An environmental assessment system for correlating environmental data with traffic data having, a traffic/environment system including a) a traffic count system for obtaining the traffic data of moving objects on a road, and classifying the moving objects into several vehicle types, b) an environmental sensor system including a particulate matter (PM) sensor for measuring PM concentration and/or a carbon dioxide (CO.sub.2) concentration sensor, c) a microphone for obtaining sound signals originated from the moving objects on the road, d) a communication device and e) a microcomputer/memory for running computer programs, the computer programs including a function for adding a time stamp showing a time onto the traffic data, and a sever having functions for correlating the traffic data with the environmental data, wherein the traffic count system is arranged to classify the traffic data by referring to the sound signals obtained by the microphone.

An environmental assessment system for correlating environmental data with traffic data having, a traffic/environment system including a) a traffic count system for obtaining the traffic data of moving objects on a road, and classifying the moving objects into several vehicle types, b) an environmental sensor system including a particulate matter (PM) sensor for measuring PM concentration and/or a carbon dioxide (CO.sub.2) concentration sensor, c) a microphone for obtaining sound signals originated from the moving objects on the road, d) a communication device and e) a microcomputer/memory for running computer programs, the computer programs including a function for adding a time stamp showing a time onto the traffic data, and a sever having functions for correlating the traffic data with the environmental data, wherein the traffic count system is arranged to classify the traffic data by referring to the sound signals obtained by the microphone.

A method for extracting road capacity based on traffic big data includes the following steps: selecting a specific traffic flow model; reading massive road lane traffic flow parameters; calibrating a model parameter of the selected traffic flow model by using the road lane traffic flow parameters read in the previous step; and fitting the calibrated model parameter to obtain a fitted traffic flow model. The present invention solves the problems that traditional methods for traffic capacity calibration have a heavy workload, inadequate samples and unreliable results due to their reliance on manual information acquisition, thereby providing support for automatic, long-term, large-scale and precise acquisition of the capacity.

A system of active lane markers in a roadway, the active lane markers comprising sensors, transmitters and receivers to monitor the status of the roads and provide vehicle guidance to vehicles in response to analysis of sensor data.

The present disclosure provides a vehicle control method, including: acquiring current driving information of a vehicle; determining, according to the current driving information and a preset risk map, whether the vehicle drives to a risky road segment; and controlling a driving state of the vehicle accordingly when it is determined that the vehicle drives to the risky road segment. By presetting a risk map, defining road and roadside risky areas in the map, defining risky road segments of roads according to the road and roadside risky areas, determining in real time, whether the vehicle drives to a risky road segment, and controlling a driving state of the vehicle when it is determined that the vehicle drives to the risky road segment, so as to avoid risks that the risky areas cause to the driving of the vehicle, the driving safety of the vehicle can be effectively improved.

The present disclosure provides a vehicle control method, including: acquiring current driving information of a vehicle; determining, according to the current driving information and a preset risk map, whether the vehicle drives to a risky road segment; and controlling a driving state of the vehicle accordingly when it is determined that the vehicle drives to the risky road segment. By presetting a risk map, defining road and roadside risky areas in the map, defining risky road segments of roads according to the road and roadside risky areas, determining in real time, whether the vehicle drives to a risky road segment, and controlling a driving state of the vehicle when it is determined that the vehicle drives to the risky road segment, so as to avoid risks that the risky areas cause to the driving of the vehicle, the driving safety of the vehicle can be effectively improved.

A method and apparatus includes at least one first sensor configured to be mounted to an infrastructure component, wherein the at least one sensor comprises an environmental sensor. At least one second sensor is configured to be mounted to the infrastructure component, and is configured to sense movement of the infrastructure component. The method and apparatus also includes a system that uses the at least one first sensor to detect an object position and determines an amount of correction for the object position based on movement detected by the at least one second sensor.

A method and apparatus includes at least one environmental sensor mounted to an infrastructure component that is capable of moving relative to ground. The at least one environmental sensor determines an object position. A system uses environmental data collected over time to learn a fixed environment that includes at least one learned reference point. The system determines an amount of movement of the infrastructure component in relation to the learned reference point. The system corrects object position to compensate for movement of the infrastructure component.

A traffic monitoring system includes a roadside traffic sensor unit and an off-site device remote from the traffic sensor unit. The roadside traffic sensor unit includes an imaging device that captures images of vehicles within a field-of-view of the imaging device, an image processing unit that generates a recognition record from the captured images of a vehicle, and a transceiver that transmits the recognition record. The off-site device includes a transceiver that receives the recognition record and a hot list identifying one or more vehicles-of-interest, a memory that stores the received recognition record for up to a retention period, and a processor that sets the retention period based on a comparison of the recognition record to the hot list identified vehicles-of-interest. The retention period can be variably set according to the severity of any “hits” to the hot list.

A traffic monitoring system includes a roadside traffic sensor unit and an off-site device remote from the traffic sensor unit. The roadside traffic sensor unit includes an imaging device that captures images of vehicles within a field-of-view of the imaging device, an image processing unit that generates a recognition record from the captured images of a vehicle, and a transceiver that transmits the recognition record. The off-site device includes a transceiver that receives the recognition record and a hot list identifying one or more vehicles-of-interest, a memory that stores the received recognition record for up to a retention period, and a processor that sets the retention period based on a comparison of the recognition record to the hot list identified vehicles-of-interest. The retention period can be variably set according to the severity of any “hits” to the hot list.