Abstract: A network system provides interventions to providers to reduce the likelihood that its users will experience safety incidents. The providers provide service to the users such as transportation. Providers who are safe and have positive interpersonal behavior may be perceived by users as high quality providers. However, other providers may be more prone to cause safety incidents. A machine learning model is trained using features derived from service received by users of the network system. Randomized experiments and trained models predict the effectiveness of various interventions on a provider based on characteristics of the provider and the feedback received for the provider. As interventions are sent to providers, the change in feedback can indicate whether the intervention was effective. By providing messages proactively, the network system may prevent future safety incidents from occurring.

Abstract: A network system provides interventions to providers to reduce the likelihood that its users will experience safety incidents. The providers provide service to the users such as transportation. Providers who are safe and have positive interpersonal behavior may be perceived by users as high quality providers. However, other providers may be more prone to cause safety incidents. A machine learning model is trained using features derived from service received by users of the network system. Randomized experiments and trained models predict the effectiveness of various interventions on a provider based on characteristics of the provider and the feedback received for the provider. As interventions are sent to providers, the change in feedback can indicate whether the intervention was effective. By providing messages proactively, the network system may prevent future safety incidents from occurring.

Abstract: A network system provides interventions to providers to reduce the likelihood that its users will experience safety incidents. The providers provide service to the users such as transportation. Providers who are safe and have positive interpersonal behavior may be perceived by users as high quality providers. However, other providers may be more prone to cause safety incidents. A machine learning model is trained using features derived from service received by users of the network system. Randomized experiments and trained models predict the effectiveness of various interventions on a provider based on characteristics of the provider and the feedback received for the provider. As interventions are sent to providers, the change in feedback can indicate whether the intervention was effective. By providing messages proactively, the network system may prevent future safety incidents from occurring.

Abstract: A network system provides interventions to providers to reduce the likelihood that its users will experience safety incidents. The providers provide service to the users such as transportation. Providers who are safe and have positive interpersonal behavior may be perceived by users as high quality providers. However, other providers may be more prone to cause safety incidents. A machine learning model is trained using features derived from service received by users of the network system. Randomized experiments and trained models predict the effectiveness of various interventions on a provider based on characteristics of the provider and the feedback received for the provider. As interventions are sent to providers, the change in feedback can indicate whether the intervention was effective. By providing messages proactively, the network system may prevent future safety incidents from occurring.

Abstract: Examples provided herein describe a navigation system to identify routes that take safety considerations into account. The system accesses safety data from various sources and computes safety data for individual road segments of a map database at multiple times for each road segment. The road segment safety data is used when determining routes for requested trips, including estimating the times that the various road segments on the route will be traversed and determining the safety of each road segment at that time.

Abstract: A safety system associated with a travel coordination system collects safety data describing safety incidents by providers and generates a plurality of safety incident prediction models using the safety data. The safety incident prediction models predict likelihoods that providers in the computerized travel coordination system will be involved in safety incidents. Two types of safety incidents predicted by the safety system include dangerous driving incidents and interpersonal conflict incidents. The safety system uses the plurality of safety incident prediction models to generate a set of predictions indicating probabilities that a given provider in the travel coordination system will be involved in a safety incident in the future. The safety system selects a safety intervention for the given provider responsive to the set of predictions and performs the selected safety intervention on the given provider.

Abstract: Examples provided herein describe a navigation system to identify routes that take safety considerations into account. The system accesses safety data from various sources and computes safety data for individual road segments of a map database at multiple times for each road segment. The road segment safety data is used when determining routes for requested trips, including estimating the times that the various road segments on the route will be traversed and determining the safety of each road segment at that time.

Abstract: Examples provided herein describe a navigation system to identify routes that take safety considerations into account. The system accesses safety data from various sources and computes safety data for individual road segments of a map database at multiple times for each road segment. The road segment safety data is used when determining routes for requested trips, including estimating the times that the various road segments on the route will be traversed and determining the safety of each road segment at that time.

Abstract: A network system provides interventions to providers to reduce the likelihood that its users will experience safety incidents. The providers provide service to the users such as transportation. Providers who are safe and have positive interpersonal behavior may be perceived by users as high quality providers. However, other providers may be more prone to cause safety incidents. A machine learning model is trained using features derived from service received by users of the network system. Randomized experiments and trained models predict the effectiveness of various interventions on a provider based on characteristics of the provider and the feedback received for the provider. As interventions are sent to providers, the change in feedback can indicate whether the intervention was effective. By providing messages proactively, the network system may prevent future safety incidents from occurring.

Abstract: A network system provides interventions to providers to reduce the likelihood that its users will experience safety incidents. The providers provide service to the users such as transportation. Providers who are safe and have positive interpersonal behavior may be perceived by users as high quality providers. However, other providers may be more prone to cause safety incidents. A machine learning model is trained using features derived from service received by users of the network system. Randomized experiments and trained models predict the effectiveness of various interventions on a provider based on characteristics of the provider and the feedback received for the provider. As interventions are sent to providers, the change in feedback can indicate whether the intervention was effective. By providing messages proactively, the network system may prevent future safety incidents from occurring.

Abstract: Examples provided herein describe a navigation system to identify routes that take safety considerations into account. The system accesses safety data from various sources and computes safety data for individual road segments of a map database at multiple times for each road segment. The road segment safety data is used when determining routes for requested trips, including estimating the times that the various road segments on the route will be traversed and determining the safety of each road segment at that time.

Abstract: Examples provided herein describe a navigation system to identify routes that take safety considerations into account. The system accesses safety data from various sources and computes safety data for individual road segments of a map database at multiple times for each road segment. The road segment safety data is used when determining routes for requested trips, including estimating the times that the various road segments on the route will be traversed and determining the safety of each road segment at that time.

Abstract: Examples provided herein describe a navigation system to identify routes that take safety considerations into account. The system accesses safety data from various sources and computes safety data for individual road segments of a map database at multiple times for each road segment. The road segment safety data is used when determining routes for requested trips, including estimating the times that the various road segments on the route will be traversed and determining the safety of each road segment at that time.

Abstract: Examples provided herein describe a navigation system to identify routes that take safety considerations into account. The system accesses safety data from various sources and computes safety data for individual road segments of a map database at multiple times for each road segment. The road segment safety data is used when determining routes for requested trips, including estimating the times that the various road segments on the route will be traversed and determining the safety of each road segment at that time.

Abstract: A system predicts event type and event intensity within specified regions. The system trains a computer model to predict event intensity score values indicative of human activity within a certain geofence and occurring within a certain timeframe. The predicted event intensity score values are based on event data received from third party systems and analyzed by the system. The predicted event intensity score values may be further based on trip data related to trips facilitated by the system. With the predicted activity, the system can modify trips and provide adequate resources for predicted events.

Abstract: Examples provided herein describe a navigation system to identify routes that take safety considerations into account. The system accesses safety data from various sources and computes safety data for individual road segments of a map database at multiple times for each road segment. The road segment safety data is used when determining routes for requested trips, including estimating the times that the various road segments on the route will be traversed and determining the safety of each road segment at that time.

Abstract: A safety system associated with a travel coordination system collects safety data describing safety incidents by providers and generates a plurality of safety incident prediction models using the safety data. The safety incident prediction models predict likelihoods that providers in the computerized travel coordination system will be involved in safety incidents. Two types of safety incidents predicted by the safety system include dangerous driving incidents and interpersonal conflict incidents. The safety system uses the plurality of safety incident prediction models to generate a set of predictions indicating probabilities that a given provider in the travel coordination system will be involved in a safety incident in the future. The safety system selects a safety intervention for the given provider responsive to the set of predictions and performs the selected safety intervention on the given provider.

Abstract: Examples provided herein describe a navigation system to identify routes that take safety considerations into account. The system accesses safety data from various sources and computes safety data for individual road segments of a map database at multiple times for each road segment. The road segment safety data is used when determining routes for requested trips, including estimating the times that the various road segments on the route will be traversed and determining the safety of each road segment at that time.