EP-4740194-A1 - METHOD, SYSTEM, COMPUTER PROGRAM PRODUCT AND COMPUTER READABLE MEDIUM FOR TRAFFIC PREDICTION

Abstract

The invention related to a method for traffic flow prediction, comprising the steps of receiving traffic-related information from a static unit (20) and/or a dynamic unit (22), performing traffic flow prediction by a prediction unit (60), wherein at least one future state of traffic is estimated based on the received traffic-related information, and sharing the estimated at least one future state, by a prediction sharing module (58). The method is characterized by creating a road graph representation comprising nodes (14, 150, 150') and edges (16) between the nodes (14, 150, 150'), wherein the road graph representation has a lane-level resolution, assigning the received traffic-related information to a corresponding lane before performing the traffic flow prediction step to generate a lane-matched traffic-related information, if the received traffic-related information is not yet a lane-matched traffic-related information, performing the traffic flow prediction step by deep learning based on the road graph representation and on the lane-matched traffic-related information. The invention further relates to a system, a computer program product and a computer readable medium for implementing the method.

Inventors

• LÖVEI, Péter

Assignees

• COMMSIGNIA KFT.

Dates

Publication Date

20260513

Application Date

20240705

Claims (1)

1. CLAIMS 1 . A method for traffic flow prediction, comprising the steps of - receiving traffic-related information from a static unit (20) and/or a dynamic unit (22), - performing traffic flow prediction by a prediction unit (60), wherein at least one future state of traffic is estimated based on the received traffic-related information, and - sharing the estimated at least one future state, by a prediction sharing module (58), characterized by - creating a road graph representation comprising nodes (14, 150, 150’) and edges (16) between the nodes (14, 150, 150’), wherein the road graph representation has a lane-level resolution, - assigning the received traffic-related information to a corresponding lane before performing the traffic flow prediction step to generate a lane-matched traffic-related information, if the received traffic-related information is not yet a lane-matched traffic-related information, - performing the traffic flow prediction step by deep learning based on the road graph representation and on the lane-matched traffic-related information. 2. The method according to claim 1 , wherein the traffic-related information is extracted from a message received from the static unit (20) and/or the dynamic unit (22). 3. The method according to claim 2, wherein the traffic-related information received through a message is aggregated and associated with a node (14, 150, 150’) or an edge (16) of the road graph representation. 4. The method according to any of claims 1 - 3, wherein the traffic-related information is one or more of the following: a speed of traffic, an occupancy, a volume of traffic, a density of traffic, a point-of-interest, a location of an event, weather data, or road work. 5. The method according to claim 4, wherein the traffic-related information further includes at least one of the following: a turn signal status of a vehicle, a lane change request of a vehicle, an emergency brake activation of a vehicle. 6. The method according to any of claims 1 - 5, wherein the estimated at least one future state is shared with a connected traffic orchestrator unit (64), and/or with a traffic management centre (TMC), and/or as part of a V2X message (62). 7. The method according to any of claims 1 - 6, wherein the step of creating the road graph representation having a lane-level resolution is performed by - defining the nodes (14, 150, 150’) of the road graph representation based on locations of static units (20), and/or based on a predefined map, and/or based on predefined locations, and - defining the edges (16) between the nodes (14, 150, 150’) based on physical connectivity of the nodes (14, 150, 150’) or based on a logical connectivity of the nodes (14, 150, 150’). 8. The method according to claim 7, wherein physical connectivity is determined between the nodes (14, 150, 150’) based on a physical distance between the nodes (14, 150, 150’) or based on map-based information. 9. The method according to any of claims 1 - 8, further comprising a step of updating the road graph representation via transfer learning to a new road graph representation. 10. The method according to claim 9, wherein transfer learning is performed by the following steps: - applying a trained graph neural network having a first layer, a last layer and one or more intermediate layers between the first layer and the last layer, wherein the trained graph neural network is trained for a road graph, - removing the first layer and the last layer of the trained graph neural network, - freezing all the parameters of the one or more intermediate layers, and - re-training the graph neural network in an E2E fashion on the new road graph representation to create a new first layer and a new last layer. 11 . The method according to any of claims 1 - 10, wherein the step of assigning the received traffic-related information to a corresponding lane is performed by point-based map-matching and/or by trajectory-based map-matching. 12. A system for traffic flow prediction, adapted to implement the method according to claim 1 , the system comprising a prediction unit (60) adapted to - receive traffic-related information from a static unit (20) and/or a dynamic unit (22), and - perform traffic flow prediction and estimate at least one future state of traffic on the received traffic-related information and comprising, characterized in that the prediction unit (60) comprises - a road graph generator (54) generating a road graph representation of a road network, wherein the road graph representation has nodes (14, 150, 150’) and edges (16), and the road graph representation has a lane-level resolution, an inference module (40) adapted to receive lane-matched traffic- related information, and the inference module (40) is connected to the road graph generator (54) to receive the road graph representation, wherein the inference module (40) is further adapted to perform the traffic flow prediction based on the traffic-related information and the road graph representation, and generate an estimation of at least one future state of traffic as an output, and - a prediction sharing module (58) connected to the inference module (40), wherein the prediction sharing module (58) is adapted to share the output of the inference module (40). 13. The system according to claim 12, wherein the prediction module (60) further comprises a point-based map-matching module (30) and/or a trajectorybased map-matching module (28) for lane-matching the received traffic- related information, if the received traffic-related information is not already lane-matched. 14. The system according to claim 12 or claim 13, further comprising a connected traffic orchestrator unit (64) connected to the prediction unit (60), wherein the connected traffic orchestrator unit (64) is adapted to receive the output of the inference module (40). 15. The system according to claim 14, wherein the connected traffic orchestrator unit (64) comprises - a traffic state evaluator (84) adapted to evaluate the output of the inference module (40) and generate an evaluated traffic state, - a road infrastructure rule generator (86) for generating a rule based on the evaluated traffic state in connection with an infrastructure element, an infrastructure response module (88) to forward the rule generated by the road infrastructure rule generator (86) to the infrastructure element, - a vehicle rule generator (94) for generating a rule based on the evaluated traffic state in connection with a vehicle, and - vehicle response module (96) to forward the rule generated by the vehicle rule generator (94) to the vehicle. 16. The system according to any of claims 12 - 15, wherein the prediction unit (60) is implemented in a road-side unit; a MEC application on a MEC server; or a central instance, such as a virtual cloud environment. 17. The system according to any of claims 12 - 16, wherein the static unit (20) is a road-side unit, a loop detector or any other static infrastructure sensor adapted to record a passing-by traffic on a lane-level, and the dynamic unit (22) is a vehicle, a smartphone, or an on-board unit of a vehicle. 18. A non-transitory computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of any of claims 1-11. 19. A non-transitory computer readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the method of any of claims 1-11.

Description

METHOD, SYSTEM, COMPUTER PROGRAM PRODUCT AND COMPUTER READABLE MEDIUM FOR TRAFFIC PREDICTION TECHNICAL FIELD The invention relates to a method and a system for traffic prediction. The invention relates also to a computer program product and computer readable medium implementing the method. Due to the global industry trends of automotive, connected mobility and to the widespread use of loT (Internet-of-Things) devices, there are thousands of entities that are in a communication connection with each other in an urban street environment, and the number of entities changes in a dynamical manner. Managing such a large number of entities can be a difficult task, however, the communication connection between the entities allows for developing new systems and methods in the field. Intelligent Transportation System (ITS) is an indispensable part of smart cities, i.e. , areas that typically uses different types of electronic methods and sensors to collect specific data. Traffic prediction is an important component of ITS and smart cities. Accurate traffic prediction is essential to many real-world applications. It is connected to several smart city use-cases, such as Green Light Optimal Speed Advisory (GLOSA) or Traffic Lights Control (TLC), which are often driven by V2X (Vehicle-to-Everything) messaging technology. For instance, traffic flow prediction can help cities alleviate congestion; car-hailing demand prediction can prompt car-sharing companies to pre-allocate cars to high- demand regions. Another example is model-based accident detection a.k.a. anomaly detection, where the deviance from the predicted values of traffic parameters from the real measured data may be used to evaluate if the difference is outside of a predefined confidence region. BACKGROUND ART A general traffic flow prediction task is to provide information about a difference of a speed and/or a volume between a current and future time instances at measurement points over an input road network. Due to its large-scale nature, and complex, highly dynamic behaviour, traffic prediction is a challenging problem that is traditionally dealt with by means of modelbased and data-driven approaches. As traffic flow can change swiftly and abruptly, approximating this problem by using e.g., the model-based Average Kinematic Wave principle cannot always be satisfactory. Many recent State-of-the-art (SOTA) models utilize neural-network structures to deal with complex data. Some of these use Graph Neural Networks, or attention mechanisms to have the best performance. Although there are a variety of approaches attempted, most of them focus on the mathematically formulated vanilla traffic prediction problem whose main goal is to reach accuracy as high as possible. A paper of Peter Ldvei et al. “Extending Graph Convolutional Network With Attention Mechanism To Estimate Traffic Flow Information In Real-time Smart City Environments” (28th ITS World Congress, Los Angeles, September 18-22, 2022, Paper ID 1228410) focuses on analysing various state of the art (SOTA) Deep Learning methods for Traffic Flow Prediction and suggests a novel model architecture for that by combining two SOTA models into one. The paper solely aims to solve the underlying mathematical problem, that is, to make predictions with the highest accuracy. Another instance is Traffic4cast, which also has been organizing traffic prediction- related competitions annually. Traffic4cast aims to predict travel time and congestion levels on a node and super segment (a set of interconnected road segments) level. Consequently, the problem formulation is almost the same, competitors suggest similar deep learning methods to solve this challenge in the best way possible. Another example is Google Maps ETA (estimated time of arrival) API, which provides an estimation of arrival time. Google (and DeepMind) formulates once again the problem in a similar fashion, dividing the road into (super)segments that consist of edges and interconnected nodes. Then the model utilizes spatio-temporal embeddings along with Graph Neural Network model architecture to make the predictions. Across the prior art, the general problem formulation is similar; however, these models belong to one of the two data collection technique categories mentioned above, i.e., GPS-based or infrastructure based, which leads to limitations. For example, as these solutions only utilize speed or travel time as an input, they are not fit for making lane-level predictions. There are some prior art documents that adopt CV-based data collection. For example, Ranwa Al Mallah et al. “Prediction of Traffic Flow via Connected Vehicles” aims to predict traffic over segments of a road network via CV and V2X-based data collection. However, the method described in this paper is not adapted for large- scale graph-based prediction, but only uses a vanilla neural network. Furthermore, it does not make predictions on a lane-level. There are other prior art solutions