Road condition management

Road condition management is provided. A first road problem at a first location is identified, based, at least in part, on a current traffic pattern and current environmental conditions at the first location. An optimized plan for road repairs is generated. The optimized plan identifies one or more road problems. The plan is optimized based on a severity index of each of the one or more road problems and an availability of at least one resource. An alert is issued to at least one interface device, wherein the alert describes the first road problem.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of road infrastructure and more particularly to road condition management.

Monitoring the condition of road infrastructure has become increasingly important over recent years. Construction projects (such as water system and fiber optic network installations, inclement weather) and high usage are common causes of poor road conditions. Poor road conditions, including lane closures and potholes, cause traffic congestion, especially in large developing cities. Poorly maintained road surfaces also result in motorists incurring higher vehicle maintenance costs.

SUMMARY

According to one embodiment of the present invention, a method for road condition management is provided. The method includes identifying, by one or more processors, a first road problem at a first location based, at least in part, on a current traffic pattern and current environmental conditions at the first location; generating, by one or more processors, an optimized plan for road repairs, wherein the optimized plan identifies one or more road problems that include the first road problem, and wherein the optimized plan is optimized based on a severity index of each of the one or more road problems and an availability of at least one resource; and issuing, by one or more processors, an alert to at least one interface device, wherein the alert describes the first road problem.

According to another embodiment of the present invention, a computer program product for road condition management is provided. The computer program product comprises a computer readable storage medium and program instructions stored on the computer readable storage medium. The program instructions include program instructions to identify a first road problem at a first location based, at least in part, on a current traffic pattern and current environmental conditions at the first location; program instructions to generate an optimized plan for road repairs, wherein the optimized plan identifies one or more road problems that include the first road problem, and wherein the optimized plan is optimized based on a severity index of each of the one or more road problems and an availability of at least one resource; and program instructions to issue an alert to at least one interface device, wherein the alert describes the first road problem.

According to another embodiment of the present invention, a computer system for road condition management is provided. The computer system includes one or more computer processors, one or more computer readable storage media, and program instructions stored on the computer readable storage media for execution by at least one of the one or more processors. The program instructions include program instructions to identify a first road problem at a first location based, at least in part, on a current traffic pattern and current environmental conditions at the first location; program instructions to generate an optimized plan for road repairs, wherein the optimized plan identifies one or more road problems that include the first road problem, and wherein the optimized plan is optimized based on a severity index of each of the one or more road problems and an availability of at least one resource; and program instructions to issue an alert to at least one interface device, wherein the alert describes the first road problem.

DETAILED DESCRIPTION

Embodiments of the present invention recognize that road conditions are monitored and inspected manually by governmental maintenance agencies or their representatives. Auditors visit roads to ensure safe and efficient conditions, taking note of problem areas. Roads are often in a state of disrepair or maintenance for long periods of time.

Embodiments of the present invention recognize that manually monitoring and inspecting road infrastructure is inefficient and time-consuming. In addition to contributing to higher costs for the governments or agencies to perform these functions, delays in detecting poor road conditions result in more traffic congestion and accidents, increased vehicular damage and repair costs, and higher motorist fuel usage.

Embodiments of the present invention provide for road condition management. Various embodiments provide for identifying road problems, predicting the impact of road problems on traffic patterns, assigning severity indices to road problems, dynamically prioritizing road repairs based on newly detected road problems, creating road repair plans, and alerting travelers of potential traffic and safety issues. Road problems include, for example, potholes, debris, lane closures, disabled vehicles, or any other conditions related to road maintenance that impede or detract from normal travel flow.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order.

The present disclosure will now be described in detail with reference to the Figures.

FIG. 1is a functional block diagram illustrating a computing environment, in accordance with an embodiment of the present disclosure. For example,FIG. 1is a functional block diagram illustrating computing environment100. Computing environment100includes interface devices110, server130, sensors140, mapping database150, road infrastructure database160, road condition database170, and model and rules database180connected over network120. Server130includes road condition analyzer132, and planning program134.

In various embodiments, server130is a computing device that can be a standalone device, a server, a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), or a desktop computer. In another embodiment, server130represents a computing system utilizing clustered computers and components to act as a single pool of seamless resources. In general, server130can be any computing device or a combination of devices with access to sensors140, mapping database150, road infrastructure database160, road condition database170, model and rules database180, and interface devices110, and with access to and/or capable of executing road condition analyzer132and planning program134. Server130may include internal and external hardware components, as depicted and described in further detail with respect toFIG. 4.

In this exemplary embodiment, road condition analyzer132and planning program134are stored on server130. In other embodiments, one or more of road condition analyzer132and planning program134may reside on another computing device, provided that each can access each other and sensors140, mapping database150, road infrastructure database160, road condition database170, model and rules database180, and interface devices110. In yet other embodiments, one or more of road condition analyzer132and planning program134may be stored externally and accessed through a communication network, such as network120. Network120can be, for example, a local area network (LAN), a wide area network (WAN) such as the Internet, or a combination of the two, and may include wired, wireless, fiber optic or any other connection known in the art. In general, network120can be any combination of connections and protocols that will support communications between interface devices110, server130, sensors140, mapping database150, road infrastructure database160, road condition database170, and model and rules database180, in accordance with a desired embodiment of the present invention.

Interface devices110represents one or more devices that present information to a user. In various embodiments, interfaces device110include a computing device that can be a standalone device, a server, a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), or a desktop computer. In other embodiments, interfaces device110include a street sign, a billboard, a smartphone, a mobile device, a navigation device, a street light, a siren, or a public alert system. In one embodiment, interface devices110include a computing device of a vehicle that controls at least one onboard sensor or safety system on a vehicle. In one embodiment, interface devices110include a smartphone that presents information to a user via a text message, email, or other form of notification. For example, the text message indicates a road problem. In one embodiment, interface devices110include an electronic street sign that presents information to a user (e.g., travelers on a road) via a textual message. For example, the digital message indicates that motorists are approaching a section of the road that is closed during a specified time of day. In one embodiment, interface devices110include a sign that alerts motorists that they are approaching a portion of the road that contains a road problem. For example, the sign indicates that motorists are approaching the scene of an accident and that the speed limit is now forty-five miles per hour (rather than the normal sixty-five miles per hour). In another embodiment, one or more of interface devices110represents a computing system utilizing clustered computers and components to act as a single pool of seamless resources. In general, interface devices110can be any computing device or a combination of devices with access to server130over network120. Interface devices110may include internal and external hardware components, as depicted and described in further detail with respect toFIG. 4.

Sensors140operate to detect information about a roadway, its traffic patterns, and its surrounding environment. In one embodiment, sensors140include devices that measure and characterize traffic flow. For example, sensors140are in-roadway vehicle detection sensors (e.g., bending plates, inductive loops, magnetic sensors, pneumatic road tubes, or weigh-in-motion sensors) or above-ground vehicle detection sensors (e.g., infrared detectors, Doppler radar, passive acoustic devices, video image detection systems). In various examples, these sensors are used to detect the presence of a vehicle at a particular point, measure traffic volume (i.e., count vehicular traffic across a point in the road over a given time interval), measure traffic speed, and measure traffic density. In another example, sensors140are a pair of pneumatic road tubes that are separated by a predetermined distance in the direction of traffic flow and allow for the measurement of the speed of a vehicle based on the predetermined distance and the amount of time between the vehicle passing over each tube of the pair. In another example, sensors140are passive infrared traffic detectors that sense changes in thermal radiation, relative to the background thermal radiation, that result from an object (e.g., a vehicle) in the field of view. In one embodiment, sensors140monitor and determine the condition of road surfaces (e.g., the presence and amount of water, ice or snow). In one example, sensors140include a laser sensor system that measures one or more conditions such as road surface temperature, a coefficient of friction of the road surface, and an amount of ice on the road surface. In one embodiment, sensors140include devices that monitor and record traffic patterns. For example, sensors140are traffic cameras that are placed along roads at certain spacing intervals, intersections, or places of frequent traffic congestion. These cameras record video and still pictures of road traffic, and the information can be used to identify changes in traffic patterns and their associated locations. In one embodiment, sensors140provide sensor data used (by, for example, road condition analyzer132) to monitor and record weather conditions. In one embodiment, sensors140include on-vehicle sensors. For example, sensors140include one or more sensors (e.g., camera, accelerometer, or other computerized sensor) on board a vehicle that gather information about the depth of potholes that the vehicle passes over or near. In various examples, sensors140include a thermometer, a wind meter, a frost meter, a rain gauge, a lightning detector, a barometer, or a light meter. In some embodiments, sensors140are adjustable. For example, a camera may move (e.g., pan or tilt), responsive to an instruction requesting a movement, in order to provide better imagery of a portion of a road where there is an accident. Sensors140are discussed in further detail in connection withFIG. 2.

Mapping database150is a data repository that stores mapping data. Mapping database150may be written to and read by one or more of road condition analyzer132and planning program134. In one embodiment, mapping database150stores geographic information system (GIS) data about a city's infrastructure. For example, mapping database150stores description and location information about all roads and traffic devices in a city. In a further example, traffic devices include, for example, traffic control devices (e.g., street lights), traffic monitoring devices (e.g., video cameras), and programmable electronic signs. In some embodiments, mapping database150may be written to and read by programs and entities outside of computing environment100in order to populate the repository with information relevant to the system operation, such as, for example, the location of a new streetlight.

Road infrastructure database160is a data repository that stores road infrastructure data. Road infrastructure database160may be written to and read by one or more of road condition analyzer132and planning program134. In one embodiment, road infrastructure database160stores data pertaining to maintaining roads under a governmental or other organization's jurisdiction. For example, road infrastructure database160contains road maintenance information such as, for example, road repair budgets, work crew information (e.g., quantity, skillsets, availability), road repair equipment information (e.g., types, quantities, availability, skills required to operate), road repair project plans (e.g., schedules, status), historical project cost and schedule data, average time allotted for different types of road maintenance projects, emergency operation plans, and union regulations. In some embodiments, road infrastructure database160may be written to and read by programs and entities outside of computing environment100in order to populate the repository with information relevant to the system operation.

Road condition database170is a data repository that stores road condition data. Road condition database170may be written to and read by one or more of road condition analyzer132and planning program134. In one embodiment, road condition database170stores data relating to the conditions (e.g., road conditions) and circumstances (e.g., weather) that affect travel patterns. For example, road condition database170stores information indicating locations and descriptions of irregularities in road surfaces, such as potholes and roads under repair. In one embodiment, road condition database170stores information about obstacles or blockages that affect traffic patterns. For example, road condition database170identifies locations of lane closures and the timeframe during which they are closed. In one embodiment, road condition database170stores data regarding road closures and alternate routes. For example, road condition database170stores information about a section of road that is closed for one week due to a paving project, and stores alternate route suggestions and the best times of day to use each route. In some embodiments, road condition database170may be written to and read by programs and entities outside of computing environment100in order to populate the repository with information relevant to the system operation. Such information includes, for example, a Coast Guard notice of a drawbridge closure due to a boating accident.

Model and rules database180is a data repository that stores traffic pattern models and exclusionary rules data. Model and rules database180may be written to and read by one or more of road condition analyzer132and planning program134. In one embodiment, model and rules database180stores models that correlate historical traffic pattern data (e.g., vehicle types, traffic volume, average traffic speed, traffic deviations) with condition data (e.g., locations, time of day, weather conditions). For example, based on historical traffic pattern data and relevant sensor data, such as, for example, weather and time information, road condition analyzer132models normal traffic patterns under different conditions and at different times of day. In a further example, the model indicates that traffic speeds are fifteen miles per hour slower when roads are icy than when roads are dry.

In one embodiment, model and rules database180stores exclusionary rules that define exceptions to the model. In one embodiment, each exclusionary rule identifies a date, a time, and a location of an exception. For example, an exclusionary rule corresponds to a predefined event that occurs either one time or on a predefined recurring schedule. For example, an exclusionary rule describes traffic delays and slower minimum vehicle speeds on roads within a one mile radius of a professional football stadium for the three hours prior to and after a game or other event at the stadium. In another example, a rule describes traffic pattern deviations, such as road closures during a holiday parade.

In some embodiments, model and rules database180may be written to and read by programs and entities outside of computing environment100in order to populate the repository with information relevant to the system operation. Model and rules database180is discussed in further detail in connection withFIG. 2.

Road condition analyzer132operates to determine whether there is a road problem. In one embodiment, road condition analyzer132models normal traffic patterns based on historical traffic patterns and environmental data. The model correlates conditions with expected traffic patterns. For example, the model generated by road condition analyzer132correlates conditions such as clear and sunny skies between the hours of 2:00 PM and 4:00 PM with expected traffic patterns such as travel speed between fifty-five and sixty-five miles per hour for a given portion of road. In a further example, the model indicates that traffic speeds between forty and fifty-five miles per hour are expected for the same portion of road under different conditions, such as when snowfall rates exceed one inch per hour. Road condition analyzer132is discussed in further detail in connection withFIG. 2.

Planning program134operates to determine an optimized road repair plan. Planning program134determines an impact of a road repair issue on a current traffic pattern. Planning program134projects an impact of a road problem on a future traffic pattern. Planning program134determines a severity index of a road problem. Planning program134determines one or more resource requirements of a road problem. Planning program134determines an optimized road repair plan. Planning program134issues an alert to one or more interface devices. Planning program134is discussed in further detail in connection withFIG. 3.

FIG. 2is a flowchart depicting operations for road condition management within the computing environment ofFIG. 1, in accordance with an embodiment of the present disclosure. For example,FIG. 2is a flowchart depicting operations200of road condition analyzer132, on server130within computing environment100.

In step202, road condition analyzer132models normal traffic patterns based on historical traffic information. The historical traffic information includes conditions and traffic patterns. Road condition analyzer132generates a model that correlates conditions (e.g., environmental conditions, road conditions, location) with traffic patterns (e.g., traffic volume, speed, paths of travel, types of vehicles) occurring concurrently with the conditions. Thus, the model predicts expected traffic patterns under various conditions. In one embodiment, road condition analyzer132receives the historical traffic information. For example, road condition analyzer132receives the historical traffic information from one or more of mapping database150, road infrastructure database160, road condition database170, and model and rules database180. In another embodiment, road condition analyzer132builds the historical traffic information. For example, road condition analyzer132builds the historical traffic information based on observed sensor data (e.g., air temperature, precipitation rate, precipitation accumulation, time of day, location, camera imagery, traffic flow information). In one example, the model correlates an amount of snow accumulation with an expected decrease in traffic speed. In this example, the model predicts that traffic speed decreases by ten miles per hour when snow accumulation equals one inch over a one hour time frame. In another embodiment, the model correlates different times of day with different traffic patterns. For example, the model correlates times from 7:00 AM through 9:00 AM and from 4:00 PM through 6:00 PM with vehicle volume between eight and eleven hundred vehicles per hour and an average speed of forty-five miles per hour with a standard deviation of ten miles per hour. Conversely, the model correlates 3:00 AM with vehicle volume between one hundred fifty and two hundred fifty vehicles per hour and an average speed of sixty miles per hour with a standard deviation of five miles per hour. In one embodiment, the predictions of the model require a predetermined level of statistical power. For example, road condition analyzer132generates a model that predicts traffic patterns within confidence intervals having ninety-five percent confidence levels.

In step204, road condition analyzer132monitors sensor data from sensors140. In one embodiment, road condition analyzer132determines a current traffic pattern based on sensor data from sensors140. For example, based on sensors140data, road condition analyzer132determines that highway traffic is moving at an average speed of sixty-one miles per hour at a volume of ten thousand vehicles per hour. In one embodiment, road condition analyzer132determines current conditions based on sensors140data. For example, road condition analyzer132determines, based on sensors140data, that it is 1:30 PM on a sunny weekday. In one embodiment, road condition analyzer132determines current conditions and a current traffic pattern based on the sensor data. For example, road condition analyzer132determines current environmental conditions based on sensor data indicating dry and sunny weather. In another example, road condition analyzer132determines a current traffic pattern based on sensor data indicating that vehicles are traveling at fifty-five miles per hour. In some embodiments, road condition analyzer132uses sensor data to refine the traffic patterns created in step202. Road condition analyzer132utilizes the current conditions and traffic pattern as feedback to improve the accuracy of the modeled normal traffic patterns. For example, responsive to receiving sensor data indicating deviations in modeled traffic patterns after an additional lane was added to a section of highway (e.g., as reflected by an update to mapping database150), road condition analyzer132refines the traffic pattern models by updating the modeled normal traffic patterns on this section of highway.

In decision206, road condition analyzer132determines whether a current traffic pattern matches the model. Road condition analyzer132determines whether the current traffic pattern matches the model based on the sensor data and the model of normal traffic patterns. Road condition analyzer132determines the current traffic pattern based on sensor data received from sensors140. Road condition analyzer132identifies a traffic pattern of the model that corresponds to the current conditions. If the current traffic pattern matches the traffic pattern of the model (decision206, YES branch), then road condition analyzer132determines that the road conditions are normal (step208). If the current traffic pattern does not match the traffic pattern of the model (decision206, NO branch), then road condition analyzer132determines whether an exclusionary rule applies (decision210).

In one example, road condition analyzer132determines a current traffic pattern indicating that traffic on a dry portion of a highway is moving at an average speed of twenty-five miles per hour at 2:00 PM on a sunny, warm Sunday afternoon in fall. Responsive to correlating this traffic pattern and these conditions with traffic pattern data (i.e., the model) in model and rules database180, road condition analyzer132determines that this traffic pattern does not match the model based on the corresponding traffic pattern of the model indicating a traffic speed range of fifty-five to seventy miles per hour for this portion of highway under these conditions. In another example, road condition analyzer132receives weather sensor data, video camera footage, timestamp information, and vehicle speed sensor data indicating weather conditions are sunny and dry, and traffic is moving at twenty miles per hour slower than the model predicts for that time of day on that stretch of road, for the current conditions. Based on this information, and the normal traffic patterns modeled in step202, road condition analyzer132determines that the current traffic pattern does not match the model and, in response, road condition analyzer132proceeds to decision210.

In other embodiments, the traffic pattern indicates a path of the vehicular traffic at a location. The path of travel indicates a path followed by one or more of the vehicles traversing a portion of road that includes the location. The path that is generated (by, e.g., road condition analyzer132) represents an average path of travel, based on an observed path of travel of one or more vehicles traversing the portion of road. For example, road condition analyzer132determines a location of one or more vehicles at various points along the path of travel and determines the average path of travel based on a geometric average of the location of each vehicle at each corresponding point. In one embodiment, the path of travel includes a precision or tolerated deviation. For example, a traffic pattern of the model indicates a path of travel that is parallel to lane markings on the portion of the road, within a tolerated deviation of two feet. In one embodiment, road condition analyzer132determines whether the traffic pattern matches a model (decision206) based, at least in part, on the path of travel of the current traffic pattern and a path of travel of a traffic pattern of the model. For example, road debris causes vehicles to swerve out of a normal path of travel. In this case, road condition analyzer132determines a path of travel of the current conditions that does not match the traffic pattern of the model that corresponds to the current conditions. In response, road condition analyzer132determines that the traffic pattern does not match the model (decision206, NO branch) and, in response, road condition analyzer132proceeds to decision210.

In decision210, road condition analyzer132determines whether an exclusionary rule applies. An exclusionary rule includes one or more criteria (e.g., one or more environmental conditions, dates, times, locations, or any combination thereof). An exclusionary rule applies if the criteria of the exclusionary rule are met. An exclusionary rule corresponds to a predefined event. In one embodiment, an event is a one-time event. A holiday parade that blocks traffic on a particular route during a specific timeframe on a specific date is an example of a predefined one-time event. An exclusionary rule for such an event includes criteria identifying portions of road along the route, the timeframe, and the date. A road construction project that blocks or diverts traffic on a specific road during the scheduled time of the project is another example of a predefined one-time event. In one embodiment, an event is a recurring event. For example, an exclusionary rule for a city with a professional football team includes criteria identifying a timeframe on specific dates (e.g., home football game days) when vehicle traffic within a two mile radius of the stadium moves twenty miles per hour or more below the average rate of travel, including not moving at all. In another embodiment, an event relates to extreme weather conditions. For example, an exclusionary rule indicates that traffic patterns deviate from normal when rainfall accumulation over a twenty-four hour period exceeds eight inches.

If road condition analyzer132determines that an exclusionary rule applies (decision210, YES branch), then road condition analyzer132determines that road conditions are normal (step208). If road condition analyzer132determines that an exclusionary rule does not apply (decision210, NO branch), then road condition analyzer132identifies a road problem (step212).

In one example, road condition analyzer132determines whether an exclusionary rule applies (decision210) based on current conditions. Based on data from sensors140, road condition analyzer132determines current conditions indicating snow accumulation of one inch and a time of 7:30 PM. Road condition analyzer132compares these conditions to the exclusionary rules stored in model and rules database180. In this example, model and rules database180includes a first exclusionary rule with criteria specifying weekdays and times between 5:00 PM and 7:00 PM. Model and rules database180further includes a second exclusionary rule with criteria specifying snow accumulation greater than three inches. In this case, road condition analyzer132fails to identify an exclusionary rule having criteria that are all satisfied by the conditions, based on which road condition analyzer132determines that no exclusionary rules apply (decision210, NO branch). In response, road condition analyzer132, identifies a road problem (step212).

In another example, road condition analyzer132determines whether an exclusionary rule applies (decision210). Based on data from sensors140, road condition analyzer132determines the current conditions, which indicate vehicular traffic along a particular portion of road with an average speed of ten miles per hour at 2:00 PM on a particular Saturday. In this example, model and rules database180includes an exclusionary rule with criteria identifying a timeframe from 1:00 PM to 3:15 PM on that particular Saturday and a location of several portions of road that happen to be the route to a popular concert venue. Road condition analyzer132determines that the current conditions meet the criteria of the exclusionary rule stored in model and rules database180, based on which road condition analyzer132determines that the exclusionary rule applies (decision210, YES branch). In response, road condition analyzer132determines that road conditions are normal (step208).

In step212, road condition analyzer132identifies a road problem and updates road condition database170. In one embodiment, road condition analyzer132uses sensor data to analyze the road problem. For example, road condition analyzer132acquires GIS coordinate data, video imagery, and still photographs from multiple cameras to identify a portion of a highway that is damaged (e.g., steep, irregularly sided grooves in the road surface). In one embodiment, road condition analyzer132adjusts one or more sensors140. For example, road condition analyzer132, using data in one or both of mapping database150and road infrastructure database160, identifies an adjustable traffic camera located near the road problem and sends a command instructing the camera to adjust (e.g., pan, tilt or zoom) in order to provide a better view of the road problem. In a further example, the camera imagery indicates a stranded vehicle in the left lane of a busy city street, and traffic congestion in both directions. In one embodiment, road condition analyzer132estimates the project resources (e.g., budget, number and type of work crew personnel, project duration) that will be required to repair the road problem. In one example, the estimate is based on identifying project resources that were required to fix a previously-identified road problem with similar characteristics. In one embodiment, road condition analyzer132updates road condition database170with information about the road problem. This information includes, for example, a description of the road problem, the location of the road problem, the date the road problem was identified, and the traffic pattern deviations (compared to models of normal traffic patterns) surrounding the road problem.

FIG. 3is a flowchart depicting operations for determining an optimized road repair plan within the computing environment ofFIG. 1, in accordance with an embodiment of the present disclosure. For example,FIG. 3is a flowchart depicting operations300of planning program134, on server130within computing environment100.

In step302, planning program134determines an impact of a road problem on a current traffic pattern. In one embodiment, planning program134acquires details of a road problem from road condition database170. In another embodiment, planning program134also acquires data from one or more of mapping database150, road infrastructure database160, and model and rules database180. For example, the details indicate a road problem located in a rightmost lane of a northbound portion of a highway, a quarter mile before the rightmost lane splits into two lanes, one leading into an eastbound highway and one continuing as part of the northbound highway. Planning program134determines the impact based on the deviation of the current traffic pattern from the normal traffic pattern. The normal traffic pattern is the traffic pattern of the model corresponding to the current conditions. For example, planning program134compares the current traffic pattern to the normal traffic pattern to determine that the average vehicular speed at the location of the road problem is twenty miles per hour slower than normal.

In step304, planning program134projects an impact of a road problem on a future traffic pattern. Planning program134projects the impact on a future traffic pattern utilizing predictive analytics to project the impact of the road problem on the speed of vehicular traffic. Planning program134utilizes predictive analytics to determine the rate of deterioration of the road problem, which is the rate of increase of the deviation between the normal traffic pattern and the projected traffic pattern. Planning program134determines the rate of deterioration based, at least in part, on at least one of weather information, traffic pattern information, and exclusionary rule information. In one embodiment, planning program134determines the rate of deterioration based on weather information. For example, planning program134determines a higher rate of deterioration of a road problem based on weather information forecasting icy conditions relative to weather information forecasting sunny and dry conditions. In another embodiment, planning program134determines the rate of deterioration based on traffic pattern information. For example, planning program134predicts a lower rate of deterioration for a road problem on a road with lower vehicular volume and lower average speed relative to a road problem on a road with higher vehicular volume and higher average speed. In another embodiment, planning program134determines the rate of deterioration based on exclusionary rule information. For example, if an exclusionary rule indicates that a road having a road problem is closed for a specified time period, planning program134determines the rate of deterioration of the road problem without regard to the normal traffic pattern during the specified time period.

In some embodiments, planning program134projects an impact of a road problem on future traffic patterns of more than one road. For example, planning program134predicts the impact of the pothole described previously (in the rightmost lane of a northbound portion of a highway) on an eastbound highway to which it connects. Planning program134analyzes current traffic pattern data (from sensors140) and historical traffic pattern data and models (from model and rules database180), and determines that traffic patterns are different than they were before the pothole existed. Planning program134determines that average traffic speeds are ten miles per hour slower where the two highways merge, traffic volume coming from the northbound highway is seven percent lower, and the number of traffic accidents near where the two highways merge is five percent higher than before the pothole existed.

In step306, planning program134determines a severity index of a road problem. The severity index is a measure of the degree to which the road problem disrupts (impacts) traffic patterns. Planning program134determines the severity index based on at least one of the impact of the road problem on a current traffic pattern, the current environmental conditions, the impact of the road problem on a future traffic pattern, and a forecast of future environmental conditions. In one embodiment, a high severity index indicates a high degree of traffic pattern disruption; a low severity index indicates a low degree of traffic pattern disruption. For example, using road problem examples given previously, planning program134determines that the severity index of the pothole on a northbound highway is higher than the severity index of the pothole on a small country road.

In one embodiment, the severity index is based, at least in part, on the predicted rate of deterioration of a road problem over time. In one embodiment, the higher the rate of deterioration of a road problem, the higher the severity index that planning program134assigns to a road problem. In one embodiment, weather conditions affect road problem deterioration. Examples of weather-related factors in road problem deterioration include expanding and contracting of surfaces due to temperature changes, snow plow blades hitting and grinding road surfaces, erosion over time caused by precipitation (e.g., rain or snow), and erosion from materials used in road maintenance (e.g., salt, sand). In one embodiment, planning program134correlates weather conditions with road problem deterioration rates. For example, based on historical data stored in road infrastructure database160, planning program134determines that potholes deteriorate faster during months with high snowfall (months in which snowfall accumulations exceed eight inches during any twelve hour period) than during months without snowfall. Based on this data, planning program134assigns a higher severity index to a pothole identified during a high snowfall month than to a pothole identified during a low snowfall month.

In another embodiment, the severity index is based, at least in part, on traffic pattern data. Planning program134correlates traffic pattern data with road problem deterioration rates. In one example, based on analyzing historical data about potholes and traffic patterns on roads with potholes, planning program134identifies a correlation between pothole deterioration and vehicular traffic types and volumes. For example, planning program134determines that, on roads where at least twenty percent of the vehicles travelling over the potholes are commercial vehicles weighing twenty-six thousand pounds or more (i.e., class7and above rated vehicles) pothole deterioration increases fifteen percent every two weeks and average vehicular speed decreases five miles per hour every two weeks. In another example, planning program134determines that, on road where less than five percent of the vehicles travelling over the potholes are class7and above rated vehicles, pothole deterioration increases one percent every two weeks and average vehicular speed does not decrease over time.

In one embodiment, the severity index is based, at least in part, on the availability of an alternate route of travel to the road affected by the road problem. For example, if the road problem is located on the only entrance road to a heavily visited national park, planning program134will assign that road problem a high severity index. In another example, if the road problem is located on a small country road that was replaced by a four-lane highway as the main route connecting two towns, planning program134assigns a low severity index to the road problem.

In step308, planning program134determines one or more resources required to repair a road problem. In some embodiments, planning program134determines resources required to repair a road problem by analyzing historical road repair plans for repairing similar road problems under similar conditions. In one embodiment, planning program134identifies similar road problems by identifying road problems with similar severity indices. For example, planning program134identifies a historical road repair plan (in road infrastructure database160, for example) for a similar road problem (e.g., a pothole on a highway). Planning program134further identifies that the project resources required to repair that pothole included four workers (e.g., one traffic control person and three laborers), three pieces of equipment (e.g., pavement grinder, surface grader, surface paver), and ten roadwork warning items (e.g., handheld red flags, plastic orange cones, temporary warning signs, flashing lights). The road crew was at the repair site for four hours, and the total cost of the project was five thousand dollars. Planning program134uses this set of resources as a baseline for determining resources required for the current pothole, and modifies resource requirements to take into account current road repair practices and other relevant economic conditions (e.g., updated specifications of road repair equipment and efficiency improvements, updated labor rates, raw material prices). For example, planning program134identifies that the current method for pothole repair uses a machine that takes the place of the surface grinder and the surface grader and reduces the required road crew from four members to three members. Planning program134analyzes the effects of all variables that have changed, and determines the required list of resources and the cost to repair the pothole, if the project were to begin immediately. In order to account for possible changes (in the required resources and associated costs) that will occur by the time the project begins, planning program134assigns rates of change to some variables of the road repair plan. For example, planning program134assumes a five percent per month deterioration rate of the road problem, resulting in a five percent per month increase in raw materials. In another example, planning program134determines the rate of deterioration as explained above. In one embodiment, planning program134updates road condition database170with information about the road problem and the associated road repair plan.

In step310, planning program134determines an optimized road repair plan. The road repair plan includes information about existing road problems and a schedule for repairing the road problems. Planning program134determines the optimized road repair plan based, at least in part, on information regarding one or more road problems. In various examples, the information regarding a road problem includes the severity index of the road problem, the resource requirements of repairing the road problem, the location and description of the road problem, and the impact of the road problem on current and future traffic patterns. In one embodiment, planning program134determines the optimized road repair plan based on a mathematical optimization algorithm utilizing a set of constraints. In some examples, constraints include current environmental conditions, traffic patterns, availability of budget and other project resources (as described previously), and events affected by or depending on the completion of a road repair (e.g., repairing a pothole on a road over which a presidential inaugural parade is scheduled to travel).

In one embodiment, the plan is optimized to schedule the maximum number of repairs. For example, based on road repair budget data (as stored in road infrastructure database160), and the estimated cost of resources required to repair each road problem (as determined in step308), planning program134determines that there is sufficient funding available to complete either one of the more costly repair projects or four of the less expensive repair projects before the end of the fiscal year. Planning program134optimizes the plan by scheduling four of the less expensive repair projects.

In one embodiment, the plan is optimized to prioritize repairing road problems with the highest severity level while minimizing traffic collisions. In one embodiment, the plan is optimized to minimize harmful effects on natural resources. In one example, when creating a repair plan for a pothole that is on a bridge that spans an ocean inlet, planning program134allocates project resources (e.g., cleaning materials, road filler materials) that are not harmful to marine life.

In one embodiment, the plan is optimized to maximize the reduction of impact of road problems on traffic patterns. For example planning program134determines that a road problem near a professional football stadium will not be scheduled for repair on any day that there is a football game or any other event in the stadium (i.e., days when there is a previously-identified event that will impact traffic patterns). In one embodiment, the plan is optimized to minimize the traffic pattern disruptions during a road repair project. For example, if planning program134determines that there are sufficient project resources within a week to repair three out of five potholes with identical severity indices, and two of the potholes are located on the same street, planning program134determines that those two potholes will be repaired in the same week, rather than disrupt traffic patterns on two separate occasions. In one embodiment, the plan is optimized to best utilize the skills of the road crew workers. For example, if only one worker is qualified as a field mechanic for a specialty piece of equipment that will be used for a road repair project, the plan identifies the timeframe for that project as an unavailable timeframe for that worker to schedule vacation.

In step312, planning program134issues an alert to one or more interface devices. In one embodiment, planning program134issues the alert over network120. In one embodiment, interface device110includes a programmable electronic highway sign. In this case, planning program134issues an alert to the programmable electronic sign, identifying a road problem and instructing the programmable sign to display a warning message such as, for example, “Road obstruction in two miles. Take alternate route.” In one embodiment, interface device110includes an onboard computer in a vehicle that controls a sensor of sensors140and a safety system of the vehicle. In this case, planning program134issues an alert to the onboard computer, identifying an upcoming road problem and instructing the onboard computer to adjust one or both of the sensor and the safety system. For example, the onboard computer can dynamically adapt to road conditions and make adjustments to increase passenger safety or comfort (e.g., tighten seatbelts, adjust headrests, change sensitivity settings on shock absorbers), or enable additional data gathering (e.g., increase the sensitivity of an accelerometer) before the vehicle arrives at the road problem.

FIG. 4is a block diagram of components of a computing device, generally designated400, in accordance with an embodiment of the present disclosure. In one embodiment, computing device400is representative of server130within computing environment100, in which case server130includes road condition analyzer132and planning program134.

It should be appreciated thatFIG. 4provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made.

Computing device400includes processor(s)404, cache416, memory406, persistent storage408, communications unit410, input/output (I/O) interface(s)412and communications fabric402. Communications fabric402provides communications between processor(s)404, cache416, memory406, persistent storage408, communications unit410, and input/output (I/O) interface(s)412. Communications fabric402can be implemented with any architecture designed for passing data and/or control information between processor(s) (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric402can be implemented with one or more buses or a crossbar switch.

Memory406and persistent storage408are computer readable storage media. In this embodiment, memory406includes random access memory (RAM). In general, memory406can include any suitable volatile or non-volatile computer readable storage media. Cache416is a fast memory that enhances the performance of processor(s)404by holding recently accessed data and data near accessed data from memory406.

Program instructions and data used to practice embodiments of the present invention may be stored in persistent storage408and in memory406for execution by one or more of the respective processor(s)404via cache416. In an embodiment, persistent storage408includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage408can include a solid state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information.

Communications unit410, in these examples, provides for communications with other data processing systems or devices. In these examples, communications unit410includes one or more network interface cards. Communications unit410may provide communications through the use of either or both physical and wireless communications links. Program instructions and data used to practice embodiments of the present invention may be downloaded to persistent storage408through communications unit410.

I/O interface(s)412allows for input and output of data with other devices that may be connected to each computer system. For example, I/O interface(s)412may provide a connection to external devices418such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External devices418can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention can be stored on such portable computer readable storage media and can be loaded onto persistent storage408via I/O interface(s)412. I/O interface(s)412also may connect to a display420.