Mixed Vehicle Research Articles

Cooperative control of mixed vehicle platoon based on pinning consensus of heterogeneous multi‐agent system

AbstractThis paper proposes a longitudinal controller for the mixed vehicle platoon in the presence of driver response time‐delay and communication time‐delay based on the pinning consensus of heterogeneous multi‐agent system. Firstly, an adaptive pinning consensus control protocol and derive sufficient conditions for the heterogeneous non‐linear multi‐agent system are designed to achieve pinning consistency. Then, the heterogeneous characteristics of the vehicle are described based on the car‐following model, then the mixed vehicle platoon system model is constructed and a mixed vehicle platoon controller based on heterogeneous multi‐agent pinning consensus is proposed. Besides, the driver response time‐delay and communication time‐delay are introduced into the model, and a controller based on time‐delay heterogeneous multi‐agent pinning consensus is designed. Finally, the effectiveness of the proposed controller is verified by numerical simulations, and the effect of number of connected autonomous vehicle, different types of vehicle order, driver response time‐delay, communication time‐delay and the parameter of the controller on stability of mixed vehicle platoon is also quantitatively demonstrated.

Cold Chain Distribution Route Optimization for Mixed Vehicle Types of Fresh Agricultural Products Considering Carbon Emissions: A Study Based on a Survey in China

With the improvement of people’s living standards and the widening of circulation channels, the demand for fresh agricultural products continues to increase. The increase in demand will lead to an increase in delivery vehicles, costs, and carbon emissions, among which the increase in carbon emissions will aggravate pollution and is not conducive to sustainable development. Therefore, it is very important to balance economic and environmental benefits in the distribution of fresh agricultural products. Based on the analysis of the distribution characteristics of fresh agricultural products, this paper studies the optimization of the cold chain distribution route of fresh agricultural products considering carbon emission. Firstly, the cold chain distribution route planning of fresh agricultural products was investigated and analyzed by the interview method, and the basis for establishing the model objective and constraint conditions was obtained. Then, taking the minimum total cost including carbon emission cost as the optimization goal, the cold chain distribution route optimization model for mixed vehicle types is established considering electric refrigerated vehicles, gasoline refrigerated vehicles, and so on. Genetic algorithm was used to solve the model, and MATLAB2018b was used to substitute specific case data for simulation analysis. The analysis results show that increasing the consideration of carbon emission and mixed vehicle types in the distribution route of fresh agricultural products can not only reduce the distribution cost but also reduce the carbon emission. To some extent, the research content of this paper can provide a reference for enterprises in planning cold chain distribution routes of fresh agricultural products.

Modelling the fundamental diagram of traffic flow mixed with connected vehicles based on the risk potential field

AbstractThe fundamental diagram (FD) of traffic flow can effectively characterize the macroscopic characteristics of traffic flow and provide a theoretical foundation for traffic planning and control. The rapid development of connected vehicles (CVs) has led to changes in traffic flow characteristics. However, research on the FD of traffic flow involving CVs and non‐connected vehicles (NCVs) is still in its early stages. Most FDs do not well characterize the motion behaviour of different vehicles, nor do they study the interaction between mixed vehicles. Therefore, in this study, the FD of mixed traffic flows (i.e. with CVs and NCVs) was constructed within a unified framework. First, the car‐following behaviours of CVs and NCVs were modelled based on risk potential field theory. Subsequently, the FD of mixed traffic flows was derived based on the relationship between car‐following behaviour and the macroscopic traffic flow under steady‐state conditions. To validate the model, rigorous verifications were conducted via numerical experiments using the Monte Carlo method. The results indicate significant agreement between the scatter plots obtained from the experiments and the theoretical curves for different penetration rates. The proposed FD has a unified framework and a more rigorous mathematical structure.

A variable time headway model for mixed car-following process considering multiple front vehicles information in foggy weather

Abstract The aim of this work is to investigate the Fog-related variable time headway (FVTH) model of connected and automated mixed flow considering multi-front vehicles information in a reduced visibility environment for driving safety. The car-following modes of mixed vehicles are analysed and the vehicle ratio for each mode based on Markov chain models is derived, where the number of front vehicles, CAVs penetration rate, and platoon intensity are considered. The combined coupling effect of visibility and driving speed on time headway is explored, and a variable time headway strategy is proposed. Their relationship equations are deduced as the FVTH model. The perturbation method is used to discuss the stability of traffic flow and obtain its stability judgment conditions. The proposed model was validated and the effects were discussed via simulation experiments. The results indicate that the acceleration and deceleration times of vehicles and collision possibility decrease significantly using the proposed method. When penetration rate is 50%, the number of front vehicles is three and platoon intensity is zero, the time to return to a stable state is reduced by 18.9%-30.3% and 24.7%-39.4%, respectively, and Time Exposed Time-to-collision (TET) is reduced by 26.1%-48.9% and 43.7%%-65.4%, respectively, compared with the basic IDM method and IDM method that considers multi-front vehicles information. As visibility decreases, the reduced degree of these indicator values increases. The driving efficiency and safety level can be enhanced.

Analyzing the Impact of Road Accidents on Carbon Dioxide Emissions in Freeway Traffic: A Simulation and Statistical Modeling Approach

Roadway accidents significantly contribute to intermittent congestion and increased CO2 emissions on freeways. This research introduces a statistical approach designed to predict the rise in CO2 emissions resulting from traffic disturbances or jams triggered by such incidents. It also assesses the influence of varying levels of accident management effectiveness in different situations. To construct these scenarios, the study employs VISSIM, a traffic modeling software, incorporating diverse factors such as traffic volume, vehicle types, incident durations, and freeway lane counts. It then produces traffic flow characteristics in the form of vehicle paths. The emission estimates are derived by correlating these simulated vehicle paths with emission rates from the MOVES model. The study then applies a regression analysis to examine the connection between the increase in emissions and various influencing factors. The findings indicate that this approach efficiently reflects the impact of variables like accident duration, vehicle mix, and traffic volume on CO2 emissions across different lane configurations. The accuracy of these predictions is also confirmed. The outcomes suggest the model’s potential usage in guiding efforts to lower emissions and determining the optimal duration of incident management, particularly in terms of lane closure, to mitigate emission impacts. This paper also discusses the limitations of the model and the future improvement direction.

Joint optimisation of vehicle trajectory and signal control at intersections mixed with connected automated vehicles: a departure sequence estimation-based approach

With advanced connected automated vehicles (CAVs) technologies, joint optimisation of trajectories and signal control can significantly improve traffic mobility and safety. This study presents a joint optimisation method of trajectory and signal control at intersections mixed with human-driven vehicles (HVs) and CAVs based on vehicle departure sequence estimation. First, a signal control optimisation method based on the trajectories detected by CAVs was proposed. By estimating the virtual arrival time of HVs and CAVs, the optimal departure sequence with minimal delay was determined. Then, a joint optimisation of trajectories and signal control was developed depending on the position of CAVs in the mixed vehicles platoon, i.e. leader and follower. Simulation experiments under different scenarios were conducted to analyse the performance of the proposed method. Comparison results show that the method decreases the average delay by 19.66% when the CAV’s penetration rate ranges from 10% to 20%.

Sliding-mode control for heterogeneous vehicular platoons with unknown matched and mismatched disturbances under intermittent communications

In this work, we investigate a longitudinal platooning control problem of heterogeneous vehicles with a focus on external unknown disturbances, parameter uncertainties and intermittent communications. When vehicle platooning encounter intermittent communications, the performance of the platooning will be degraded. Nevertheless, the existing researches can not deal with the aforementioned three issues effectively. To this end, a novel nonsingular dynamic terminal sliding-mode control (NDTSMC) law is contrived. First, for a heterogeneous cooperative adaptive cruise control (CACC) or adaptive cruise control (ACC) platooning system of mixed vehicles, a hybrid mathematical reference model is developed. Then, we propose a CACC-ACC switched approach which activates either a CACC mode or an enhanced ACC mode relied on communication reliability. The unknown disturbances and parameter uncertainties can be together served as a unknown lumped matched (or mismatched) disturbance, depending on the circumstances. The unknown lumped matched (or mismatched) disturbance can be estimated by a finite-time disturbance observer (FTDO). Based on the observation, a novel switched controller consisting of a baseline controller part and an observation-based NDTSMC law part is proposed. Furthermore, combined with Lyapunov stability theory, it can be demonstrated that the stability of the string of mixed vehicles in the heterogeneous platoon can be robustly guaranteed after switching. Simulation examples show that the proposed approach exhibits satisfactory control properties for addressing intermittent communications. The convincing performances for attenuating parameter uncertainties and external unknown disturbances are achieved, which are also shown in simulations.

A Dynamic Urban Mobility Index from Clustering of Vehicle Speeds in a Tourist-Heavy City

The rapid urbanization of cities often brings about complex mobility issues, such as traffic congestion that, when unplanned, results in decreased productivity and quality of life. While many cities have adopted smart city initiatives to capture and monitor mobility, applying these in a developing country context remains a challenge when infrastructure and high-resolution spatial and temporal data are lacking. In this work, we use GPS data obtained from probe vehicles (a mix of public and private transport vehicles) within the city of Baguio, The Philippines, to develop and propose the Zone-based Speed Index (ZSI), a mobility index based on the speed clusters observed in this city. The ZSI dynamically infers monthly speed thresholds to classify zones as fast or slow and successfully captures the decrease in vehicle mobility associated with the impact of typhoons and holidays. Thus, it can be used to characterize urban vehicle mobility with high (hourly) resolution. Insights from the use of our dynamic mobility index are useful in the development and optimization of transportation systems, in monitoring the ease of vehicle mobility, and in the performance assessment of smart city initiatives, which are much needed in tourism hotspots.

Dual-decoder attention model in hierarchical reinforcement framework for dynamic crowd logistics problem with batch-matching

Dynamic routing problem with crowd-sourced driver can be challenging as the dynamically arrived crowd drivers are utilized to meet the dynamically placed customer demand. To guarantee a certain service level, the company also operates a set of employed vehicles as a backup capacity to serve the tasks for which the use of a crowd driver is not feasible or not efficient. Thus, in this paper, we study a special dynamic routing problem that arises in such an environment, in which a mix of company vehicles and dynamically arrived crowd drivers serves dynamically placed orders. The company seeks route policies for crowd drivers and company vehicles so as to minimize the total costs. We present a hierarchical reinforcement learning method that combines upper-level agent for balancing the opportunities and risks brought by delayed batch-matching and lower-level agents for route planning. The effectiveness of our approach is demonstrated through extensive numerical analyses, with the results showing the benefits derived from the upper-level agent and the designed dual-decoder architecture of lower-level agent. The solution quality, computation time, and generalization ability of the proposed approach are all proven to be superior over existing methods.

Long Short-Term Memory-Assisted Mixed Vehicle Platoon Control Strategy Considering Message Recovery Under Nonideal Information Environment

Typical communication and detection issues in a nonideal information environment, such as sensing failure, communication and sensing delay, and packet loss, further aggravate the adverse impacts of human-driven vehicle (HV) uncertainty on a mixed vehicle platoon. To guarantee the performance of the mixed vehicle platoon featuring HVs and connected and automated vehicles under the nonideal information environment, this article proposes a platoon control strategy integrating a combined longitudinal and lateral control and message recovery. Specifically, by building the dataset associated with HV behaviors, a long short-term memory (LSTM) predictor is established to recover the problematic HV messages (i.e., position, velocity, and heading) caused by the nonideal information environment. Furthermore, based on the boundary of the HV states, an evaluation and correction (EC) method is presented to suppress the adverse impacts of prediction failures. Then, a combined longitudinal and lateral controller cooperating with the LSTM predictor and EC method is developed to enhance the stability and safety of the mixed vehicle platoon under the nonideal information environment. In a theoretical analysis, the relatedness between the asymptotic stability and string stability of the platoon and predictor accuracy is strictly proved. Finally, comparative experiments verify the effectiveness of the proposed control strategy by employing driver-in-the-loop simulations.

On Energy Consumption and Productivity in a Mixed-Model Assembly Line Sequencing Problem

Mixed and multi-model assembly line sequencing problems are more practical than single-product models. The methods and selection criteria used must keep up with the constantly increasing level of variability, synchronize flows between various—often very energy-intensive production departments—and cope with high dynamics resulting from interrupted supply chains. The requirements for conscious use of Earth’s limited natural resources and the need to limit energy consumption and interference in the environment force the inclusion of additional evaluation criteria focusing on the environmental aspect in optimization models. Effective sustainable solutions take into account productivity, timeliness, flow synchronization, and the reduction of energy consumption. In the paper, the problem of determining the sequence of vehicles for a selected class of multi-version assembly lines, in which the order restrictions were determined taking into account the above criteria, is presented. Original value of the paper is the development of the Grey Wolf Optimizer (GWO) for the mixed-model assembly lines sequencing problem. In the paper, a comparative analysis of the greedy heuristics, Simulated Annealing and GWO for a real case study of a mixed vehicle assembly line is presented. The GWO outperforms other algorithms. Overall research performance of the GWO on the sequencing problem is effective.

A Multi-Period Charging Service Pricing Game for Public Charging Network Operators Considering the Dynamics of Coupled Traffic-Power Systems

The proliferation of electric vehicles (EVs) couples the operations of traffic systems and power systems, necessitating the interdependent traffic-power modeling to optimize the on-road EV charging decisions. In this paper, the multi-period charging service pricing interactions between multiple charging network operators (CNOs) are discussed considering the dynamics of traffic-power systems. A multi-period user equilibrium model considering mixed vehicle types is formulated to describe the possible vehicle trip transitions between different periods in urban transportation network. For the power distribution network, a convexified multi-period AC optimal power flow model is adopted for local electricity market clearing. With the dynamics of traffic-power systems, the pricing interaction between multiple CNOs is a non-cooperative game. We analyze the existence of Nash Equilibrium with fixed-point theory, and propose an iterative method based on the best response strategy to find <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon$</tex-math> </inline-formula> -Nash Equilibrium. Numerical studies based on an interdependent power-traffic system consisting of two 18-node power distribution networks and one revised Nguyen-Dupuis transportation network demonstrate that with the proposed CNOs’ charging pricing strategy, the CNOs’ service profits can increase by 1%-3% while the PDNs’ operation costs decrease about 1%-2% for the interdependent traffic-power system.

Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study

Fleet Size and Mix Vehicle Routing Problem (FSMVRP), Adapted Large Neighbourhood Search Heuristic Optimization ProposalWith a Plant-capacity and Multi-day Planning Algorithm: A Livestock Feed Industry Case Study

The impact of traffic mobility measures on vehicle emissions for heterogeneous traffic in Lagos City

Emissions from vehicular traffic are one of the key drivers of urban environmental air pollution, degrading the ambient air quality in many African cities and across the globe. This study sought to assess the impact of traffic mobility measures on the emission levels of air pollutants from vehicle exhaust in Lagos, Nigeria. Traffic flow and vehicle mix were collected daily during morning, afternoon, and evening peak periods along selected arterial and two-lane collector roadways. Simultaneously, five (5) air pollutants from vehicular traffic emissions were observed at distinct monitoring stations along the roadways using portable gas detectors. The traffic flow and mobility measures were inputted into a multiple exponential model to estimate the concentration of each pollutant. The result of the vehicle fleet composition revealed 3.5%, 4.5%, 57%, 32%, 1.5%, and 2% for motorcycles, tricycles, personal cars, minibuses, large buses, and heavy goods vehicles, respectively, on the two-lane road and 10%, 35%, 27%, 10%, and 18% for motorcycles, personal cars, minibuses, large buses, and heavy goods vehicles, respectively, on the arterial. The results of a multiple exponential regression model (MER) showed significant contributions from traffic flow, speed, vehicle fleet proportion, and pollutant concentration (p <0.05). However, the impact on ambient air quality revealed severe pollution levels for CO and PM2.5, while SO2, NO2, and PM10 showed moderate, poor, and poor pollution levels. This study has provided evidence on how pollutant emissions increase rapidly during peak traffic conditions in heterogeneous traffic on different classes of roads and recommended policies for managing vehicle fleets and traffic congestion with the aim of reducing the impact on the ambient air quality and health of the public on heavily trafficked roadways in sub-Saharan African cities.

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

With the increasing number of Connected and Autonomous Vehicles (CAVs), the heterogeneous traffic flow on highways now consists of a mix of CAVs and Non-networked Autonomous Vehicles (NAVs). The current deployment of Roadside Units (RSUs) on highways is mostly based on uniform or hotspot locations. However, when the permeability of CAVs on the road varies, the communication network may face challenges such as excessive energy consumption due to closely spaced RSU deployments at high CAV permeability or communication interruptions due to widely spaced RSU deployments at low CAV permeability. To address this issue, this paper proposes an improved D-LEACH clustering algorithm based on vehicle clustering; analyzes the impact of RSU and vehicle communication radius, mixed traffic density, and different CAV permeabilities in the heterogeneous traffic flow on the RSU deployment interval; and calculates the rational and effective RSU deployment interval schemes under different CAV permeabilities on highways in the heterogeneous traffic flow. When the heterogeneous traffic flow density is stable and CAV continues to penetrate, the RSU communication radius and deployment interval can be adjusted to ensure that the network connectivity is maintained at a high level. When the RSU and vehicle communication radius are stable, the mixed traffic density is 0.05, and the CAV permeability is 0.2, the RSU deployment interval can be set to 1235 m; when the mixed traffic density is 0.08 and the CAV penetration rate is 0.7, the RSU deployment interval can be set to 1669 m to ensure that the network connectivity is maintained at a high level.

Histological features of non-alcoholic fatty liver disease revealed in response to mixed vehicle emission exposure and consumption of a high-fat diet in wildtype C57Bl/6 male mice

Non-alcoholic fatty liver disease (NAFLD) is currently plaguing the population at pandemic proportions and is expected to become more prevalent over the next decade. Recent epidemiological studies have demonstrated a correlation between the manifestation of NAFLD and ambient air pollution levels, which is exacerbated by other risk factors, such as diabetes, dyslipidemia, obesity, and hypertension. Exposure to airborne particulate matter has also been associated with inflammation, hepatic lipid accumulation, oxidative stress, fibrosis, and hepatocyte injury. While prolonged consumption of a high-fat (HF) diet is associated with NAFLD, little is known regarding the effects of inhaled traffic-generated air pollution, a ubiquitous environmental pollutant, on the pathogenesis of NAFLD. Therefore, we investigated the hypothesis that exposure to a mixture of gasoline and diesel engine emissions (MVE), coupled with the concurrent consumption of a HF diet, promotes the development of a NAFLD phenotype within the liver. Three-month-old male C57Bl/6 mice were placed on either a low-fat or HF diet and exposed via whole-body inhalation to either filtered (FA) air or MVE (30 µg PM/m3 gasoline engine emissions + 70 µg PM/m3 diesel engine emissions) 6 hr/day for 30 days. Histology revealed mild microvesicular steatosis and hepatocyte hypertrophy in response to MVE exposure alone, compared to FA controls, yielding a classification of "borderline NASH" under the criteria of the modified NAFLD active score (NAS) system. As anticipated, animals on a HF diet exhibited moderate steatosis; however, we also observed inflammatory infiltrates, hepatocyte hypertrophy, and increased lipid accumulation, with the combined effect of HF diet and MVE exposure. Our results indicate that inhalation exposure to traffic-generated air pollution initiates hepatocyte injury and further exacerbates lipid accumulation and hepatocyte injury induced by the consumption of a HF diet, thereby contributing to the progression of NAFLD-related pathologies.

Development and Application of Field Mixing Process and Intelligent Mixing Vehicle for Plateau Type Emulsified Explosives for Open Pit Mining

Abstract To improve mine production efficiency, reduce blasting costs, and meet the needs of mine capacity expansion, the use of an on-site mixing emulsion explosives truck for open pit iron ore deep hole step blasting charging operation. This paper firstly briefly introduces the process flow of emulsion explosives production and makes a detailed introduction to the working principle and process control requirements of each section of emulsion explosives production. Based on the analysis of the basic principle of adaptive control, the adaptive PID controller is designed to control the discharge temperature and density of emulsion explosives production and to achieve self-tuning of parameters, which improves the control accuracy of production parameters and product quality. The research results show that the use of field mixing emulsion explosives, and blasting operations personnel was reduced by 40% to 50%, and the drilling workload was reduced by about 7%. The application of field mixing emulsion technology improves the blasting effect, increases shovel loading efficiency, reduces the labor intensity of workers, saves comprehensive mining costs, and reduces the cost of temporary explosives depot guarding and disposal of used packaging in mines.

Ecological Cooperative Adaptive Control of Connected Automate Vehicles in Mixed and Power-Heterogeneous Traffic Flow

The development of vehicle electrification and intelligent network technologies has led to a new type of mixed and power-heterogeneous traffic flow, comprised of regular vehicles (RVs) and connected and automated vehicles (CAVs), fuel vehicles (FVs) and electric vehicles (EVs). To reduce the energy consumption of mixed and power-heterogeneous traffic flow operating at a signalized intersection, the Ecological Control Unit–Cooperative Adaptive Control (ECU-CACC) is proposed in this paper. The vehicle platoon is divided into units which are named minimum ecological control units (min-ECUs). A bi-level control framework is designed to improve traffic efficiency and reduce energy consumption. The lower-level aims to plan the best ecological trajectory for every min-ECU, and the upper-level optimizes the passing strategies for efficiency through speed coordination. Scenario numerical experiments are performed to verify the effectiveness of the bi-level optimal control model and analyze the energy-saving effect of ECU-CACC under different vehicle mixing situations. The results from the experiment prove the excellent energy-saving potential of the proposed ECU-CACC, which helps the min-ECUs save about 10–20% energy consumption compared with a regular pattern.

Consensus-Based Control Strategy for Mixed Platoon under Delayed V2X Environment

This paper develops a mixed platoon control strategy incorporating vehicle dynamics and time-varying vehicle-to-everything (V2X) delays to guarantee the consensus of a mixed platoon and reduce the impacts caused by the inconstant driving behavior of human-driven vehicles (HVs). In particular, a system control framework is established for a mixed vehicle platoon, which bears HVs and connected automated vehicles (CAVs). More precisely, this system control framework considers directly controlling CAVs and indirectly guiding the HVs to improve the consensus of the whole platoon with respect to velocity error and headway. Furthermore, based on the third-order closed-loop dynamic model, the consideration of vehicle dynamics and time-varying delays are taken into account. Then, theoretical analysis employs Lyapunov–Krasovskii theory to derive the delay boundary that determines the asymptotic stability and local string stability. Finally, a performance comparison with the existing algorithm is carried out to further demonstrate the advantages of the proposed strategy.

Multi-objective route optimization of urban cold chain distribution using electric and diesel powered vehicles

Increasingly serious urban road congestion and environmental pollution have led some cities to impose restrictions such as limiting certain types of vehicles and the areas in which they can operate and introducing subsidies and other incentives for electric refrigerated vehicles. As a result, both internal combustion engine (ICE) refrigerated vehicles and electric refrigerated vehicles will be used together in urban cold chain distribution for a long time into the future. Most current studies are concerned with mixed vehicle type allocation or mixed vehicle routing problems. Although there are some studies that combine the two, there has been little research into the effects of low carbon emission and multiple objective optimization on distribution and routing for a mixture of vehicle types. This paper describes the creation of an optimized decision-making cold chain distribution model that includes different types of distribution vehicles and different route optimization criteria that represent the three enterprise objectives of minimal cost, minimum carbon emissions and minimum distance. The decision criteria are influenced by conflicting benefits offered by ICE and electric refrigerated vehicles as well as government subsidies for electric refrigerated vehicles and carbon emission trading by logistics enterprises. A chaotic particle swarm optimization algorithm was used to analyze a cold chain distribution system in Wuhan, Hubei Province, China. The results show that: (1) The choice of enterprise distribution models and the vehicles and routes determined are closely related to enterprise goals. To minimize total cost, ICE refrigerated trucks with less load capacity and electric refrigerated trucks with greater load capacity are preferred. To minimize carbon emissions, ICE refrigerated trucks and electric refrigerated trucks with less load capacity are preferred. To minimize total distance, ICE refrigerated trucks and electric refrigerated trucks with greater load capacity are preferred. (2) As the value of government subsidies increases, the total load capacity of electric refrigerated vehicles also increases, the distribution routes change, and total carbon emissions and total distribution costs decrease. (3) When carbon emission trading prices increase, the number of electric refrigerated vehicles gradually increases, the distribution path changes, the total distribution costs increase, and total carbon emissions decrease. Some suggestions are made for government to determine subsidies for electric refrigerated vehicles and carbon emission trading policies that will guide the choice of distribution vehicles and affect the criteria used for route optimization by cold chain logistics enterprises.