In the effort to urgently develop new and improved methodologies to compute and assess CO₂ emissions from transportation, results have been less than ideal; this article provides a review of the methodologies currently available. When it comes to the discharge of harmful gases into the air, transportation is the biggest offender. Methods are still being developed to calculate and analyze the transportation sector’s carbon footprint, despite the fact that the need to limit the emission of gases that contribute to global warming has now become urgent. Previous studies have calculated the carbon footprint of transportation; however, there are some discrepancies in the terminology and methodologies utilized. The commonalities between CO₂ emission measurement techniques and assessment techniques are the primary subject of this review. This study helps to increase public awareness of environmental concerns and promotes the use of reliable methodologies for calculating transportation-related CO₂ emissions. It is hoped that choosing the optimal available method will contribute to a decrease in CO₂ emissions from transportation.

Prediction of incident duration is very important in Advanced Traffic Incident Management (ATIM). Predicting accurately the duration of a traffic incident is necessary to effectively reroute traffic around the incident and to clear traffic, in general, away from the incident's area. In this paper, Support Vector Regression (SVR) is employed to predict the incident duration. In our experiments, we used one incident data set collected from an expressway in the Netherlands. The data set is divided into two parts; one is for model development, and the other is for the model validation. The experiment results, including error analysis, indicate that the prediction model based on Support Vector Regression (SVR) can obtain high accuracy for incident duration. This model can effectively be applied to traffic incident detection and clearance systems.

Real-time driving behavior identification has a wide range of applications in monitoring driver states and predicting driving risks. In contrast to the traditional approaches that were mostly based on a single data source with poor identification capabilities, this paper innovatively integrates driver expression into driving behavior identification. First, 12-day online car-hailing driving data were collected in a non-intrusive manner. Then, with vehicle kinematic data and driver expression data as inputs, a stacked Long Short-Term Memory (S-LSTM) network was constructed to identify five kinds of driving behaviors, namely, lane keeping, acceleration, deceleration, turning, and lane changing. The Artificial Neural Network (ANN) and XGBoost algorithms were also employed as a comparison. Additionally, ten sliding time windows of different lengths were introduced to generate driving behavior identification samples. The results show that, using all sources of data yields better results than using the kinematic data only, with the average F1 value improved by 0.041, while the S-LSTM algorithm is better than the ANN and XGBoost algorithms. Furthermore, the optimal time window length is 3.5 s, with an average F1 of 0.877. This study provides an effective method for real-time driving behavior identification, and thereby supports the driving pattern analysis and Advanced Driving Assistance System.