Abstract: A method, apparatus and computer program product are provided for quantifying public transport coverage. In this regard, for a traveler journey beginning at a starting location, transit time data is determined. The transit time data is indicative of a predicted amount of time to travel between the starting location and a departure location associated with a public transport boarding location for a public transport. Furthermore, waiting time data is determined. The waiting time is indicative of a predicted amount of time to wait at the departure location prior to departure via the public transport. Additionally, time to departure data is computed based on a combination of the transit time data and the waiting time data. For the traveler journey, a time to departure probability prediction is also determined.

Abstract: Processors obtain instances of service event data and generate a plurality of territory sets. The processors determine a plurality of respective servicing values based at least in part on the instances of service event data. A respective servicing value is determined for each territory set of the plurality of territory sets. A respective servicing value for a respective territory set is determined based at least in part on at least one path that links the respective service locations located within a particular territory of the territory set on a particular service date. Based on the plurality of respective servicing values, a preferred territory set is identified from the plurality of territory sets. Responsive to determining that the preferred territory set satisfies one or more provision criteria, the processors cause information corresponding to the preferred territory set to be provided.

Abstract: A method, apparatus, and non-transitory computer-readable storage medium for determining an intersection condition are provided. An example embodiment may obtain entity count data of an area comprising an intersection, obtain map feature data of the area comprising the intersection, obtain temporal context data of the area comprising the intersection, and generate an intersection deadlock score based on the entity count data, the map feature data, the temporal context data, or a combination thereof. The generated intersection deadlock score may further be used to provide a prediction of a deadlock condition based on the intersection deadlock score to a traffic application and/or a navigation application.

Abstract: An approach is provided for associating an object with a spatial budget. The approach, for example, involves initiating a scan of an object, an available space, or a combination thereof using a sensor to determine a spatial volume of the object, a spatial volume of the available space for a storage or a placement of the object, or a combination thereof. The approach also involves determining a spatial cost of an object based on the spatial volume of the object. The approach further involves determining an available spatial budget based on the spatial volume of an available space. The approach further involves determining a spatial budget for the object based on the spatial cost of the object and the available spatial budget. The approach further involves providing the spatial budget of the object, the spatial cost of the object, the available spatial budget, or a combination thereof as an output.

Abstract: An approach is provided for determining object utility for personalizing service for space utilization. The approach, for example, involves using a sensor to capture sensor data of an environment including an object over a period of time. The approach also involves processing the sensor data to track one or more interactions between at least one user and the object over the period of time. The approach further involves determining a utility value of the object based on the one or more interactions. The approach further involves providing the utility value as an output. In one example embodiment, the approach further involves determining a service, an action, or a combination thereof to apply to the object based on the utility value.

Abstract: Systems and methods for determining a public transportation score are provided. For example, a method of determining a public transportation score includes receiving a public transportation metric associated with a first location within a public transportation network. The method also includes determining one or more elements of the public transportation network capable of affecting the public transportation metric. The method also includes determining a status of the one or more elements of the public transportation network. The method also includes based on the status of the one or more elements, determining a score corresponding to the public transportation metric.

Abstract: An apparatus, method and computer program product are provided for predicting events in which drivers render aggressive behaviors while maneuvering vehicles. In one example, the apparatus receives input data indicating a target location and including attribute data associated with the target location. The apparatus causes a machine learning model to generate output data as a function of the input data. The output data indicate a likelihood in which a target driver will render an aggressive behavior at the target location while maneuvering a target vehicle. The machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which drivers rendered the aggressive behavior while maneuvering vehicles.

Abstract: An approach is provided for machine learning of vehicular wait events. The approach, for instance, involves processing a sensor observation to determine a wait event. The wait event indicates that at least one person is in a wait state. The approach also involves processing the sensor observation to determine one or more contextual features associated with a location of the wait event, a time of the wait event, the at least one person, or a combination thereof. The approach further involves determining a ground truth of the wait state. The approach further involves vectorizing the one or more contextual features and the ground truth into a training vector. The approach further involves using the training vector to train a machine learning model to determine predicted waiting data based on one or more input vectors. The approach further involves providing the trained machine learning model as an output.

Abstract: An apparatus, method and computer program product are provided for predicting overtaking maneuver events. In one example, the apparatus receives input data including attribute data associated with a location and first travel data associated with a first occupant of a first vehicle. The apparatus causes a machine learning model to generate output data as a function of the input data. The output data indicate a likelihood in which the first vehicle will execute an overtaking maneuver at the location. The machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which second vehicles executed the overtaking maneuver. The historical data include second travel data associated with second occupants of the second vehicles.

Abstract: An apparatus, method and computer program product are provided for predicting slipping events for micromobility vehicles. In one example, the apparatus receives input data indicating a target location and including contextual data associated with the target location. The apparatus causes a machine learning model to generate output data as a function of the input data. The output data indicate a likelihood in which a target micromobility vehicle will slip at the target location. The machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which micromobility vehicles have slipped. The historical data indicate slip-inducing objects within locations of the events, proximity of sources of the slip-inducing objects relative to the locations, and one or more factors that cause the slip-inducing objects to be disposed within the locations.

Abstract: Processors obtain instances of service event data and generate a plurality of territory sets. The processors determine a plurality of respective servicing values based at least in part on the instances of service event data. A respective servicing value is determined for each territory set of the plurality of territory sets. A respective servicing value for a respective territory set is determined based at least in part on at least one path that links the respective service locations located within a particular territory of the territory set on a particular service date. Based on the plurality of respective servicing values, a preferred territory set is identified from the plurality of territory sets. Responsive to determining that the preferred territory set satisfies one or more provision criteria, the processors cause information corresponding to the preferred territory set to be provided.

Abstract: A system, method, and computer program product may be provided for generating a routing graph for a user. A system may include a memory configured to store computer program code instructions; and a processor configured to execute the computer program code instructions to define a geographic area of interest for the user; and identify a cluster of one or more mobility hubs in the area of interest. The processor may be further configured to create the routing graph from an edge of the one or more mobility hubs based on one or more preferences of the user, and provide the routing graph to a routing engine for calculating an intermodal route.

Abstract: An approach is provided for providing data-driven traffic simulations for ad-hoc reconfigurations of a smart-city infrastructure. The approach involves retrieving training traffic data collected from a geographic area supported by the smart-city infrastructure. The approach also involves determining one or more configurations of the smart-city infrastructure corresponding to one or more times at which the training traffic data was collected, wherein the one or more configurations indicate respective states of one or more traffic-related actions supported by the smart-city infrastructure. The approach further involves training a predictive model to predict a traffic-related key performance indicator based on the training traffic data and the one or more configurations, wherein the predictive model is used to predict the traffic-related key performance indicator for a reconfiguration of at least one of the one or more traffic-related actions.

Abstract: Provided herein is a method for a framework to predict the population density for an area based on indirect measurements and contextually similar areas. Methods may include: receiving ground truth population data corresponding to a first region; determining map features associated with the first region; receiving dynamic mobility data associated with the first region; training a machine learning model based on the ground truth population data corresponding to the first region, the map features associated with the first region, and the dynamic mobility data associated with the first region; receiving dynamic mobility data associated with a second region; determining map features associated with the second region; processing the dynamic mobility data associated with the second region and the map features associated with the second region using the machine learning model; and receiving, from the machine learning model, a population estimate for the second region.

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: A method, apparatus and computer program product are provided for quantifying public transport coverage. In this regard, for a traveler journey beginning at a starting location, transit time data is determined. The transit time data is indicative of a predicted amount of time to travel between the starting location and a departure location associated with a public transport boarding location for a public transport. Furthermore, waiting time data is determined. The waiting time is indicative of a predicted amount of time to wait at the departure location prior to departure via the public transport. Additionally, time to departure data is computed based on a combination of the transit time data and the waiting time data. For the traveler journey, a time to departure probability prediction is also determined.

Abstract: An approach is provided for providing taxi routing involving comparison among routes with and without taxi lanes. The approach involves, for example, computing a taxi route between at least one origin-destination pair using a routing engine configured to include at least one taxi lane in the taxi route. The taxi lane permits use by a taxi vehicle while restricting use by a different vehicle type (e.g., an on-demand mobility provider vehicle). The approach also involves computing a non-taxi route using the routing engine or another routing engine configured to not include the at least one taxi lane in the non-taxi route. The approach further involves providing the taxi route for the at least one origin-destination pair as an output based on a comparison of the taxi route to the non-taxi route with respect to an estimated time of arrival, a travel time, a travel distance, and/or a travel price.

Abstract: An approach is provided for providing biometrically-signed location trace data at a device as a proof of user presence. The approach involves initiating a transmission of location data generated by one or more location sensors of a device. The approach also involves, in response to the transmission, receiving a challenge generated for the location data. The approach further involves generating a biometric mapping message based on the challenge and biometric data of a user. The user is associated with the device at a time the location data was generated. The approach further involves providing the biometric mapping message as an output. The output of the biometric mapping message indicates a proof of a presence of the user within a proximity of the device at the time the location data was generated.

Abstract: An approach is provided for argumentative navigation routing. The approach involves, for example, determining a recommended route and an alternative route. The approach also involves iteratively presenting one or more justification messages in support of the recommended route until a response is detected from a user that either rejects or accepts the one or more justification messages. The one or more justification messages are selected from among a plurality of argumentative reason classes. The approach further involves determining an accepted argumentative reason class from among the plurality of argumentative reason classes that corresponds to an accepted justification message of the one or more justification messages. The approach further involves prioritizing the accepted argumentative reason class to generate one or more subsequent justification messages in a subsequent argumentative routing interaction.