A traffic condition detection method comprises: obtaining a plurality of traffic parameters associated with a monitoring area and obtaining a normal parameter range based on the traffic parameters, wherein at least half of the traffic parameters fall within the normal parameter range; and performing a monitoring procedure on the monitoring area, wherein the monitoring procedure comprises: determining whether a real-time traffic parameter falls within the normal parameter range; and outputting a traffic abnormality notification associated with the monitoring area when the real-time traffic parameter does not fall within the normal parameter range.

A simulation device that on the basis of: a traffic network (10) as a graph of route sections (3) and nodes (4), with starting places (1) and destinations (2).
so as to determine the traffic volume on the route sections (3) performs the following steps: for each starting place (1) and for each of a multiplicity of traffic participants of the starting place (1): determining allocated to each of the destinations (2) respectively a traffic participant fraction, corresponding to an allocation of the traffic participants to the destinations; determining allocated to each of the destinations (2) respectively an optimal route (6) from the starting place (1) to the destination (2); determining for each of the destinations (2) and for each route section (3) of the optimal route (6) a dwell portion (5) of the traffic participant in the route section (3) as a function of the traffic participant fraction that is allocated to this destination (2); for each of the route sections (3) of the traffic network (10): determining the traffic volume on the route section (3) by means of summating the dwell portions (5) of all traffic participants in this route section (3).

A simulation device that on the basis of: a traffic network (10) as a graph of route sections (3) and nodes (4), with starting places (1) and destinations (2).
so as to determine the traffic volume on the route sections (3) performs the following steps: for each starting place (1) and for each of a multiplicity of traffic participants of the starting place (1): determining allocated to each of the destinations (2) respectively a traffic participant fraction, corresponding to an allocation of the traffic participants to the destinations; determining allocated to each of the destinations (2) respectively an optimal route (6) from the starting place (1) to the destination (2); determining for each of the destinations (2) and for each route section (3) of the optimal route (6) a dwell portion (5) of the traffic participant in the route section (3) as a function of the traffic participant fraction that is allocated to this destination (2); for each of the route sections (3) of the traffic network (10): determining the traffic volume on the route section (3) by means of summating the dwell portions (5) of all traffic participants in this route section (3).

The invention relates to a traffic signal control field, discloses method and system of dynamic adjust signal time according to traffic flows in order to decrease stops/starts and green-light idle time: method includes: 1) get parameters of roadnet, signals; 2) get traffic flows; 3) I-neurons predict traffic flows about over thresholds; 4) P-neuron determine pre-judges according to over thresholds of intersection; 5) overall trade-off accept/reject, priority, schedule, and management of pre-judges, make and send I-instructions; system includes: 1) predict method package; 2) traffic data center or vehicle queue detecting equipments; 3) or vehicle in/out detectors; 4) traffic signals controllers. The predicting math model based signals net universal “A-A” serial method, support String mode control, enable roadnet traffic always run low energy consumption signals, avoid redundant stops/starts one time per period per vehicle per road-segment about 60 seconds and idle gasoline consumption, 30 vehicles about 30 minutes idle gasoline consumption per road-segment, and with solitary wave technique for dissolving jam-core, suddenly-happened big queue, provide a serial continuity solution means for signal control to dissolve congestion core, early congestion, delay arrival of a large cluster of congestion, improve efficiency of traffic signal response.

The invention relates to a traffic signal control field, discloses method and system of dynamic adjust signal time according to traffic flows in order to decrease stops/starts and green-light idle time: method includes: 1) get parameters of roadnet, signals; 2) get traffic flows; 3) I-neurons predict traffic flows about over thresholds; 4) P-neuron determine pre-judges according to over thresholds of intersection; 5) overall trade-off accept/reject, priority, schedule, and management of pre-judges, make and send I-instructions; system includes: 1) predict method package; 2) traffic data center or vehicle queue detecting equipments; 3) or vehicle in/out detectors; 4) traffic signals controllers. The predicting math model based signals net universal “A-A” serial method, support String mode control, enable roadnet traffic always run low energy consumption signals, avoid redundant stops/starts one time per period per vehicle per road-segment about 60 seconds and idle gasoline consumption, 30 vehicles about 30 minutes idle gasoline consumption per road-segment, and with solitary wave technique for dissolving jam-core, suddenly-happened big queue, provide a serial continuity solution means for signal control to dissolve congestion core, early congestion, delay arrival of a large cluster of congestion, improve efficiency of traffic signal response.

A method for determining space allocation and signal timing of an isolated signalized intersection consists of at least one remote server and a processing module that is communicably coupled with the at least one remote server. A plurality of traffic-related data, wherein the plurality of traffic-related data reflects activity at the isolated signalized intersection, is received through the processing module. A space determination process is performed on the plurality of traffic-related data through the processing module. Next, a timing determination process is performed on the plurality of traffic-related data through the processing module in order to minimize the average intersection delay at the isolated signalized intersection. Based upon the results from the space determination process and the timing determination process a cycle length is determined for the isolated signalized intersection.

This technology describes one type of CAVH services focusing on fixed-route trips such as commuting, shopping, school, and other trips that users previously travel recurrently and frequently. The technology describes the system architecture of the proposed fixed-route services. The technology includes methods of calibrating, providing, and optimizing the functionalities of such fixed-route services. The detailed methods are proposed for pre-trip, enroute, trip chaining, and post-trip operations, the cyber-physical security, and privacy protection for the users and participating vehicles.

A system for determining a vehicle queue length in a connected vehicle infrastructure environment includes a vehicle traffic data evaluation module with a processor configured via executable instructions to receive vehicle traffic data of multiple vehicles, the vehicle traffic data comprising basic safety messages, create a geometric representation of a vehicle queue, the vehicle queue comprising one or more segments, each segment comprising a set length, extract data of speed, location, date and time from the basic safety messages, aggregate extracted data with the geometric representation of the vehicle queue, assign a speed value of a basic safety message of a vehicle to a segment when the vehicle is identified to be located within the segment of the vehicle queue, detect a sequence of segments comprising assigned speed values, and determine a vehicle queue length based on the set length of each segment.

A system for determining a vehicle queue length in a connected vehicle infrastructure environment includes a vehicle traffic data evaluation module with a processor configured via executable instructions to receive vehicle traffic data of multiple vehicles, the vehicle traffic data comprising basic safety messages, create a geometric representation of a vehicle queue, the vehicle queue comprising one or more segments, each segment comprising a set length, extract data of speed, location, date and time from the basic safety messages, aggregate extracted data with the geometric representation of the vehicle queue, assign a speed value of a basic safety message of a vehicle to a segment when the vehicle is identified to be located within the segment of the vehicle queue, detect a sequence of segments comprising assigned speed values, and determine a vehicle queue length based on the set length of each segment.

Methods, systems, and processor-readable media for generating a temporal detector scan image for traffic signal control are described. An intelligent adaptive cycle-level traffic signal controller uses a deep learning module for traffic signal control, applying image processing techniques to traffic environment data formatted as image data, called “temporal detector scan image” data. A temporal detector scan image is generated by formatting point detector data collected by point detectors (e.g. inductive-loop traffic detectors) over time into two-dimensional matrices representing the traffic environment state in a plurality of lanes over a plurality of points in time, combined with traffic signal data indicating the state of a traffic signal of each lane. The deep learning module may be trained using temporal detector scan image data collected from a traffic environment, and then may be deployed to control the traffic signal for the traffic environment once trained.