An intersection is a typical emission hot spot in the urban traffic network. And frequent violations such as running the red light have been a critical social problem at signalized intersections in developing countries. This article aimed to quantify the impact of violations (behaviors which will block the fleet) on emissions at signalized intersections. Increased emissions of vehicles affected by violations are of two levels: (1) trajectory level for the first four affected vehicles and (2) traffic flow level for the subsequent vehicles. At the trajectory level, the study focuses on the second-by-second activities of the first four affected vehicles. First, the trajectory model of the first affected vehicle is developed. Then, the trajectory of the other three vehicles is constructed using the Gipps car-following model. At the traffic flow level, a linear emission model is developed to describe the relationship between emission factors and idling time in the one-stop (vehicle stop once) and two-stop (vehicle stop twice) scenarios based on the global position system (GPS)-collected data at 44 intersections in Beijing. Based on the linear emission model, increased emissions at the traffic flow level are calculated as knowing the number of stops and idling time before and after violations. The analysis of the subsequent vehicles is divided into unsaturated and saturated conditions. Under the unsaturated condition, the emissions have barely increased due to the increase of idling time for one-stop vehicles caused by the violations. Under the saturated conditions, the emission increment increases sharply as the one-stop vehicle gradually transforms to a two-stop vehicle because of violations, and the maximum emission increment reaches 45% in half an hour in the case. The increment of emissions decreases steadily as the proportion of two-stop vehicles reaches 100% after violations, while the proportion before violations keeps increasing.

The potential increase/decrease in energy consumption of traffic operation modes is a hot topic in the studies of connected and autonomous vehicles (CAVs), but there are few theoretical studies on the fuel consumption of human driving vehicles (HDVs) and CAV mixed platoon as a result of limited data. Based on the power distribution of light‐duty vehicles, this study developed a fuel consumption model for HDV and CAV mixed platoons by theoretical hypothesis considering the oscillation along with the platoon. Firstly, a vehicle‐specific power (VSP) distribution conforming to normal distribution is constructed with hypothesis‐based VSP standard deviation and constant VSP average value at each speed bin under different platoon arrangement conditions. Then, the fuel factors (mL/km) are calculated by weighted fuel rate (mL/s) according to the VSP distribution. Finally, a library of fuel consumption values for multiple penetration rates, platoon intensities, and speeds was established. In the case, at a speed of 30 km/h, the fuel consumption in the CAV group was reduced by 0.8–5%, 1.9–12.5%, 8.6–12%, and 12.4%, respectively, at the penetration rate of 20%, 50%, 80%, and 100%, compared to the HDV group. The fuel consumption reduction effect of CAV was negatively correlated with the speed.

A detailed and accurate fuel model fuel consumption model that reflects real-world fuel consumption is required as input for devising and executing a model policy for prospective regulatory tools. The fuel consumption model based on the vehicle-specific power (VSP) has rapidly become the primary development direction since the release of the Motor Vehicle Emissions Simulator (MOVES) model. However, fuel consumption cannot be accurately characterized under high-speed scenarios. This work develops two fuel consumption models for the light-duty (gasoline) vehicles that can better characterize fuel consumption for light-duty vehicles under high-speed scenarios. For model 1, the VSP of −5kW/ton is a crucial turning point. When VSP∈ [−30, −5] kW/ton, the fuel rate is only determined by speed. When VSP∈(−5, 30], the fuel rate will gradually increase with VSP, and the growth characteristics will vary with speed. Model 2 develops the new interpretations for VSP and forms the one-to-one correspondence between the fuel rate and the new VSP. The two models can separately improve the accuracy by 12.2% and 13.8% compared with the conventional model. The fuel factor differences become significant when speed is higher than 65 km/h, which are separately 30.66% and 28.13% higher than the conventional VSP model when the speed is 100 km/h. Further, the fuel factors of the two models for freeways are, respectively, 6.33% and 7.56% higher than the conventional VSP model, and the distinction for arterial, collector, and local street roads is not notable.

No abstract