In the regional highway network, there are numerous toll stations generating massive amounts of data on a daily basis. However, due to equipment and network issues, there may be delays in data transmission for some stations. In such cases, the transmitted data may not be sufficient to meet the requirements for real-time traffic flow prediction. To achieve real-time traffic data imputation and dynamic traffic flow prediction, this paper firstly proposed a method for data imputation of highway traffic flow data based on self-supervised learning, which adopts time series model based on attention mechanism (Seq2Seq-Att). Then the self-supervised learning method was used to train the model. Finally, the reliability of the method was verified by taking 80 toll stations in the highway network of Guangdong province as an example. The results show that the method in this paper can flexibly capture the missing pattern in traffic data and give a reasonable value according to the internal correlation of the data. This method is generally superior to other methods and has good performance under different missing rates. The overall MAPE is about 17.7% and the WMAPE is 12.8%. In the case of high missing rate, this method has obvious advantages over other methods. The results of traffic volume prediction indicate that the prediction accuracy of traffic flow prediction using the data completed by this method is close to the situation of using complete data.

At present, the freight car overload phenomenon is coming from bad to worse, in order to improve the efficiency of freight car control on the highway and the level of safety in the freight transport, a freight transport risk level identification model based on user portrait of freight risk was proposed. Firstly, based on highway toll data, taking freight car as the research object, a user portrait system for freight transport risk identification was developed from the aspects of driving behavior and operation status. Then, the sample data was cleaned and the label index was extracted and analyzed. Then, K-means++ algorithm was applied to obtain the classification results of freight transport risk feature portraits. Next, the entropy weight method was used to score the freight risk of all kinds of freight car to determine the risk level of all kinds of freight car. Finally, by combining with the relevant indicators of various types of vehicles, the vehicle portrait was completed. Based on the trucking toll data of the entire highway network in Guangdong Province from March to May 2022, the proposed model was used to divide the trucking vehicles into five categories. Among them, “the freight car of high risk and high workload” accounted for 5.42%, the freight car of higher risk and night-driving and overloaded ”accounted for 19.12%, “the freight car of mediumrisk and overspeed” accounted for 12.85%, “the freight car of low risk and low-frequency” accounted for 37.00%, and “the freight car of low risk and high-frequency” accounted for 25.61%. The validity of the model was verified by the data of an accident database in Guangdong Province in the same period. The data showed that the relative risk coefficient of high risk vehicles is much higher than that of low risk vehicles. The research shows that the proposed model can effectively identify trucks with high freight risk characteristics. Based on the results of risk grade identification, traffic management departments can carry out high-risk vehicle identification, key inspection of overload and over-limit, and specific message push to guide vehicle driving safety, so as to improve the safety management level of the industry.

Under the background of dual-carbon policy, it is imperative to maintain ecological balance and reduce carbon emissions. Nowadays, it is very important to accurately measure the carbon emission index of vehicles on a wide range of road networks. Therefore, this study proposed a measuring method of vehicle carbon emission in expressway network based on multi-source data fusion. Firstly, a basic data cleaning method for carbon emission statistics was proposed to clean the basic data required for subsequent carbon emission calculation. Secondly, the highway carbon emission calculation model was established, and then the related calculation process was designed. Finally, taking the whole highway network of Guangdong province as an example, this paper calculated the vehicle carbon emission from September 2020 to June 2021, and compared the calculation results with China's carbon accounting database. Through this method, the proposed method was proved to be scientific and reliable. The research shows that the average carbon emission of mini buses in Guangdong province is small, but the total carbon emission accounts for the largest proportion of all types of vehicles, up to 52.1%; the total carbon emission of gasoline vehicles accounts for 49.8%, which is higher than that of diesel vehicles (45.4%) and of other energy vehicles (4.8%). Vigorously promoting new energy vehicles can effectively reduce the carbon emission of expressways. In addition, the study finds that there are significant differences in the travel patterns of different vehicles under COVID-19, but the overall impact on the transportation economy is limited.

In the context of “dual-carbon” policy, the task of carbon emission reduction in transportation industry is arduous, but the vehicle carbon emission currently cannot be measured accurately at present. In order to realize the fine calculation of carbon emission, this paper proposes a precise calculation method of traffic carbon emission in expressway segment based on multi-source data. Firstly, KD-tree algorithm is used to match the GPS data of operational vehicles with the road points, thus implementing the real-time monitoring of dynamic vehicles. Then, the calculation model of vehicle carbon emission in road segment is established, and the relevant calculation process is designed. Finally, the main section of Humen Bridge is taken as an example to calculate the carbon emission of the section. Through VISSIM simulation and relative comparison experiments, the science and reliability of the proposed algorithm are verified. The results show that, for different vehicle types, the carbon emission of minibus is the highest, accounting for 74.36%; and that, for different fuel types, the carbon emission of gasoline automobile is the highest, accounting for 80.50%. The new energy vehicles in operation account for 12.60% of the total vehicles but the corresponding carbon emission only accounts for 4.27%, which means that energetically developing new energy trucks is the key to the carbon emission reduction of expressways. It is also found that, when the traffic saturation is controlled at 0.32～0.38, the average carbon emission of equivalent standard vehicle is lower; while when the traffic saturation is greater than 0.62, the average carbon emission of standard vehicle increases significantly. These conclusions provide theoretical basis for traffic management departments to formulate relevant strategies.