An approach is provided for providing probe anomaly detection. The approach, for example, involves determining an expected vehicle volume for a geographic region over a time epoch. The expected vehicle volume is calculated from historical vehicle volume data. The approach also involves determining an actual vehicle volume for the geographic region over the time epoch. The actual vehicle volume is based on current vehicle volume data collected from a plurality of vehicles traveling in the geographic region. The approach further involves computing a vehicle volume change based on the expected vehicle volume and the actual vehicle volume. The approach then involves providing the vehicle volume change as an indicator of a probe anomaly in the geographic region.

The present disclosure provides a method for controlling a traffic scheduling strategy in a smart city. The method includes obtaining traffic data information in a preset area during a current time period, the traffic data information at least including a speed of at least one vehicle on at least one road, predicting one or more target areas where traffic congestion is likely to occur from the preset area during a next time period based on the traffic data information in the preset area during the current time period, determining whether the traffic scheduling strategy is needed to be switched based on traffic data information in the one or more target areas during the next time period, and in response to determining that the traffic scheduling strategy is needed to be switched, switching a first traffic scheduling strategy to a second traffic scheduling strategy.

The present disclosure provides a method for controlling a traffic scheduling strategy in a smart city. The method includes obtaining traffic data information in a preset area during a current time period, the traffic data information at least including a speed of at least one vehicle on at least one road, predicting one or more target areas where traffic congestion is likely to occur from the preset area during a next time period based on the traffic data information in the preset area during the current time period, determining whether the traffic scheduling strategy is needed to be switched based on traffic data information in the one or more target areas during the next time period, and in response to determining that the traffic scheduling strategy is needed to be switched, switching a first traffic scheduling strategy to a second traffic scheduling strategy.

A control apparatus is a control apparatus for causing light emitting devices to display a crosswalk when the control apparatus detects a pedestrian who is about to cross a road. The control apparatus includes a controlling portion configured to perform: detect, as a first vehicle, a vehicle traveling in a crosswalk display region where a crosswalk is to be displayed; determine whether or not the speed of the first vehicle is less than a reference speed; and in a case where the speed is less than the reference speed and a predetermined safety standard is satisfied, determine to cause the light emitting devices to display the crosswalk.

A control apparatus is a control apparatus for causing light emitting devices to display a crosswalk when the control apparatus detects a pedestrian who is about to cross a road. The control apparatus includes a controlling portion configured to perform: detect, as a first vehicle, a vehicle traveling in a crosswalk display region where a crosswalk is to be displayed; determine whether or not the speed of the first vehicle is less than a reference speed; and in a case where the speed is less than the reference speed and a predetermined safety standard is satisfied, determine to cause the light emitting devices to display the crosswalk.

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.

A method for assessing possible trajectories of road users in a traffic environment includes capturing the traffic environment with static and dynamic features, identifying at least one traffic user, determining at least one possible trajectory for at least one road user in the traffic environment, and assessing the at least one determined possible trajectory for the at least one road user with an adapted/trained recommendation service and the captured traffic environment.

An alert system and method comprising at least one alert beacon having one or more sensors (e.g., LiDAR sensor). The alert beacon further including a processor operable to poll the LiDAR sensor for a predefined number of beta readings in response to receiving an initial reading from the LiDAR sensor indicating a vehicle is within a predefined distance away from the alert beacon. The processor further being operable to calculate an average distance and an average velocity for the vehicle in response to receiving the predefined number of beta readings when the vehicle is within the predefined distance from the alert beacon. The processor also being operable to activate an audible alert and a visual alert when the average distance is below a distance threshold and the average velocity exceeds a velocity threshold in response to calculating the average distance and the average velocity.

System and methods are disclosed for capturing license plate (LP) information of a vehicle in relative motion to a camera device. In one example, the camera system detects the LP in multiple frames, then aligns and geometrically rectifies the image of the LP by scaling, warping, rotating, and/or performing other functions on the images. The camera system may optimize capturing of the LP information by executing a temporal noise filter on the aligned, geometrically rectified images to generate a composite image of the LP for optical character recognition. In some examples, the camera device may include an image sensor, such as a high dynamic range (HDR) sensor, modified to set long and short exposures of the HDR sensor to capture frames of a vehicle's LP, but without consolidating the images into a composite image. The camera system may set optimal exposure settings based on detected relative speed of the vehicle.

System and methods are disclosed for capturing license plate (LP) information of a vehicle in relative motion to a camera device. In one example, the camera system detects the LP in multiple frames, then aligns and geometrically rectifies the image of the LP by scaling, warping, rotating, and/or performing other functions on the images. The camera system may optimize capturing of the LP information by executing a temporal noise filter on the aligned, geometrically rectified images to generate a composite image of the LP for optical character recognition. In some examples, the camera device may include an image sensor, such as a high dynamic range (HDR) sensor, modified to set long and short exposures of the HDR sensor to capture frames of a vehicle's LP, but without consolidating the images into a composite image. The camera system may set optimal exposure settings based on detected relative speed of the vehicle.