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1.INTRODUCTIONWith the innovation of automobile industry technology and the development of autonomous driving technology, the net-connected vehicles have been realized in the highway environment, but the introduction of net-connected vehicles is still in the preliminary stage for the urban transportation network with complex and changing operating environment, and the condition of mixed driving of net-connected vehicles and manual driving vehicles on urban roads will exist for a long time. A large proportion of greenhouse gases and air pollutants are emitted by vehicles [1]. Conventional public transport is widely promoted in various countries because of its advantages, such as high capacity and low pollution. However, due to the characteristics of longer body and frequent stop, temporary traffic bottlenecks are often formed at bus stops, which causes traffic congestion and leads to higher exhaust emissions in the bus stop area. As the carbon emission hotspot area, it is necessary to study the characteristics of carbon emission in the area of bus stop. For the longitudinal and lateral control models. The Intelligent Driving Model (IDM) proposed by Treiber M et al. [2] is used to simulate the following behavior of conventional vehicles. Cooperative Adaptive Cruise Control (CACC) and Adaptive Cruise Control (ACC) is a technique to control the longitudinal motion of vehicles. The CACC model and ACC model proposed by the PATH lab at the University of California, Berkeley through real-vehicle tests were often used as the car-following model for net-connected vehicle [3][4]. In terms of lane change model research, Rickert M and Chowdhury D et al. [5][6] proposed a series of lane change rules based on the NaSch model. For traffic emission models, the EMFAC model was developed as early as 1991 by the European California Air Resources Board, and with the continuous research, many emission models have been proposed. Palaclis J et al. [7] proposed the concept of motor vehicle specific power (VSP) to represent the relationship between motor vehicle emissions and driving behavior. This paper uses the VT-Micro model proposed by Ahn K et al. [8], which is based on the instantaneous velocity and acceleration of the vehicle and can be used to calculate CO, HC, NOX, fuel consumption, and other indicators for each vehicle. For the study of bus emissions, Wang C et al. [9] analyzed the factors influencing bus emissions and the degree of influence. The results showed that the delay and the bus stop had significant effects on emissions. Özener O et al. [10] evaluated the pollutant emissions at the bus stop, focusing on the emission characteristics of bus idling and starting phases. Summarizing the relevant literature, it can be found that the studies on bus emissions focus on conventional buses, but rarely involve the scenarios after the introduction of net-connected vehicles. There is also a lack of research on the carbon emission characteristics of mixed traffic flows in public stop areas. In this paper, we use a continuous simulation model to study the impact of bus stopping on the traffic flow characteristics and carbon emission distribution characteristics, which can provide corresponding suggestions and measures for the traffic operation control and carbon emission management at bus stopping areas under the introduction of intelligent net-connected vehicles in the future. 2.TRAFFIC FLOW MODEL2.1Basic assumptions and traffic sceneFor the purpose of the study, the following assumptions were made regarding the content and methodology of the study:
The traffic scene of this paper is a one-way three-lane straight section, and the bus stop is the linear bus stop, which is set on the machine-off-road divider. The study area is 500 meters upstream and downstream of the bus stop. The total length of the stop is 20 m. The traffic scene is shown in Figure 1. 2.2Car-following modela. Manual driving vehicle model Where: an is the acceleration of the following vehicle; a0 is the maximum acceleration; vn is the speed of the following vehicle; v0 is the desired speed; s0 is the stationary safety distance in congestion; T is the safe headway of the following vehicle; Δν is the speed difference between the following vehicle and the leading vehicle; b is the desired deceleration; hn is the headway distance; l is the car length. b. ACC vehicle model In order to better adapt to the urban traffic scenario, this paper adopts the dynamic expected headway model to simulate the following behavior of ACC, whose expression is as follows: Where: an is the acceleration of the following vehicle; vn is the speed of the following vehicle; s0 is the stationary safety distance in congestion; Δν is the speed difference between the following vehicle and the leading vehicle; hn is the headway; l is the vehicle length. k5 and k4 are coefficients. c. CACC vehicle model The dynamic expectation CACC nonlinear dynamic expectation headway model is used to simulate the following behavior of the CACC with the following expressions: Where: an is the acceleration of the following vehicle; vn is the speed of the following vehicle; s0 is the stationary safety distance in congestion; Δν is the speed difference between the following vehicle and the leading vehicle; hn is the headway; l is the body length. k1, k2, k3 are coefficients. 2.3Lane-changing modelThe lane change behavior can be divided into arbitrary lane change and forced lane change, where arbitrary lane change can occur at any location on the mainline, while forced lane change mainly occurs in the bus stop area. a. Discretionary lane-changing model The lane change behavior is executed with probability PChange when the vehicle satisfies the following conditions: Where: vn(t) is the speed of vehicle n at moment t; vq is the desired speed; df,n(t), dl,n(t) are the distance between vehicle n and the adjacent lane in front of and behind the vehicle at moment t; dn (t) is the distance between vehicle n and the vehicle in front of this lane at moment t; dsafe is the safe distance, that is, the distance between the vehicle in front of the emergency brake and the vehicle in the back without collision: where: a is the maximum vehicle deceleration; L is the vehicle length; treaction is the response time; b. Mandatory lane-changing model Ahmed proposed a hierarchical tree gap acceptance model, which is simplified in this paper to represent the probability of generating a Mandatory lane change intention, and the probability is calculated as follows: Where: Xn(t) is the distance of bus n from the stop; εn is a standard normally distributed random number; α、 β are control parameters, αεn is used to describe the driver’s observation/judgment error. For net-connected vehicles, the lane change is done by the control system, so Pn = 1. 2.4Bus-stopping modelBased on the operating characteristics of the bus near the bus stop, the range of the bus deceleration stop upstream of the stop and the range of acceleration convergence downstream are determined. The bus deceleration stop impact range L1 and acceleration start impact range L2 are determined as follows: Where L1 is the distance from the bus stop when the bus is upstream, L2 is the distance from the bus stop when the bus is downstream, v is the travel speed of the vehicle, ac is the comfortable deceleration of the vehicle, and ab is the comfortable acceleration of the vehicle. 2.5Fuel consumption and traffic emission modelIn this paper, we use the VT-Micro model proposed by Ahn to Calculate the carbon emission of the vehicle. Where: MOE is the emission and fuel consumption rate of the vehicle, i is the speed index of the vehicle, and j is the acceleration index of the vehicle. K is the regression coefficient, v is the speed of the vehicle, and a is the acceleration of the vehicle. 3.NUMERICAL SIMULATION AND ANALYSIS OF RESULTS3.1Characterization of carbon emissions within the stopping area when only buses are connectedAssume that all vehicles on the road are manual driving vehicles, and all buses are net-connected vehicles. The initial traffic volume is 1000veh/h, the initial percentage of buses is set to 0.1, and the buses are randomly generated in three lanes. The simulation is carried out based on MATLAB software with open boundary conditions, and the vehicles are generated from the starting point with a headway time distance showing Poisson distribution, and the simulation lasts for 1800 simulation steps with a step size of 1 s. Three simulation experiments are carried out in parallel under different conditions, and the average value is taken as the result. a. Analysis of the spatial and temporal distribution of carbon emissions In the above given simulation scenario, change the bus ratio to 0.05, 0.1 and 0.15 to obtain the spatial and temporal distribution of carbon emissions corresponding to different bus ratios, as shown in the figure2. Figure 2.Spatial and temporal distribution of carbon emissions corresponding to different bus ratios  It can be seen from the figure (a) that there is a clear bright color area upstream of the bus stop, which is more obvious than that in figure (b) and (c), and has a tendency to spread upstream. In contrast, the bright-colored areas in figure (c) are more scattered. It can be seen that, under the premise that all buses are net-connected vehicles and all cars are manual driving vehicles, increasing the percentage of buses will significantly increase the emissions at bus stops. This may be due to the fact that buses need to complete lane changes upstream of the bus stop and switch to the outside lane for stopping. When the proportion of buses increases, it is difficult for buses to complete the lane change behavior. In addition, due to the increase in the number of buses in the traffic flow, the lane changing behavior increases significantly, which leads to an increase in the confusion of the traffic flow upstream of the stop. Therefore, as the percentage of buses increases, the carbon emissions upstream of the stop increases significantly. b. Analysis of the carbon emissions hotspot In this paper, carbon emission hotspots are defined as the concentrated distribution area of carbon emissions on the road within a certain period of time. Based on the above analysis combined with the average percentage of bus in the traffic flow of the actual test, the percentage of bus is determined as 0.1 as the basis of the subsequent study. Under the given simulation conditions, the distribution of carbon emissions in a total of 1km upstream and downstream of the bus stop during the whole simulation time was counted, and the heat map was used to identify and analyze the carbon emission hotspots in the stop area, as shown in the figure3: As can be seen from figure 3, the carbon emission statistics are mainly concentrated in the upstream of the bus stop; the carbon emission statistics of the inner lane are larger than those of the middle and outer lanes in turn. The carbon emission hotspot is the area upstream of the bus stop in the inner lane (within 200m upstream of the bus stop). The reason for this phenomenon may be that the bus in the outside lane needs to complete the lane change to reach the bus stop and drop off passengers, which will inevitably cause the vehicles around the bus to adopt the acceleration and deceleration behavior to avoid the bus. The above analysis shows that the stability of the traffic flow is reduced due to the lane changing behavior of buses in the upstream part, and the frequent acceleration and deceleration of vehicles in the upstream of the bus stop leads to significantly higher carbon emissions in this area than in other areas, thus forming a carbon emission hotspot. 3.2Impact of Traffic Volume on Carbon Emissions in Bus Stop AreasKeep the proportion of buses as 0.1, variable traffic volume as 750veh/h, 1000veh/h, 1250veh/h, study the influence of different traffic volumes on the traffic emission distribution characteristics and values in the stopping area. As can be seen from the figure 4, the distribution of carbon emission hotspots is concentrated near the inner lane upstream of the stop at different traffic volumes. With the increase of traffic volume, the carbon emission hotspot area gradually becomes larger and the carbon emission value also increases. The reason is that with the increase of traffic volume, it is more difficult for buses, especially those driving in the inner lane, to complete the lane change at the upstream of the stop, resulting in frequent acceleration and deceleration of vehicles at the upstream of the stop, which contributes to the increase of carbon emissions. Therefore, traffic volume is an important factor affecting the distribution of carbon emissions at the bus stop. 3.3The impact of the proportion of net-connected vehicles on traffic emissions in the bus stop areaIn the following, we introduce the net-connected vehicles among the small cars and study the effect of the proportion of net-connected vehicle on the traffic emission in the bus stop area. The proportion of buses is 0.1, and the traffic volume is constant at 1000veh/h, the proportion of net-connected vehicles among small vehicles is 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9. The impact of different percentages of net-connected vehicles on the carbon emission distribution characteristics of the docking station area is analyzed as follows: Figure 5 shows that the bright area at the bus stop area decreases significantly with the increase of the proportion of net-connected vehicles. After the proportion of small vehicles in the net-connected vehicle is greater than 0.6, the bright area is almost gone. This indicates that the stability of the traffic flow at the bus stop is significantly improved and the traffic carbon emission is significantly reduced with the increase of the proportion of net-connected vehicles in the small vehicles, especially the acceleration and deceleration behavior caused by bus lane change is significantly reduced after the proportion of net-connected vehicles is greater than 0.6. This is because the increase of net-connected vehicles in the traffic flow allows vehicle-to-vehicle communication between vehicles, which can avoid frequent acceleration and deceleration behaviors when buses need to change lanes in the first place, thus reducing carbon emissions at the upstream of the bus stop. 4.CONCLUSIONSThis paper investigates the carbon emission distribution characteristics of traffic flow in a three-lane linear bus stop area in a net-connected environment based on a microscopic traffic flow model. The main findings include:
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