Vehicle loads have significant impacts on the emissions of heavy-duty trucks, even in the same traffic conditions. Few studies exist covering the differences in emissions of diesel semi-trailer towing trucks (DSTTTs) with different loads, although these vehicles have a wide load range. In this context, the operating modes and emission rates of DSTTTs were analyzed under varying loads scenarios to understand the effect of vehicle loads on emission factors. First, second-by-second field speed data and emission data of DSTTTs with different loads were collected. Then, the methods for calculating the scaled tractive power (STP) and the emissions model for DSTTTs were proposed to evaluate the effect of different loading scenarios. The STP distributions, emission rate distributions, and emission factor characteristics of different loaded trucks were analyzed and compared. The results indicated that the STP distributions of DSTTTs that under the unloaded state were more narrow than those under fully loaded or overloaded conditions. The emission rates of carbon dioxide (CO₂), carbon monoxide (CO) and total hydrocarbon (THC) for DSTTTs under a fully loaded state were significantly higher than those under an unloaded state. However, due to the influence of exhaust temperature, the emission rates of nitrogen oxides (NO_x) among fully loaded trucks were lower than those under the unloaded state when STP bin was above 4 kW/ton. The emission factors of CO₂, CO, THC, and NO_x for fully loaded trucks demonstrated the largest increases at low-speed intervals (0–30 km/h), which rose by 96.2%, 47.9%, 27.8%, and 65.2%, respectively.

The selective catalytic reduction (SCR) is the most commonly used technique for decreasing the emissions of nitrogen oxides (NOx) from heavy-duty diesel vehicles (HDDVs). However, the same injection strategy in the SCR system shows significant variations in NOx emissions even at the same operating mode. This kind of heterogeneity poses challenges to the development of emission inventories and to the assessment of emission reductions. Existing studies indicate that these differences are related to the exhaust temperature. In this study, an emission model is established for different source types of HDDVs based on the real-time data of operating modes. Firstly, the initial NOx emission rates (ERs) model is established using the field vehicle emission data. Secondly, a temperature model of the vehicle exhaust based on the vehicle specific power (VSP) and the heat loss coefficient is established by analyzing the influencing factors of the NOx conversion efficiency. Thirdly, the models of NOx emissions and the urea consumption are developed based on the chemical reaction in the SCR system. Finally, the NOx emissions are compared with the real-world emissions and the estimations by the proposed model and the Motor Vehicle Emission Simulator (MOVES). This indicates that the relative error by the proposed method is 12.5% lower than those calculated by MOVES. The characteristics of NOx emissions under different operating modes are analyzed through the proposed model. The results indicate that the NOx conversion rate of heavy-duty diesel trucks (HDDTs) is 39.2% higher than that of urban diesel transit buses (UDTBs).

A persistent disconnect between traffic modeling and environmental emissions modeling, stemming from their independent disciplinary evolution, continues to impede the accurate integration of traffic dynamics into emissions prediction. This misalignment frequently results in inconsistencies in simulation outputs and limits the reliability of traffic-based environmental assessments. From a traffic engineering perspective, it is essential that emissions models more precisely reflect real-world vehicle behavior and the complexities of dynamic traffic conditions. In addressing this gap, the present study offers a comprehensive and critical review of the integration between traffic dynamics and emissions modeling across macro-, meso-, and micro-scales. Emissions models are systematically classified into four categories—driving cycle-based, speed–acceleration matrix-based, engine power-based, and vehicle-specific power-based—and assessed in terms of their responsiveness to dynamic traffic inputs. Furthermore, the review highlights the emerging challenges associated with connected and autonomous vehicles and AI-driven modeling techniques, underscoring the urgent need for modular, real-time adaptable modeling frameworks. Through a detailed examination of parameter requirements, data integration issues, and validation challenges, this study provides structured insights to guide the development of scientifically robust and operationally relevant emissions models tailored to the demands of increasingly complex and intelligent transportation systems.

The large-scale integration of autonomous vehicles (AVs) has led to fundamental changes in the energy consumption evolution mechanism of new hybrid traffic flows, rendering conventional mapping between traffic flow parameters and energy invalid. This study develops a “flow–speed–density–energy” model for hybrid traffic composed of autonomous and human-driven vehicles (HVs). In this study, energy (kJ/km/h) is mathematically expressed as the product of the energy factor (kJ/km/veh) and traffic flow (veh/h). By constructing flow and energy factor curves with varying penetration rates and platoon intensities, the study derives four–factor diagrams for hybrid traffic flows. The analysis yields several key findings. First, for internal combustion engine vehicles (ICEVs), energy in HV platoons decreases with increasing speed, whereas in AV platoons it first decreases and then rises as flow intensifies under high-speed conditions. Second, energy is inversely correlated with density, reflecting the negative speed–density relationship. Under uncongested conditions, energy increases with flow, while in congested regimes, sharp reductions in the energy factor leads to overall energy decline. Finally, the primary difference between electric vehicles (EVs) and ICEVs lies in the low-speed range, where the energy of ICEVs decreases rapidly with increasing speed, whereas the energy of EVs increases due to the slower decline of the energy factor and faster flow growth. This study addresses the challenges in the operation and energy consumption of hybrid traffic flow at the macroscopic traffic level, providing foundational support for energy management.

In car-following scenarios, trailing vehicles tend to fluctuate around the motion of the lead vehicle and gradually converge toward their preferred trajectories. With the large-scale integration of autonomous vehicles (AVs) into traffic streams, differences in desired trajectories among vehicles are amplified. This amplification intensifies fluctuation propagation and complicates platoon-level energy consumption mechanisms. This study addresses these dynamics through the development of a mixed-platoon energy consumption estimation model that accounts for gradually evolving traffic flows (MP-ECEMG), incorporating real-world data from light-duty human-driven vehicles (HVs) and AVs. Results indicate that the model leads to a broader range of energy consumption variation than conventional models due to complex vehicle interactions, while the transitions of energy consumption between vehicles are smoother than those predicted for theoretical AVs. For the three powertrains examined, potential energy savings at a 0.5 AV penetration rate (PR) are 11.4%, 18.1%, and 41.8% for internal combustion engine vehicles (ICEVs), recovery electric vehicles (EVs), and non-recovery EVs, respectively. In contrast, actual savings based on AV testing data in Apolloscope are 2.9%, 4.7%, and 11.5%. Regional energy performance is comparable between Apolloscope and Waymo, with both slightly outperforming A2D2. This study provides key insights into the effects of real-world AV technology on energy consumption.