Journal of Advanced Transportation最新文献

The driving risk is assessed using the theory of unascertained measures to determine the presence of a conditional switch in the control system of a human-machine codriving vehicle. Relevant risk indicators for driving are selected, including five driver-related indicators and three vehicle-related indicators. Subsequently, each indicator’s threshold range and associated risk level are analyzed and defined. The methodologies for establishing unascertained measure and their corresponding functions for both single and multiple indicator unascertained measure are then elucidated. A game theory–based weighting method is proposed, employing ordinal relationship analysis (ORA) and entropy weighting (EW) to determine indicator weights while utilizing confidence identification criteria to ascertain risk levels. Finally, experimental analyses are conducted on the driving risk assessment model, and the simulation results demonstrated the model’s ability to distinguish between normal and risky driving. In a continuous driving simulation, the model successfully identified a peak risk period (Level V) and, following a system alert, driving behavior returned to normal risk levels within 5 min. The model demonstrated utility for control switching decisions in human-machine codriving scenarios, identifying instances where driver risk (Level IV) significantly exceeded vehicle risk (Level II), indicating a need to transfer control to the vehicle system. Consequently, the study’s findings can provide theoretical support for control switching mechanisms in human-machine codriving vehicles.

The main material used in the construction of roads is asphalt. Therefore, the recognition of asphalt’s mechanical aspects is very important. One of the important features of asphalt is its shear strength, which should be measured accurately. However, the methods that have been presented to measure this important factor of asphalt always encounter weaknesses. So, it is necessary to find a suitable method to determine the shear strength of asphalt with more accurate results and high compatibility with reality. In this regard, the purpose of the present research was to design jaws in order to measure the shear strength in the direction and opposite direction of the traffic path and provide a model to predict shear strength using Marshall stability resulting from invented jaws. In order to examine the accuracy of the designed jaw in this study, two different types of asphalt, Binder 0–25 and Topeka 0–19 grading, were used. For this purpose, Marshall stability and shear strength tests in the direction and opposite direction of the Marshall were conducted with 12 repetitions on these samples. Also, the genetic programming (GP) evolutionary algorithm was applied in this study to provide a prediction model of shear strength. The results of this study indicated that there was a significant relationship between the Marshall stability and the shear strength in the direction and opposite direction of the Marshall applying the invented jaws in both asphalt types, and the coefficient of determination (R²) for the Binder and Topeka were 0.93 and 0.97 in the Marshall’s direction and 0.96 and 0.95 for the Marshall’s opposite direction, respectively. Also, the results of the GP method indicated that the relationships between predicted and actual values of shear strength for Binder and Topeka asphalt types were appropriately described by R² of 99.47% and 99.21% with RMSE of 8.0177 and 5.0143 in the traffic direction, and R² of 97.45% and 98.08% with RMSE of 1.2684 and 0.7035 in the traffic opposite direction, respectively. Therefore, GP provided a more suitable fit of all experimental data for both Binder and Topeka asphalts, and it can be said that with the help of new designed jaws, the shear strength in the direction and opposite direction of the Marshall can be estimated with high accuracy.

This systematic literature review investigates the application of machine learning (ML) techniques for predicting traffic incident durations, a crucial component of intelligent transportation systems (ITSs) aimed at mitigating congestion and enhancing environmental sustainability. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, we systematically analyze literature that overviews models for incident duration prediction. Our review identifies that while traditional ML models like XGBoost and Random Forest are prevalent, significant potential exists for advanced methodologies such as bilevel and hybrid frameworks. Key challenges identified include the following: data quality issues, model interpretability, and the complexities associated with high-dimensional datasets. Future research directions proposed include the following: (1) development of data fusion models that integrate heterogeneous datasets of incident reports for enhanced predictive modeling; (2) utilization of natural language processing (NLP) to extract contextual information from textual incident reports; and (3) implementation of advanced ML pipelines that incorporate anomaly detection, hyperparameter optimization, and sophisticated feature selection techniques. The findings underscore the transformative potential of advanced ML methodologies in traffic incident management, contributing to the development of safer, more efficient, and environmentally sustainable transportation systems.

Accurate prediction of electric bus energy consumption is a key step to realize the orderly planned charging of electric buses. Meanwhile, to address the problem that the current electric bus energy consumption prediction model is not conducive to realistic application, this paper proposes an energy consumption prediction model that considers actual electric bus operation data to predict trip energy consumption. First, based on the operation data of six routes in Beijing, the influencing factors of electric bus energy consumption are summarized, including route name, travel direction, weekday and nonweekday, operation time, vehicle number, and driver’s name. Secondly, the energy consumption influencing factors were used to extract trip energy consumption features, including departure moment features, vehicle performance features, and driver attribute features. A new simple method is proposed to deal with un-ordered characteristic data to solve the problem of quantifying the influencing factors. The energy consumption prediction model considering actual quantifiable features utilizes the concept of distance to identify several historical trips that have characteristics most similar to the predicted trip in terms of energy consumption. The new prediction model is essentially a machine learning model based on k-means clustering algorithm, which leverages feature extraction and data analysis to make predictions. Finally, the real data are used to predict the energy consumption of different routes and different driving directions on weekdays, respectively. The energy consumption prediction error is as low as 7.112%, and the prediction results are compared with other traditional prediction models, and the model accuracy is high.

For a developing country to flourish sustainably, the transport sector needs to be balanced yet compete with its peers to support the growth of diverse sectors of the urban economy. Encouraging active mobility is one of the vital steps for the development of sustainable urban transportation. It indicates any mode of transport that involves physical activity, for example, cycling, walking, skateboarding and skiing. This paper is an attempt to identify the performance indicators that majorly affect the walkability and cyclability of people in cities capable of promoting active mobility. The objective is to corroborate the presence of qualitative and quantitative indicators in various sustainable transportation practices. Based on analytical hierarchy process, modified Delphi approach and user perception survey were utilised for the identification of performance indicators for Bhopal city. The indicators were segregated using exploratory factor analysis into five dimensions to categorise the performance indicators: sociodemographic, socioeconomic, physical and built environment and safety. It was found that supportive facilities were crucial for developing existing land use, physical and built environment and safety for users in a beginner city wanting to encourage users to switch to active modes choices. It was also found that urban design and built environment were the most influential factors which affect the various performance indicators for the establishment of active mobility modes for sustainable urban transportation.

The prediction of operating speed plays a crucial role in road design and safety assessment, especially on complex urban expressway interchange ramps. This task is challenging due to various influences like road conditions, traffic dynamics, and driver behavior. This study aims to identify the optimal model configuration for predicting operating speeds on urban expressway interchange ramps. Three models are established: a short-term operating speed model based on a generalized linear model (GLM), a GLM incorporating for spatial correlation (GLMS), and a deep neural network model considering spatial correlation (DNNS). Each model incorporates considerations for the impact of the plan, profile, and other facets of the interchange ramp in urban expressways. Naturalistic driving experiments are conducted in Shanghai, 70% for model calibration and 30% for validation. Comparative analysis shows that the DNNS model outperforms the others, effectively capturing speed fluctuations along the interchange ramp, demonstrating its robustness and generalization capabilities.

With the rapid development of transport technology and the increasing complexity of traffic patterns, integrating multiple data sources for traffic flow prediction has become crucial to overcome the defects of a single data source. This paper introduces a multisource data fusion approach with graph convolutional neural networks (GCNs) for node-level traffic flow prediction. Specifically, it extracts different types of traffic flows from multiple data sources and constructs a unified graph structure by using global traffic nodes to interpolate the traffic flow. In addition, a GCN combined with gated recurrent units (GRUs) is proposed for spatiotemporal modeling of data fusion and traffic flow prediction. The main contributions are: (1) The approach significantly improved prediction accuracy by leveraging multiple data sources compared to a single source. (2) A unified graph structure was created via global traffic nodes to interpolate traffic flow and address data sparsity. (3) The proposed model demonstrates an over 11% improvement in accuracy compared to other baseline models, as measured by the weighted mean absolute percentage error (WMAPE). It also exhibits stability in multitime scale predictions, highlighting the effectiveness of multisource data fusion, data imputation, and node-level prediction capabilities. The approach provides valuable insights for managing urban traffic data from multiple sources and predicting traffic flow, and it shows stability in multitime scale predictions.

Current airport ground operations, relying on single and fixed aircraft taxiing rules, struggle to handle dynamic traffic flow changes during peak flight times at large airports. This leads to inefficient taxiing routes, prolonged taxiing times, and high fuel consumption. This paper addresses these issues by proposing a new adaptive method for dynamic taxiway routing in airport ground operations. This method aims to reduce ground taxiing time and fuel consumption while ensuring the safety of aircraft taxiing. This study proposes a multiobjective A^∗ algorithm with time windows which takes into account the allocation of resources on airport taxiways and introduces factors such as turning angles, dynamic turning speeds, and dynamic characteristics of the ground operations. Experiments conducted over the 10 busiest days in the history of Tianjin Binhai International Airport demonstrate that the algorithm excels in minimizing total taxiing time, differing only by 0.5% from the optimal solution. It also optimizes multiple objectives such as fuel consumption and operates at a solving speed approximately three orders of magnitude faster than the optimal solution algorithm, enabling real-time calculation of aircraft taxiing paths. The results of the study indicate that the proposed multiobjective A^∗ algorithm with time windows can effectively provide decision support for dynamic routing in airport ground operations.

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.