Journal of Advanced Transportation最新文献

The frequent occurrence of secondary traffic accidents, characterized by vehicles losing control and straying into opposing lanes on highways, has emerged as a pressing concern. To address this issue, attention has been focused on the pivotal role of median guardrails as safety barriers. While conventional guardrails have effectively hindered vehicles from veering off course, mitigating accident severity, they are now inadequate in meeting the heightened protective standards necessitated by the surge in truck traffic and advancements in vehicle capabilities. To evaluate and enhance the protective capabilities of guardrails, this research employs a vehicle finite element (FE) model in conjunction with a W-beam guardrail system. Collision trajectories, acceleration, and displacement metrics were analyzed to compare the effectiveness of three improved guardrail designs in preventing crossing in the event of a runaway truck. Furthermore, based on the design of the retrofitted guardrail, the optimization of the structural parameters was carried out by a multiobjective optimization method using radial basis function (RBF) and NSGA-II algorithms with the size of the guardrail as the design variable. The collision simulation comparisons reveal that the double W-beam arch-reinforced guardrail surpasses both the double W-beam and the arch-reinforced guardrail regarding protective performance. Notably, the double W-beam design offers a viable option for disposing of obsolete guardrails postdemolition. The optimized design underscores that optimal structural protection is achieved when meticulously adjusting the thickness of the upper girder plate and the arch to precise dimensions. This refined guardrail system enhances safety and achieves material efficiency, utilizing less steel in its construction. By elucidating effective design modifications and the determination of optimal structural dimensions, this study provides its ideas for safer roads and more efficient infrastructure development.

To ensure the safety of operations in the airfield area, it is crucial to address the increased conflict risks resulting from the growing number of vehicles and aircraft. Based on the complex network theory, this study takes aircraft and vehicles in the airfield area as nodes and selects five different indicators (average degree, average node weight, average weighted clustering coefficient, network density, and network efficiency) to characterize the operation state of the airfield area, so as to identify conflict risks. Building on this framework, an ATT-Bi-LSTM innovation prediction model based on LSTM network architecture is established to forecast the evolution of network indicators over time. By leveraging the algorithm to predict the temporal evolution of indicators, valuable insights into the future evolution of conflict risk can be gleaned from the prediction results. Real operational data from Xi’an Xianyang Airport are utilized as a demonstrative example in this study. The results of the experiments illustrate that the analytical approach proposed in this study achieves a precise identification of the indicators. The experimental results are then compared with data from other predictive models that operate on the same data set. Compared to alternative prediction models, the accuracy is increased by nearly 10%, reaching 89.78%. The results of the study help to accurately identify conflict risks in the airfield area in advance and provide strategic conflict avoidance strategies for relevant staff. This is essential to ensure the security of airfield area.

The traffic environment of mountainous highways is more complex than that of nonmountainous highways, with higher driving loads, which increases the risk in overtaking. The changes in the driver’s pupils, eye gaze behavior, and heart rate can be used to evaluate the level of driving tension and safety. To analyze the driving load while overtaking on two-lane highways in mountainous areas, an actual vehicle test was conducted. Twenty-one drivers were divided into a skilled group and an unskilled group. The gaze time, gaze transfer characteristics, heart rate changes, and pupil area changes during the three stages of overtaking (intention, execution, and return) were compared and analyzed. The comprehensive evaluation of driving load during the overtaking process used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and Rank Sum Ratio (RSR) method. The results show that the two groups of drivers had the highest driving load during the overtaking execution stage and the lowest driving load during the intention stage. The driving load of overtaking on sections with poor-sight distance was significantly higher than that on sections with good-sight distance, and the risk in overtaking during the execution and return stages was highest on sections with poor-sight distance. It is possible to reduce the driving load if the driver is familiar with the road conditions or has a rich driving experience. Compared to the unskilled group, the skilled group had lower driving loads at all stages of overtaking. The research results can provide a theoretical basis for optimizing traffic safety prevention and control technology on mountainous highways and for designing intelligent driving assistance.

The objective of this study is to minimize the overall transportation cost through the joint decision-making for air-cargo hub network design and fleet planning under the uncertain environment. This joint decision-making considers various factors, including hub location, node connectivity, fleet size, and flight frequency. It takes into account several uncertain parameters such as air-cargo demand and transportation cost in a realistic setting. We propose a mixed-integer programming model tailored to the characteristics of such problem, which utilizes interval numbers to address these challenges. This model aims to provide a robust scheme for the joint hub network design and the fleet planning in the uncertain environment. An improved probability-based interval ranking method is proposed to solve the model. This transformation converts the proposed model into an equivalent real-number one, simplifying the solving process. Then a hybrid heuristic algorithm, combining the advantages of Memory-Based Genetic Algorithm (MBGA) and Greedy Heuristic Procedure (GHP), is introduced to enhance the solving speed. Finally, the performance of our proposed model and algorithm is verified using real-world data from the Australian postal dataset. The results show that the proposed model reduces hub construction costs by 1.37% and fleet operational costs by 7.60%, respectively, as opposed to the use of traditional approaches. The computational time of the proposed algorithm is reduced by 28.4% and 36.5%, respectively, when compared to the use of Genetic Algorithm (GA) and Variable Neighborhood Search (VNS) algorithm.

Although Type A personality traits have been confirmed to be more frequently engaged in risky driving behaviours, the existing research on the decision-making of queue-jumping behaviours has not considered this personality trait. This study aimed to explore the decision-making processes of the subject driver’s queue-jumping and the follower vehicle driver’s yielding behaviours with Type A and Type B personalities. First, the decision-making utility variables for both players were selected, and a payoff matrix considering utility variable weights was constructed. Next, a decision-making utility evaluation questionnaire was designed, and this questionnaire and the existing Type A behaviour pattern scale were investigated simultaneously. Then, the weight coefficients of the decision-making utility variables were calculated; the replicated dynamic equations of four game combinations were constructed and the local stability principle of a dynamic system was used to determine the evolutionarily stable strategy for each game combination. Finally, the evolutionary process in which subject vehicle drivers select jumping the queue strategy and follower vehicle drivers select giving way strategy was simulated using MATLAB software based on empirical data to verify the validity of the constructed evolutionary game model. The results indicated some differences in the weight coefficients of decision utility variables between Type A and Type B personalities. The constructed game model can effectively reflect the decision-making processes of subject and follower vehicle drivers of different personality types. The dynamic evolution processes of strategy selection were different for the four game combinations. This study revealed the evolutionary game process between subject and follower vehicle drivers, laying a theoretical foundation for traffic management departments to manage queue-jumping behaviours.

Transit-oriented development (TOD) strategies on subway stations have been implemented in many high-density cities globally to enhance public transportation system efficiency and promote public transportation mobility. Focusing on the developments of intricate metropolitan systems, researchers attempted to elicit “latent rules” by proposing a generic TOD performance evaluation system. This study suggests a multi-indicator TOD performance evaluation method based on a multi-indicator approach grounded in the analysis of multisource urban big data, revealing the role of rail transit TOD station characteristics on critical indicators of station operation through an interpretable machine learning approach. Using Shanghai, China, as a case study, the methodology employed 26 widely used indicators related to TOD development and utilized a BP neural network model trained in a sample space of 77 rail transit TOD stations, aiming to predict the four critical station performance indicators. The robustness of the explanatory variables in the model has been verified by various methods, affirming their consistencies with the development characteristics of the city and the stations. The performance assessment methodology achieves significant predictive results and is computationally feasible, with potential values in applications in other high-density cities worldwide.

Advances in vehicle intelligence have ushered in the rapid development of intelligent connected vehicles and the emergence of the Internet of Vehicles (IoV), greatly improving the passenger travel experience. However, as a new mode of transport, flexible public transportation presents challenges for operators in terms of reducing costs and improving passenger experiences through complex route planning. The present study introduces B^∗ as a heuristic multiobjective route planning algorithm that addresses these challenges. Using the trajectory extraction procedure (TEP) and route assignment procedure (RAP), B^∗ filters out inaccessible routes and plans efficient routes on the fly to save money and enhance the passenger experience. Experimental results show that B^∗ outperforms traditional methods in terms of shorter driving distances and reduced passenger waiting times, highlighting its potential to optimize bus utilization and improve travel experiences.

As a new, highly complex, and far-reaching technology, autonomous driving can be associated with various fears and uncertainties. However, recent findings show that high trait anxiety can positively contribute to the intention to use (ITU) autonomous vehicles (AVs). An explanation for this is that the possibility of handing over one’s driving control to artificial intelligence (AI) is even more relieving for more anxious people. Our study aimed to test whether this explanation can be supported by investigating to what extent this relationship can be applied to buses in which control is handed over per se–in the conventional bus to a driver, and in the autonomous bus to the AI. We also analyzed how the fear of giving up control mediates the relationship between trait anxiety and ITU. In a quasi-experimental study, 253 subjects were surveyed while riding an autonomous or conventional electric bus. The results confirmed a positive association between trait anxiety and ITU in the overall sample but not in the autonomous and conventional subsamples. Contrary to our assumptions, fear of giving up control served as a slightly suppressive but not significant mediator. The results were independent of whether control was handed over to a human driver in the conventional electric bus or to AI in the autonomous bus. Our study thus provides fundamental new insights into the acceptance of AVs and buses in general and opens the door for subsequent research based on these findings.

In an urban road network, the ability of the traffic flow itself to alleviate congestion caused by external disruptions is overlooked. This study applied the two-fluid model to simulate mesoscopic traffic flow, focusing on the resilience of urban road networks under normal disturbances. Three resilience indices—plasticity, transition of elasticity, and elasticity—were introduced based on the failure deformation process of rigid materials. These indices were used to modify the two-fluid model’s parameters, considering the effects of bus operations and temporary roadblocks on traffic flow and service quality. A hidden Markov model (HMM) was employed to predict service quality transitions (distinction, merit, and pass), with validation using dynamic bayonet traffic data from Xuancheng and video recordings from Xi’an, China. The results confirmed that resilience varies significantly across different times and locations, with peak hours and dense urban areas exhibiting lower resilience and higher susceptibility to disruptions. Bus queuing was found to degrade service quality, and rainstorms had a more severe impact than construction zones. The study can aid in the development of management efficiency of urban road networks.

Enhancing hazmat truck safety through advanced driving assistance systems (ADAS) relies on both system efficacy and driver reactions. This study investigates the driving behaviors of hazmat truck drivers in response to forward collision warnings (FCWs). Traditional warning triggering methods struggle to capture diverse and immediate driver responses; therefore, our research employs a vision-based framework for driving data extraction and utilizes the K-means++ clustering method for response-based classification. Moreover, we propose an enhanced version of the intelligent driver model (IDM) based on the concept of a virtual vehicle to reproduce hazmat truck drivers’ differential behaviors during risky car-following periods, achieving results that depict improved driving simulations. This model is compared with classic benchmarks, including the IDM, optimal velocity model (OVM), and full velocity difference (FVD) model, demonstrating superior performance in terms of traffic stability and safety in extreme scenarios. Our findings highlight that preaction drivers tend to accelerate before receiving warnings, opting to overtake rather than maintain safe distances. In contrast, calm drivers decelerate in anticipation of the warning, showcasing their awareness of maintaining safety. The analysis reveals that aggressive drivers are predominantly in the 41–45 age group, indicating a higher skill level, while calm drivers are more commonly older, reflecting a trend in cautious driving behaviors. Overall, our research contributes to the development of effective ADAS by considering real-time driver responses and emphasizes the potential of our model to revolutionize commercial ADAS adoption and enhance road safety for hazmat operations.