# US 12,190,720 B2 · Method of determining the amount of pollutant emissions from a vehicle over a road network section > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,190,720 B2 - Document ID: US12190720B2 - Status: Granted - Kind: B2 - Application number: 17/692,677 - Filed: 2022-03-11 - Published: 2022-10-20 - Granted: 2025-01-07 - Earliest claimed: 2021-04-14 - Assignees: Ifp Energies Nouvelles - Applicants: IFP Energies nouvelles - Inventors: Giovanni De Nunzio; Mohamed Laraki; Bassel Othman; Laurent Thibault - Web page: https://patentgrep.com/patent/US12190720B2 ## CPC - G08G 1/0129: {for creating historical data or processing based on historical data} - G08G 1/0137: {for specific applications} - G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model - G06N 3/0499: Feedforward networks - G06N 3/09: Supervised learning - G08G 1/0116: {from roadside infrastructure, e.g. beacons} - G08G 1/0125: {Traffic data processing} - G08G 1/02: using treadles built into the road (pads or other sensitive devices responsive to passage of vehicles E01F11/00) - G08G 1/04: using optical or ultrasonic detectors - G08G 1/052: with provision for determining speed or overspeed {(speed measuring in general G01P)} - G08G 1/20: {Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles} - G06N 3/08: Learning methods - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06N 20/20: Ensemble learning - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract The present invention is a method for determining the amount of pollutant emissions (Q) from at least one vehicle over a road network section, wherein a pollutant emission model (MFE) is constructed by machine learning (APP) using macroscopic data (MAC) of a learning road network and traffic data (TRA). This model (MFE) is then applied to a road network section. ## Claims ### Claim 1 A method for determining an amount of pollutants emitted from for at least one vehicle travelling over at least one section of a road network, comprising steps of: a) constructing a training set from at least macroscopic data from a learning road network and from at least one of measured data from traffic on the road network and simulated data relative to vehicle traffic occurring on the learning road network; b) constructing a model of pollutant emissions using a machine learning method trained by using the constructed training set, the model of pollutant emissions associating with the at least one road network section according to the macroscopic data of the at least one section of the road network and to at least one of the measured data and the simulated data relative to traffic travelling on the road network; c) measuring the traffic data by using at least one sensor located on the at least one road network section and the traffic data is simulated by traffic simulation with the traffic data being at least one of an average speed of at least one vehicle travelling on the at least one road network section; and d) determining the amount of at least one pollutant emitted from at least one vehicle travelling on at least one of the section of the road network by applying the pollutant emission model to macroscopic data of the at least one section of the road network and to at least one of the measured traffic data and simulated traffic data relative to the traffic on the at least one road section. ### Claim 2 (depends on 1) A method as claimed in claim 1, wherein the training set is constructed by additionally using data of a vehicle fleet associated with the learning road network. ### Claim 3 (depends on 1) A method as claimed in claim 1, wherein an amount of at least one pollutant emission from the at least one vehicle on the at least one section of the road network is further determined using data of a vehicle fleet associated with the road network. ### Claim 4 (depends on 2) A method as claimed in claim 2, wherein an amount of at least one pollutant emission from the at least one vehicle on the at least one section of the road network is further determined using data of a vehicle fleet associated with the road network. ### Claim 5 (depends on 1) A method as claimed in claim 1, wherein the training set is constructed by carrying out steps of: i. determining at least one instantaneous vehicle speed profile by measuring the position, speed and altitude of at least one vehicle on at least one of the learning road network and by generating at least one instantaneous speed profile of at least one vehicle; ii. categorizing each section of the learning road network according to the macroscopic data of the learning road network; and iii. constructing a microscopic pollutant emission model at a scale of vehicles travelling on the at least one section of the road network connecting an instantaneous speed profile of the at least one vehicle and the category of the at least one section of the learning road network to an average amount of at least one pollutant and a standard deviation. ### Claim 6 (depends on 3) A method as claimed in claim 3, wherein the training set is constructed by carrying out steps of: i. determining at least one instantaneous vehicle speed profile by measuring the position, speed and altitude of at least one vehicle on at least one of the learning road network and by generating at least one instantaneous speed profile of at least one vehicle; ii. categorizing each section of the learning road network according to the macroscopic data of the learning road network; and iii. constructing a microscopic pollutant emission model at a scale of vehicles travelling on the at least one section of the road network connecting an instantaneous speed profile of the at least one vehicle and the category of the at least one section of the learning road network to an average amount of at least one pollutant and a standard deviation. ### Claim 7 (depends on 4) A method as claimed in claim 4, wherein the training set is constructed by carrying out steps of: i. determining at least one instantaneous vehicle speed profile by measuring the position, speed and altitude of at least one vehicle on at least one of the learning road network and by generating at least one instantaneous speed profile of at least one vehicle; ii. categorizing each section of the learning road network according to the macroscopic data of the learning road network; and iii. constructing a microscopic pollutant emission model at a scale of vehicles travelling on the at least one section of the road network connecting an instantaneous speed profile of the at least one vehicle and the category of the at least one section of the learning road network to an average amount of at least one pollutant and a standard deviation. ### Claim 8 (depends on 5) A method as claimed in claim 5, wherein the training set is constructed by carrying out steps of: i. determining at least one instantaneous vehicle speed profile by measuring the position, speed and altitude of at least one vehicle on at least one of the learning road network and by generating at least one instantaneous speed profile of at least one vehicle; ii. categorizing each section of the learning road network according to the macroscopic data of the learning road network; and iii. constructing a microscopic pollutant emission model at a scale of vehicles travelling on the at least one section of the road network and connecting an instantaneous speed profile of the at least one vehicle and the category of the at least one section of the learning road network to an average amount of at least one pollutant and a standard deviation. ### Claim 9 (depends on 1) A method as claimed in claim 1, wherein the model of pollutant emissions is constructed by machine learning using a microscopic pollutant emission model, a category of the sections of the learning road network, at least one speed profile and the measured data of the traffic. ### Claim 10 (depends on 1) A method as claimed in claim 1, wherein at least one speed profile of at least one vehicle is generated by use of the macroscopic data of the learning road network and at least one of the measured traffic data and simulated traffic data using a vehicle speed model constructed by use of a machine learning method from at least one of the measured traffic data and the simulated traffic data and the macroscopic data from the learning road network. ### Claim 11 (depends on 2) A method as claimed in claim 2, wherein at least one speed profile of at least one vehicle is generated by use of the macroscopic data of the learning road network and at least one of the measured traffic data and simulated traffic data using a vehicle speed model constructed by use of a machine learning method from at least one of the measured traffic data and simulated traffic data and the macroscopic data from the learning road network. ### Claim 12 (depends on 1) A method as claimed in claim 1, wherein an amount of pollutant emissions is determined over the at least one section of the road network by carrying out the steps of: i. categorizing the at least one section of the road network; ii. at least one of measuring traffic and simulating the traffic on the at least one section of the road network by using at least one of a counting loop and a traffic simulation respectively; and iii. determining an amount of at least one pollutant emission from the at least one vehicle travelling over the at least one section of the road network by use of the pollutant emission model according to a category of the section of the road network and the traffic measurement. ### Claim 13 (depends on 2) A method as claimed in claim 2, wherein an amount of pollutant emissions is determined over the at least one section of the road network being considered by carrying out the steps of: i. categorizing the at least one section of the road network being considered; ii. at least one of measuring traffic and simulating the traffic on the at least one section of the road network by use of at least one of a counting loop and a traffic simulation respectively; and iii. determining an amount of at least one pollutant emission from the at least one vehicle over the at least one section of the road network by use of the pollutant emission model according to a category of the section of the road network and the traffic measurement. ### Claim 14 (depends on 3) A method as claimed in claim 3, wherein an amount of pollutant emissions is determined over the at least one section of the road network being considered by carrying out the steps of: iv. categorizing the at least one section of the road network being considered; v. at least one of measuring traffic and simulating the traffic on the at least one section of the road network by use of at least one of a counting loop and a traffic simulation respectively; and vi. determining an amount of at least one pollutant emission from the at least one vehicle over the at least one section of the road network by use of the pollutant emission model according to a category of the section of the road network and the traffic measurement. ### Claim 15 (depends on 8) A method as claimed in claim 8, wherein the category of the at least one section is selected from a roundabout, a highway, a traffic light, a speed bump, an underpass, a section with no intersection, a priority intersection, and an urban highway. ### Claim 16 (depends on 8) A method as claimed in claim 8, wherein the machine learning is a supervised machine learning method selected from a multiple linear regression, a neural network, a random forest or a combination of these methods. ### Claim 17 (depends on 1) A method as claimed in claim 1, wherein the macroscopic data of the road network is one of topology and traffic signs and the speed limit and the macroscopic data of the road network is provided by a geographic information system. ### Claim 18 (depends on 1) A method as claimed in claim 1, wherein an amount of at least one pollutant emission from the at least one vehicle on the at least one section of the road network is displayed on a road map, by a smartphone or a computer system.