miles inland—across the family’s planned route—within 30 minutes and two hours, respectively. The Sarnos quickly query their in-vehicle communication system for an alternate route that has not been closed, is not overly congested, will take them farther west, and will avoid the windiest areas as well as those that are likely to flood. Computers at the regional transportation center compute a new route using information about all emergency- and construction-related road closures, real-time traffic observations, and geographic information system data. Following the new route, the Sarnos avoid the worst of the hurricane.
ENHANCED TRAFFIC AND EMERGENCY MANAGEMENT
Traffic managers in the year 2020 will have many powerful tools at their disposal for optimizing the capacity and efficiency of the roadway system. They will use sophisticated traffic simulation models that dynamically forecast how traffic would most likely respond to weather, construction, accidents, and other road closures. These models will take as input real-time traffic and weather data obtained from a much-enhanced observational infostructure. The traffic simulations will help identify ways to modify traffic flow and routing to respond to weather and other factors; for example,
through changing signal timing and dynamic message signs that change the speed limit or advise drivers of detours or closures. The output also will provide guidance and recommendations directly to drivers before travel, when routes are being selected, and then in their vehicles once travel has begun.
Likewise, in 2020 responses to weather-related emergencies and evacuations due to weather hazards will take full advantage of advances in meteorology and traffic management through integrated decision support systems. This will be a significant improvement over the current “swivel-chair integration” approach, in which emergency managers consult separate information sources for weather, traffic, and emergency response practices. Further, these integrated decision support systems will be available to help determine optimum evacuation routes for individual drivers, just as the Sarno family was able to do en route.