On-road carbon dioxide (CO2) emissions from light-duty diesel trucks (LDDTs) are greatly affected by driving conditions, which may be better predicted with the sequence deep learning model as compared to traditional models. In this study, two typical LDDTs were selected to investigate the on-road CO2 emission characteristics with a portable emission measurement system (PEMS) and a global position system (GPS). A deep learning-based LDDT CO2 emission model (DL-DTCEM) was developed based on the long short-term memory network (LSTM) and trained by the measured data with the PEMS. Results show that the vehicle speed, acceleration, VSP, and road slope had obvious impacts on the transient CO2 emission rates. There was a rough positive correlation between the vehicle speed, road slope, and CO2 emission rates. The CO2 emission rate increased significantly when the speed was >5 m/s, especially at high acceleration. The correlation coefficient (R2) and the root mean square error (RMSE) between the monitored CO2 emissions with PEMS and the predicted values with the DL-DTCEM were 0.986–0.990 and 0.165–0.167, respectively. The results proved that the model proposed in this study can predict very well the on-road CO2 emissions from LDDTs.

/pdf/a-deep-learning-micro-scale-model-to-estimate-the-co2-1bz5sggs.pdf

A Deep Learning Micro-Scale Model to Estimate the CO2 Emissions from Light-Duty Diesel Trucks Based on Real-World Driving

In the China VI regulations for light-duty vehicles, the (RDE) test is introduced as a supplementary test procedure. In the actual test process, the RDE results are more significantly influenced by driving behavior and vehicle type. To reduce the test cost, the NOx and PN prediction models are established based on the GA-BP method. The results showed that the coefficients of determination of the GA-BP model for NOx and PN predictions are all greater than 0.9 and are linearly highly correlated at the instantaneous emission level. At the overall emission level, the overall error of the GA-BP model is less than 7% for NOx prediction and less than 6% for PN prediction. The model has high accuracy for both instantaneous and overall emissions of light-duty vehicles. This provides practical engineering value for guiding the RDE test.

/pdf/prediction-of-real-driving-emission-of-light-vehicles-in-2ihe6irp.pdf

Prediction of Real Driving Emission of Light Vehicles in China VI Based on GA-BP Algorithm

Emissions from the road transport sector are a major contributor to the climate change. Commonly used heavy vehicles powered by diesel engines emit toxic and harmful compounds that adversely affect the environment. For this reason, increasingly newer Euro emission standards are being introduced. Therefore, the emission performance of heavy-duty vehicles used in the transportation processes of diverse loads was investigated. The first research object was a set of vehicles equipped with a crane used for loading and unloading timber. The measurements were carried out in a specially designed research test reflecting the way the vehicle is used. It consisted of the drive to the loading site, the transport of timber to the destination and the processes of loading and unloading timber. The test route included driving on both unpaved forest roads and asphalt roads. The second research object was a set of vehicles used in long-distance road transport. The research route characterizing the traffic of vehicles from the cited group was selected here. There were three stages of the route–urban, extra-urban and highway. The whole trip was carried out with a load of 24,800 kg. The testing equipment consisted of SEMTECH DS and AVL Micro Soot Sensor devices belonging to the PEMS (Portable Emission Measurement System) group. Based on the obtained results, the influence of the operating conditions on the exhaust emissions and fuel consumption was determined.

/pdf/analysis-of-exhaust-emissions-from-heavy-duty-vehicles-on-195rtsi9.pdf

Analysis of Exhaust Emissions from Heavy-Duty Vehicles on Different Applications

Vehicles generally move smoothly and with high speeds on elevated roads, thereby producing specific traffic-related carbon emissions in contrast to ground roads. Hence, a portable emission measurement system was adopted to determine traffic-related carbon emissions. The on-road measurement results revealed that the instantaneous emissions of CO2 and CO from elevated vehicles were 17.8% and 21.9% higher than those from ground vehicles, respectively. Based on it, the vehicle specific power was confirmed to exhibit a positive exponential relationship with instantaneous CO2 and CO emissions. In addition to carbon emissions, carbon concentrations on roads were simultaneously measured. The average CO2 and CO emissions on elevated roads in urban areas were 1.2% and 6.9% higher than those on ground roads, individually. Finally, a numerical simulation was performed, and the results verified that elevated roads could deteriorate the air quality on ground roads but improve the air quality above them. What ought to be paid attention to is that the elevated roads present varied traffic behaviour and cause particular carbon emissions, indicating that comprehensive consideration and further balance among the traffic-related carbon emissions are necessary when building elevated roads to alleviate the traffic congestion in urban areas. 

Quantification of traffic-related carbon emission on elevated roads through on-road measurement

Green Light Optimized Speed Advisory (GLOSA) is an emerging smart transportation technology that guides drivers through signalized intersections during green light phases, showing promise in mobile source pollution control. However, there is still a lack of comprehensive understanding regarding its impact on driving behavior, fuel consumption, and CO2 emissions. In this study, we used heavy-duty diesel truck (HDDT) as an example and conducted a comprehensive path study from the changes in driving behavior to fuel consumption and CO2 emission reduction after the application of GLOSA, using a portable emission measurement system for real-world driving tests at two research scales: signal-controlled road section and signalized intersections. The study was innovatively conducted in three scenarios: Without GLOSA, With GLOSA, and GW GLOSA, which considers the simultaneous application of Green Wave (GW) traffic control system. Results show that applying GLOSA, the passing rate of vehicles at signalized intersections significantly improves, the driving cycle curve tends to be smoother, the cruise time ratio increases to 67.60%, and the travel time is reduced by 5%–13%. HDDT's average speed on the road segments (Seg) decreases by 25.15%, and the aggressive acceleration and deceleration behavior near signalized intersections are significantly reduced. The trip fuel consumption and CO2 emissions of HDDT decrease by 16.63%–23.51% and 16.06%–24.12%, respectively. These fuel savings and CO2 emission reductions mainly occur in the Seg and Signalized intersection-after sections. After the simultaneous application of GLOSA and GW, the trip average speed increases by 60.90%, and traffic efficiency is greatly improved. Although the fuel consumption and CO2 emissions are slightly higher compared to GLOSA alone due to the higher driving speed, GW GLOSA still offers relatively considerable fuel and environmental benefits. This study deepens people's understanding of GLOSA and GW and provides support for the formulation of transportation efficiency improvement and environmental protection strategies. 

Green light optimized speed advisory achieves fuel savings and CO2 emission reduction by profoundly impacting driving behavior

On-road exhaust emissions from light-duty vehicles are greatly influenced by driving conditions. In this study, two light-duty passenger cars (LDPCs) and three light-duty diesel trucks (LDDTs) were tested to investigate the on-road emission factors (EFs) with a portable emission measurement system. Emission characteristics of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NOx) emitted from vehicles at different speeds, accelerations and vehicle specific power (VSP) were analyzed. The results demonstrated that road conditions have significant impacts on regulated gaseous emissions. CO, NOx, and HC emissions from light-duty vehicles on urban roads increased by 1.1–1.5, 1.2–1.4, and 1.9–2.6 times compared with those on suburban and highway roads, respectively. There was a rough positive relationship between transient CO, NOx, and HC emission rates and vehicle speeds, while the EFs decreased significantly with the speed decrease when speed ≤ 20 km/h. The emissions rates of NOx and HC tended to increase and then decrease as the acceleration increased and the peak occurred at 0 m/s2 without considering idling conditions. For HC and CO, the emission rates were low and changed gently with VSP when VSP 0. For NOx NOx emission rates were lower and had no obvious change when VSP 0.

Characterization of Exhaust CO, HC and NOx Emissions from Light-Duty Vehicles under Real Driving Conditions

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Characterization of Exhaust CO, HC and NOx Emissions from Light-Duty Vehicles under Real Driving Conditions