BACKGROUND

Traffic data collection and analysis are routinely conducted by road agencies, for example, government organizations responsible for traffic management, road maintenance and/or road network planning. Road agencies may obtain measurements of traffic metrics, (e.g., traffic performance), from traffic data, to understand current traffic conditions at a roadway section of interest, such as a portion of a road or a traffic junction—a location where multiple roads intersect, allowing traffic to change from one road to another (e.g., intersection, roundabout, interchange). Traffic metrics are generally used to ascertain an existing, or to predict a future, need of changes to roadway infrastructure and/or traffic control equipment. Examples of traffic metrics include traffic volume, average vehicle speed, vehicle delay, among others.

Modifications to a roadway network may result from traffic metrics related to traffic flow, driving behaviour, or another traffic/driver information. For example, to increase traffic flow at a heavily congested roadway section an additional lane(s) may be added, to reduce wait times at an intersection control signal timing may be modified, and to deter speeding on a portion of road a traffic calming device, (e.g., a speed bump), may be installed.

Various data collection techniques are employed for collecting traffic data. A first technique includes employment of manual counters positioned near a roadway section of interest, (i.e., people observing traffic, then measuring and manually recording vehicle movement.) For instance, to collect traffic data related to interruption of traffic flow at an intersection, a person/people may time the duration each vehicle is stopped prior to entering the intersection. Such a method is manually intensive and prone to human error.

Another technique for collecting traffic data includes use of sensing equipment. For example, movement sensors, (e.g., pneumatic tubes, video cameras), are temporarily and/or permanently installed near a roadway section of interest to sense vehicle movement and collect traffic data. This technique requires the purchase or rental, installation, and potentially maintenance, of sensing equipment. Extracting traffic data from video footage necessitates image processing which is usually complex and resource intensive.

Yet another technique for collecting traffic data includes use of a GPS tracker device installed onboard a vehicle. Traffic data collected via this technique is limited to the data collected by the particular devices, thus multiple passes through a roadway section of interest are needed to gather a sufficient amount of data for the data to be meaningful. Once collected, data from the devices must be uploaded for analysis/processing.

SUMMARY

According to another broad aspect there is a traffic analytics system comprising a processing resource, a first datastore and a network interface for communicatively coupling to a communication network, the traffic analytics system configured for: defining a plurality of zones corresponding to a plurality of sample vehicle ways, each of the plurality of zones comprising a plurality of first contiguous subzones, the plurality of first contiguous subzones for each of the plurality of zones forming a plurality of second contiguous subzones, selecting vehicle data indicative of vehicle operating conditions of a plurality of corresponding vehicles, generating a plurality of features for each of the plurality of second contiguous subzones based on the vehicle data, generating training data for each of the plurality of second contiguous subzones comprising the plurality of features and defining a classifier using at least one machine learning technique with the training data, the classifier for classifying a subzone as one of a portion of a vehicle way and not a portion of a vehicle way.

In an embodiment the vehicle data includes a subset of raw vehicle data or a subset of raw vehicle data and data interpolated therefrom.

In an embodiment the traffic analytics system is configured for receiving from a remote second datastore the subset of raw vehicle data, the second datastore communicatively coupled to the communication network.

In an embodiment the traffic analytics system is configured for communicating with a first data management system communicatively coupled to the communication network for receiving the subset of raw vehicle data therefrom.

In an embodiment the first data management system is a managed cloud data warehouse for performing analytics on data stored therein.

In an embodiment the traffic analytics system comprises a second data management system having the subset of raw vehicle data stored therein. In another embodiment the second data management system is a managed cloud data warehouse for performing analytics on data stored therein.

In an embodiment the traffic analytics system is configured for storing the subset of raw vehicle data in the first datastore.

In an embodiment the classifier classifies a subzone of a type of vehicle way indicative of one of the types of vehicle ways of the plurality of sample vehicle ways.

In an embodiment each zone of the plurality of zones defines an area encompassing a corresponding sample vehicle way.

In an embodiment defining the plurality of zones, for at least one zone, comprises: obtaining geographic coordinate data indicating a location of a reference point proximate the corresponding sample vehicle way, defining a reference area relative to the location of the reference point for encompassing the corresponding sample vehicle way and partitioning the reference area into the plurality of first contiguous subzones. In another embodiment partitioning the reference area comprises partitioning all portions of the reference area into the plurality of first contiguous subzones.

In an embodiment, for each of the plurality of zones, the reference area is defined at a same relative location to the reference point proximate the corresponding vehicle way. In another embodiment the reference area is defined at a distance substantially 25 m radially from the reference point.

In an embodiment each contiguous subzone of the plurality of second contiguous subzones is defined by latitude and longitude pairs.

In an embodiment partitioning the reference area into the plurality of first contiguous subzones comprises subdividing the reference area into a grid of latitude and longitude lines. In another embodiment partitioning the reference area into the plurality of first contiguous subzones comprises subdividing the reference area into the plurality of first contiguous subzones according to a hierarchical geospatial indexing system.

In an embodiment subdividing the reference area into the plurality of first contiguous subzones according to a hierarchical geospatial indexing system comprises subdividing the reference area into first contiguous subzones according to a Geohash indexing system. In another embodiment subdividing the reference area into the plurality of first contiguous subzones according to a Geohash indexing system comprises subdividing the reference area into the plurality of first contiguous subzones according to the Geohash indexing system having a Geohash precision of substantially 9. In yet another embodiment subdividing the reference area into the first contiguous subzones according to a hierarchical geospatial indexing system comprises subdividing the reference area into first contiguous subzones according to a H3 hierarchical geospatial indexing system.

In an embodiment partitioning the reference area into the plurality of first contiguous subzones comprises defining a subzone encompassing the reference point and building a grid of contiguous subzones therefrom until all portions of the reference area are partitioned by the grid of the plurality of first contiguous subzones.

In an embodiment generating a plurality of features based on vehicle data indicative of vehicle operating conditions for a plurality of corresponding vehicles comprises generating the plurality of features based on vehicle data indicative of at least one of position, speed, and ignition state of a vehicle, the ignition state indicating a state of one of ON or OFF.

In an embodiment generating a plurality of features based on vehicle data comprises generating a plurality of features based on date and time data indicative of a date and time vehicle operating conditions is logged.

In an embodiment the vehicle data comprises raw vehicle data corresponding to a location within the plurality of zones. In another embodiment the vehicle data comprises raw vehicle data corresponding to a position within at least one traffic zone encompassing at least one zone of the plurality of zones. In yet another embodiment the at least one traffic zone encompasses each zone of the plurality of zones.

In an embodiment data interpolated from the subset of raw vehicle data is dependent on the dimensions of each of the plurality of second contiguous subzones.

In an embodiment the traffic analytics system is configured for filtering data indicative at least one of an outlier or duplicate value from the vehicle data.

In an embodiment generating the plurality of features for each of the plurality of second contiguous subzones, for at least one second contiguous subzone, comprises generating subzone-related features from a first subset of vehicle data corresponding to a location within the at least one second contiguous subzone.

In an embodiment generating subzone-related features from a first subset of vehicle data corresponding to a location within the at least one second contiguous subzone comprises generating subzone related features selected from the group of: minimum vehicle speed, maximum vehicle speed, average vehicle speed, median vehicle speed, standard deviation of vehicle speed, minimum ignition, maximum ignition, total number of ignitions on, total number of ignitions off, average number of ignitions, ignition ratio, minimum number of vehicle visits/day, maximum number of vehicle visits/day, average number of vehicle visits/day, median number of vehicle visits/day, standard deviation of number of vehicle visits/day, minimum unique number of vehicle visits/day, maximum unique number of vehicle visits/day, median unique number of vehicle visits/day, standard deviation of unique number of vehicle visits/day, average unique number of vehicle visits/day, total number of vehicle visits, total number of unique vehicle visits, and average number of visits/vehicle.

In an embodiment generating the plurality of features for each of the plurality of second contiguous subzones, for at least one second contiguous subzone, comprises generating subzone-related features based on the first subset of vehicle data and a second subset of vehicle data including vehicle data for a same vehicle temporally subsequent thereto.

In an embodiment generating the subzone-related features for each of the plurality of second contiguous subzones, for at least one second contiguous subzone, comprises generating subzone related features selected from the group of: average time to park, minimum time to park, maximum time to park, median time to park, and standard deviation of time to park.

In an embodiment generating the plurality of features for each of the plurality of second contiguous subzones, for at least one second contiguous subzone, comprises generating subzone-related features based on the first subset of vehicle data and a second subset of vehicle data further including vehicle data for a same vehicle temporally preceding and subsequent thereto.

In an embodiment generating the subzone-related features for each of the plurality of second contiguous subzones, for at least one second contiguous subzone, comprises generating subzone related features selected from the group of: average dwell time, minimum dwell time, maximum dwell time, median dwell time, and standard deviation of dwell time.

In an embodiment generating the plurality of features for each of the plurality of second contiguous subzones, for at least one second contiguous subzone, comprises generating zone-related features based on a third subset of vehicle data corresponding to a position within a corresponding zone or from a portion of the subzone-related features.

In an embodiment generating the zone-related features comprises generating the zone-related features selected from the group of: zone minimum ignition off, zone maximum ignition off, zone average vehicle speed, zone maximum vehicle speed, zone minimum vehicle speed, zone average number of unique visits/day, zone minimum number of unique visits/day, zone maximum number of unique visits/day, zone average median number of unique visits/day, and zone total average number of unique visits/day.

In an embodiment generating the zone-related features are comprises generating the zone-related features based on the portion of the subzone-related features and selected from the group of: zone average time to park, zone maximum time to park, zone minimum time to park, zone maximum dwell time, zone minimum dwell time, zone median dwell time, zone average dwell time, zone minimum number of unique visits, zone average number of unique visits, zone maximum number of unique visits, zone average total number of visits, zone maximum total number of visits, and zone minimum total number of visits.

In an embodiment generating the zone-related features comprises generating the zone-related features are generated based on the third subset of vehicle data instances corresponding to a position within the corresponding zone and selected from the group of: zone total number of visits and zone total number of unique visits.

In an embodiment generating the plurality of features includes generating subzone-zone-related features from a relationship of a portion of the plurality of subzone-related features to a portion of the zone-related features of a corresponding zone.

In an embodiment generating subzone-zone-related features comprises generating subzone-zone-related features selected from the group of: minimum vehicle speed ratio, average vehicle speed ratio, maximum vehicle speed ratio, minimum ignition off ratio, maximum ignition off ratio, maximum dwell time ratio, minimum dwell time ratio, average median dwell time ratio, average dwell time ratio, minimum time to park ratio, average time to park ratio, maximum time to park ratio, minimum number of unique vehicle visits ratio, maximum number of unique vehicle visits ratio, average number of unique vehicle visits ratio, total number of vehicle unique visits ratio, minimum unique number of vehicle visits/day ratio, maximum unique number of vehicle visits/day ratio, average unique number of vehicle visits/day ratio, total unique number of vehicle visits/day ratio, average median unique number of vehicle visits/day ratio, minimum total number of vehicle visits ratio, maximum total number of vehicle visits ratio, average total number of vehicle visits ratio, and total number of vehicle visits ratio.

In an embodiment the traffic analytics system is configured for, for each first contiguous subzone of the plurality of first contiguous subzones of the plurality of zones, for obtaining spatial relationship data for each thereof to a corresponding zone and generating at least one feature from the spatial relationship data.

In an embodiment the traffic analytics system is configured for, for each first contiguous subzone of the plurality of first contiguous subzones of each of the plurality of zones, obtaining spatial relationship data for each thereof to the plurality of first contiguous subzones and generating at least one feature from the spatial relationship data.

In an embodiment the spatial relationship data is indicative of the distance between the first contiguous subzone and a centre point of a corresponding zone. In another embodiment the spatial relationship data is indicative of the distance between a centre point of the first contiguous subzone and the centre point of the corresponding zone. In yet another embodiment the spatial relationship data is indicative of the number of adjacent first contiguous subzones of the plurality of first contiguous subzones to the first contiguous subzone. In an embodiment the number of adjacent first contiguous subzones of the plurality of first contiguous subzones to the first contiguous subzone is the number of neighbours of a Geohash.

In an embodiment each training data for at least one second contiguous subzone of the plurality of second contiguous subzones comprises an indication that the at least one second contiguous subzone is one of a vehicle way class or a not-vehicle way class.

In an embodiment the traffic analytics system is configured for, for two or more zones of the plurality of zones having one or more common first contiguous subzones, associating the one or more common first contiguous subzones with a unique zone of the two or more zones.

In an embodiment associating each of the one or more common first contiguous subzones with a unique zone comprises calculating the distance between each first contiguous subzone of the one or more common first contiguous subzones and a location of a centre point of the two or more zones and assigning the one or more common first contiguous subzones to the zone of which the distance is shortest.

In an embodiment the same relative location to the reference point is defined by classifier data associated with the classifier. In another embodiment first contiguous subzone dimensions are defined by the classifier data associated with the classifier. In yet another embodiment Geohash precision is defined by classifier data associated with the classifier.

In an embodiment the spatial relationship data is indicative of the number of adjacent first contiguous subzones to a subzone having vehicle data corresponding to a location therein. In another embodiment the spatial relationship data is indicative of the number of neighbours of a Geohash having vehicle data corresponding to a location therein.

