Abstract: An apparatus, method and computer program product provide a route using a map layer of one or more sound events. For example, the apparatus receives a user preference indicating a sound type, identifies a road segment associated with the sound type from a plurality of road segments, selects a subset from the plurality of road segments as a route based on association of the subset with respect to the road segment, and outputs the route.

Abstract: An approach is provided for providing mobility insight data related to shared vehicles for a point of interest (POI). The approach involves retrieving shared vehicle data for the POI. The shared vehicle data indicates one or more shared vehicle events that have occurred, that are occurring at a given time, or a combination thereof within a threshold proximity of the POI. The approach also involves processing the shared vehicle data to determine mobility insight data. The mobility insight data includes a shared vehicle usage pattern, a shared vehicle availability pattern, or a combination thereof under one or more contexts for travel to or from the POI. The approach further involves presenting the mobility insight data in a location-based user interface.

Abstract: Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

Abstract: Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

Abstract: An approach is provided for identifying partitions associated with erratic pedestrian behaviors and their correlations to points of interest. For example, the approach involves receiving sensor data associated with a geographic area. The approach also involves based on the sensor data, determining pedestrian-behavior parameter(s) respectively for partition(s). Each respective partition of the partition(s) represents a respective subarea of the geographic area, a respective time period, or a combination thereof. The approach further involves identifying at least one erratic partition from the partition(s) based on determining that a respective pedestrian-behavior parameter associated with the at least one erratic partition deviates from a baseline pedestrian-behavior parameter by at least a threshold extent. The approach further involves determining a correlation of the at least one erratic partition to at least one map feature of a geographic database.

Abstract: An approach is provided for accessing traffic impact caused by individual driving behaviors. For example, the approach involves receiving, by one or more processors, sensor data collected from one or more sensors of a vehicle traveling on a road network. The approach also involves processing, by the processors, the sensor data to determine one or more driving behaviors associated with the vehicle. The approach further involves computing, by the processors, a traffic impact index based on the one or more driving behaviors and at least one contextual parameter associated with the vehicle, the road network, a driver of the vehicle, or a combination thereof. The traffic impact index represents an estimated impact of the vehicle on a traffic flow within at least a portion of the road network. The approach further involves providing, by the processors, the traffic impact index as an output.

Abstract: An apparatus, method and computer program product are provided for providing signature reduction for a vehicle. For example, the apparatus receives a destination for a vehicle as input, selects a subset from a plurality of road segments as a route from a location to the destination, and outputs the route or a portion thereof. The subset is selected based on association of each of the plurality of road segments with respect to a source, and the source is capable of acquiring vehicle signature information.

Abstract: Systems and methods for generating location-based information are provided. For example, an apparatus for generating location-based information includes a processor and a memory. The memory includes computer program code. The computer program code is configured to cause the processor of the apparatus to determine review information based on a user review. The computer program code is further configured to cause the processor of the apparatus to determine a location associated with the review information. The computer program code is further configured to cause the processor of the apparatus to analyze traffic data based on the location. The computer program code is further configured to cause the processor of the apparatus to, based on the analysis, provide data for modifying the user review.

Abstract: Systems and methods for messaging using contextual information are provided. For example, an apparatus for messaging using contextual information includes a processor and a memory. The memory includes computer program code. The computer program code is configured to cause the processor of the apparatus to determine a temporal element from user-submitted text. The computer program code is further configured to cause the processor of the apparatus to analyze traffic data or route data, or a combination thereof, based on the temporal element. The computer program code is further configured to cause the processor of the apparatus to provide for a display of a modification to the user-submitted text based on the analysis. The modification corresponds to the temporal element.

Abstract: System and methods for determining traffic violation hotspots based on roadway feature and/or sensor data. Traffic violation hotspots include, for example areas where traffic violations are more likely to occur such as traffic light jumping, wrong way driving, over speeding, not wearing seatbelt, avoiding stop signs and red lights, distracted driving, passing other vehicles in a no-passing zone, among others. Embodiments further provide predictions for traffic accidents hotspots based on the presence or absence of the traffic violation hotspots.

Abstract: Systems and methods for the detection and analysis of road markings and other road objects are described. A method for detection of road markings comprises identifying image data including lane markings associated with a road segment, defining a plurality of subsections for the road segment, identifying boundary recognition observations for the lane markings from the image data corresponding to the at least one of the plurality of subsections for the road segments, calculating one or more clusters for the boundary recognition observations according to color or intensity, and outputting a lane marking indicator indicating the color or the intensity, for the at least one of the plurality of subsections for the road segments, in response to the one or more clusters.

Abstract: An apparatus, method and computer program product are provided for predicting wrong-way-driving events. A WWD event defines an event in which a vehicle travels in a direction opposing a traffic direction designated for a portion of a road. In one example, the apparatus estimates a likelihood of portion of the road inducing a wrong-way-driving (WWD) event by using a machine learning model as a function of map data, sensor data, or a combination thereof. If the likelihood satisfies a threshold, the apparatus updates a map layer to indicate the WWD event at the portion of the road.

Abstract: An approach is disclosed for determining a non-specific device location according to an observed mobility pattern derived from non-positioning related sensors. The approach involves, for example, determining non-positioning related sensor data collected from one or more sensors of a device. The approach also involves processing the non-positioning related sensor data to determine an observed mobility pattern. The approach further involves making a determination that the observed mobility pattern corresponds to reference data associated with a non-specific location, the non-specific location being at a designated location specificity level. Based at least on the determination, the approach further involves providing an output indicating that the device is located at the non-specific location.

Abstract: An apparatus, method and computer program product are provided for determining a state of visibility for a road object. In one example, the apparatus receives temporal data, calculates an orientation of a light source with respect to a road object using the temporal data, and predicts a state of visibility for the road object based on the orientation of the light source. In another example, the apparatus determines an artificial light source associated with a road object, receives attribute data associated with the artificial light source, determines a state of the artificial light source using the attribute data, and predicts a state of visibility for the road object based on the state of the artificial light source.

Abstract: An approach is provided for proactive booking of a shared vehicle. A routing platform determines that a user is leaving a location for a destination. The routing platform calculates a probability that the user will use a transport mode employing the shared vehicle for a route segment to the destination based on a context and a profile of the user. The routing platform initiates a creation of a proactive reservation of the shared vehicle based on determining that the probability meets or exceeds a threshold value. The proactive reservation is created without intervention of the user.

Abstract: An approach is provided for using a passenger-based driving profile. The approach involves detecting an identity of a user of a vehicle. The approach also involves retrieving a passenger profile of the user based on the identity. The passenger profile is generated based on sensor data indicating a reaction of the user to at least one vehicle driving behavior previously experienced by the user as a vehicle passenger. The approach further involves configuring the vehicle based on the passenger profile.

Abstract: Systems and methods for selecting a navigation map for utilization in a region are provided. For example, a method includes determining, for one or more areas, a plurality of scores corresponding respectively to a plurality of digital maps. The method further includes selecting a digital map of the plurality of digital maps based on a value of a score of the plurality of scores. The method further includes providing the selected digital map to a device to utilize one or more navigation functions in a location within the one or more areas.

Abstract: System and methods are provided for computing visibility values for locations for parking shared vehicles (e.g., light micro-mobility vehicles, electric vehicles, micro-mobility vehicles, etc.), based on the location's surrounding and map related information. A visibility value for a location of a shared vehicle is computed based on multiple criteria such as visibility from the street using street level imagery and sight lines among other data. The visibility values may be used by a shared vehicle service to request that users not park or store a shared vehicle in areas where the visibility index is below a threshold.

Abstract: An apparatus, method and computer program product are provided for determining a state of visibility of a road object, such as a road sign, using vehicle sensor data. For example, the apparatus determines whether one or more sensors of a first vehicle observes a road sign. If the road sign is not observed by the one or more sensors, the apparatus determines whether one or more second vehicles is obscuring the road sign. If the one or more second vehicles is not obscuring the road sign, the apparatus determines whether the road sign is obscured due to a weather condition. If the road sign is obscured due to the weather condition, the apparatus generates a signal indicating that the road sign was obscured due to the weather condition.

Abstract: A method, apparatus and computer program product are provided for predicting a state of visibility for a road object. For example, at least one processor receives road sign attribute data indicating at least one attribute of a road sign. The processor further receives weather forecast data indicating a weather forecast of a location in which the road sign is disposed, and using the road sign attribute data and the weather forecast data, a state of visibility for the road sign is identified.