Fusion of Vehicle Weight and Activity Data for Improved Vehicle Emission Modeling

2015 ◽  
Vol 2503 (1) ◽  
pp. 153-162 ◽  
Author(s):  
Kanok Boriboonsomsin ◽  
Guoyuan Wu ◽  
Peng Hao ◽  
Matthew Barth
Keyword(s):  
Los Angeles ◽  
Vehicle Emissions ◽  
Motor Vehicle ◽  
Operating Mode ◽  
Vehicle Emission ◽  
Average Weight ◽  
Activity Data ◽  
Vehicle Weight ◽  
Weight Data

Vehicle weight is one of several factors that affect vehicle emissions. Vehicle weight is especially important when modeling emissions from heavy-duty trucks (HDTs). The motor vehicle emission simulator (MOVES) model provides a way to account for vehicle weight during the construction of vehicle emissions inventories. To date, vehicle weight has not received much attention, although reliable vehicle weight data have become increasingly available in the past several years with the deployment of weigh-in-motion (WIM) technology. This study developed a method for fusing vehicle weight data from WIM stations and traffic data from vehicle detector stations (VDS) to obtain HDT activity data for input in MOVES as vehicle operating mode distributions. The study identified trucks recorded by a WIM station that were likely to travel over a VDS during a specified time period. The measured weight data of the trucks were fused with the second-by-second speed and acceleration values in truck trajectories that were created based on the knowledge of truck traffic speed at the VDS. The study used freeways in Los Angeles County, California, as a case study. The case study showed that the distributions of vehicle operating mode were quite different between the proposed method and the existing method, which assumed an average weight value for all HDTs in the same class. In the example case illustrated in this paper, the proposed method resulted in 78% higher nitrogen oxide emissions and 30% higher particulate matter emissions than the existing method.

Energy Consumption and Emissions Modeling of Individual Vehicles

2017 ◽  
Vol 2627 (1) ◽  
pp. 93-102 ◽  
Author(s):  
Randall Guensler ◽  
Haobing Liu ◽  
Yanzhi (Ann) Xu ◽  
Alper Akanser ◽  
Daejin Kim ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
High Performance ◽  
Vehicle Emissions ◽  
Motor Vehicle ◽  
Operating Conditions ◽  
Modeling Framework ◽  
Activity Data ◽  
Emissions Modeling ◽  
Vehicle Activity ◽  
Fuel Consumption And Emissions

This study demonstrated an approach to modeling individual vehicle second-by-second fuel consumption and emissions on the basis of vehicle operations. The approach used the Motor Vehicle Emission Simulator (MOVES)–Matrix, a high-performance vehicle emissions modeling system consisting of a multidimensional array of vehicle emissions rates (pulled directly from EPA’s MOVES emissions model) that could be quickly queried by other models to generate an applicable emissions rate for any specified on-road fleet and operating conditions. For this project, the research team developed a spreadsheet-based MOVES-Matrix calculator to simplify connecting vehicle activity data with multidimensional emissions rates from MOVES-Matrix. This paper provides a walk-through of the calculation procedures, from basic vehicle information and driving cycles to second-by-second emissions rates. The individual vehicle emissions modeling framework was incorporated into Commute Warrior, a trademarked travel survey application for Android smartphones, to provide real-time fuel consumption and emissions rate estimates from concurrently obtained GPS-based speed data.

scholarly journals Vehicle Emission Computation Through Microscopic Traffic Simulation Calibrated Using Genetic Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 67-80 ◽  
Author(s):  
Yun Wei ◽  
Ying Yu ◽  
Lifeng Xu ◽  
Wei Huang ◽  
Jianhua Guo ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Vehicle Emissions ◽  
Traffic Simulation ◽  
Urban Region ◽  
Vehicle Emission ◽  
Activity Data ◽  
Microscopic Traffic Simulation ◽  
Emission Models ◽  
Vehicle Activity

Abstract Vehicle emission calculation is critical for evaluating motor vehicle related environmental protection policies. Currently, many studies calculate vehicle emissions from integrating the microscopic traffic simulation model and the vehicle emission model. However, conventionally vehicle emission models are presented as a stand-alone software, requiring a laborious processing of the simulated second-by-second vehicle activity data. This is inefficient, in particular, when multiple runs of vehicle emission calculations are needed. Therefore, an integrated vehicle emission computation system is proposed around a microscopic traffic simulation model. In doing so, the relational database technique is used to store the simulated traffic activity data, and these data are used in emission computation through a built-in emission computation module developed based on the IVE model. In order to ensure the validity of the simulated vehicle activity data, the simulation model is calibrated using the genetic algorithm. The proposed system was implemented for a central urban region of Nanjing city. Hourly vehicle emissions of three types of vehicles were computed using the proposed system for the afternoon peak period, and the results were compared with those computed directly from the IVE software with a trivial difference in the results from the proposed system and the IVE software, indicating the validity of the proposed system. In addition, it was found for the study region that passenger cars are critical for controlling CO, buses are critical for controlling CO and VOC, and trucks are critical for controlling NOx and CO2. Future work is to test the proposed system in more traffic management and control strategies, and more vehicle emission models are to be incorporated in the system.

scholarly journals Motor Vehicle Emission Modeling and Software Simulation Computing for Roundabout in Urban City

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Haiwei Wang ◽  
Huiying Wen ◽  
Feng You ◽  
Jianmin Xu ◽  
Hailin Kui
Keyword(s):  
Vehicle Emissions ◽  
Motor Vehicle ◽  
Road Traffic ◽  
Vehicle Emission ◽  
Signal Control ◽  
Emission Model ◽  
Emission Rates ◽  
Integration Model ◽  
Urban City ◽  
Total Vehicle

In urban road traffic systems, roundabout is considered as one of the core traffic bottlenecks, which are also a core impact of vehicle emission and city environment. In this paper, we proposed a transport control and management method for solving traffic jam and reducing emission in roundabout. The platform of motor vehicle testing system and VSP-based emission model was established firstly. By using the topology chart of the roundabout and microsimulation software, we calculated the instantaneous emission rates of different vehicle and total vehicle emissions. We argued that Integration-Model, combing traffic simulation and vehicle emission, can be performed to calculate the instantaneous emission rates of different vehicle and total vehicle emissions at the roundabout. By contrasting the exhaust emissions result between no signal control and signal control in this area at the rush hour, it draws a conclusion that setting the optimizing signal control can effectively reduce the regional vehicle emission. The proposed approach has been submitted to a simulation and experiment that involved an environmental assessment in Satellite Square, a roundabout in medium city located in China. It has been verified that setting signal control with knowledge engineering and Integration-Model is a practical way for solving the traffic jams and environmental pollution.

How Many Trajectories Are Needed to Develop Facility- and Speed-Specific Vehicle-Specific Power Distributions for Emission Estimation? Case Study in Beijing

2019 ◽  
Vol 2673 (11) ◽  
pp. 779-790
Author(s):  
Zeyu Zhang ◽  
Guohua Song ◽  
Zhiqiang Zhai ◽  
Chenxu Li ◽  
Yizheng Wu
Keyword(s):  
Sample Size ◽  
Motor Vehicle ◽  
Specific Power ◽  
Activity Data ◽  
Light Duty ◽  
Emission Estimation ◽  
Vehicle Activity ◽  
Light Duty Vehicles ◽  
Mean Square Errors

Vehicle-specific power (VSP) distributions, or operating mode (OpMode) distributions, are one of the most important parameters in VSP-based emission models, such as the motor vehicle emission simulator (MOVES) model. The collection of second-by-second vehicle activity data is required to develop facility- and speed-specific (FaSS) VSP distributions. This then raises the problem of how many trajectories are needed to develop FaSS VSP distributions for emission estimation. This study attempts to investigate the adaptive sample size for developing robust VSP distributions for emission estimations for light-duty vehicles. First, vehicle activity data are divided into trajectories and categorized into different trajectory pools. Then, the uncertainty of FaSS VSP distribution caused by sample size is analyzed. Further, the relationship between VSP distribution sample size and emission factor uncertainty is discussed. The case study indicates that error in developing FaSS VSP distributions decreases significantly with increased sample size. In different speed bins, the sample size required to develop robust FaSS VSP distributions and estimate emission factors is significantly different. In detail, in each speed bin, for a 90% confidence level, 30 trajectories (1,800 s) are enough to develop robust FaSS VSP distributions for light-duty vehicles with the root mean square errors (RMSEs) lower than 2%, which means errors in calculating fuel consumption and greenhouse gas (GHG) emissions are lower than 5%. However, 35 trajectories (2,100 s) are needed to estimate emissions of carbon monoxide (CO), nitrogen oxide (NOX), and hydrocarbons (HC) with an estimation error lower than 5%.

scholarly journals Estimasi Beban Pencemar Dari Emisi Kendaraan Bermotor di Ruas Jalan Kota Pekanbaru

2014 ◽  
Vol 1 (2) ◽  
pp. 71
Author(s):  
Nurhadi Hodijah ◽  
Bintal Amin ◽  
Mubarak Mubarak
Keyword(s):  
Air Quality ◽  
Vehicle Emissions ◽  
Motor Vehicle ◽  
Ambient Air ◽  
Motor Vehicles ◽  
Vehicle Emission ◽  
Pollutant Load ◽  
Diesel Vehicles ◽  
Motor Vehicle Emissions ◽  
Pm 10

Increasing population and economy in Pekanbaru City was clearly followed by anincrease in the number of motor vehicles has the potential to cause air pollution andendanger human health. This research was aimed to analyze the pollutant load gases of CO,HC, NO 2 , SO 2 and PM 10 emissions from motor vehicles at at Pekanbaru City. Survey on thevolume of motor vehicles, roadside air quality and vehicle emission test was conducted onthree different road in Pekanbaru city. The volume of motor vehicles and pollutants loadsfrom motor vehicle emissions was highest at Sudirman road and the lowest at Diponegororoad. There are very significant differences between Sudirman road with Diponegoro roadand Tuanku Tambusai road with Diponegoro road. Higher pollutant load was found for gasCO (76,4 %), than gas HC (19,4 %), gas NO 2 (3,6 %), gas SO 2 (0,1 % ) and PM 10 ( 0,7 % ).The largest contribution of pollutant load gas CO, HC and PM 10 comes from motorcycles, gasNO 2 from the city cars and gas SO 2 coming from the truck. The quality of roadside air in thethird road to the gases CO, NO 2 , SO 2 and PM 10 are still below the ambient air qualitystandards, whilest gas HC had passed the ambient air quality standard. A positive correlationbetween concentrations of roadside air pollutants with a load of motor vehicle emissions wasfound. The percentage of motor vehicle emission test results explain that the rates of vehiclesfueled with gasoline were higher than diesel vehicles and that do not pass of the emission testwere generally produced before 2007, while for diesel vehicles that do not pass the emissionstest opacity value that were produced in the 2010 onward. 

Development of Local Emissions Rate Model for Light-Duty Gasoline Vehicles: Beijing Field Data and Patterns of Emissions Rates in EPA Simulator

2017 ◽  
Vol 2627 (1) ◽  
pp. 67-76 ◽  
Author(s):  
Chenxu Li ◽  
Lei Yu ◽  
Weinan He ◽  
Ying Cheng ◽  
Guohua Song
Keyword(s):  
Field Data ◽  
Motor Vehicle ◽  
Vehicle Emission ◽  
Emission Model ◽  
Regular Pattern ◽  
Activity Data ◽  
Light Duty ◽  
Gasoline Vehicles ◽  
Er Model ◽  
Local Emissions

A local emissions rate (ER) model is an important tool that is often combined with vehicle activity data in assessing the effect of traffic control strategies on emissions. Such a model is especially critical in developing countries where local emissions data are either unavailable or limited. This study sought to develop a local ER model for light-duty gasoline vehicles (LDGVs) based on limited emissions testing data from Beijing, emissions factors in the China vehicle emission model, and the regular patterns of ERs in the Motor Vehicle Emission Simulator (MOVES) program. To this end, the research team first analyzed the characteristics of vehicle emissions on the basis of field data collected in Beijing. Then the authors summarized the regular pattern of ERs for LDGVs embedded in the MOVES model and examined consistency of normalized ERs derived from Beijing and the MOVES program. The normalized mean square error was used to evaluate the level of consistency. When consistency was sufficiently high, the regular pattern of ERs in the MOVES program was used to fill the missing field emissions data. Development of the model involved four essential elements: ( a) data-driven ERs, ( b) a supplement for high-power operating modes, ( c) modeling ERs of zero-mile-level emissions, and ( d) development of a deterioration model of ERs. On the basis of the proposed model, a local database of ERs for LDGVs was established and applied to assess the emissions benefit of electronic toll collection lanes.

motor vehicle emissions and atmospheric lead concentrations in the los angeles area

1978 ◽  
Vol 28 (12) ◽  
pp. 1200-1206 ◽  
Author(s):  
Margaret C. Hoggan ◽  
Arthur Davidson ◽  
Margaret F. Brunelle ◽  
John S. Nevitt ◽  
John D. Gins
Keyword(s):  
Los Angeles ◽  
Vehicle Emissions ◽  
Motor Vehicle ◽  
Motor Vehicle Emissions

On-Road Vehicle Emission and Activity Data Collection and Evaluation in Houston, Texas

2005 ◽  
Vol 1941 (1) ◽  
pp. 60-71 ◽  
Author(s):  
Fengxiang Qiao ◽  
Lei Yu ◽  
Michal Vojtisek-Lom
Keyword(s):  
Vehicle Emissions ◽  
Vehicle Emission ◽  
Emission Measurement ◽  
Passenger Cars ◽  
Emission Data ◽  
Activity Data ◽  
Road Vehicle ◽  
System Data ◽  
Vehicle Activity ◽  
Road Emission

The newly developed on-road emission measurement device OEM-2100 was used to collect emissions in the Houston, Texas, area. The device can measure second-by-second fuel consumption and emissions of nitrogen oxides, hydrocarbons, carbon monoxide, carbon dioxide, and particulate matter. A total of 459.0 mi of on-road tests and 813.9 min of idling tests were conducted on three passenger cars and two trucks under 170 different test conditions (170 bags placed). Global Positioning System data were recorded simultaneously in line with the emission data. Data were analyzed by a six-step data processing procedure. The bag-based analysis indicated that vehicle emissions varied strongly, not only with vehicle activity data but also with roadway facility types and vehicle specifications. Spatial distributions of tested emissions illustrated how the emissions altered along the driving routes. The tested vehicle emissions were compared with the MOBILE6.2 estimates, and significant differences were found for all vehicles and for most testing conditions. Among the roadway facility types, the largest difference was on arterial roads, where the tested on-road emissions were higher than MOBILE6.2 estimates. As for idling conditions, the tested emissions were much higher than MOBILE6.2 estimates and indicates a need for further investigation of idling emissions. The large amount of emission and vehicle activity data collected initiated a useful database in Houston with promising potential uses. More on-road vehicle emission tests are necessary to obtain more accurate and reliable local vehicle emission individuality and to establish a richer on-road emission database.

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