Analysis Of Multi-Scale Traffic Environment Optimization Strategy Based On Vehicle-Road Information

The rapid development of the vehicle industry and the continuous advancement of the urbanization process have caused an enormous increase in the number of vehicles,which have been causing serious exhaust pollution and has become one of the important sources of urban environmental pollution in China.There are some studies including emission model establishment,path planning,and vehicle emission reduction measures to alleviate vehicle emissions,but problems such as inaccurate model data and single planning goals still exist.We propose analysis of multi-scale traffic environment optimization strategy based on vehicle-road information,which combined model construction,simulation analysis and platform verification to study the optimization of transportation environment from the micro and meso levels respectively,the content and conclusion are as follows:To address the problems associated with inaccurate calculation caused by existing emission model,we used on-board measurement experiments to acquire basic data.Firstly,the on-board experimental platform was comprised of the METDH6.3 portable emissions measurement system,on-board diagnostics system II,and a global positioning system receiver,and then selected a representative vehicle model as the experimental vehicle to complete more than 500 hours of on-board measurement experiments in the experimental area.Lastly,some correction factors such as basic emission factors,natural environment,vehicle operating characteristics,and road characteristics were obtained to accurately evaluate the vehicle emissions of urban road networks after data processing.To address the problems associated with existing emission model does not consider the impact of traffic operation conditions,this article established a two-level planning model that considers emissions.Firstly,we established a comprehensive emissions measurement model that considers traffic operating parameters such as real-time traffic flow,traffic occupancy to characterize the impact of urban traffic conditions on emissions;Due to individual travel decisions have an important influence on vehicle emissions,herein,the article took the minimum vehicle emissions as the upper-level optimization goal,and used the user balance as the lower-level model to establish a two-level planning model that considers emissions to describe the coupling relationship between various factors in the transportation system.To address the problems associated with single-objective path planning cannot meet the travel needs,we studied on multi-objective path planning considering environmental factors.Firstly,based on the actual survey data,an experimental regional road network model was established in MATLAB,and then time,distance,fuel consumption,and emissions were used as optimization goals to complete the multi-objective path planning between the same starting points;the results showed that when emissions were used as the optimization goal,the optimal value is 8 g,compared with other routes,the emissions are reduced by 22.3%,24.5% and 18.4% respectively.Finally,the Baidu Map API interface was developed to complete the "vehicle real-time path planning" platform to complete model verification and the error of each index was maintained within 15%,which verified the validity of the model established.To address the problems associated with vehicle emissions are not feedback timely,this article completed the dynamic display of the emissions heat map by combining the traffic simulation model and the Baidu map heat map.Firstly,to build the road network model by accumulating data in VISSIM,and then set up a blank experiment to analyze the difference in traffic operating efficiency and vehicle emissions caused by trunk optimization,vehicle restrictions,and development of new energy vehicles.As a result,CO,NOx and HC emissions were reduced by about 27%,which means emission reduction effect was significant.Finally,the model verification was completed through the heat map module of the "Real-time Vehicle Path Planning" platform,and the results obtained are consistent with the VISSIM simulation results which verify accuracy of the model.