Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can determine locations and geospatial scores for pick-up events and other transportation events relative to geocoded areas at a given time period and generate geospatial-based proportion metrics for provider devices corresponding to such transportation events as proportional premiums for performing the events. For example, the systems determine locations of provider devices across geocoded areas for a particular time period. The systems can generate geospatial scores for transportation events of the provider devices occurring within the time period based on the specific locations and times of the events relative to the geocoded areas. Based on the geospatial scores and cumulative metrics accounting for such transportation events relative to the geocoded areas, the systems can generate geospatial-based-proportion metrics corresponding to such events for a graphical display on provider devices.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can iteratively adjust an allocation of transportation providers across geocoded areas for a time period until identifying a final allocation of transportation providers corresponding to an improved transportation-value metric for the geocoded areas. By iteratively adjusting both a transportation-value metric and a transportation-provider allocation across geocoded areas, the disclosed systems intelligently generate a final allocation of transportation providers that increases a transportation efficiency reflected in the transportation-value metric.

Abstract: The present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to disclosed embodiments, a transportation matching system trains a predictive request model to generate a metric predicted to trigger an increase in transportation provider activity within the geographic area for a given time period. Furthermore, the system determines a predicted gap between expected request activity and expected transportation provider activity for the geographic area during a future time period, utilizes the predictive request model and the predicted gap to generate a metric for the geographic area, and generates an interactive map associated with a customized schedule for the geographic area and the future time period based on the generated metric.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can determine locations and geospatial scores for pick-up events and other transportation events relative to geocoded areas at a given time period and generate geospatial-based proportion metrics for provider devices corresponding to such transportation events as proportional premiums for performing the events. For example, the systems determine locations of provider devices across geocoded areas for a particular time period. The systems can generate geospatial scores for transportation events of the provider devices occurring within the time period based on the specific locations and times of the events relative to the geocoded areas. Based on the geospatial scores and cumulative metrics accounting for such transportation events relative to the geocoded areas, the systems can generate geospatial-based-proportion metrics corresponding to such events for a graphical display on provider devices.

Abstract: The present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to disclosed embodiments, a transportation matching system trains a predictive request model to generate a metric predicted to trigger an increase in transportation provider activity within the geographic area for a given time period. Furthermore, the system determines a predicted gap between expected request activity and expected transportation provider activity for the geographic area during a future time period, utilizes the predictive request model and the predicted gap to generate a metric for the geographic area, and generates an interactive map associated with a customized schedule for the geographic area and the future time period based on the generated metric.

Abstract: The present application discloses systems, methods, and computer-readable media that can dynamically control a number of provider devices operating in a prioritized-dispatch mode by determining a range of prioritized-dispatch-mode slots for a target time based on differences between value metrics received by provider devices operating in multiple dispatch modes and sending availability nudges to provider devices based on the range of prioritized-dispatch-mode slots. For instance, the disclosed systems can generate a threshold-noticeable-value difference between historical value metrics received by provider devices while operating in a prioritized-dispatch mode and in a basic-dispatch mode. Based on the threshold-noticeable-value difference, the disclosed systems determine a range of slots for the prioritized-dispatch mode during a target time period for a geographic area.

Abstract: The present application discloses systems, methods, and computer-readable media that can dynamically control a number of provider devices operating in a prioritized-dispatch mode by determining a range of prioritized-dispatch-mode slots for a target time based on differences between value metrics received by provider devices operating in multiple dispatch modes and sending availability nudges to provider devices based on the range of prioritized-dispatch-mode slots. For instance, the disclosed systems can generate a threshold-noticeable-value difference between historical value metrics received by provider devices while operating in a prioritized-dispatch mode and in a basic-dispatch mode. Based on the threshold-noticeable-value difference, the disclosed systems determine a range of slots for the prioritized-dispatch mode during a target time period for a geographic area.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that validate the performance of a dispatch model, such as a directive-based model, utilized for facilitating transportation matching services. For example, the disclosed systems can generate critical situations clusters composed of groups of critical transportation provider performance scenarios. From the critical situation clusters, the disclosed systems can sample one or more critical transportation provider situations that present a risk of degradation for the transportation matching services. Accordingly, the disclosed systems can measure a performance of the dispatch model by employing a simulation model that utilizes the dispatch model to propagate the critical transportation provider situations through time.

Abstract: The present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to disclosed embodiments, a transportation matching system trains a predictive request model to generate a metric predicted to trigger an increase in transportation provider activity within the geographic area for a given time period. Furthermore, the system determines a predicted gap between expected request activity and expected transportation provider activity for the geographic area during a future time period, utilizes the predictive request model and the predicted gap to generate a metric for the geographic area, and generates an interactive map associated with a customized schedule for the geographic area and the future time period based on the generated metric.

Abstract: The present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to disclosed embodiments, a transportation matching system trains a predictive request model to generate a metric predicted to trigger an increase in transportation provider activity within the geographic area for a given time period. Furthermore, the system determines a predicted gap between expected request activity and expected transportation provider activity for the geographic area during a future time period, utilizes the predictive request model and the predicted gap to generate a metric for the geographic area, and generates an interactive map associated with a customized schedule for the geographic area and the future time period based on the generated metric.

Abstract: The present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to disclosed embodiments, a transportation matching system trains a predictive request model to generate a metric predicted to trigger an increase in transportation provider activity within the geographic area for a given time period. Furthermore, the system determines a predicted gap between expected request activity and expected transportation provider activity for the geographic area during a future time period, utilizes the predictive request model and the predicted gap to generate a metric for the geographic area, and generates an interactive map associated with a customized schedule for the geographic area and the future time period based on the generated metric.

Abstract: The present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to disclosed embodiments, a transportation matching system trains a predictive request model to generate a metric predicted to trigger an increase in transportation provider activity within the geographic area for a given time period. Furthermore, the system determines a predicted gap between expected request activity and expected transportation provider activity for the geographic area during a future time period, utilizes the predictive request model and the predicted gap to generate a metric for the geographic area, and generates an interactive map associated with a customized schedule for the geographic area and the future time period based on the generated metric.

Abstract: The present application discloses systems, methods, and computer-readable media that can dynamically control a number of provider devices operating in a prioritized-dispatch mode by determining a range of prioritized-dispatch-mode slots for a target time based on differences between value metrics received by provider devices operating in multiple dispatch modes and sending availability nudges to provider devices based on the range of prioritized-dispatch-mode slots. For instance, the disclosed systems can generate a threshold-noticeable-value difference between historical value metrics received by provider devices while operating in a prioritized-dispatch mode and in a basic-dispatch mode. Based on the threshold-noticeable-value difference, the disclosed systems determine a range of slots for the prioritized-dispatch mode during a target time period for a geographic area.

Abstract: This disclosure describes a transportation matching system that manages the allocation of transportation providers by training and utilizing multiple machine-learning models to identify, allocate, and serve specific transportation providers with customized opportunities to relocate the transportation providers between geocoded areas in a geocoded region. For instance, the transportation matching system trains and utilizes an incremental provider model, a provider allocation model, and personalized provider behavioral models as well as a customized provider interface generator to satisfy anticipated transportation requests and improve transportation matching within a geocoded region.

Abstract: This disclosure describes a transportation matching system that manages the allocation of transportation providers by training and utilizing multiple machine-learning models to identify, allocate, and serve specific transportation providers with customized opportunities to relocate the transportation providers between geocoded areas in a geocoded region. For instance, the transportation matching system trains and utilizes an incremental provider model, a provider allocation model, and personalized provider behavioral models as well as a customized provider interface generator to satisfy anticipated transportation requests and improve transportation matching within a geocoded region.

Abstract: This disclosure describes a transportation matching system that manages the allocation of transportation providers by training and utilizing multiple machine-learning models to identify, allocate, and serve specific transportation providers with customized opportunities to relocate the transportation providers between geocoded areas in a geocoded region. For instance, the transportation matching system trains and utilizes an incremental provider model, a provider allocation model, and personalized provider behavioral models as well as a customized provider interface generator to satisfy anticipated transportation requests and improve transportation matching within a geocoded region.