Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis

被引：24

作者：

Xu, Junshi ^{[1
]}

Hilker, Nathan ^{[2
]}

Turchet, Matheus ^{[1
]}

Al-Rijleh, Mohamad-Kenan ^{[1
]}

Tu, Ran ^{[1
]}

Wang, An ^{[1
]}

Fallahshorshani, Masoud ^{[1
]}

Evans, Greg ^{[2
]}

Hatzopoulou, Marianne ^{[1
]}

机构：

[1] Univ Toronto, Civil Engn, 35 St George St, Toronto, ON M5S 1A4, Canada

[2] Univ Toronto, Chem Engn & Appl Chem, Toronto, ON, Canada

来源：

TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT | 2018年 / 62卷

关键词：

Traffic simulation; Emission modelling; Speed aggregation; Microwave radar; Traffic camera; PM concentrations; URBAN; PM2.5;

D O I：

10.1016/j.trd.2018.02.010

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This study investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Data from a microwave radar and video cameras were first used directly for emission modelling. They were then used as input to a traffic simulation model whereby vehicle drive cycles were extracted to estimate emissions. To reach this objective, hourly traffic data were collected from three periods including morning peak (6-9 am), midday (11-2 pm), and afternoon peak (3-6 pm) on a weekday (June 23, 2016) along a high-volume corridor in Toronto, Canada. Traffic volumes were detected by a single radar and two video cameras operated by the Southern Ontario Centre for Atmospheric Aerosol Research. Traffic volume and composition derived from the radar had lower accuracy than the video camera data and the radar performance varied by lane exhibiting poorer performance in the remote lanes. Radar speeds collected at a single point on the corridor had higher variability than simulated traffic speeds, and average speeds were closer after model calibration. Traffic emissions of nitrogen oxides (NOx) and particulate matter (PM10 and PM2.5) were estimated using radar data as well as using simulated traffic based on various speed aggregation methods. Our results illustrate the range of emission estimates (NOx: 4.0-27.0 g; PM10: 0.3-4.8 g; PM2.5: 0.2-1.3 g) for the corridor. The estimates based on radar speeds were at least three times lower than emissions derived from simulated vehicle trajectories. Finally, the PM10 and PM2.5 near-road concentrations derived from emissions based on simulated speeds were two or three times higher than concentrations based on emissions derived using radar data. Our findings are relevant for project level emission inventories and PM hot-spot analysis; caution must be exercised when using raw radar data for emission modeling purposes.

引用

页码：90 / 101

页数：12

共 27 条

[1] Performance evaluation of vehicles emissions prediction models [J].

Abo-Qudais S. ;

Qdais H.A. .

Clean Technologies and Environmental Policy, 2005, 7 (4) :279-284

[2] VISSIM/MOVES integration to investigate the effect of major key parameters on CO2 emissions [J].

Abou-Senna, Hatem ;

Radwan, Essam .

TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2013, 21 :39-46

[3] Investigating the isolated and combined effects of congestion, roadway grade, passenger load, and alternative fuels on transit bus emissions [J].

Alam, Ahsan ;

Hatzopoulou, Marianne .

TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2014, 29 :12-21

[4]

Bing X., 2014, Advances in Transportation Studies, V32, P23

[5] Compliance with the annual NO2 air quality standard in Athens.: Required NOx levels and expected health implications [J].

Chaloulakou, A. ;

Mavroidis, I. ;

Gavriil, I. .

ATMOSPHERIC ENVIRONMENT, 2008, 42 (03) :454-465

[6]

Chen H., 2005, QUALITY, V5

[7] Concentrations and emission factors for PM2.5 and PM10 from road traffic in Sweden [J].

Ferm, Martin ;

Sjoberg, Karin .

ATMOSPHERIC ENVIRONMENT, 2015, 119 :211-219

[8] Simulating the air quality impacts of traffic calming schemes in a dense urban neighborhood [J].

Ghafghazi, Golnaz ;

Hatzopoulou, Marianne .

TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2015, 35 :11-22

[9] A review of fundamental drivers governing the emissions, dispersion and exposure to vehicle-emitted nanoparticles at signalised traffic intersections [J].

Goel, Anju ;

Kumar, Prashant .

ATMOSPHERIC ENVIRONMENT, 2014, 97 :316-331

[10] Performance evaluation of air quality models for predicting PM10 and PM2.5 concentrations at urban traffic intersection during winter period [J].

Gokhale, Sharad ;

Raokhande, Namita .

SCIENCE OF THE TOTAL ENVIRONMENT, 2008, 394 (01) :9-24

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