Search

Disruptive events cause decreased functionality of transportation infrastructures and enormous financial losses. An effective way to reduce the effects of negative consequences is to establish an optimal restoration plan, which is recognized as a method for resilience enhancement and risk reduction in the transportation system. This study takes the total travel time as the resilience measure to formulate a bilevel optimization model for a given scenario. However, the uncertainties involved in restoration activities cannot be overlooked. In this context, the inherent uncertainty is represented with a set of scenarios generated via the Latin hypercube technique. To assess the risk under uncertainty, a conditional value at risk with regret (CVaR-R) measure is introduced when considering the existence of worst-case scenarios. Then, the bilevel programming model is transformed from the deterministic case to the stochastic case, where the upper-level problem determines the restoration sequence to minimize CVaR-R and the lower-level problem is a traffic assignment problem. An integrated framework based on a novel genetic algorithm and the Frank—Wolfe algorithm is designed to solve the stochastic model. Numerical experiments are conducted to demonstrate the properties of the proposed bilevel programming model and the performance of the solution algorithm. The proposed methodology provides new insights into the restoration optimization problem, which provides a reference for emergency decision-making. [ABSTRACT FROM AUTHOR]

To address the shortcomings in decision-making methods for ground motion threshold warning models in high-speed rail earthquake early warning systems (HSREEWs), we propose a dual judgement method and corresponding early warning process for earthquake early warning decisions based on joint peak ground acceleration (PGA) and complex earthquake environmental risk evaluation (ERE) values. First, we analyse the characteristics of four complex earthquake environments based on the characteristics of high-speed rail (HSR) operating environments. Second, we establish an earthquake environmental risk evaluation index system and propose an adversarial interpretive structure modelling method-based complex earthquake situation evaluation model (AISM-based ESEM). The AISM method firstly evaluates the proximity by the TOPSIS (technique for order preference by similarity to an ideal solution) method, then effectively rank targets with fuzzy attributes through opposite hierarchical extraction rules without sacrificing system functionality. Since PGA can reflect the current size of earthquake energy, combining PGA thresholds with ESEM-derived values of ERE can effectively determine the risk status of each train and make decisions on the most appropriate alarm form and control measures for that status. Finally, case analysis results under the background of Wenchuan Earthquake show that the new early warning decision-making method accurately assesses environmental risks in affected areas and provides corresponding warning levels as a supplement to existing HSREEWs warning models. [ABSTRACT FROM AUTHOR]

This paper identifies salient beliefs that influence e-bike couriers' traffic violation behavior based on the theory of planned behavior (TPB). Two surveys were conducted in Nanjing, China, in 2018. The first survey extracted the key psychological beliefs, which were used to design a questionnaire. The second survey assessed TPB components and reported e-bike couriers' traffic violation behavior. A structural equation model was adopted to analyze the data. The results revealed that attitudes, descriptive norms, and perceived behavioral control explained 55.7% of the variance in intention to perform traffic violation behavior, and intentions together with perceived behavior control accounted for 28.5% of the variance in self-reported violation riding behavior. All of the belief composites had strong direct impacts on their respective TPB constructs. Salient beliefs were applied to develop effective intervention strategies. Age, education level, whether one possessed a driver's license, and past traffic violation behaviors had significant effects on belief composites and behavior. The quantitative analysis results obtained in the study can provide theoretical support for designing more effective interventions for reducing the traffic violation rate of e-bike couriers., Author(s): Qian Cheng (corresponding author) [1]; Wei Deng [2]; Qizhou Hu [3] 1. Introduction With the rapid development of e-commerce and the logistics industry, demand in the logistics market is [...]

Aiming at the problem that traditional deep learning algorithms have limited effect on long sequence feature extraction, a vehicle trajectory prediction model based on Transformer was proposed. The data of position, speed, acceleration and working condition of surrounding vehicles obtained by the Internet of vehicles (IoV) technology were used as model input, and the state vector of motor vehicles in the region was obtained through the processing of the self-attention layer and full connection layer of Transformer. The vehicle state information features were further refined by a convolutional social pooling layer, and the vehicle trajectory distribution in the future was obtained through the decoder. The model was deployed in the simulation environment, and NGSIM dataset was used to verify and analyze the model. The results show that the prediction accuracies of the future lateral trajectory and longitudinal trajectory of vehicle are 97.4% and 88.4%, respectively. Compared with other deep learning models, the root-mean-square error (RMSE) and the negative log likelihood loss (NLLL) values between the prediction value and the true value are minimum. The model has a significant effect in extracting the historical trajectory features of vehicles and improves the accuracy of trajectory prediction. [ABSTRACT FROM AUTHOR]