Research Based on vehicle exhaust emission estimation

Abstract

With the development and progress of society, short-term traffic flow prediction has become an increasingly important topic. Accurate prediction of traffic flow can not only provide necessary guidance for the transportation department, but also bring convenience to travel, but also play an important role in the prevention and control of urban air pollution. This paper mainly studies the prediction of vehicle flow, and then estimates the vehicle exhaust through the calculated vehicle flow, trying to establish the estimation model of vehicle flow and vehicle exhaust. Through Gaode map interface and WGS-84 (GPS) coordinates of each bayonet provided, the road name corresponding to the bayonet equipment is obtained according to the distance vector, and the data is preprocessed. For the interval road motor vehicle flow, the dynamic prediction can be realized by using the interdependence between the time series observations. According to the time dimension of prediction, traffic flow prediction can generally be divided into long-term traffic flow prediction (in year), medium-term traffic flow prediction (in month and day) and short-term traffic flow prediction (in hour and minute). According to the data provided, due to the weak regularity of short-term traffic flow data of interval roads, strong random error interference, and high uncertainty, it is difficult to predict accurately, The long-term and medium-term traffic flow data have strong periodicity and weak random interference. Considering the accuracy and data provided, this paper mainly selects the prediction method based on endogenous variables represented by autoregressive moving average model ARIMA and time cycle neural network LSTM. Because the use conditions of the two models are different, it is necessary to improve the model and modify the use scenario of the model. The advantage of this method is that the data acquisition cost is low and easy to implement. Finally, the prediction effect of traffic flow is evaluated by cellular automata to improve the performance of the model.