No abstract

Car-following models have been studied for a long time, and many traffic engineers and researchers have devoted attention to them. With the increase in machine learning, this paper proposes a fusion model based on the physics-informed deep learning framework. The purpose of this paper is to inherit the predecessors’ ideas, transform them to fit a new framework, and improve the framework’s accuracy. The IDM-D (intelligent driver model development) involves reenabling the effect of the following vehicle to form a complementary model (not car-following model) with the IDM (intelligent driver model). The pretreated NGSIM data are used for calibration and validation. The IDM and the IDM-D are combined with the LSTM under the framework of physics-informed deep learning, and the results are mixed in a ratio to form the final result. Using test data for simulation, the results reveal that the IDM-informed LSTM shows better performance than the LSTM and that the fusion model further improves the MSE (mean square error) of the IDM-informed LSTM. The fusion increases the accuracy during the deceleration process, which is better than just a single IDM-informed LSTM. The fusion model further explains drivers’ deceleration behaviors.

To investigate the effect of activated crumb rubber content on the molecular interactions and the properties of crumb rubber-modified asphalt (CRMA) with waste oil, six models of asphalt with various rubber dosages were developed using Materials Studio software, and the molecular dynamics performance of the system was further examined. Then, the fatigue and high- and low-temperature performances of the CRMA binders were characterized by dynamic mechanical experiments in the laboratory. The mean square displacement and diffusion coefficient were used to quantify the migration of molecules. The aggregation state of the components was evaluated using a radial distribution function. The bulk modulus of the CRMA models was calculated to study the mechanical properties. Dynamic shear and bending beam rheometer tests were implemented to evaluate the road performances of the CRMA binders. The results show that increasing the amount of powder could improve the mechanical properties of the asphalt, that is, the modulus of 70% of the asphalt was improved by 57.5%. The rubber and waste oil were evenly dispersed in the system, and the distribution of asphalt components was in accordance with the colloid theory. The temperature-sensitive properties of the rubber led to the improvement of road properties of the CRMA binders with the increase of the admixture. Combined with the distribution of molecules in the asphalt model, the results of rheological indexes show that the waste oil could improve the rheology and stability of binders. This will provide theoretical support for upgrading the content of crumb rubber in CRMA binders.

In the case of a fire, the choice of exit in the highway tunnel is strictly limited by fire location, which seriously affects the evacuation time. A spontaneous or disorderly exit choice might result in a decreased evacuation efficiency and utilization rate of exits. In this paper, we propose a strategy to obtain the optimal exit choice based on fire location during highway tunnel evacuations. In our strategy, first, the vehicle distributions and locations of evacuating occupants are determined in the traffic simulation program VISSIM. The evacuation simulation software BuildingEXODUS is employed to obtain the corresponding parameters of the evacuation process and analyze the impacts of different fire locations on the evacuation time. During the analysis, the optimal productivity statistics (OPS) is selected as the evaluation index. Then, the feature points of the crowding occupants are captured by the fuzzy c-means (FCM) cluster algorithm. Next, based on the feature points, the relationship between the location of the fire and boundary of the optimal exit choice under the optimal OPS is obtained through the polynomial regression model. It is found that the R-squared(R2) and sum of squares for error (SSE) of the polynomial regression model, reflecting the accuracy estimation, are 98.02% and 2.79×10−4, respectively. Moreover, different fire locations impact the evacuation time of tunnel entrance and evacuation passageway. This paper shows that the location of the fire and boundary of optimal exit choice have a negative linear correlation. Taking the fire 110 m away from the evacuation passageway as an example, the OPS of our strategy can be decreased by 35.6% when compared with no strategies. Our proposed strategy could be applied to determine the location of variable evacuation signs to help evacuating occupants make optimal exit choices.