Song et al., 2020 - Google Patents
Prediction of pedestrian exposure to traffic particulate matters (PMs) at urban signalized intersectionSong et al., 2020
- Document ID
- 5025747238481214356
- Author
- Song J
- Qiu Z
- Ren G
- Li X
- Publication year
- Publication venue
- Sustainable Cities and Society
External Links
Snippet
To evaluate the prediction performance of three models (CAL3QHC–California Line Source Model 3 with Queuing and Hot Spot calculations, BPNN-Back Propagation Neural Network, and WNN-Wavelet Neural Network) on pedestrian exposure to traffic-related particulate …
- 230000001537 neural 0 abstract description 20
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
- G01C21/26—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
- G01C21/20—Instruments for performing navigational calculations
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
- G01C21/26—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Song et al. | Prediction of pedestrian exposure to traffic particulate matters (PMs) at urban signalized intersection | |
Forehead et al. | Review of modelling air pollution from traffic at street-level-The state of the science | |
Bharadwaj et al. | Impact of congestion on greenhouse gas emissions for road transport in Mumbai metropolitan region | |
Lyu et al. | Review of the studies on emission evaluation approaches for operating vehicles | |
Quaassdorff et al. | Microscale traffic simulation and emission estimation in a heavily trafficked roundabout in Madrid (Spain) | |
Pinto et al. | Traffic data in air quality modeling: a review of key variables, improvements in results, open problems and challenges in current research | |
Pan et al. | Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information | |
Pinto et al. | Kriging method application and traffic behavior profiles from local radar network database: A proposal to support traffic solutions and air pollution control strategies | |
Wang et al. | Investigation of the spatiotemporal variation and influencing factors on fine particulate matter and carbon monoxide concentrations near a road intersection | |
Shahbazi et al. | Investigating the influence of traffic emission reduction plans on Tehran air quality using WRF/CAMx modeling tools | |
Kutlimuratov et al. | Modelling traffic flow emissions at signalized intersection with PTV vissim | |
Amirjamshidi et al. | Integrated model for microsimulating vehicle emissions, pollutant dispersion and population exposure | |
Samaranayake et al. | Real‐time estimation of pollution emissions and dispersion from highway traffic | |
Bigazzi et al. | Traffic congestion and air pollution exposure for motorists: comparing exposure duration and intensity | |
Wang et al. | Identifying contributions of on-road motor vehicles to urban air pollution using travel demand model data | |
Kurz et al. | Projection of the air quality in Vienna between 2005 and 2020 for NO2 and PM10 | |
Kan et al. | Understanding space-time patterns of vehicular emission flows in urban areas using geospatial technique | |
Craig et al. | Modeled and measured near-road PM2. 5 concentrations: Indianapolis and Providence cases | |
Jiang et al. | The impact of Cold-start emissions on air pollution exposure during active travel | |
Gonçalves et al. | Air quality models sensitivity to on-road traffic speed representation: effects on air quality of 80 km h− 1 speed limit in the Barcelona metropolitan area | |
Etuman et al. | Integrated air quality modeling for urban policy: A novel approach with olympus-chimere | |
Matara et al. | ‘An assessment of the contribution of vehicular traffic to ambient air quality—A case study of nairobi expressway corridor | |
Kho et al. | A Predictive Study: carbon monoxide emission modeling at a signalized intersection | |
Kamigauti et al. | Enhancing spatial inference of air pollution using machine learning techniques with low-cost monitors in data-limited scenarios | |
Lao et al. | Air quality model for Barcelona |