Method and apparatus for utilizing estimated patrol properties and historic patrol records

It is an object of the present invention to provide a predictive traffic law enforcement profiler apparatus and method which incorporates a means to determine current location, time, velocity and also incorporates a means to utilize a database derived from historic traffic law enforcement records, crowd sourced records and historical traffic data and also incorporates a predictive processing means to provide historic traffic law enforcement records and estimates of enforced speed limits and enforcement profiles, patrol locations and schedules of traffic law enforcement to a driver.

CROSS REFERENCE OF RELATED APPLICATIONS

This patent is related to Pending U.S. patent application Ser. No. 16/895,000 with filing date Jun. 8, 2020 by the same inventor.

BACKGROUND OF THE INVENTION

The present invention relates to electronic devices used to provide information to drivers, driver assist controllers, automated and autonomous vehicle controllers and more particularly relates to a method and apparatus for utilizing historical data to predict traffic law enforcement patrol patterns, locations, schedules, speed traps, and enforcement profile.

It is well known that road condition information is very important to drivers to improve efficiency and safety of travel. In particular it is beneficial to maximize the amount of relevant road information that is available to drivers and present it in an optimally beneficial way. Heretofore, the most common road condition information has been real time and available from radar detectors for locating immediate traffic law enforcement patrol locations, radar detectors equipped with GPS for detecting locations of red light cameras, fixed speed traps and from the Department of Transportation through GPS based vehicle navigation systems for providing real time road condition data.

However, these techniques primarily provide only real time road condition information and do not provide historic and probabilistic or statistical data. More specifically, data available from traditional radar detectors only provides the driver with immediate law enforcement locations with very little warning. Additionally, current generation radar detectors and smart cell phones equipped with GPS for detecting red light cameras or fixed speed traps only provide fixed location of traffic law enforcement. Additionally, onboard vehicle navigation systems provide only near real time road accident, hazard and condition information. Additionally, exclusive crowd sourced databases of traffic law enforcement encounters are limited and compromised by accuracy of reports. Heretofore, none of the existing driver information apparatus provide the driver with historical statistical and probabilistic data and none predict likely locations of traffic law enforcement or patrol locations, patrol schedules, enforcement profiles, or enforced speed limit. Additionally, heretofore, no existing driver assisted, automated or autonomous vehicles incorporate a means to identify and report traffic law enforcement encounters as contributors to crowd sourced databases without human driver intervention. Furthermore, no existing driver assisted, automated or autonomous vehicle controllers provide a means to utilize real time, historical statistical or probabilistic traffic law enforcement patrol locations, patrol schedules, enforcement profiles, or enforced speed limit estimates to control movement.

It is an object of the present invention to provide historic traffic law enforcement patrol information. An additional object of the present invention is to utilize historic traffic law enforcement information to statistically predict the locations, schedules, and enforcement profile where drivers are likely to encounter traffic law enforcement patrols, speed traps, fixed and mobile speed cameras, red light cameras and provide the enforced speed limit.

It is an additional object of the present invention to utilize historic traffic law enforcement patrol citation records to statistically predict the probabilistic locations of traffic law enforcement patrols, enforcement profiles, speed traps and fixed and mobile speed cameras and red light cameras.

An additional object of the present invention is to provide historic and probabilistic traffic law enforcement patrol information, derived from historical traffic law enforcement data, and statistically predict the locations where it is more likely to encounter traffic law enforcement and speed traps and provide maximum safe speeds to avoid citation

An additional object of the present invention is to incorporate driver assisted, automated and autonomous vehicle sourced traffic law enforcement encounter information, and road condition information with crowd sourced databases.

An additional object of the present invention is to provide real time, historic and probabilistic traffic law enforcement patrol information, and road condition information to driver assisted, automated or autonomous vehicle controllers to control movement.

An additional object of the present invention is to utilize real time crowd sourced traffic law enforcement encounter information to provide real time locations of traffic law enforcement to drivers, driver assisted vehicle controllers, and automated or autonomous vehicles controllers.

SUMMARY OF THE INVENTION

It is a general object of the present invention to provide a method for supplying statistical and historical traffic related data to drivers. It is a more specific object of the present invention to provide a predictive traffic law enforcement profiler apparatus which incorporates a means to determine current location, date/time, velocity and also incorporates a means to access a database derived from historical traffic law enforcement records, historical crowd sourced traffic law enforcement encounter records, and historical traffic data and also incorporates a predictive processing means to statistically predict likely patrol locations, schedules, enforcement profiles of traffic law enforcement, enforcement austerity, the enforced speed limit and combinations thereof, and a means to provide this information to drivers, and to driver assisted, automated or autonomous vehicle controllers to control vehicle movement.

It is a more specific object of the present invention to provide a predictive traffic law enforcement profiler apparatus which incorporates a means to determine current location, date/time, velocity and also incorporates a means to access a database derived from historical traffic law enforcement records, historical crowd sourced traffic law enforcement encounter records, and historical traffic data of predicted traffic law enforcement patrol schedules, locations, enforcement profiles, enforcement austerity and enforced speed limit and combinations thereof, and provide this information to drivers.

It is yet another object of the present invention to provide an apparatus and method which provides any combinations of predicted traffic law enforcement patrol locations, schedules, enforcement profiles, enforced speed limits, and the locations of fixed and mobile speed cameras, speed traps, and red light cameras.

It is yet another object of the present invention to provide an apparatus and method which provides historic traffic law enforcement patrol information and a means for filtering and presenting historic traffic law enforcement data.

It is yet another object of the present invention to provide a method for determining likely patrol locations, schedules, enforcement profiles, enforcement austerity, enforced speed limit, locations of speed traps, speed cameras, red light cameras, and combinations thereof, of traffic law enforcement and providing this information to consumers.

It is yet another object of the present invention to incorporate driver assisted, automated and autonomous vehicle sourced traffic law enforcement encounter information with crowd sourced databases of traffic law enforcement encounter records.

It is yet another object of the present invention is to provide real time, historic and probabilistic traffic law enforcement patrol information to driver assisted, automated or autonomous vehicle controllers to control movement.

It is yet another object of the present invention is to utilize real time crowd sourced traffic law enforcement encounter information to provide real time locations of traffic law enforcement to drivers, driver assisted vehicle controllers, and automated or autonomous vehicles controllers.

It is yet another object of the present invention to utilize real time historic and probabilistic traffic law enforcement patrol information, road condition information, traffic flow information to perform navigation and route planning.

The present invention provides an innovational design which incorporates state of the art data processing predictive technology to provide precise action, increased accuracy, lower cost, and added functionality over known existing products.

In a preferred embodiment, the predictive traffic law enforcement profiler apparatus includes a location determining means, a velocity determining means, direction determining means, current time and date determining means, a database means, user control means, a predictive processor means, an indicator means, a computer interface means, a vehicle controller means, a vehicle sensor input means, a telemetry means, and a network interface communication means. Said user control means may be a local control means or a remote control means through said said network interface communication means to configure, control and monitor operation of said apparatus. Said database means may contain any combinations of real time and historic records of traffic law enforcement encounters and records of estimated properties of traffic law enforcement. Said user control means may communicate road hazards, road conditions preferably including weather and ice conditions, and said real time records of traffic law enforcement encounters to said database. Said predictive processing means accesses said database means to provide estimated traffic law enforcement profiles and may perform any algorithmic functions including any combinations of statistical analysis, inferential statistics, data analytics and artificial intelligence on said real time and historic records of traffic law enforcement encounters to predict said properties of traffic law enforcement. Said vehicle sensor means may provide an ability to sense the environment of said vehicle which could include the ability to identify traffic law enforcement, road hazards, road conditions preferably including ice and precipitation and weather. An output from said vehicle sensor means could also be provided to said database or said network interface means. Said vehicle controller means provides ability for said predictive processing means to control vehicle speed and preferably vehicle direction and route. Said telemetry means provides an ability to forward an output from any of said vehicle sensor means, said velocity determining means, said location determining means, said direction determining means to said said database or said network interface means. Said indicator means provides an ability for said predictive processing means to provide said properties of traffic law enforcement preferably using both visual, audible, and electronic signal annunciators. Said predictive traffic law enforcement profiler apparatus further may be partitioned into one or more client means and one or more server means wherein said client means may communicate with said server means through said network interface communication means. Wherein said client means preferably comprises said location determining means, said velocity determining means, said direction determining means, said current time and date determining means, said user input means, said indicator means, said vehicle controller means, and said vehicle sensor input means. Wherein said server means preferably comprises said a database means and said predictive processor means.

In another preferred embodiment, the predictive traffic law enforcement profiler apparatus includes a database means, a predictive processor means, and an indicator means. The database means includes a means for providing the locations where traffic law enforcement has historically issued citations and the information associated with said citations which preferably includes combinations of type of violation, direction of travel, speed of vehicle if cited for speeding, location time and date and may also include reason for stop and type of vehicle. Said predictive processing means cross correlates records in the database to statistically predict the locations and schedules at which it is more probable to encounter traffic law enforcement, and enforcement profile further including enforcement austerity, speed traps, speed cameras, red light cameras and also estimates the enforced speed limit and combinations thereof, of traffic law enforcement. Said indicator means which preferably includes visual and or audible annunciators presents combinations of predicted locations, schedules and enforcement profile further including enforcement austerity, speed traps, speed cameras, red light cameras and also estimated enforced speed limit at said locations, of traffic law enforcement.

In another preferred embodiment, the predictive traffic law enforcement profiler apparatus includes a means to provide estimates of patrol schedules, locations and enforcement profile and combinations thereof, of traffic law enforcement at a singular or plurality of locations comprising: a database means, access means and an indicator means. Said database means provides records, derived from issued citations of traffic law enforcement and crowd sourced encounters with traffic law enforcement and combinations thereof, of patrol schedules, patrol locations, and estimated enforcement profile including any combinations of: enforced speed limits, speed traps, speed cameras, red light cameras and enforcement austerity of traffic law enforcement. Said access means to retrieve combinations of said patrol schedules, patrol locations, and enforcement profile including any combinations of: enforced speed limits, speed traps, speed cameras, red light cameras and enforcement austerity of traffic law enforcement. Said indicator means to present combinations of said patrol schedules, patrol locations, and estimated enforcement profile including any combinations of: enforced speed limits, speed traps, speed cameras, red light cameras of traffic law enforcement.

In another preferred embodiment, the predictive traffic law enforcement apparatus for providing estimates of the enforced speed limit, patrol schedules and enforcement profiles of traffic law enforcement and combinations thereof at a singular or plurality of first locations comprising: a database means, location and velocity determining means, time determining means, record access means and indicator means. Said database means for providing records of estimated traffic law enforcement patrol schedules, enforced speed limits and enforcement profiles derived from a combination of issued citations and crowd sourced encounters of traffic law enforcement at a second plurality of locations. Said location and velocity determining means for determining the location and current velocity of said apparatus. Said record access means to retrieve combinations of said estimated traffic law enforcement patrol schedules, enforced speed limits and enforcement profiles at said first locations from said database means. Said indicator means to present combinations of said estimated patrol schedules, enforced speed limits, and enforcement profiles of traffic law enforcement at said first locations. Said indicator means to notify when said apparatus location is within or approaching locations of said estimated patrol schedules, enforcement profiles and combinations thereof; and a second indicator means to notify when said apparatus velocity exceeds said estimated enforced speed limit at said location of said apparatus.

In another preferred embodiment, the predictive traffic law enforcement profiler apparatus includes a location determining means, current time and date determining means, a database means, user input means, a predictive processor means and an indicator means. Said location determining means includes a means to determine the latitude and longitude location and current velocity. Said time of day determining means includes a means to determine the current date and time. Said database means includes a means for providing the locations where traffic law enforcement has historically issued citations and the information associated with said citations which preferably includes combinations of type of violation, direction of travel, violation speed if cited for speeding, location time and date and may also include reason for stop and type of vehicle. Said predictive processing means correlates current location, velocity, time of day, and user criteria with entries in the database to statistically predict the locations where it is more likely to encounter traffic law enforcement, red light cameras, speed cameras and speed traps and provide maximum safe speeds to avoid citation and provide said information via the indicator means which preferably includes both visual and audible annunciators.

In yet another preferred embodiment, the predictive traffic law enforcement profiler apparatus includes a client means and a server means. Said client means includes a location determining means, a time of day determining means, an indicator means and a means to communicate with one or more said server means. Said server means preferably includes a database means, a predictive processor means and a means to communicate with one or more clients. Said server database means includes a means for providing the locations where traffic law enforcement has historically issued citations and the information associated with said citations which preferably includes combinations of type of violation, direction of travel, speed of vehicle if cited for speeding, location time and date and may also include reason for stop and type of vehicle. Said client location determining means includes a means to determine the latitude and longitude location and current velocity. Said client time of day determining means includes a means to determine the current date and time. Said client communication means provides a means to communicate the client location, client time of day, and client velocity to said server means. Said server predictive processor means correlates said client location, said client velocity, said client time of day, and user client criteria with records in said server database means to statistically predict the locations where it is more likely for said client to encounter any combinations of traffic law enforcement, speed traps, red light cameras, speed cameras and provide maximum safe client speeds to avoid citation derived from said server database of historical issued citations and provide said predicted locations, speed traps, speed cameras and maximum client speeds to avoid citation through said server communication means to said client. Said client indicator means preferably includes combinations of visual and audible annunciators to present any combinations of said server predicted locations of traffic law enforcement, speed traps, red light cameras, speed cameras and maximum client speeds to avoid citation.

In yet another preferred embodiment the predictive traffic law enforcement profiler apparatus includes a means to provide estimates of traffic law enforcement austerity comprising: a location determining means, database means, database access means, and an indicator means. Wherein said database means comprising estimates of enforcement austerity derived from historic records of issued citation warnings, historic records of traffic flow rates and volumes, records of issued citations of traffic law enforcement, and crowd sourced traffic law enforcement encounters and combinations thereof. Said access means to retrieve a combination of said estimated enforcement austerity and an indicator means to present enforcement austerity at said location.

In yet another preferred embodiment, a method is for providing estimates of the enforced speed limit, patrol schedules, enforcement profile, and combinations thereof, of traffic law enforcement at a singular or plurality of locations comprising the steps of: Retrieving a historical record derived from issued traffic law enforcement citations at said location. Correlating citation time, location, violation velocity and combinations thereof to determine enforced speed limit, patrol schedules, and enforcement profile of traffic law enforcement at said locations; presenting said estimated enforced speed limit, estimated patrol schedules, enforcement profiles, and combinations thereof, at said locations.

In yet another preferred embodiment, the predictive law enforcement traffic profiler driver information apparatus also includes a means for monitoring current weather conditions and a database means. The database means includes a means for storing the coordinate locations where accidents have occurred and the recorded details associated with said accidents which preferably includes cause of accident, time of said accident, and weather conditions at time of said accident. In this preferred embodiment, the said predictive processing means correlates current location and current weather conditions with said database to determine relevant locations of probable road hazards via the indicator means which preferably includes both visual and audible annunciators.

Further objects and advantages of the present invention will become apparent to those skilled in the art from a consideration of the following detailed description of the preferred embodiment and drawings.

DETAILED DESCRIPTION

It is well known that traffic law enforcement agencies have patrol patterns, enforcement profiles and schedules that vary by location, time of day, month and year, weather conditions and traffic flow rates. Generally, traffic law enforcement typically allow some flexibility in enforcing regulations; examples of which could include allowing motorists to exceed the posted speed limit by some margin before issuing a citation or allowing motorists extra time on expired vehicle registrations. The local traffic law enforcement customs may vary by location and time. For instance, in some areas the posted speed limit may be enforced strictly, while in other areas traffic law enforcement may allow much higher speed limits over the posted speed limits prior to issuing a speeding citation. Additionally, it is not uncommon for traffic speed limits to be enforced more rigorously at night than during the day or during certain holidays there may be increased traffic law enforcement patrols and the enforcement profile may be more strict and more focused in certain regions. Additionally, the use of fixed and mobile automated traffic law enforcement systems are commonly used to enforce traffic laws, examples of which include Red Light cameras which photograph and issue citations to operators or registered owners of vehicles passing through an intersection with a red light and Speed Camera systems which photograph and issue a speeding citation to operators or registered owners of vehicles exceeding the speed limit at a location. Automated traffic law enforcement systems are typically at fixed locations, or change locations periodically and also frequently exhibit predetermined enforcement profiles, which could include the enforced speed limit.

In the present invention, the terms estimated patrol schedule, patrol locations and enforcement profiles are defined in Table 1 to characterize the enforcement patterns of traffic law enforcement and utilized in subsequent descriptions of the present invention.

TABLE 1Traffic Law Enforcement Properties and Patterns which includescombinations of: Estimated and Predicted Patrol Schedules, Patrol Locations,and Enforcement Profiles.Traffic LawEnforcementPropertyDescriptionLocationThe location for which these enforcement properties pertain.PatrolThe times at which traffic law enforcement is predicted to be present at theScheduleLocation and also predicts the times with which traffic law enforcement ispredicted to not be present at a given location and the confidence of theprediction.Location PatrolThe predicted intensity with which traffic law enforcement patrols theIntensityLocation and may be as a function of time and the confidence of theprediction. Locations with higher Patrol Location Intensity may beconsidered Patrol Locations.PatrolThe locations at which traffic law enforcement is predicted to patrol and alsoLocationspredicts the locations which traffic law enforcement is predicted to not patroland may be as a function of time and the confidence of the prediction. APatrol Location may have higher Location Patrol Intensity.EnforcementThe estimated and predicted characteristics which describe traffic lawProfileenforcement at the Location which may include any combinations of:a)Enforced speed limit at location and timeb)Speed trap enforced locationc)Speed camera enforced locationd)Red light camera enforced locatione)Aircraft speed limit enforced locationf)Histogram of enforced traffic lawsg)Histogram of citation speedsh)Location citation densityi)Enforcement Profile of Traffic Lawsj)Probability of traffic citation at location and timek)Probability of encountering traffic law enforcement at location and timel)Enforcement Austerity - traffic law enforcement inflexibility and Austerenessa.Enforcement Leniency Profile -probability of receiving a citation versesa warning as a function of violation typeb.Speed Limit Enforcement Austerity Relative to Average Traffic FlowVelocity - estimated enforced speed limit relative to the traffic velocityc.Speed Limit Enforcement Austerity Relative to Average Traffic FlowVolume - estimated enforced speed limit relative to the traffic volumed.Location Enforcement Austerity Relative to Average Traffic FlowVolume - estimated amount a location is patrolled relative to trafficvolume

State and city law enforcement agencies maintain databases of traffic violations that were issued. Examples of traffic violations may include but are not limited to speeding citations, expired vehicle registration, and other moving and non-moving traffic violations. Each citation record typically includes information relevant to the violation. In the case of speeding citations, typically the time, date, location, direction and speed of the vehicle are recorded. In the case of expired registrations, typically the time, date, location, and duration of expiration are recorded. In the present invention, the term “citation record” or “citation” refers to the collection of data captured with an issued citation. Additionally, it is also possible to maintain databases of crowd or user sourced data which could include information provided by individuals that experienced an encounter with traffic law enforcement. Such encounters could include traffic stops by traffic law enforcement and the reason for stop could be provided, location, time, date, and preferably the speed at which the driver was travelling could be provided to the database. Additionally, an encounter could also be a vehicle passing a traffic law enforcement vehicle in which case the location, time, date, direction and preferably speed at which the user was travelling could be contained in the database. In the present invention, the term “encounter record” or “encounter” refers to the collection of data captured with a crowd sourced encounter event with traffic law enforcement.

In the present invention the term “driver” refers to a controller of a vehicle which includes human or machine controllers or any combination thereof. Machine controllers provide a level of automation of a vehicle system and include sensors to sample the environment which could include sensing and identifying the location of traffic law enforcement vehicles, other vehicle locations, directions and speeds in the vicinity of the vehicle, traffic signs such as posted speed limit signs, stop signs, caution signs, school zones, construction zones, road surface conditions, and weather conditions, processors to evaluate environmental information and human driver preferences to produce control decisions, and actuators to effect vehicle movement. Examples of machine controllers may include cruise controllers which control the speed of a vehicle, adaptive cruise controllers which control the speed of a vehicle but may vary the speed based on environmental conditions such as posted speed limit signs, school zones, constructions zones or caution signs, lane assist controllers which assist a human driver with directional control to travel in lanes which are less likely to be monitored by traffic law enforcement, semi-autonomous vehicle controllers and fully autonomous vehicle controllers which can perform route planning, and control the direction and speed of a vehicle and adapt based on environmental conditions.

In the present invention the term “vehicle” refers to both human controlled or automated machine controlled vehicles or combinations thereof. The level of vehicle machine control can range from exclusively human control to fully autonomous and can be categorized into five levels of automation as presented in Table 2.

TABLE 2Levels of autonomous vehicles.LevelDescription0No driving automation - Full-time performance by the human driver of allaspects of the dynamic driving task, even when enhanced by warning orintervention systems.1Driver Assistance - system of either steering or acceleration/deceleration usinginformation about the driving environment and with the expectation that thehuman driver performs all remaining aspects of the dynamic driving task.Examples include: stability control, cruise control, and automatic braking2Partial Driver Automation - The driving mode-specific execution by one or moredriver assistance systems of both steering and acceleration/decelerationusing information about the driving environment and with the expectationthat the human driver performs all remaining aspects of the dynamic drivingtask. Examples include: adaptive cruise control in combination with lanekeeping.3Conditional Driver Automation - The driving mode-specific performance by amachine controlled Automated Driving System of all aspects of the dynamicdriving task with the expectation that the human driver will respondappropriately to a request to intervene.4High Automation: The driving mode-specific performance by a machinecontrolled Automated Driving System of all aspects of the dynamic drivingtask, even if a human driver does not respond appropriately to a request tointervene. May include machine controlled automated route planning.5Full Automation: The full-time performance by a machine controlledAutomated Driving System of all aspects of the dynamic driving task underall roadway and environmental conditions that can be managed by a humandriver. Could include fully autonomous unoccupied vehicles.

In the present invention the term “crowd sourced” refers to contributors of records of encounters with traffic law enforcement and includes human contributors and automated contributors. More specifically, sensors on vehicles can identify encounters with traffic law enforcement and autonomously contribute encounter records.

The historic and predictive traffic law enforcement profiler apparatus preferably utilizes databases of traffic citations, traffic stop, and arrest records maintained by law enforcement agencies including state highway patrols, city and county police departments, municipal courts, state courts, departments of motor vehicles, and in general government or private agencies collective referred to as citations, and preferably includes databases of crowd sourced encounters with traffic law enforcement collectively referred to as encounters to profile and predict the enforcement profile, patterns, locations and schedules where traffic law enforcement agencies patrol. The apparatus provides an indication to a driver when approaching an area where there is a historic or statistically significant chance of encountering traffic law enforcement personnel allowing precaution to be taken such as driving cautiously and within enforced speed limits.

Additionally, the apparatus may also provide the driver with historically significant traffic law enforcement information which may include a maximum estimated speed which it is safe to drive with an acceptably low chance of being cited by law enforcement for violating the speed limit. Additionally, the apparatus preferably may provide the driver with a historical record of traffic law enforcement citations and preferably a set of database processing methods to enable searching, filtering, extracting, statistical processing, and viewing of results preferably including histogram creation, distributions, scatter plots, tables and lists.

A block diagram of a preferred embodiment of the historic and predictive traffic law enforcement profiler apparatus is shown inFIG.1. As can be seen, the profiler apparatus,14preferably consists of a client device21and a server resource3. The client device21may further include a historic and predictive processing unit1, a location determining unit7, time determining unit25, direction determining unit26, velocity determining unit28, vehicle sensor inputs27, a historical database2, real time data base30, a road map database15, a traffic flow database16, a visual display9, an audible output8, a user control10, vehicle control24and a network interface6. The server resource3preferably includes a historic database4, a real time database5, a traffic flow database23, a roadway map database17, a network interface19, and a predictive processing unit18. Databases2,15,16,30,4,5,17,23are presented as distinct databases; however, those skilled in the art will recognize that any physical or logical combinations of databases2,15,16,30,4,5,17,23are realizable and considered within the scope of the present invention. Those skilled in the art will also recognize that the user control10enables configuration and operation of the profiler apparatus14, which may include touch, touchscreen or audio/voice user interfaces.

A preferred embodiment of the profiler apparatus14includes a client device21and a server resource3. In a more specific preferred embodiment the client device21could be implemented as a standalone device and the server resource3may preferably not be required. Or in another preferred embodiment the client device21could be implemented as a thin device, utilizing server resource3for providing databases4,5,17,23and historic predictive processing unit18through the network interfaces6,19wherein network interfaces6,19could be either wireless or wired and the client device21may not require databases2,15,16,30or predictive processing unit1. In an additional preferred embodiment, client device21and server resource3are both present and share responsibility for providing database content4,5,17,23,2,15,16and predictive compute resources1,18.

In a preferred portable navigation device embodiment, the client device21of the profiler apparatus14could be integrated into a standalone portable navigation devices such as a TomTom, Garmin, Magellan, Nuvi or similar road navigation device in which case preferably the client device21could use the common resources of the portable navigation device including but not limited to the display9, audio output8, location determining unit7, user control10, predictive processing unit1and local databases2,15,16,30. To provide access to real time databases and physically separate historical databases and predictive processing resources, the preferred portable navigation device embodiment may also include wired and/or wireless instances of network interfaces6,19. Interfaces6,19provide data communication with server resource3preferably providing records of traffic law enforcement citations and encounters from real time database5, historical database4, traffic flow database23, and map database17and preferably server resource predictive processing unit18.

In a preferred smart phone or tablet embodiment, the historic and predictive client device21could be a smart phone or tablet executing an application which may be interoperating with a web application executing on a web server3. In the preferred smart phone embodiment, the client device21of the profiler apparatus14could be integrated with a smart phone or tablet device such as an iPhone, iPad, Google phone, Microsoft Surface tablet or device operating the Android OS, Windows OS, iOS or similar device and could use the common resources of the device preferably including but not limited to the display9, audio output8, location determining unit7, time determining unit25, direction determining unit26, velocity determining unit28, user control10, predictive processing unit1and local databases2,15,16,30. Additionally, the preferred smart phone or tablet embodiment could optionally utilize wired or wireless network interfaces6,19which could preferably support G3, G4, G5 or other wireless data standards to provide data communication with a server resource3preferably providing traffic law enforcement citations and encounters from real time database5, historical database4, traffic flow database23, and map database17and preferably server resource predictive processing unit18. The user control10may enable configuration and operation of the profiler apparatus14. User control10may include touch and touchscreen user interfaces, and in addition to or alternatively may include audio/voice user interfaces, utilizing the screen and or audio resources of the smart phone or tablet device. Examples of voice control applications which could be used to configure and control the profiler apparatus14may include Amazon Alexa, Google Voice, Apple Siri, and Microsoft Cortana or in general native voice control. User control10may also be performed remotely from the server3, through the network interfaces6,19, and the server terminal31,815can enable one or more client devices21to be configured and controlled remotely as a federated fleet. Additionally, in the preferred smart phone or tablet embodiment, the client device21.102could perform vehicle control to achieve driver assisted, partial driver automation, conditional driver automation, high automation, or full automation. The camera of the smart phone can function as vehicle sensors105,106,107,101which are passed to the predictive processing unit1through the vehicle sensor inputs27. The predictive processing unit1can control vehicle100speed, direction and navigation/routing though the vehicle control unit24which preferably interfaces to the vehicle controller103providing command of the vehicle100steering or motor systems or any combination thereof.

Additionally, in the preferred smart phone or tablet embodiment, the client device21could perform driver assisted functions such as cruise control or adaptive cruise control by controlling the speed of the vehicle100to be consistent with the speed determined by the predictive processing unit1through the vehicle control unit24which preferably interfaces to the vehicle controller103providing command of steering or motor systems or any combination thereof. The vehicle control unit24preferably interfaces to the vehicle controller103utilizing wired or wireless protocols which could include the network interface6, the computer interface12, wifi, bluetooth or USB. The smart phone or tablet camera sensor could serve as a vehicle sensor105,106,107,101in conjunction with the predictive processing unit1through the vehicle sensor inputs27to perform adaptive cruise control by slowing the vehicle100to maintain a safe distance behind any other sensed vehicles in the path. Additionally, utilizing the smart phone camera sensor105,106,107,101, vehicle control unit24, and the vehicle controller103, the predictive processing unit1could also perform partial driver automation, conditional driver automation, high automation, or full automation by controlling the direction of the vehicle100to change lanes or pass a slower moving vehicle. Additionally, the smart phone camera sensor105,106,107,101in conjunction with the predictive processor1could identify and report as crowd sourced records encounters with traffic law enforcement to the client real time database30and to the server database5utilizing network interface6where the records could be shareable with other users. Further, if the predictive processor1identifies the location of traffic law enforcement, the predictive processor1can control the speed of the vehicle100to comply with posted limits or estimated enforcement profiles in addition to controlling the position of the vehicle in a lane that is advantageous to avoid citation. The The predictive processing unit1can also receive indications or monitor the client real time database30and server real time data base5through the network interface6to learn of crowd sourced records of encounters with traffic law enforcement. The predictive processing unit1can control the speed, direction, and navigation route of the vehicle100to comply with posted speed limits, enforcement profiles or traffic law enforcement locations to avoid citation. Additionally the smart phone camera sensor105,106,107,101in conjunction with the predictive processing unit1can sense identify and interpret roadway signs preferably including speed limit signs, stop signs, and caution signs to interpret the actual posted speed limit.

In a preferred personal computer embodiment, the client device21of the profiler apparatus14could be a personal computer and the server device3could be a web server. In the preferred personal computer embodiment, Client device21could use the common resources of the device preferably including but not limited to the computer screen as the display9, computer speaker output as the audio output8, location determining unit7, time determining unit25, graphical user interface as the user control10, the central processor unit as the predictive processing unit of the personal computer and local databases2,15,16,30which could be on a computer drive. Additionally, the preferred personal computer embodiment could utilize internet network interfaces6,19to provide internet data communication with a server resource3preferably providing traffic law enforcement encounters from real time database5, historical database4, traffic flow database23, and map database17and preferably server resource predictive processing unit18. Furthermore, the preferred personal computer embodiment could be a web browser or application executing on the personal computer client device21with server resource3providing predictive processing services in unit18.

In a thin predictive processor preferred embodiment, databases2,15,16,30could be combined into a unified database which contains combinations of pre-computed predicted patrol schedules, and enforcement profiles which may include estimated enforced speed limits, patrol schedules, patrol locations, enforcement profiles, and estimated enforcement austerity, and other properties as defined in Table 1, at a singular or plurality of locations and a map of the roadway system. The plurality of locations would preferably include locations and be limited to within the geographic region of travel. Additionally, the maximum plurality of locations would be limited by the size of the database2,15,16,30. Typically 10 billion locations could be stored with current client database technologies however, those familiar with the art, would recognize that future database technologies could exist which conceivably support additional locations. Additionally, in this preferred embodiment databases4,5,17,23could similarly be combined into a unified database which contains combinations of the predicted enforcement profiles, estimated patrol schedules, estimated enforced speed limits, and estimated enforcement austerity at a singular or plurality of locations and a map of the roadway system. Typically, 100 billion locations could be stored with current server database technologies however, those familiar with the art, would recognize that future database technologies could exist which conceivably support additional locations. In the thin predictive processor preferred embodiment, the predictive processor unit of the client1, and server18could perform access functions to database2,15,16,30and4,5,17,23respectively, to retrieve any combinations of pattern properties presented in Table 1 including for example the predicted enforcement profiles, estimated patrol schedules, estimated enforced speed limits, and estimated enforcement austerity, at locations of interest and provide indications to the user through the audio8and visual9.

In a preferred machine controlled or automated vehicle embodiment, the client device21,102of the profiler apparatus14could be integrated into a vehicle100. The vehicle100could incorporate any level of automation presented in Table 2 including driver assistance, partial driver automation, conditional driver automation, high automation, or full automation (fully autonomous). and could use the common resources of the vehicle100preferably including but not limited to the display9, audio output8, location determining unit7, time determining unit25, direction determining unit26, velocity determining unit28, user control10, predictive processing unit1and local databases2,15,16,30. Additionally, the preferred machine controlled or automated vehicle embodiment can also provide an indication of the historical and or predicted traffic law enforcement profile, schedules and location to computer interface12or network interface6to provide an indication to another system such as a vehicle integrated navigation system or a vehicle integrated communication system. Additionally, the preferred machine controlled or automated vehicle embodiment, the client device21,102of the profiler apparatus14could optionally utilize wired or wireless network interfaces6,19which could preferably support G3, G4, G5 or other wireless data standards to provide data communication with a server resource3,104preferably providing traffic law enforcement citations and encounters from real time database5, historical database4, traffic flow database23, and map database17and preferably server resource predictive processing unit18. The automated vehicle100typically has a range of sensors105,106,107,101for determining a number of environmental conditions which could include, speed and direction of other vehicles, objects and road hazards such as stalled vehicles, icy weather and slippery road condition, construction zones, school zones, lane closures, posted speed limit signs, posted caution signs, and location, lane position, speed and direction of the vehicle100. Sensors105,106,107,101could interpret posted speed limit signs and posted caution signs to determine the posted speed limit. Sensors105,106,107,101could be a GPS, Lidar system, stereo vision system, camera system, machine vision system, Internet Protocol address locating system, radio location system, a Simultaneous localization and mapping (SLAM) system, dead reckoning system, radio location system, real-time locating system (RTLS), radar proximity detection, light field moment imaging or an Inertial Navigation system or any combinations thereof. Sensors105,106,107,101could interface to the enforcement profiler client device21,102through the vehicle sensor inputs27to provide the functions of the location determining unit7, time determining unit25, direction determining unit26, velocity determining unit28, The client device21,102may determine any combinations of properties presented in Table 1 including for example the predicted enforcement profiles, estimated patrol schedules, estimated enforced speed limits, and estimated enforcement austerity in addition to real time locations of traffic law enforcement from database5,30to provide vehicle control preferably including any combinations of vehicle speed, direction, route, or navigation route planning through the vehicle control unit24, which preferably interfaces with the vehicle controller103providing command of the vehicle100steering or motor or any combinations thereof to improve travel efficiency and decrease probability of citation. For instance, the client device21,102may control speed in conjunction with adaptive cruise control to ensure a vehicle does not violate enforcement profiles such as enforced speed limit. Further, the client device21,102may also control speed to ensure a vehicle complies with the posted or enforced speed limit at real time or predicted patrol locations. In addition, the client device21,102may also preferably control the direction, route, and or navigation route planning of a vehicle to travel on road ways with the highest enforced speed limits or roadways with fewer numbers of estimated patrol locations or real time locations of traffic law enforcement. The Profiler apparatus14performs navigation and route planning preferably in conjunction with historic and real time traffic flow rates16,23to estimate the speed at which traffic is currently moving or expected to be moving on the available route options. The client device21,102may also utilize sensors105,106,107,101in conjunction with the predictive processor1to identify and report crowd sourced records of encounters with traffic law enforcement to the client real time database30and to the server database5utilizing network interface6where the records could be shareable with other users. Further, if the predictive processor1identifies the location of traffic law enforcement, the predictive processor1can control the speed of the vehicle100to comply with posted limits or estimated enforcement profiles in addition to controlling the position of the vehicle in a lane that is advantageous to avoid citation. The predictive processing unit1can also receive indications or monitor the client real time database30and server real time data base5through the network interface6to learn of crowd sourced records of encounters with traffic law enforcement. The predictive processing unit1can control the speed, direction, and navigation route of the vehicle100to comply with posted speed limits, enforcement profiles or traffic law enforcement locations to avoid citation. The user control10may enable configuration and operation of the profiler apparatus14. User control10may include touch and touchscreen user interfaces, and in addition to or alternatively may include audio/voice user interfaces, utilizing the screen and or audio resources of the vehicle100. Examples of voice control applications which could be used to configure and control the profiler apparatus14may include Amazon Alexa, Google Voice, Apple Siri, and Microsoft Cortana or in general native voice control. User control10may also be performed remotely from the server3, through the network interfaces6,19, and the server terminal31,815can enable one or more client devices21to be configured and controlled remotely as a federated fleet. Those skilled in the art will recognize that additional vehicle control scenarios are possible and should be considered within the scope of the present invention.

As can be seen in a preferred embodiment ofFIG.1, the Client device21of the Historic and Predictive Processing Unit (HPPU)1may accept input from the User Control10, location determining unit7, time determining unit25, direction determining unit26, velocity determining unit28, vehicle sensor inputs27, Weather Monitor11, and database2,15,16and30. The User Control10, provides a means for a user including a human user or a machine user such as a driver assisted, automated or autonomous vehicle to configure and control the client device21. The user control10may enable configuration and operation of the profiler apparatus14. User control10may include touch and touchscreen user interfaces, and in addition to or alternatively may include audio/voice user interfaces, utilizing the screen and or audio resources of the vehicle100. Examples of voice control applications which could be used to configure and control the profiler apparatus14may include Amazon Alexa, Google Voice, Apple Siri, and Microsoft Cortana or in general native voice control. User control10may also be performed remotely from the server3, through the network interfaces6,19, and the server terminal31,815can enable one or more client devices21to be configured and controlled remotely as a federated fleet. The Historic and Predictive Processing Unit1may provide annunciation output to the Audio Output8, and Display9and also may have coupling to a computer interface12and or network interface6for interfacing to a server resource3for uploading and downloading content from server resource databases4,5,17,23and or interacting with server applications running on server predictive processing unit18. Additionally, the Historic and Predictive Processing Unit1may provide navigation route planning and vehicle control preferably including speed control, direction control, and route control or any combinations thereof, through the Vehicle Control Unit24. Additionally, a user of the client device21may upload or add entries as crowd sourced records, to client databases2,30or to server databases4,5using the client device network interface6and server network interface19. Crowd sourced records could be shared with multiple instances of client devices21. Furthermore, vehicles equipped with sensors such as Lidar, stereo vision, cameras, or machine vision could autonomously identify and report encounters with traffic law enforcement to the client device21through the vehicle sensor inputs27wherein the encounters would be added as crowd sourced records to client databases2,30or to server databases4,5using the client device network interface6. The network interface6may also enable multiple instances of client devices21to communicate with a server resource3through network interface19which could be wired or wireless. Examples of the server resource3could include a web server hosting the predictive processing algorithms application executing on predictive processor18which could be the server's central processing unit. In this preferred embodiment server resource3could serve web pages containing any combination of real time, historic and or predicted traffic law enforcement patterns, schedules, locations, enforcement profiles defined in Table 1, to client devices21or could also be a file server providing client device21access to databases4,5,17,23which may partially or totally eliminate the need for client device local databases2,16,15,30. Further, the server resource3could include a social media site implementing a protocol such as ActivityPub, and the client devices21could support ActivityPub actor protocol to receive real time and predicted traffic law enforcement patrol schedules, locations and enforcement profiles. Additionally, the ActivityPub actor client device21could publish real time encounters with traffic law enforcement to the ActivityPub server resource3. Preferably a plurality of client devices21could establish network interfaces6to a single instance of server resource3through network interface19, wherein the number of client devices21may be limited to less than 10,000,000; however, as the capacity of server resource3increases, those skilled in the art would recognize additional client devices21could communicate with a server resource3. Additionally, a network of client devices21could establish communications with each other through their respective network interface6preferably enabling multiple client devices21to share respective databases2,15,16,30with other client devices21preferably forming a distributed database and possibly eliminating the need for server resource3. Similarly, a plurality of server resources3could establish network interfaces with each other through their respective network interfaces19preferably enabling multiple server resources3to share respective databases4,5,17,23with other server resources3preferably forming a distributed database, wherein the number of server resources3may be limited to less than 10,000,000; however, those skilled in the art will recognize additional server resources3could be included as network19capacities are increased.

The Location Determining Unit7may preferably provide the current location, speed, direction of travel, date and time. The location determining unit7could be realized using Global Positioning (GPS) technology, a GPS receiver, and it is well known that speed, direction of travel, date, and time can be derived from GPS data. Additionally, the location determining unit7could be a GPS, Lidar system, stereo vision system, camera system, machine vision system, Internet Protocol address locating system, radio location system, a Simultaneous localization and mapping (SLAM) system, dead reckoning system, radio location system, real-time locating system (RTLS), radar proximity detection, light field moment imaging, or an Inertial Navigation system or any combinations thereof. Other location determining systems could also be used to implement the location determining unit7and should be considered within the scope of the present invention. The time determining unit25could be a quartz based real time clock, network time protocol synchronized digital clock, or a GPS receiver that provides time and date. Other time determining systems could also be used to implement the time determining unit25and should be considered within the scope of the present invention. The velocity determining unit28could be a speedometer, GPS receiver that differentiates location over time, or an inertial measuring unit that determines speed. Other velocity determining systems could also be used to implement the velocity determining unit28and should be considered within the scope of the present invention. The direction determining unit26could be a digital compass, GPS receiver that differentiates location over time, or an inertial measuring unit that determines direction. Other direction determining systems could also be used to implement the direction determining unit26and should be considered within the scope of the present invention. Utilizing the current position, time of day, speed and direction of travel provided by the Location Determining Unit7or the time determining unit25, velocity determining unit28, direction determining unit26, the HPPU1,18may access the historical database of traffic law enforcement citation and encounter records2,4and real time database5,30of current traffic law enforcement location, citation and encounter records, and statistically profile and predict any combination of properties in Table 1 which includes the locations, schedules and enforcement profiles of traffic law enforcement patrols. Alternatively or additionally, the historical databases2,4and real time database5,30may contain any combination of previously computed properties in Table 1 which include combinations of predicted locations, schedules and enforcement profiles of traffic law enforcement patrols, collectively referred to as predictive record, in which case the HPPU1,18may access the database2,4,5,30to retrieve the predicted locations, schedules, and enforcement profiles of traffic law enforcement. Additionally, the HPPU1can provide a visual representation of the historical and or predicted traffic law enforcement profile, schedules and locations to the display9and can also provide an acoustic representation of said historical and or predicted traffic law enforcement profile, schedules, and location information to the Audio Output8. The Audio Output8preferrably in conjunction with the user control10, may enable the Client Device21to support voice user interface driven applications which could include Amazon Alexa, Google Voice, Apple Siri, and Microsoft Cortana or in general native voice annunciations. HPPU1can also provide a visual representation of the historical and or predicted traffic law enforcement profile to the display9combined with a map of the roadway preferably provided by client device database15and or server resource database17. The HPPU1can also provide an indication of the historical and or predicted traffic law enforcement profile, schedules and location to computer interface12or network interface6to provide an indication to another system such as a vehicle integrated navigation system or a vehicle integrated communication system. The HPPU1can also utilize the vehicle controller unit24to control the vehicle preferably including any combinations of vehicle speed, direction, route, or route planning.

Vehicle telemetry preferably including vehicle speed, location, direction, and time can be observed by the HPPU1from the Velocity Determining Unit28, Location Determining Unit7, Direction Determining Unit26, and Time Determining Unit25respectively. The HPPU1may store vehicle telemetry in client Traffic Flow Database16and or in server Traffic Flow Database23by utilizing Network Interfaces6,18. The Traffic Flow Databases16,23may contain real time and historic telemetry for a plurality of vehicles which could be up to 100,000,000 vehicles but those skilled in the art will recognize the Traffic Flow Databases16.23could be scaled to accommodate additional telemetry records. The HPPU1,18may access the vehicle telemetry stored in the Traffic Flow Databases16,23to infer traffic flow rates. The HPPU1preferably may perform navigation route planning utilizing real time and historic traffic flow rates from the Traffic Flow databases16,23in conjunction with predicted traffic law enforcement patrol schedules, locations and enforcement profiles and the Roadway Map Database15,5to determine an efficient route. The HPPU1,18performs navigation and route planning preferably in conjunction with historic and real time traffic flow rates16,23to estimate the speed at which traffic is currently moving or expected to be moving on the available route options.

Additionally, crowd sourced road conditions which preferably may include the locations of road hazards, slippery road conditions, accidents, lane closures, construction zones, and slow traffic can be provided to the HPPU1using the user control10or automatically detected from vehicle sensor inputs27. The HPPU1preferably can store the road conditions to the client traffic flow database16or the server traffic flow database23through the Network Interfaces6,19. Traffic Flow Databases16,23may contain real time and historic road condition entries for a plurality of vehicles which could be up to 100,000,000 vehicles but those skilled in the art will recognize the Traffic Flow Databases16.23could be scaled to accommodate additional telemetry records. The HPPU1preferably may perform navigation route planning utilizing real time and historic road conditions from the Traffic Flow Databases16,23in conjunction with predicted traffic law enforcement patrol schedules, locations and enforcement profiles and the Roadway Map Database15,5to determine an efficient route. Further, the HPPU1preferably utilizes real time road conditions from the Traffic Flow Databases16,23to generate an alert using the Audio Output8, or Display9take precautionary action in addition to information regarding the hazard, when approaching a location identified as a hazard. If the vehicle100incorporates driver assist features the HPPU1can notify the vehicle control unit24to take precautionary measures such as slowing the vehicle100or changing lanes to avoid the hazard.

The HPPU1,18preferably can also utilize traffic flow rates provided by client device traffic flow database16and or server resource traffic flow database23at a given location to determine traffic velocity and density as a function of time and location. Traffic flow databases16and23preferably contain historic and real time measured traffic volume and or velocity at given locations and associated times. Utilizing historic and real time an historic traffic volume and velocity from databases16and or23and real time and historic encounter databases2and or4, the HPPU1,18can determine combinations of traffic law enforcement austerity properties presented in Table 1.

More specifically, the HPPU1,18can preferably determine the Enforcement Leniency Profile of Table 1 which provides an indication of traffic law enforcement patterns for issuing citation warnings verses citations. The HPPU1,18may determine the Enforcement Leniency Profile for locations and time intervals of interest by utilizing database citation records2,4,5,30to determine the ratio or magnitude of citation warnings verses citations at locations and time frames as a function of violation type. The Enforcement Leniency Profile provides an indication of how rigidly different types of violations are enforced. Additionally, the HPPU1,18can preferably determine the Speed Limit Enforcement Austerity Relative to Average Traffic Flow Velocity also in Table 1 which provides a measure of the enforced speed limit relative to the average flow velocity of traffic. Speed Limit Enforcement Austerity Relative to Average Traffic Flow Velocity can be determined by the HPPU1,18by determining the difference between traffic flow velocity from real time and historic traffic flow database16, and23to traffic law enforcement issued citation velocities from real time and historic citation and encounter databases2,4,5,30at given locations and times.

The HPPU1,18can determine the Location Enforcement Austerity Relative to Average Traffic Volume of Table 1, which quantifies the patrol location patterns as a function of traffic volume and provides an indication as to which locations are patrolled more frequently under specific traffic volume conditions and times. The HPPU1,18can determine the Location Enforcement Austerity Relative to Average Traffic Volume by utilizing the historic traffic speed and density16,23to determine the probability of a vehicle having an encounter with traffic law enforcement at a given time and location and the probability at each location could provide a relative indication of the location patrol intensity or equivalently the relative amount a location is patrolled. Alternatively, if historic traffic speed and density16,23are not utilized, the location patrol intensity could be approximated as the location citation density which quantifies the number of citations issued within a location and time interval. The location citation density may be interpreted relative to other locations within the region to provide an indication of relative location patrol intensity. Additionally, the HPPU1,18can preferably determine the Speed Limit Enforcement Austerity Relative to Average Traffic Flow Volume, presented in Table 1, which provides a measure of the enforced speed limit relative to the average flow volume of traffic. Speed Limit Enforcement Austerity Relative to Average Traffic Flow Volume can be determined at a location by the HPPU1,18by determining the historic estimated enforced speed limit from databases2,4,5,30as presented in Table 1, at the location as a function of traffic flow volume provided by database16,23, and then inferring the enforced speed limit as the historic estimated enforced speed limit at said location with similar traffic flow volume.

Alternatively or additionally, combinations of the predicted traffic law enforcement patrol schedules, locations and enforcement profiles, including enforced speed limits and enforcement austerity characteristics can be previously computed and stored in databases2,4,5,30at said given locations and times and the HPPU1,18may access the database2,4,5,30to retrieve the predicted traffic law enforcement patrol schedules, locations and enforcement profiles, including enforcement austerity and enforced speed limit and combinations thereof. Additionally, the HPPU1can provide a visual representation of combinations of the historical and or predicted patrol locations, schedules, enforcement profiles, and austerity of traffic law enforcement to the display9and can also provide an acoustic representation of said historical and or predicted locations, schedules, enforcement profiles, and austerity to the Audio Output8. HPPU1can also provide a visual representation of the historical and or predicted traffic law enforcement patrol locations, schedules, enforcement profiles, austerity and combinations thereof, to the display9combined with a map of the roadway preferably provided by client device database15and or server resource database17. Further the HPPU1can use real time, historical and or predicted traffic law enforcement patrol locations, schedules, enforcement profiles, austerity and combinations thereof, and the vehicle control unit24, which preferably interfaces with the vehicle controller103providing command of the vehicle100steering or motor systems or any combinations thereof, preferably including any combinations of vehicle speed, direction, route, or navigation route planning. For instance, the HPPU1may control speed in conjunction with adaptive cruise control to ensure a vehicle does not violate enforcement profiles such as enforced speed limit. Further, the HPPU1may also control speed to ensure a vehicle complies with the posted or enforced speed limit at real time or predicted patrol locations. Further the HPPU1may include a rules engine to control the speed, route and route planning of the vehicle100. The rules engine preferably contains a list of rules configured through the user control10which define maximum speed limits either relative to any commination of the enforcement profile enforced speed limit, real time and or historic traffic flow rates provided by Traffic Flow Database15,23, or the posted speed limit at which the vehicle100can travel in various locations such as school zones, construction zones, crowd sourced real time locations of traffic law enforcement, normal road condition zones, or locations where crowd sourced road conditions are identified in the Traffic Flow Databases16,23. As a further example the rules engine may define a vehicle100should never travel above 10 miles per hour above the posted speed limit unless of an emergency regardless of the enforcement profile enforced speed limit. In addition, the HPPU1may also control the direction and or route of a vehicle100to travel on road ways with the highest enforced speed limits or roadways with the fewest numbers of estimated patrol locations. Those skilled in the art will recognize that the HPPU1could utilize artificial intelligence algorithms and machine learning algorithms to process real time, historical and or predicted traffic law enforcement patrol locations, schedules, enforcement profiles, austerity and combinations thereof to provide vehicle control through the vehicle control unit24, which preferably interfaces to the vehicle controller103providing command of the vehicle steering or motor systems or any combinations thereof, to improve travel efficiency and decrease probability of citation, and additional vehicle control scenarios should be considered within the scope of the present invention.

Databases2,4,5,30may contain combinations of previously computed predicted locations, schedules and enforcement profiles of traffic law enforcement, historical records and real time records derived from citations which were issued by traffic law enforcement agencies and or crowd sourced encounters with traffic law enforcement collectively referred to as citations or encounters in the present invention. Records derived from issued traffic law enforcement citations are typically considered more reliable than crowd sourced records and generally public information and are preferably compiled and maintained by law enforcement and government agencies and said agencies preferably include but are not limited to State Highway Patrols, City and County Police agencies, Department of Motor Vehicles and Municipal Courts. Data bases2,4,5,30could also contain crowd sourced citations or encounters with traffic law enforcement contributed by individuals. Databases2,4,5,30could contain an entry for each issued citation or encounter but could be in various compressed formats to improve storage efficiency. Each entry of databases2,4,5,30preferably may contain a combination of the fields similar to those presented in Equation 1 for the record format; however, a subset of said fields may be available per record or additional fields such as vehicle type, or color could be present in each record. Additionally, different means could be used to compress the databases2,4,5,30entries to improve storage efficiency. Equation 1, demonstrates a preferred content representation of each record in database2,4,5,30derived from citation records.
record_entry={location, time, date, direction, type, violation_speed}

Equation 1. Preferred citation and encounter record properties and members of database

Equation 1 record properties may also be referred to as members or events of said record. Said record member: location is preferably the location where the violation was observed and the citation issued. Said location field is preferably in latitude and longitude units; however, it could additionally be reported by but not limited to mile post marker, crossroads location, or street address. Said record member: time and date are preferably the time and date at which the encounter occurred or citation was issued, time could include both the time and date. Said record member: direction is preferably the direction of travel at which time the encounter or citation was issued. Said record member: type is preferably the type of encounter or citation which may include speeding, expired registration and other infraction types or could be the reason for traffic law enforcement stopping vehicle. Said record member: speed preferably represents the speed of the vehicle when a speeding citation was issued or could also represent the velocity which would specify both the speed and direction. Crowd sourced records of encounters with traffic law enforcement can also be stored in databases2,4,5,30in a representative format of Equation 1, wherein crowd sourced data may be a sighting encounter of traffic law enforcement such that possibly only location, time and direction might be present in the record entry Equation 1.

Furthermore, in another preferred embodiment, databases2,4,5,30could contain predicted traffic law enforcement properties and patterns at locations and times, derived from processed citation records similar to Equation 1, preferably including a combination of the properties defined in Table 1, and represented in Equation 1 a. Those skilled in the art will recognize that additional enforcement characteristics and predicted profile properties could be derived from traffic law enforcement citation records and crowd sourced encounters with traffic law enforcement and stored in databases2,4,5,30and should be considered within the scope of the present invention. Equation 1 a, demonstrates a preferred format representation of each entry of database2,4,5,30containing predictive entries. Said predictive_record_entries of Equation 1a preferably contain any combination of traffic law enforcement properties presented in Table 1.
Predictive_record_entry={location, patrol schedule, patrol location, location patrol intensity, enforcement profile}

Equation 1 a. A preferred post processed predictive database record format. Reference Table 1 for definition of predicted traffic law enforcement properties and patterns.

Most state and city departments of transportation monitor and report historical and current traffic flow volume and rate information for roadways. Client device21preferably incorporates a traffic flow data base16which preferably contains records of historic traffic flow volumes and or historic traffic velocities at locations verses time. Additionally, server resource3may contain a traffic flow and velocity database23. Databases16and23may contain real time and or historic traffic flow records. Vehicle telemetry preferably including vehicle speed, location, direction, and time can be observed by the HPPU1from the Velocity Determining Unit28, Location Determining Unit7, Direction Determining Unit26, and Time Determining Unit25respectively. The HPPU1may store vehicle telemetry in client Traffic Flow Database16and or in server Traffic Flow Database23by utilizing Network Interfaces6,18. The Traffic Flow Databases16,23may contain real time and historic telemetry for a plurality of vehicles which may provide the traffic flow volume and rate. The HPPU1,18can preferably utilize the traffic rate and volume from databases16,23to determine the probability of a vehicle having an encounter with traffic law enforcement at a given time and location and the probability at each location preferably provides a relative indication of patrol locations. Additionally, HPPU1,18can preferably determine the enforced speed limit relative to the rate and flow of traffic by comparing the traffic speed from databases16,23to traffic law enforcement issued citation speeds from databases2,4,5,30at given locations and times. Preferably HPPU1,18can compare the traffic flow rate with speeding citation issued speeds and determine the estimated speed at which traffic law enforcement issues citations at given times and locations relative to the average traffic rate and or may determine the austerity with which traffic law enforcement enforces the speed limit relative to average traffic rates and volumes at given locations and times. Equation 2 demonstrates a preferred traffic flow entry format of databases16,23.
traffic_flow_entry={location, volume, velocity, time}

Equation 2. A preferred traffic flow entry format

Said location field of Equation.2is preferably in latitude and longitude coordinate units and the volume preferably represents the number of vehicles per second travelling through the location at said time and with the average said velocity.

In a preferred embodiment, the HPPU1,18utilizes traffic flow and rate historical and real time records16,23to enable improved prediction of traffic law enforcement speed traps, speed cameras and or patrol locations and or austerity of enforced speed limits relative to the natural flow of traffic. More specifically, in a preferred embodiment the HPPU1,18may determine the relative intensity of patrols at given locations as the relative ratio of issued traffic citations to traffic volume at said given locations. In a preferred embodiment the HPPU1,18may determine the location of estimated speed traps and speed cameras as locations with relative ratios of issued traffic citations to traffic volume that exceed a threshold relative to the ratios of issued traffic citations to traffic volume in surrounding areas. Furthermore, in a preferred embodiment the HPPU1,18may compute the schedule of speed traps, speed cameras, and or the schedule of patrol locations by determining said ratios of issued traffic citations to traffic volume at historic times and locations and then cross correlating said ratios as a function of time to identify patrol schedules which are time correlated. Those skilled in the art will realize the austerity of enforced speed limits at corresponding locations and the locations of predicted speed traps could be computed antecedently and contained within databases2,4,5,30and the HPPU1,18could retrieve austerity of enforced speed limits and locations of predicted speed traps and speed cameras from databases2,4,5,30.

Additionally, in a preferred embodiment HPPU1,18may utilize the difference between the traffic flow average velocity from database16,23at locations and the velocities at which citations have been issued at said locations from databases2,4,5,30and determine an indication of speed limit enforcement profile and austerity relative to traffic flow velocities. Furthermore, in a preferred embodiment the HPPU1,18may estimate the schedule of speed limit austerity relative to traffic flow velocities by determining the difference between the historic traffic flow average velocity16,23at locations and times and the velocities at which historic citations have been issue at corresponding locations and times from database2,4,5,30. Said differences are then cross correlated as a function of time to identify speed limit traffic law enforcement austerity at corresponding locations which are time correlated and thus estimate future speed limit enforcement profile austerity. Those skilled in the art will realize the austerity of enforced speed limits relative to traffic flow average velocities and volumes at corresponding locations could be computed antecedently and contained within databases2,4,5,30and the HPPU1,18could retrieve austerity of enforced speed limits relative to traffic flow rates from databases2,4,5,30.

In a preferred embodiment the HPPU1,18can determine an optimal driving route to maximize travel speed and minimize the probability of receiving a citation by utilizing the ratio of traffic volume16,23verses traffic citation density2,4,5at locations along optional routes to determine an optimal route which maximizes the said ratio and minimizes the travel travel time. Further, the HPPU1,18can utilize the estimated enforced speed limits, patrol schedules, patrol locations, enforcement profiles from databases2,30,4,5and roadway map databases15,17, and preferably the time25to determine an optimal driving route between starting and ending locations to minimize travel time and minimize probability of receiving a citation.

Traffic law enforcement encounters and citations may be reported based on the milepost marker or address thus, databases2,4preferably could contain entries for mapping Mile Post Marker to latitude and longitude coordinates, and street address latitude and longitude coordinates.
coordinate={location by mile post, street address, lat-long}

Equation 3. Mile Post, address to latitude and longitude example entry format

FIG.7shows a preferred deployment view816of a federated fleet of vehicles803,804,805,806equipped with Client Devices21to provide and or utilize enforcement profiles which preferably includes estimated enforced speed limits, patrol schedules, patrol locations, enforcement profiles, and real time locations of traffic law enforcement or any combinations thereof. The vehicles803,804,805,806communicate with server resource3,811using Network Interfaces6,19,812. The federation of vehicles803,804,805,806may provide telemetry to the Traffic Flow Database23,814which can be visualized as807,808,809,810where each vehicles speed, posted limit and enforced limit is provided. Server terminal815preferably provides a means to visualize the deployment view816, and to enable centralized control to push policies to the HPPU1rules engine of each Client Device21in each of the vehicles803,804,805,806utilizing network interfaces6,19,812. Each of the vehicles803,804,805,806could support a different level of automation including driver assistance, partial driver automation, conditional driver automation, high automation, or full automation;

however, the server resource811can centrally monitor and define rules engine policy to control a fleet of vehicles. A subset of vehicles804,806can be grouped into a vehicle sub-group/broadcast domain.811which can further have unique rules engine policies applied with respect to the remainder of the fleet803,805. In addition to the server resource3,811deployment view816each vehicle803,804,805,806could also retrieve the telemetry data from the Traffic Flow Database23,814to visualize the deployment view816. It is also preferable to enable vehicles803,804,805,806to send voice, data, text, or video messages between members or subgroups of the federation803,804,805,806using the Network Interfaces6,19,813and to also communicate with social media sites such as ActivityPub wherein vehicles803,804,805,806could be actors and server resource3,811could be an ActivityPub server. It is preferable for members of a subgroup of federated vehicles803,804,805,806to have group chats, group texts, group video capabilities between members by utilizing the profiler apparatus14. While ActivityPub was used as the social media protocol in this example, those skilled in the art will realize other social media sites and protocols could be accessible by federated vehicles803,804,805,806. Additionally, the roads800,801,802of the deployment view816could be color coded to reflect an aspect of the enforcement profile such as the enforcement intensity. Similarly the real time and historic traffic flow rate could be encoded or overlayed on the roadways800,801,802to communicate the traffic flow rates relative to the posted speed limits.

FIG.2shows a preferred method212, for profiling traffic law enforcement. The objectives of method212may include combinations of predicting the likely traffic law enforcement patrol locations, schedules, enforcement profiles, speed trap locations, red light camera locations, speed camera locations, enforcement austerity, enforcement histograms, maximum driving speed to avoid citation and are summarized in Table 1. Further objectives of method212may include providing historical and real time records of traffic patrol and traffic enforcement citations and encounters. Further objectives of method212may include utilizing traffic law enforcement patrol locations, schedules, and enforcement profiles in Table 1 to control a vehicles speed, direction, route, or any combinations thereof to improve travel efficiency while decreasing the probability of citation. Method212could be implemented by a preferred embodiment of the traffic law enforcement profiler apparatus14. More specifically, in a preferred embodiment, method212could be implemented on the client device21or in combination with server resource3or in combination with a plurality of client devices21or a plurality of server resources3. Said plurality of client devices21and plurality of server resources3could be limited to 10 million, but those skilled in the art will recognize that the limitations of the plurality of client devices21and server resources3could utilize the cellular networks, wireless data networks, internet or other broadband network which could scale to support greater than 10 million devices21and servers3.

In the preferred method212, context operation201may determine current location coordinates, time, date, direction and speed225and combinations thereof from the location determining unit7. Operation202preferably determines the geographical region and historic time period of interest which preferably results in location coordinates defining the boundaries of the region and historic time intervals223, said boundaries are preferably in the range of 100 feet to 5000 miles. Said historic time intervals preferably may extend 20 years into the past relative to current time; however, those skilled in the art will recognize the historic time intervals223could extent to the duration for which records were maintained and should be considered within the scope of the present invention. Furthermore, the historic time intervals223could actually extend into the future relative to current time preferably 20 years for retrieving records which may include estimated and predicted patrol schedules, patrol locations, enforcement profiles, and properties presented in Table 1, and combinations thereof.

The database retrieve operation203, preferably utilizes the regions and times of interest223to access the historical databases2,4and real time database5,30to retrieve a plurality of records derived from traffic law enforcement citations and encounters, and records of estimated and predicted patrol schedules, patrol locations, and enforcement profiles and combinations thereof222. The plurality of records222is preferably limited to the records in the region and time interval of interest223and is typically less than 100 million records but those skilled in the art will recognize as the length historical records databases increase and the region of interest increases, the number of records may also increase and should be considered within the scope of the present invention. The plurality of records222may consist of record entries similar to Equation1, Equation 1 a, and Table 1 and any combinations of the individual subfields thereof, referred to as database entry (dbe). Other options for storing the content or dbe records in historical databases2,4and5,30are possible which may be more efficient and should be considered within the scope of the present. Additionally or alternatively, databases2,4,5,30may store dbe records which may be predicted traffic law enforcement property fields in Table 1 and combinations thereof. Storing predicted enforcement profiles, locations and schedules of traffic law enforcement provides the benefits of reduced processing requirements of the HPPU1,18and reduced database2,4,5,30sizes. Additionally, in a preferred embodiment the database retrieve operation203may retrieve a plurality of historic traffic flow volume and velocity database16,23records and roadway map database15,17entries, for the region and timeframe of interest223, and provide on222.

In a preferred embodiment, predicted traffic law enforcement patrol locations can be determined by the patrol location estimator operation204, predicted traffic law enforcement schedules can be determined by the patrol schedule estimator operation205, and estimated enforcement profile can be determined by the patrol profile estimator operation206by utilizing the dbe222, and optionally the traffic flow and map database entries222.

In a preferred embodiment, operations204,205,206may all be present; however, in other preferred embodiments any combination or subset of operations204,205and206may be present. Operations204,205,206preferably process the plurality of historical and real time data base entries222provided from databases2,4,5,30,16,23to produce statistical estimates of past and present patrol locations in operation204, estimates of schedules in operation205and enforcement profiles in operation206, and time extrapolate statistical estimates to produce predicted patrol locations215in operation204, patrol schedules213in operation205and enforcement profiles214in operation206.

The Indication operation207may accept and present any combination of the predicted patrol schedules213, locations215, profiles214, database records222, and current location, date, time and velocity225of apparatus14. Additionally, Indication operation207may enunciate an audio or visual indication or alarm if apparatus14location and velocity225is approaching predicted patrol locations215or is within predicted patrol schedules213with historical patrol location or historical patrol schedule correlation. Additionally, Indication operation207may enunciate an audio or visual indication or alarm if the apparatus14location, time and velocity225are approaching a location with a statistically significant chance of encountering traffic law enforcement as indicated by the predicted patrol location215and with a predicted patrol schedule213and combinations thereof.

The vehicle control operation226may accept any combinations of the predicted patrol schedules213, locations215, enforcement profiles214, database records222, and current location, date, time, direction and velocity225of apparatus14. Additionally, the vehicle control operation226may control a vehicles speed, direction, route, or any combinations thereof to improve travel efficiency while decreasing the probability of citation. More specifically, the vehicle control operation226may control speed in conjunction with adaptive cruise control to ensure a vehicle does not violate enforcement profiles such as enforced speed limit at a given location. The vehicle control operation226may also control speed to ensure a vehicle complies with the posted or enforced speed limit at real time or predicted patrol locations. The vehicle control operation226may also control the direction and or route of a vehicle to travel on road ways with the highest enforced speed limits or roadways with the fewest numbers of estimated patrol locations to reduce travel time. Those skilled in the art will recognize that the vehicle control operation226could utilize artificial intelligence algorithms and machine learning algorithms to process patrol schedules213, locations215, enforcement profiles214, database records222, and combinations thereof to provide vehicle control to improve travel efficiency and decrease probability of citation, and additional vehicle control scenarios should be considered within the scope of the present invention.

In another preferred embodiment, operation203may provide records of historic citation and encounter records to operation207, over the region of interest223, which may present the historical citation information222. Additionally, any combination of record fields of Equation 1, 1a or Table 1 maybe be presented by operation207. In a preferred embodiment the traffic enforcement profiler method212provides historic traffic law enforcement data222. From the presented historic data222, a user could preferably interpret, and or estimate patrol locations, schedules and enforcement profile including enforced speed limits. Those skilled in the art will recognize the historic records222could further be filtered by a user and the properties the records could be visually encoded to communicate the enforcement profile and should be considered within the scope of the present invention.

Estimating Patrol Locations, Schedules, and Enforcement Profiles of Traffic Law Enforcement

In a preferred embodiment of the present invention, apparatus14may utilize the principles of statistical analysis and probability theory in estimating the properties of traffic law enforcement presented in Table 1. In particular, traffic law enforcement properties of Table 1 may be modeled as a stochastic process and the predicted patrol locations, schedules and enforcement profiles could be considered random variables. Records derived from historic traffic law enforcement citations and encounters can be considered events of the underlying stochastic process and utilized to estimate the probability distribution of patrol locations, schedules, patrol profiles and the properties presented in Table 1 to provide a historical characterization of traffic law enforcement properties as a function of time and location. Additionally, the historic records of traffic law enforcement citations and encounters may be utilized to probabilistically predict the locations, schedules, patrol profiles, and properties of Table 1 of traffic law enforcement.

In a preferred embodiment, properties including those of Equation 1, derived from issued citations and encounters, which can be considered events of the traffic law enforcement stochastic process, may be correlated to estimate the statistical relationship and dependencies between said properties and enable predicting current or future events of traffic law enforcement which may include patrol locations, schedules and enforcement profiles. Correlation refers to any statistical relationship between two or more random variables or sets of events from a stochastic process. Equation 4a presents a general algorithm implemented by a preferred embodiment of apparatus14. Equation 4a may determine the correlation between a plurality of random variables X1, X2, XN, wherein μNrepresents the mean of the associated random variable and σNrepresents the variance of the associated random variable. E is the expected value operator. Random variables X1, X2, XNof traffic law enforcement may include patrol locations, schedules, enforcement profiles and properties of presented in Table 1 and Equation 4a, may determine the correlation and hence statistical predictability of patrol locations, schedules, and enforcement profiles.

Equation 4a. A preferred multi-variable correlation method to predict events of traffic law enforcement.

Equation 4a1 may utilize a plurality of observed events C1, C2, CNof random variables X1, X2, XNto determine the sample correlation which approximates the correlation of Equation 4a, which may statistically predict patrol locations, schedules and enforcement profiles.

Equation 4a1. A preferred multi-variable sample correlation method to predict events of traffic law enforcement

Equation 4a1 is presented in continuous integral form; however, discrete versions of Equation 4a1 are also possible and should be considered within the scope of the present invention. In Equation 4a1, the plurality of observed events C1, C2, CNcould be derived from encounters and citations of traffic law enforcement222and the properties associated with each citation as presented in Equation 1. The φ operator may represent the variable to perform correlation over and could be any combination of time, location, citation speed, citation type or any combination of properties of Equation 1.1,Nmay be considered the plurality of functions for which the correlation is computed and could also be any combination of time, location, citation speed, and any combination of properties of Equation 1. Relative maximum in the correlation result Equation 4a1, may be identified as periodic patrol properties as a function of1,Nand could be time extrapolated into the present or future to predict events of traffic law enforcement including patrol locations, schedules, enforcement profiles and the properties provided in Table 1.

Statistical analysis of the properties of event records, derived from issued citations and encounters of traffic law enforcement, preferably including the properties of Equation 1, can be utilized for characterizing the properties of the traffic law enforcement stochastic process. More specifically, descriptive statistics principles can be utilized to quantitatively describe the historical characteristics of traffic law enforcement from the event records which may include determining the mean, variance, frequency, histogram, distributions, accumulations, minimum and maximum of properties derived from historical citation records including those of Equation 1. Further, statistical inference can be utilized to infer and predict the properties presented in Table 1, of traffic law enforcement, from historical encounters with and citation records of traffic law enforcement, which may include patrol locations, schedules, and enforcement profiles. Correlation is also a statistical measure which refers to a broad class of statistical relationships involving dependence between random variables. The following sections present preferred algorithms for utilizing correlation and statistical analysis to estimate patrol locations, schedules, and enforcement profiles of traffic law enforcement and may include the properties presented in Table 1.

Patrol Location Estimation—Operation204

The patrol location estimator204preferably determines the locations where it is likely to encounter traffic law enforcement patrols. Table 1 provides a listing of estimated traffic law enforcement properties which may be provided by the current invention, and the estimation of Patrol Location and the Patrol Location Intensity properties of Table 1 may be determined by the Patrol Location Estimator operation204. In a preferred embodiment, the patrol location estimator204, may implement algorithms utilizing historic and or real time records similar to Equation 1, derived from traffic law enforcement citations and crowd sourced data222to predict patrol locations215. In yet another preferred embodiment, the patrol location estimator204may utilize records similar to Equation 1a, of previously determined predicted patrol locations and location patrol intensity of traffic law enforcement patrols222, to predict patrol locations and location patrol intensities215.

A preferred method of the patrol location estimator204to determine patrol location estimates215, calculates the correlation between a plurality of records222and identifies traffic law enforcement patrol locations. In a preferred embodiment of patrol location estimation operation204, Equation 4a, 4a1 could be utilized to compute the location correlation between citation record222locations from Equation 1, possibly as a function of citation type, time, and direction. Relative maximum in the correlation result may be identified by the location estimation operation204as periodic patrol patterns at a location and can be time extrapolated into the present or future and hence predict patrol locations215. In a preferred embodiment, the patrol location estimator204correlates a plurality of random citation events C1, C2, CNto optimize the accuracy of patrol location estimates215; however, preferably said plurality of events to be correlated could include any combination of the following enumerated records222of Equation 1:1) Citations issued at a location2) Citations issued at a location and in a direction3) Citations issued as a function of time at a location4) Citations issued as a function of time at a location and in a direction5) Traffic flow volume as a function of time at a location6) Traffic flow rate as a function of time at a location

In another preferred embodiment, the patrol location estimator204can compute the auto correlation of the time sequence of citation records222at a plurality of locations limited to be within the region of interest223wherein the relative maxima of said auto correlation indicates the presence of periodic patrol patterns at said plurality of locations. Operation204can observe the relative maxima of said autocorrelation and determine locations where patrols occur and the periodic times at which said patrols occur and time extrapolate the periodic times of the patrols to predict locations of patrols at the present and or future times and provide said predicted patrol locations and times as indication215. Equation 4b provides a preferred method of operation204for computation of the auto correlation of issued citations locations as a function of citation type and time.

Equation 4b. A preferred correlation method to estimate patrol locations

In equation 4b, the correlation can be computed for a citation type and or location and patrol interval time, between a starting time t1and an ending time t2. In a preferred embodiment, operation204can compute the correlation of equation 4b and identify relative maximums as a function of patrol interval timewhich indicate traffic law enforcement patrol locations and patrol intervals and provide said predicted patrol locations and times as indication215.

In another preferred embodiment, the patrol location estimator204can compute the accumulation of citation records222at a plurality of locations between a time interval t1and t2and determine historic patrol locations as locations with accumulation results above a threshold and or accumulation results which are relatively higher than other locations. Operation204can predict current and future patrol locations from historic patrol locations and provide said predicted patrol locations as indication215.

In a preferred embodiment, the patrol location estimator204computes the accumulation215of historical and or real time traffic law enforcement encounters preferably as a function of time, location, and by type of citation or encounter type. The accumulation215at each location may be interpreted relative to other locations to determine patrol location estimates preferably as the locations with higher relative accumulations. Alternatively or additionally, the patrol location estimation operation204could preferably compare the accumulation215at each location to a threshold to determine patrol location estimates preferably as the locations with accumulations greater than the threshold. A preferred accumulation algorithm is shown in Equation 4; however, other algorithms to compute the citation location accumulation are possible and should be considered within the scope of the present invention. Equation 4 accumulation is algorithmically equivalent to the auto correlation with time shift of zero and computed at a plurality of locations.

Equation 4. A preferred computation of encounter location accumulation.

The following terms of equation 4 are defined:

Accumulation(loc)—total occurrences of encounters at a given location loc, time t, and of type

Loc—location, and may preferably include direction

dbe(type, loc, t)—data base entry at time t and location loc and of type.

t1—start time and date of interval for calculating the total number of encounters

t2—end time and date of interval for calculating the total number of encounters

Equation 4 preferably computes the total number of occurrences of citations and or encounters issued at a given location loc within a specified time period t1 to t2 referred to as citation accumulation and of a type of citation. Operation204preferably computes the citation location accumulation for each location223to produce a complete histogram of citation accumulations at each location loc and for each citation type. Preferably the time period t1 to t2 is large enough to give an accurate representation of issued citations at a given loc, said period t1 to t2 being preferably in the range of 1 minute to several years.

In another preferred embodiment, the patrol location estimator204may compute the patrol location estimates utilizing traffic flow database entries16and or23to compute the ratio of accumulated traffic encounters as a function of time, location and type to traffic volume as a function of time and location to normalize said accumulated traffic law enforcement encounters and preferably enable more accurate prediction of estimated patrol locations. Equation 4c demonstrates an example algorithm for computing the citation accumulation normalized by traffic volume.

Equation 4c. A preferred computation of normalized encounter location accumulation.

The following terms of equation 4 are defined:

Normalized Accumulation(type, loc, t)—total occurrences of encounters at a given location loc, time range t2 to t1, and of type

Traffic_flow(loc,t)—traffic volume at location loc and time interval t2 to t1.

Loc—location and may preferably include direction dbe(type, loc,t)—data base entry at time t and location loc of type

t1—start time and date of interval for calculating the total number of citations

t2—end time and date of interval for calculating the total number of citations

In yet another preferred embodiment, the patrol location estimator204may utilize records similar to Equation 1 a, of previously determined predicted locations of traffic law enforcement patrols222, to predict patrol locations215.

The predicted patrol locations produced by the patrol location estimator204are provided on indication215.

Patrol Schedule Estimator Operation205

In a preferred embodiment, the patrol schedule estimator205predicts the schedules of traffic law enforcement patrols at a plurality of locations within the region of interest223. Table 1 provides a listing of estimated traffic law enforcement properties which may be provided by the current invention, and the Estimated Patrol Schedule property of Table 1 may be determined by the Patrol Schedule Estimator operation205. The patrol schedule estimator205could implement algorithms utilizing historic and real time records derived from traffic law enforcement citations and crowd sourced data222to predict patrol schedules. In another preferred embodiment, the patrol schedule estimator205could compute the time-location correlation Equation 4a, 4a1 of records similar to Equation 1, which are derived from historic traffic law enforcement citations and crowd sourced data222, to estimate traffic law enforcement patrol schedules213, preferably as a function of location and by type of citation or encounter type. In yet another preferred embodiment, the patrol schedule estimator205may utilize records similar to Equation 1 a, of previously predicted schedules of traffic law enforcement patrols222, to predict traffic law enforcement patrol schedules213, at a plurality locations within the region of interest223.

In a preferred embodiment, the patrol schedule estimator205preferably computes the time-location correlation of encounters213retrieved from database2,4,5,30and preferably uses the time-location correlation of historical encounters to predict patrol schedules at a plurality of locations within the region of interest223. The patrol schedule estimator205could implement the correlation method of Equation 4a, 4a1 and the autocorrelation method of Equation 4b to estimate patrol schedules. In equation 4b, the correlation can be computed as a function of citation type and or location and patrol interval time, between a starting time t1and an ending time t2. In a preferred embodiment, the patrol schedule estimator operation205can compute the correlation of equation 4a1 or 4b and identify relative maximums as a function of patrol interval timewhich indicates traffic law enforcement patrol locations and patrol intervals. From said patrol intervals, the patrol schedule estimator operation205can infer the historic schedules of traffic law enforcement and time extrapolate said historic schedules to predict current and future schedules of traffic law enforcement and provide said predicted patrol locations and associated patrol schedules as indication213.

FIG.3graphically demonstrates a preferred correlation method of patrol schedule estimator205for estimating traffic law enforcement patrol schedules and locations.FIG.3shows a representative plot of the accumulation of traffic law enforcement encounters700at a location as a function of time and demonstrates a preferred method of correlation to derive patrol schedule estimates720and time extrapolation to infer and predict patrol schedules at present and future times704. Time instances where there is an increase in law enforcement encounters are shown as encounter accumulation maximums710,711,712,713,714and indicate possible times where there may be increased patrols. Graph720may present the result of correlating the encounter accumulation700as a function of time which yields the encounter correlation result720with maxima723,724,725,726,727indicating the presence of periodic patrol patterns in historic encounter accumulations and provides an estimated patrol schedule. The amplitude of correlation720provides an indication of reliability of the predicted patrol schedule at said location and time, while the width of the correlation maxima723,724,725,726,727provides an indication of the time variance and duration of patrol interval. From the correlation maxima723,724,725,726,727predicted patrol schedules are provided and can be time extrapolated to create estimated current and future patrol schedules704at said location. The maxima706,703,707,708of the predicted patrol schedules704provide specific statistically probable patrol times and the amplitude of the maxima706,703,707,708provide a relative indication of the likelihood of patrol and the width of said maxima provides an indication of the typical duration of the patrols or time uncertainty of the patrols at said location.

In a preferred embodiment, correlation of historic traffic law enforcement records could be performed over a period of minutes, hours, days, weeks or even years to provide estimates of traffic law enforcement patrol schedules. However, correlation of historic traffic law enforcement records over time intervals of 10 years or less is generally sufficient to produce reliable estimates of patrol schedules213. The correlation results from historical law enforcement encounters can be time extrapolated to infer real time and future patrol schedules213.

Conceptually various algorithms and equations can be employed by the patrol schedule estimator205to estimate patrol schedules from traffic law enforcement citation records and crowd encounter222and should be considered within the scope of the present invention. Additional preferred algorithms for estimating patrol schedules by computing the time-location correlation are shown in equation 5 and equation 5a. Equation 5a presents a preferred method of the patrol schedule estimator operation205for correlating records and encounters222as a function of location, patrol intervals and over a time period interest. Equation 5a may compute the encounter_correlation Equation 5a by auto correlating the database records222at a location and for a patrol time interval. The relative maximums of the encounter_correlation Equation 5a, correspond to periodic patrol patterns or schedules of traffic law enforcement of interval t_patrol at the associated location loc. In a preferred embodiment, the patrol schedule estimator operation205can observe said relative maximums of the encounter_correlation Equation 5a, and determines the periodic traffic law enforcement patrol intervals. The patrol schedule estimator205may time extrapolate said patrol intervals to predict the current and or future enforcement schedules at the associated locations and provides said predicted patrol locations and associated schedules as indication213.

Equation 5a. A preferred algorithm for computation of encounter time-location correlation.

The following preferable terms of equation 5a are defined:

correlation(loc, t_patrol)—the correlation of encounters at location loc and patrol interval t_patrol

Loc+/−Δα—location of said violation within +/−Δαdistance, Δα preferably ranges from 100 feet to 50 miles. Loc preferably also includes direction.

dbe(loc+/−Δα, t+/−Δε+/−t_patrol)—represents a database entry which has a matching location loc and issue time Δt+/−Δε.

tstart—the earliest time for calculating correlation time duration.

tend—the last time for calculating correlation time duration.

t+/−Δε—time of said violation within +/−Δε time, Δε preferably ranges from 1 minute to 1 day.

Equation 5, is yet another preferred algorithm for estimating patrol schedules by correlating the database entries as a function of time and location.

Equation 5. Preferred algorithm for computation of encounter location-time correlation.

The following preferable terms of equation 5 are defined:

correlation(loc, Δt)—the total correlation of citation locations at periodic times Δt

Loc+Δα—location of said violation within +/−Δαdistance, Δα preferably ranges from 100 feet to 50 miles. Loc preferably also includes direction.

dbe(loc, nΔt+Δε)—represents a database entry which has a matching location loc and issue time nΔt+Δε.

Nmin—the earliest index for calculating correlation time duration.

Nmax—the last index for calculating correlation time duration.

nt+Δε—time of said violation within +/−Δε time, Δε preferably ranges from 1 minute to 1 day.

Equation 5 preferably computes the total number of periodic occurrences of citations issued at a given location loc at the periodic times starting at time nmin*Δt+Δε.to nmax*Δt+Δε. This could be accomplished by accessing the database records222dbe and summing the number of entries with matching loc and time n*Δt+Δε. The occurrence of each matching dbe preferably has a weight of one. Said periodic times n*Δt+Δε could have a Δε term added which functions to allow a dbe with matching loc and in the span of +/−c to match. Δε preferably has a span of 1 minute to 24 hours such that any dbe with a matching location loc, and issue time not within the said span Δε would preferably produce a positive match result. The patrol schedule estimator205preferably repeats computation of Equation 5 for each location223in the geographic region of interest. Furthermore, the patrol schedule estimator operation205preferably repeats computation of equation 5 for different time spacing intervals Δt preferably ranging from one hour to one year in an attempt to identify periodic correlations between issued citations times and locations. Said citation location-time correlation results213of the patrol schedule estimator205are preferably passed to the Indication operation207and or the vehicle control operation226.

The algorithms presented in Equations 4a, 4a1, 4b, 5, 5a andFIG.4are examples of preferred methods to estimate and predict traffic law enforcement patrol schedules from historic traffic law enforcement and crowd sourced records; however, other algorithms to estimate traffic law enforcement patrol schedules are possible but should be considered within the scope of the present invention including using records of previously determined, estimated and predicted traffic law enforcement patrol schedules.

In yet another preferred embodiment, the patrol schedule estimator205may utilize records similar to Equation 1 a, of previously determined predicted locations of traffic law enforcement patrols222, to predict patrol schedules215.

Patrol Enforcement Profile Estimator Operation206

The patrol enforcement profile estimator, operation206, preferably determines the enforcement profile of traffic law enforcement. Table 1 provides a listing of estimated traffic law enforcement properties and patterns which may be provided by the current invention, and the estimation of Patrol Profile properties of Table 1 may be determined by the Patrol Enforcement Profiler operation204. In a preferred embodiment, the patrol enforcement profile estimator206, determines the traffic law enforcement profile214at a plurality of locations within the geographic region and time intervals of interest223utilizing database records derived from historic traffic law enforcement citation records and or crowd sourced encounters222. In yet another preferred embodiment, the patrol profile estimator206utilizes records, similar to Equation 1a and Table 1, of previously predicted profiles of traffic law enforcement patrols222, to predict traffic law enforcement patrol profiles214, at a plurality of locations within the region of interest223.

In a preferred embodiment, the traffic law enforcement profile estimator206may estimate traffic law enforcement profile properties214at a plurality of locations and times within the region and time frame of interest223which are summarized in Patrol Profile of Table 1 and may include any combination of: enforced speed limit, speed trap location, red light camera location, speed camera location, aircraft speed limit enforced location, histogram of issued citation types, location citation density, enforcement profile of traffic laws, histogram of citation speeds for speeding citations, number of issued citations, the distribution of enforced traffic laws, probability of receiving a traffic citation, probability of encountering traffic law enforcement, and the austerity with which traffic laws are enforced.

The austerity with which traffic laws are enforced may comprise those in Table 1 including enforcement leniency profile, speed limit enforcement austerity relative to average traffic flow velocity, location enforcement austerity relative to average traffic volume, speed limit enforcement austerity relative to average traffic volume and other austerity metrics and combinations thereof, and indicate said plurality of profile properties on214. Those skilled in the art will recognize that additional traffic law enforcement profile characteristics can be derived from historic citation records and could be implemented with the programmable patrol profile estimator206, and should be considered within the scope of the present invention.

In a preferred embodiment the patrol profile estimator206, may utilize correlation methods analogous to Equation 4a, 4a1 and statistical analysis to estimate the historical patterns and properties provided in Table 1, of traffic law enforcement from historical records of citation and encounters222. The patrol profile estimator206may correlate properties derived from citation records222, analogous to Equation 1, which could include citation record: violation speed, time, date, location, travel direction, citation type, traffic volume, and average traffic velocity to estimate historical and predicted enforcement properties of traffic law enforcement214, as presented in Table 1, at the current location225or surrounding locations within the region of interest223of apparatus14.

In yet another preferred embodiment, the enforcement profiler206may access previously computed predicted traffic law enforcement profiles222analogous to Equation 1a or Table 1, at a plurality of locations within the region of interest223, to provide predicted enforcement profile properties presented in Table 1. Alternatively or additionally, the enforcement profiler222may access historic records derived from issued citations and crowd sourced law enforcement encounters222analogous to Equation 1, at a plurality of locations with the region of interest223, to compute the predicted enforcement profile properties of Table 1. The enforcement profiler206may provide combinations of the enforcement profile properties in Equation 1 a, and Table 1 and more specifically, the enforcement profiler206, may provide any combinations of the following enforcement profiles properties:

Enforcement Profile Properties:

Enforced Speed Limit

In a preferred embodiment the enforcement profile estimator206may estimate the enforced speed limit at a plurality of locations and time periods within the region and time interval of interest223and provide an indication of the estimated enforced speed limit on214. The enforced speed limit214is equal to or higher than the posted speed limit and is preferably the maximum speed at which there is not a statistically unacceptable chance of being cited for speeding by traffic law enforcement. The enforced speed limit at a location may vary with the time of day, direction of travel, traffic flow volume and time of month or year and other events. The patrol profile estimator206, may utilize Equations 4a, 4a1 to correlate citation records222and determine the historical patterns and relationship of enforced speed limit to location, travel direction, time, date, traffic volume, and average traffic velocity. The patrol profile estimator206may also time extrapolate from the historical patterns of enforced speed limit to infer the enforced speed limit214at the location, time, date, and travel direction225of apparatus14.

In a preferred embodiment, the patrol enforcement profiler206, can compute a histogram of citation speeds from records222of issued traffic law enforcement citations at location225of apparatus14or a plurality of locations within the region of interest223. Said histogram of citation speeds provides a representation of speed limit enforcement profile for interpretation by a user and or the histogram could be further processed by the enforcement profiler206to determine an estimate of the enforcement speed limit214possibly as the maximum positive slope of the histogram.

In another preferred embodiment, the enforcement profiler operation206may determine the estimated enforced speed limit214as the minimum speed for which a speeding citation was issued from records222of issued speeding citations at the corresponding location and preferably time. Furthermore, the enforcement profiler operation206may additionally or alternatively estimate the enforced speed limit214as the average of citation speeds of records222for which speeding citations were issued at the corresponding location and preferably time. In a preferred embodiment, the enforcement profiler operation206may also calculate the variance of the violation speeds of speeding citations records222at location225of apparatus14or a plurality of locations223which represents the variability and uncertainty in the estimated enforced speed limit traffic law enforcement profile.

In addition to the presented methods of estimating the enforced speed limit, the enforcement profiler206is preferably programmable and may implement additional methods of estimating said enforced speed limit from historical records of traffic law enforcement citations and crowd sourced encounters which should be considered within the scope of the current invention. The traffic law enforcement profiles computed by operation206are preferably presented on214.

In a preferred embodiment, the enforcement profiler206may calculate the mean citation speed from records of law enforcement citations and crowd sourced data222is shown in equation 6. Equation 6 can be derived from the generalized correlation Equations 4a, 4a1 as a constrained correlation and represents an algorithm to determine the mean citation speed for which speeding citations were issued as a function of time and location.

Equation 6. A preferred constrained correlation method to determine the mean citation speed

The following terms of Equation 6 are defined:

Dbe.speed—represents database entry citation speed for which the speeding citation was issued.

Loc+Δα—location of said violation within +/−Δαdistance, Δα preferably ranges from 100 feet to 10 miles.

T+Δε—time of said violation within +/−Δε time, Δε preferably ranges from 1 minute to 1 day. 1/N—N is the total number of dbe entries which match at said location and time

In a preferred embodiment, the enforcement profiler206may additionally or alternatively compute the citation speed variance as a function of location and time from traffic law enforcement records and or crowd sourced records222and an example algorithm is shown in equation 7. Said variance may indicate the uncertainty in the enforced speed limit or speed limit traffic law enforcement profile. Said citation variance results are indicated on214and are preferably provided to the Indication operation207and or the vehicle control operation226.
citation_speed_variance (loc,t)=1/NΣ(dbe.speed(loc+Δα,t+Δε)−mean_citation_speed (loc,t))**2

Equation 7. A preferred method to determine citation speed variance as a function of location and time.

Citation_speed_variance—variance of violation speed for which citations were issued at the specified location and time.

dbe.speed—database entry violation speed at the specified location and time.

Loc+Δα—location of said violation within +/−Δαdistance, Δα preferably ranges from 100 feet to 10 miles.

T+Δε—time of said violation within +/−Δε time, Δε preferably ranges from 1 minute to 1 day. 1/N—N is the total number of dbe entries which match at said location and time

In yet another preferred embodiment, the enforcement profiler206may determine the enforced speed limit as minimum citation speed preferably as a function of location and optionally time and or direction from the records of traffic law enforcement citations and crowd sourced encounters222and a preferred algorithm is shown in Equation 8. Said minimum citation speed provides an estimation of enforced speed limit by traffic law enforcement as a function of location and optionally time and or direction and an estimation of the maximum speed to avoid receiving a citation.
min_citation_speed(loc,t)=floor(dbespeed(loc+Δα,t+Δε))

Equation 8. A preferred method to determine minimum citation speed.

Min_citation_speed—minimum violation speed for which a citation has been issued at the specified location and time.

dbe.speed—database entry violation speed at the specified location and time.

Loc+Δα—location of said violation within +/−Δαdistance, Δα preferably ranges from 100 feet to 10 miles.

T+Δε—time of said violation within +/−Δε time, Δε preferably ranges from 1 minute to 1 day.

In equation 8, the floor function accesses dbe entries222at the location225of apparatus14or within the region of interest223and extracts the lowest speed field from the set of dbe records. Said minimum citation speed results214of operation206are preferably provided on214.

The algorithms presented in Equation 6, 7, and 8 are examples of preferred methods to estimate traffic law enforcement profile from historic traffic law enforcement and crowd sourced records; however, other algorithms to estimate traffic law enforcement profile from records of traffic law enforcement citations and crowd sourced encounters222are possible and should be considered within the scope of the present invention.

Those skilled in the art will recognize that additional enforced speed limit profiling characteristics can be derived from historic citation records and crowd sourced encounters of traffic law enforcement222, and databases2,4,5,30, and could be implemented with the programmable patrol profile estimator206, and should be considered within the scope of the present invention. Additionally, those skilled in the art will recognize that the enforced speed limit estimate could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Histogram of Citation Speeds:

In a preferred embodiment, the enforcement profiler206may determine a histogram of violation speeds for which citations222were issued at location225of apparatus14and at a plurality of locations within the time intervals and region of interest223and the citation violation speed histograms may be indicated on214. The histogram may provide a statistical representation of the distribution of speeding citation violation speeds. The enforcement profiler206, preferably determines the histogram of speeds at a location for which speeding citations were issued by indicating the distribution of the accumulation of each citation violation speed derived from records of issued speeding citations222at the corresponding citation location. The histogram of citation violation speeds may provide an enforcement profile of the enforced speed limit at the associated location. Furthermore, the enforcement profiler206may determine the speed at which the histogram of violation speeds achieves maximum positive slope to estimate the enforced speed limit214. Additionally, those skilled in the art will recognize that histogram of citation speeds could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Histogram of Enforced Traffic Laws:

In a preferred embodiment, the enforcement profiler206may determine a histogram of enforced traffic laws for which citations were issued at the location of apparatus225and a plurality of locations within the time intervals and region of interest223and the histogram of enforced traffic laws may be indicated on214. The histogram of enforced traffic laws214, may provide a statistical estimation of traffic laws which are enforce at a location. The enforcement profiler206, preferably determines the histogram of enforced traffic laws at a location by indicating the distribution of the issued citation types from records of issued speeding citations222at the corresponding citation location. Citation types could consist of both moving and non-moving citation violation types such as speeding, expired registration, invalid display of license plate for example. The histogram of enforced traffic laws may provide an enforcement profile of traffic laws. Additionally, those skilled in the art will recognize that the histogram of enforced traffic laws could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Enforcement Profile of Traffic Laws:

In a preferred embodiment, the enforcement profiler206may estimate the enforcement profile of traffic laws which preferably consists of estimating combinations of the enforcement schedule and enforcement austerity for different types of traffic citations and provide an indication on214. The enforcement profiler206may determine the enforcement profile of traffic laws at the location225of apparatus14or at a plurality of locations within the region and timeframe of interest223by computing the correlation between properties of historically issued citations222which may include combinations of issue time, date, location, citation warning, and citation type. In a preferred embodiment, the enforcement profiler206may utilize the method of Equation 4a, 4a1 to compute the correlation between citation properties to determine the enforcement profile of traffic laws. Additionally, those skilled in the art will recognize that the enforcement profile of traffic laws could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Location Citation Density

The location citation density may be determined as the frequency of issued citations which are correlated to a geographic location and may provide a statistical indication of the extent to which a location is patrolled. In a preferred embodiment, the enforcement profiler206may determine the density of issued citations222at locations within the time intervals and region of interest223and provide an indication of the location citation density on214. The enforcement profiler206, preferably determines the citation density by accumulating the citations issued at each location at the current location225or at locations within the region of interest223which are derived from records of issued citations222and may be normalized by traffic volume and may provide an indication of location patrol intensity. The location citation density could be determined as a function of citation type and time may provide a geographical enforcement profile. Additionally, those skilled in the art will recognize that location citation density could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Speed Trap Enforcement Location:

In a preferred embodiment, the enforcement profiler206may utilize combinations of records of historic issued citations and crowd sourced encounters222to predict the locations of speed traps within the region and time intervals of interest223and provide an indication on214.

In a preferred embodiment, the enforcement profiler206may determine the location of estimated speed traps as locations with any statistical combination of: a region with a statistical increase in location correlation of issued citations relative to surrounding regions, an increase in location citation density relative to surrounding areas, drop in estimated enforced speed limit, an increase in probability of a vehicle having an encounter with traffic law enforcement, or an increase in relative ratios of issued traffic citations to traffic volume that exceed a programmable threshold relative to the ratios of issued traffic citations to traffic volume in surrounding locations. Furthermore, in a preferred embodiment, the enforcement profiler206, in addition to predicting the location of speed traps, may also predict the schedules of speed trap enforcement by utilizing the patrol schedule estimates213from the patrol schedule estimation205. Additionally, or alternatively the schedule of speed trap enforcement at speed trap locations can be predicted by determining said ratios of issued traffic citations222to traffic volume at historic times and locations and then correlating said ratios as a function of time to identify patrol schedules which are time correlated, and then time extrapolating said correlation to predict said speed trap enforcement schedules.

Those skilled in the art will recognize that additional speed trap detection profiling characteristics can be derived from historic citation records and could be implemented with the programmable patrol profile estimator206, and should be considered within the scope of the present invention. Additionally, those skilled in the art will recognize that locations of speed traps could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Aircraft Speed Enforcement Location:

In a preferred embodiment, the enforcement profiler206may utilize combinations of records of historic issued citations and crowd sourced encounters222to predict the locations of aircraft speed limit enforcement within the region and time intervals of interest223and provide an indication on214.

In a preferred embodiment, the enforcement profiler206may determine the location of estimated aircraft speed limit enforcement locations with any combination of: a region with a statistical increase in location correlation of issued citations relative to surrounding regions, relative to surrounding areas an increase in location citation density, drop in estimated enforced speed limit, an increase in probability of a vehicle having an encounter with traffic law enforcement, or an increase in relative ratios of issued traffic citations to traffic volume that exceed a programmable threshold relative to the ratios of issued traffic citations to traffic volume in surrounding locations. Additionally, or alternatively, the enforcement profiler206may determine locations where the speed limit is enforced by aircraft by examining the citation type of citation records presented in Equation 1 as being issued by aircraft enforcement and utilizing the citation location correlation as the location of the aircraft speed limit enforced location.

Furthermore, in a preferred embodiment, the enforcement profiler206, in addition to predicting the location aircraft speed limit enforcement, may also predict the schedules of aircraft speed limit enforcement by utilizing the patrol schedule estimates213from the patrol schedule estimation205. Additionally, or alternatively the schedule of aircraft speed limit enforcement at patrolled locations can be predicted by determining said ratios of issued traffic citations222to traffic volume at historic times and locations and then correlating, analogous to Equation 4a, 4a1, said ratios as a function of time to identify patrol schedules which are time correlated, and then time extrapolating said correlation to predict said aircraft speed limit enforcement schedules.

Those skilled in the art will recognize that additional aircraft speed limit enforcement detection profiling characteristics can be derived from historic citation records and could be implemented with the programmable patrol profile estimator206, and should be considered within the scope of the present invention. Additionally, those skilled in the art will recognize that locations of aircraft speed limit enforcement could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Red Light Camera Enforced Intersection:

In a preferred embodiment, the enforcement profiler206may utilize combinations of records of historic issued citations and crowd sourced encounters222to predict the locations of intersections with red light camera enforcement within the region and timeframe of interest223and provide an indication on214. The enforcement profiler206may determine locations of intersections equipped with red light camera enforcement by examining the citation type of citation records presented in Equation 1 as being issued by automated red light camera enforcement and utilizing the citation location correlation as the location of the red light enforced intersection. Alternatively or additionally, the enforcement profiler206may determine locations of intersections equipped with red light camera enforcement by observing the relative density of citations issued222at intersections located within the region and time frame of interest223, and infer the locations of intersections equipped with red light camera enforcement as those with a number of citations issued of type similar to red light violation exceeding a programmable threshold greater than the number of issued citations of similar type at surrounding intersections. Additionally, red light camera enforced areas may be indicated by a region with a statistical increase in location correlation of issued red light violation citations relative to surrounding regions.

Those skilled in the art will recognize that additional red light camera enforced detection profiling characteristics can be derived from historic citation records and could be implemented with the programmable patrol profile estimator206, and should be considered within the scope of the present invention. Additionally, those skilled in the art will recognize that red light camera locations could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Speed Camera Enforced Location:

In a preferred embodiment, the enforcement profiler206may utilize combinations of records of historic issued citations and crowd sourced encounters222to predict the locations with speed camera enforcement within the region and time period of interest223and provide an indication on214. The enforcement profiler206may determine locations of roadway equipped with speed camera enforcement by examining citation type of citation records presented in Equation 1 as being issued by automated speed camera enforcement and utilizing the citation location correlation as the location of the speed camera enforcement. Alternatively or additionally, the enforcement profiler206may determine locations of roadway equipped with speed camera enforcement by observing the relative location citation density of issued222on roadway located within the region and time frame of interest223, and infer the locations of roadway equipped with speed camera enforcement as those with a number of citations issued of type similar to speeding violation exceeding a programmable threshold greater than the number of issued citations of similar type at surrounding roadway.

Those skilled in the art will recognize that additional speed camera enforced detection profiling characteristics can be derived from historic citation records and could be implemented with the programmable patrol profile estimator206, and should be considered within the scope of the present invention. Additionally, those skilled in the art will recognize that speed camera locations could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Enforcement Austerity

The enforcement austerity estimate provides a means to predict the amount of flexibility, leniency or austereness of traffic law enforcement in enforcing traffic laws and may include the Enforcement Leniency Profile, Speed Limit Enforcement Austerity and Location Enforcement Austerity.

Enforcement Leniency Profile

In a preferred embodiment, the enforcement profiler206, may determine the enforcement leniency profile which is a statistical measure of the willingness of traffic law enforcement to issue warnings rather than citations, at the location of apparatus225or locations within the region and time intervals of interest223and provide an indication on214. The enforcement profiler206, preferably determines the enforcement leniency profile for citation types and is derived from the ratio of the number of citations issued222of a given type to the number of warnings issued222of said given type at each location and time interval. The enforcement leniency profile of issued citations may provide a geographical representation of enforcement tolerance for each type of citation.

Those skilled in the art will recognize that additional enforcement leniency profiling characteristics can be derived from historic citation records and could be implemented with the programmable patrol profile estimator206, and should be considered within the scope of the present invention. Additionally, those skilled in the art will recognize the enforcement leniency profile could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Speed Limit Enforcement Austerity Relative to Average Traffic Flow Velocity

In a preferred embodiment, the enforcement profiler206, may estimate the enforced speed limit austerity relative to the average flow velocity of traffic at the location of apparatus225or at a plurality of locations within the region of interest223and provide an indication on214. The enforcement profiler may determine the speed limit enforcement austerity by correlating or comparing the average traffic flow velocity from historical records of traffic flow222, contained in databases16,23to traffic law enforcement issued citation speeds222at associated locations and times providing the enforced speed limit relative to the flow of traffic. Preferably the enforcement profiler206can compare the traffic flow rate with records of issued speeding citation velocities222and determine the estimated velocity at which traffic law enforcement issues citations at given times and locations relative to the average traffic flow velocity and may determine the austerity with which traffic law enforcement enforces the speed limit relative to average traffic flow velocities and volumes at given locations and times.

In another preferred embodiment, the enforcement profiler206, may estimate the schedule of speed limit austerity relative to traffic flow velocities at the location225of apparatus14or at a plurality of locations within the region of interest223and provide an indication on214. The enforcement profiler206, preferably determines the difference between the historic traffic flow average velocity from databases16,23at location225or plurality of said locations within223and the velocities at which historic citations have been issued at corresponding plurality of locations and times222. At each location, the enforcement profiler may then cross correlate said differences as a function of time as in Equation 4a, 4a1 to estimate the enforced speed limit austerity schedule, and time extrapolate to predict current or future enforced speed limit austerity schedules at corresponding locations.

Those skilled in the art will recognize that additional speed limit enforcement austerity characteristics can be derived from historic citation records and could be implemented with the programmable patrol profile estimator206, and should be considered within the scope of the present invention. Additionally, those skilled in the art will recognize the speed limit enforcement austerity could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Location Enforcement Austerity Relative to Average Traffic Volume

In a preferred embodiment, the enforcement profiler206, may estimate the probability of receiving a citation relative to the average flow volume of traffic at location225or a plurality of locations within the timeframe and region of interest223, which provides a measure of how much a location is patrolled and may be indicated on214. The enforcement profiler may determine the location enforcement austerity relative to the average traffic volume by computing the probability of a vehicle receiving a citation at a location derived from the ratio of the number of citations issued at a location and time interval to the average flow volume of traffic at the corresponding location and time interval.

Those skilled in the art will recognize that additional location enforcement austerity profiling characteristics can be derived from historic citation records and could be implemented with the programmable patrol profile estimator206, and should be considered within the scope of the present invention. Additionally, those skilled in the art will recognize the location enforcement austerity profile could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Speed Limit Enforcement Austerity Relative to Average Traffic Volume

In a preferred embodiment, the enforcement profiler206, may estimate the enforced speed limit austerity relative to the average flow volume of traffic at the location of apparatus225or at a plurality of locations within the region of interest223and provide an indication on214. The enforcement profiler may determine the speed limit enforcement austerity by comparing the average traffic flow volume from historical records of traffic flow222, contained in databases16,23to traffic law enforcement issued citation speeds222at associated locations and times providing the enforced speed limit relative to the flow of traffic. Preferably the enforcement profiler206can compare the traffic flow rate with records of issued speeding citation velocities222and determine the estimated velocity at which traffic law enforcement issues citations at given times and locations relative to the average traffic flow velocity and may determine the austerity with which traffic law enforcement enforces the speed limit correlated to the average traffic flow velocities and volumes at given locations and times.

Those skilled in the art will recognize that additional speed limit enforcement austerity profiling characteristics can be derived from historic citation records and could be implemented with the programmable patrol profile estimator206, and should be considered within the scope of the present invention. Additionally, those skilled in the art will recognize the speed limit enforcement austerity profile could be pre computed for locations225,223and stored in the databases2,4,5,30and provided as records222which could then be provided by the patrol profile estimator206as indication214.

Those skilled in the art will recognize that in addition to the enforcement profiles outlined in the present invention, additional enforced profiles could be derived from historic records of citations and crowd sourced encounters and could be implemented with the programmable patrol profile estimator206, and should be considered within the scope of the present invention.

Presentation of Enforcement Schedules, Locations, Profiles

Indication operation207provides a means to present the records of historical traffic law enforcement citations and crowd sourced events222and or estimated traffic law enforcement locations215, schedules213and profile214to a user. In a preferred embodiment, indication operation207preferably accepts estimated patrol locations215, estimated patrol schedules213, the estimated traffic law enforcement profile214, historic records of traffic law enforcement citations222, and roadway map database entries222and apparatus speed, time, location and direction data225to present a representation which may include combinations of estimated traffic law enforcement locations215, schedules213, enforcement profile214, historic traffic law enforcement records222and the traffic law enforcement properties presented in Table 1. Said presentation of estimated traffic law enforcement locations, schedules, enforcement profile and historic data may include audio8and or visual means9.

Preferred Presentations

In a preferred embodiment,FIG.4presents a view400of display9preferably produced by the indication operation207. Many variations of view400are possible, yet still provide combinations of historic and estimated traffic law enforcement characteristics which may include historic traffic law enforcement records222, estimated traffic law enforcement locations215, estimated schedules213, and enforcement profile214and combinations thereof, and should be considered within the scope of the present invention. View400preferably presents a numerical representation of the estimated enforced speed limit401at the current location225of apparatus14and could optionally also present the estimated enforced speed limit at a plurality of points, preferably limited to less than 1000, further ahead or behind the direction of travel or in the vicinity of the apparatus225. Items403and404indicate representative examples of the estimated enforced speed limit at a point 1 mile ahead and at a point 2 miles ahead respectively. In other preferred embodiments, fewer or additional estimated speed limits at other locations could be displayed over what is presented in the preferred view400. Additionally or alternatively, a histogram or a distribution402could be presented at a plurality of locations, preferably less than 1000, which communicates the distribution of speeds at which citations222have been issued at said plurality of locations for the time period of interest223. A histogram402provides a convenient means for a user to rapidly determine the enforcement profile and infer the enforced speed limit at locations of interest223. Additionally or alternatively, view400may have indications for the locations of a plurality, preferably less than 1000, of estimated speed traps405, or speed cameras406which were indicted on215,213and214. In a preferred embodiment, audio output8may enable audio indications of a plurality of estimated speed limits401,403,404, and speed traps405in addition to or in place of displayed indications401,403,404,405in which case display9may not be present.

In yet another preferred embodiment,FIG.5presents another example view64of display9. In view64of display9, the indication operation207may combine a roadway map, preferably from databases15and or17, with any combination of said predicted traffic law enforcement locations215, schedules213, enforcement profile214and historic traffic law enforcement records222. Many variations of view64are possible, yet still provide combinations of estimated traffic law enforcement locations, schedules, and enforcement profile and historical traffic law enforcement data combined with a map of the roadway and should be considered within the scope of the present invention. View64demonstrates a preferred embodiment of many different display options for proving estimated and historic traffic law enforcement data to the driver, and preferably view64could be customizable to provide a subset or alterations of the data which the operator may select.

View64presents an example map of the roadway50,59, and58. Overlaid on the roadway50, is preferably the current location of the user62, and optionally any of the current velocity, enforced speed limit, posted speed limit, the time63of the user62. Preferably overlaid on the roadway50could be a plurality of indications51,54, and57showing estimated enforcement speed limit214derived from historical records of citations issued by traffic law enforcement or crowd sourced encounters at corresponding locations which were preferably calculated by the speed limit enforcement profiler206. Said plurality of estimated enforcement speed limits51,54,57can vary and be correlated by time of day, date, direction, and location, traffic conditions and traffic flow rates and the patrol schedule can optionally be presented for corresponding locations215.

Furthermore, in a preferred embodiment, various methods could be utilized for conveying said plurality of enforcement speeds51,54,57including color or gradient coding sections of roadway to correspond to enforced speed limit ranges as indicated by58,83,84,85or numerical representations51,54,57. Additionally, various methods could be utilized for conveying location citation density which indicates the intensity of patrols at a location as could be indicated by58,83,84,85wherein the shade or color of sections of roadway could encode the level of patrol intensity. Preferably overlaid on the roadway50is the real time and historic traffic flow rates which could preferably be color encoded whereby traffic flow rate is encoded relative to the posted speed limit at that location. Preferably overlaid on the roadway50could be a plurality of indications72showing estimated enforcement austerity214at corresponding plurality of locations. Said austerity72could preferably include providing combinations of the enforcement leniency profile, speed limit enforcement austerity relative to average traffic flow velocity, location enforcement austerity relative to average traffic volume, speed limit enforcement austerity relative to average traffic velocity and other austerity metrics of the present invention and those presented in Table 1.

Additionally, example symbols52,53,55and56may represent a plurality, of predicted patrol locations215over a time period and locations of interest223, preferably determined by the Patrol Location Estimator204, which identifies the locations where traffic law enforcement has historically issued citations. In a preferred embodiment of view64, the locations of a plurality of historically issued citations or crowd sourced encounters222, limited to the region of interest223, may alternatively or additionally be displayed as shown by example symbols73and74. Preferably the presentation of said plurality of historically issued citations222may be filtered by combinations of citation type, time, location and direction. In view64, historically issued speeding citations222over a time period and region of interest223could be displayed as a histogram at a plurality of locations limited to the region of interest223, examples of which include 70 and 71 which present the citation speed verses the number of speeding citations issued at said citation speed at said plurality of locations. Further, view64may identify a plurality of locations, within the region of interest223, as speed traps, red light cameras, speed cameras, and aircraft enforced locations as indicated on214, which preferably may be presented overlaid on view64as55,80,81,82respectively. Other indications could be employed by the present operation207to identify speed traps, red light camera locations and speed camera locations and should be considered within the scope of the current invention.

View64can provide an indication of estimated traffic law enforcement patrol schedules213at a plurality of locations within the region of interest223as example indications60,65. Estimated patrol schedule indications60,65preferably provide estimated patrol times, locations, and may preferably but not necessarily also provide any combinations of estimated enforced speed limit, the average speed at which citations were issued, citation variance from the mean, the minimum speed for which a speeding violation was issued, and a histogram of speeds for issued speeding citations. Alternative representations of60,65estimated traffic law enforcement patrol schedules213are possible and should be considered within the scope of the present invention.

The speed limit enforcement profile214, patrol schedules213, and patrol locations215can preferably be used in conjunction with road navigation planning to select a route between destinations with the fastest estimated enforcement speed limit to minimize driving time.

Additionally, the apparatus14and view64may preferably provide a set of historical citation and crowd sourced record database2,4,5,30processing methods to enable searching, filtering, extracting, statistical processing, and viewing of historical traffic law enforcement citation and crowd sourced records preferably including histogram creation, distributions, scatter plots, tables and lists.

View64is an example of one realization to present the traffic law enforcement profiled information and historical records of citations and crowd source encounters, and many alterations of the above description are possible but still within the scope of the current invention.

While the above description contains many specifics, these should not be construed as limitations on the scope of the invention, but rather as an exemplification of one preferred embodiment thereof. It will be obvious to those skilled in the art that many modifications and alterations may be made without departing from the spirit and scope of the invention which should be determined not by the embodiments illustrated, but by the appended claims and their legal equivalents.