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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.

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.

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.

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 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.

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.

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.

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.

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.

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.

Understanding the factors influencing ride-sourcing trips is crucial for enhancing the quality of personalized mobility and optimizing the allocation of transportation system resources. However, the nonlinear effects of dockless bike-sharing (DBS) and the built environment (BE) across different spatiotemporal contexts have not been adequately addressed in previous research. This study aims to bridge this gap by analyzing order data and BE characteristics in Tianjin, China. Utilizing the Gradient Boosting Decision Tree (GBDT) model and Accumulated Local Effects (ALE) plots, this study explores the relative importance and nonlinear thresholds of these factors on ride-sourcing trips. The findings reveal that DBS trips during weekday AM peak hours exert significantly negative effects on ride-sourcing, whereas the impact during weekend AM peaks and daily PM peaks is positive. Furthermore, variables such as active population density, metro accessibility, and residential, entertainment, and cultural BEs have positive nonlinear impacts on ride-sourcing trips. These insights offer policy implications and resource allocation recommendations for both government bodies and operators.

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.

The research on tunnel resilience has garnered increasing attention in recent years. Owing to prolonged exposure to natural or anthropogenic factors, the resilience level of many highway tunnels is continuously declining, rendering them susceptible to sudden accidents and challenging to restore postincident. Currently, although several scholars have employed diverse evaluation methods in their research on tunnel resilience, there is a lack of summarization and integration of these methods. In addition, there is also a dearth of a unified evaluation index system and framework for different types of natural or man-made disasters, which are crucial for advancing the development of tunnel resilience evaluation. This study commences with an introduction to the origin of resilience and the definition of tunnel resilience, and comprehensively summarizes commonly employed evaluation methods. Subsequently, this study centers on the resilience evaluation methods in tunnel engineering and analyzes their strengths and weaknesses. Besides, the distribution of resilience metrics in current researches is analyzed and the detailed explanations for the diverse choices are provided. According to the results and deficiencies of existing research, combined with the author’s perspectives, the index systems, evaluation frameworks, and resilience improvement strategies are proposed, which can be applied to the resilience evaluation of various operational highway tunnels under diverse disaster scenarios. Furthermore, this study also presents a case study on the evaluation of tunnel fire resilience to validate the applicability of the research findings. These findings aim to provide a guide for the operation and maintenance management of the operating tunnels and improve the scientific decision-making level of tunnel maintenance.

The rapid growth of cities and the increasing traffic congestion have made vehicle emissions worse, especially in developing countries. Governments worldwide are now relying on regulations and policies to manage and reduce these emissions effectively. This change in approach towards emission control is also happening in developing nations. In this study, the effects of these policy measures are quantified using a calibrated integrated traffic and emission model (AIMSUN), and two hypothetical scenarios were analyzed: one scenario is where electric vehicles (EVs) replace traditional internal combustion EVs (ICEVs) in the study area and another scenario is assuming strict implementation of Euro 4/IV emission standards. The results showed that shifting towards a higher proportion of EVs leads to significant reductions in emissions but requires increased battery consumption, highlighting the trade-off between reducing emissions and higher energy demand. Implementing Euro 4/IV standards could considerably reduce emissions, especially from motorcycles and trucks. It suggests that focusing on these categories with a phased implementation approach could bring significant environmental benefits. Policymakers in developing countries should adopt a rounded approach instead of implementing strict policies. It is crucial for them to carefully weigh the pros and cons of policy instruments before making any decisions. This study shows how traffic micro‐simulation modeling coupled with emission models can be used in evidence-based decision-making.

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.

Multimodal public transport network (MPTN) plays an important role in relieving road traffic pressure for metropolitan area. Nevertheless, the impact of an accident happened in an individual station may not only disrupt the station itself or the single lines that go through the station but also spread over the whole network. Therefore, identifying the vulnerable stations is essential for improving the MPTN management against the systematic risk caused by accidents. In this paper, we proposed a route diversity-based approach to measure the vulnerability of stations in MPTN based on the complex network theory. The route constraint parameters were established to reflect the travel time restriction in constructing the set of passengers’ acceptable routes. In addition, an algorithm was formulated to rapidly calculate the route diversity index and meanwhile avoid the “overlapping routes” problem. A simple virtual network was used as a numerical example to compare the proposed approach with the vulnerability evaluation approaches based on degree centrality and betweenness centrality. Finally, the proposed approach was applied to the MPTN of Beijing to explain its effectiveness and potential applications. The results show that the proposed method can efficaciously estimate vulnerable nodes compared with degree centrality and betweenness centrality. Meanwhile, the acceptable routes between any OD pairs in the MPTN are 1–10 according to the constrained parameter. In addition, the average number of acceptable routes between OD pairs of Beijing MPTN is 3.649. By ranking the stations according to their vulnerability, it can be found that the top 5 vulnerable stations are all external traffic hubs or the stations around famous commercial areas. The results suggest that these stations are significant for external transport as well as crucial for internal urban transportation systems. The research output could contribute to the MPTN management in accident prevention and emergency handling.

To investigate the potential lane-changing collision risks that may arise between vehicles during lane changes and those in the original lane, a model for vehicle lane-changing collision risk is constructed specifically for this scenario, and a research analysis is conducted. First, based on vehicle trajectory data, a sample set capturing the relationships between vehicles traveling in a straight line and those changing lanes laterally is extracted and built. Interpolation methods are then applied to fill in missing values, outliers are eliminated, and data noise is smoothed during preprocessing. After preprocessing, a total of 468 vehicle pairs and 265,392 data points are obtained. Second, a real-time collision time model is established based on the preprocessed data, and collision risk probabilities are calculated accordingly. Then, the collision risks are classified into four levels based on whether the vehicle on the side actually changes lanes and the severity of the collision risks. Finally, a light gradient boosting machine (LightGBM) learning method is adopted to predict the risk levels and analyze the main factors that significantly impact the severity of collision risks. The results indicate that the longitudinal distance between the target vehicle and the preceding vehicle is the most critical influencing factor, followed by the speed of the target vehicle itself, and then the speed difference between the target vehicle and the preceding vehicle. The influence of other factors is relatively similar and does not have a significant impact.

The principle of system resilience is its ability to withstand disruptions and maintain an equilibrium state. In urban network systems, adaptive traffic signal control (ATSC) has been an effective countermeasure to mitigate traffic flow disturbance and improve resilience. This research has explored the usage of a decentralised advantage actor-critic (a2c) algorithm-based ATSC in mitigating disruptions, particularly nonrecurring congestion caused by car accidents. A reward function has also been proposed, combining deduced resilience metric, safety indicator time to collision (TTC) and system performance. A virtual simulation environment was created using simulation of urban mobility (SUMO) to facilitate the evaluation of the proposed approach. In the grid simulation environment, an overall 5.8% improvement is achieved, exceeding benchmark algorithms in three metrics, especially performance with a margin of over 5.2%. Robustness against different levels of car accidents are proven as well. Further evaluation is also implemented based on a real-world case study and demonstrates an improvement of 20.08%, highlighting the correlation of proposed method’s efficiency on the traffic flow rate and road structure.

Probe vehicles and loop detectors collect traffic data for empirical MFD estimation. The accuracy of MFD estimation is contingent upon the percentage of road network links covered (road coverage) and the extent of information (penetration rate) from each link. While the previous research has explored the effects of loop detector road coverage and probe vehicle penetration rates, the impact of probe vehicle road coverage remains unaddressed. Furthermore, the unequal distribution and low penetration rates of probe vehicles undermine the reliability of the data obtained from them. Therefore, this study aimed to (i) examine the impact of probe vehicle road coverage on MFD estimation, (ii) enhance the reliability of the spatial data by using speed data reconstruction, (iii) highlight the increased role of spatial data in the accuracy of MFD estimation, and (iv) develop a performance factor of speed data quality to demonstrate that the quality of spatial data is more susceptible to the penetration rate than road coverage. The results underlined the role of FCD road coverage in MFD estimation, and the proposed methodology significantly improves spatial data quality. The study concluded that spatial and temporal data affect the MFD estimation differently, and the proposed performance factor increases the role of spatial data in MFD estimation.

Safety performance functions (SPFs) have become valuable tools for estimating the relationships between crashes and various causal factors when constructing crash-prediction models. However, the commonly used independent variable, the annual average daily traffic (AADT) is data on a yearly basis, which has limitations in capturing the temporal characteristics of traffic flows influenced by the passage of time. Accordingly, there have also been many studies using 15 min data to reflect real-time, which is an important time unit to understand changes in highway traffic flow. However, such a short time unit has the limitation of high instability and randomness. In light of this, this study recognizes the importance of the 15 min time interval and proposes a new approach by developing a modified hourly model that aggregates data at fine-grained 15 min intervals (00, 15, 30, and 45 min, both at the beginning and end), instead of the traditional hourly data that starts and ends at the peak of each hour to compensate for the existing limitations. The analysis focused on South Korea’s nationwide highways, and models were developed based on both statistical and machine-learning approaches to compare their performances for selecting the final model. Additionally, a modified temporal SPF is introduced to predict crashes by assigning weights based on a Dirichlet distribution to models with overlapping time intervals aggregated in 15 min increments. This innovative approach overcomes the limitations of existing 15 min models, where the number of crashes is too small for effective training if the model is simply developed by dividing the time. The anticipated outcome is that the proposed model will demonstrate excellent performance and serve as an effective tool for predicting highway crash risks.

Longitudinal control of autonomous vehicles (AVs) has long been a prominent subject and challenge. A hierarchical longitudinal control system that integrates deep deterministic policy gradient (DDPG) and proportional–integral–derivative (PID) control algorithms was proposed in this paper to ensure safe and efficient vehicle operation. First, a hierarchical control structure was employed to devise the longitudinal control algorithm, utilizing a Carsim-based model of the vehicle’s longitudinal dynamics. Subsequently, an upper controller algorithm was developed, combining DDPG and PID, wherein perceptual information such as leading vehicle speed and distance served as input state for the DDPG algorithm to determine PID parameters and output the desired acceleration of the vehicle. Following this, a lower controller was designed employing a PID-based driving and braking switching strategy. The disparity between the desired and actual accelerations was fed into the PID, which calculated the control acceleration to enact the driving and braking switching strategy. Finally, the effectiveness of the designed control algorithm was validated through simulation scenarios using Carsim and Simulink. Results demonstrate that the longitudinal control method proposed herein adeptly manages vehicle speed and following distance, thus satisfying the safety requirements of AVs.

Air taxis, a core service within urban air mobility (UAM), have the potential to enhance user satisfaction and address societal challenges such as traffic congestion and environmental pollution. However, the success of this service is often hindered by various concerns. To ensure successful implementation, we investigate the factors influencing public acceptance of air taxis. This study distinguishes itself from previous research in three key aspects. First, it introduces a novel classification of the factors into individual and societal dimensions. Second, it is among the first to apply a value-based adoption model to understand the intention to adopt air taxis, including UAM. Third, it uniquely considers the Korean perspective, unlike most existing studies that focus on Western cultural contexts. To identify the consumers’ perceptions, we conducted interviews with experts and surveyed a sample of 1,000 members of the general public in Korea. Our findings suggest that perceived value for society, as well as perceived value for individual users, significantly influences adoption intention. We discuss both academic insights and practical implications for policy and industry, supporting the commercialization of Korean UAM (K-UAM) promoted by the Korean government.

Based on the principle of direct linear transformation (DLT) in close-range photogrammetry, a method was proposed for reconstructing the motion states of the host vehicle and other vehicles based on vehicle-mounted videos. To verify the effectiveness and accuracy of the method, validation experiments were designed. Under two typical operating states, steering and straight driving, the motion states of the host vehicle and other vehicles (including trajectory, distance, speed, and acceleration) were reconstructed from the vehicle-mounted video. In the experiments, high-precision inertial navigation was installed on the other vehicle to record real-time motion data of the vehicle. Finally, in order to compare and analyze the reconstructed video results with the vehicle’s actual motion data, the recorded motion data were matched and synchronized to the same time axis as the vehicle-mounted videos through a GPS timing device. The experimental result shows that the reconstructed trajectory results based on this method can generally reflect the vehicle’s actual trajectory, with an average deviation of less than 7.4%; the reconstructed distance results have an average deviation of less than 9.3%; the reconstructed speed results have an average deviation of less than 7.3%; the reconstructed acceleration results can reflect the vehicle’s acceleration or deceleration states. The results of this study provide an effective solution for obtaining important parameters of vehicles in accident reconstruction research, such as the trajectory, speed, distance, and acceleration or deceleration, and it has significant practical value for applications.

Deviating to the left on two-way roads can result in fatal head-on collisions. This article presents an intelligent decision-making and path-planning algorithm aimed at avoiding collision with a vehicle that has deviated from the opposing lane. The path-planning process utilizes the model predictive control (MPC) approach, employing a linear kinematic prediction model with a horizon of 2 seconds. Considering that the deviated vehicle may abruptly return to its original lane at any moment, its motion is associated with significant uncertainty. To address this challenge, the path-planning algorithm directs the ego vehicle (EV) under specific constraints to ensure that both the left and right sides of the road are symmetrically reachable in future time steps. This enables the decision-making algorithm to select the safer direction for evasive maneuver at the appropriate moment. The motion prediction of the threat vehicle (TV) is conducted until the potential collision time, taking into account its motion history, and is utilized in the decision-making process. Once the maneuver direction is determined, the collision-free path planning continues using the MPC method. To evaluate the algorithm, six simulations are conducted, modeling various distant and close encounter states of the vehicles on roads with left- and right-hand curves. The simulation results indicate the flexibility and appropriate performance of the algorithm in planning safe and maneuverable paths.

With the development of the Shanghai International Shipping Center, the diversity of vehicle types on the highways and arterial roads near Yangshan port is continually increasing. Within such a container port corridor, large container trucks are primarily utilized for mainline transportation. Their larger size and significant inertia would increase psychological pressure on sedan drivers, and elevate their behavior risk. To investigate the effects of container trucks on drivers’ visual characteristics and driving behavior as well as to predict driving risk, firstly, this research conducted field tests in four scenarios surrounding the port. Visual characteristics and behavior data of sedan drivers were collected. Secondly, a “Visual-behavior” chain model was established. The relationship between drivers’ visual characteristics, driving behavior, and driving risk was illustrated from the perspective of time-series behavior patterns. Thirdly, three driving risk prediction models were built with Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and ARIMA-LSTM. The results indicate that the ARIMA-LSTM model shows the most effective prediction performance. This research provides a field-data comparative analysis of the driving risks influenced by a high proportion of container trucks. The findings contribute to understanding the unique mixed traffic visual environment around large-scale container ports.

In order to accurately predict the lane-changing trajectory of the vehicle and improve the driving safety of the vehicle, a lane-changing trajectory prediction model based on the combination of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) neural network is proposed by comprehensively considering the historical driving behavior, the spatial characteristics of surrounding vehicles and the bidirectional time sequence information of the vehicle trajectory. Firstly, the vehicle trajectory data are filtered and smoothed, and it is divided into three categories: left lane change, right lane change, and straight driving, and a lane change trajectory sample set is constructed. Secondly, CNN-BiLSTM model is constructed to identify the sample set of lane-changing trajectory. Considering the interaction between vehicles in the driving process, the information of predicted vehicle, and surrounding vehicles is taken as the input of the model. The extracted feature vector is input to the BiLSTM layer for prediction after the CNN layer feature extraction, and the horizontal and vertical coordinates of the target vehicle at the next time are output. Thirdly, the trajectory data of the US-101 dataset in NGSIM is selected to verify the performance of the CNN-BiLSTM model, and at the same time, it is compared with models such as CNN-LSTM, long short-term memory (LSTM), BiLSTM, and CNN-GRU-Att. Finally, the verification result shows that the overall fitting degree of the vehicle lane change trajectory prediction of the proposed model reaches 99.50%, and the mean square error and mean absolute error are 0.0003076 and 0.01417, which are improved compared with other models. In the meanwhile, the research on multistep trajectory prediction in different prediction time domains is carried out. It was found that the longer the prediction time domain is, the lower the prediction performance of the model decreases, but the prediction accuracy still reached more than 96%, and it was able to accurately predict the lane change trajectory.

Accurately predicting origin-destination (OD) passenger flows serves as the basis for implementing efficient plans, including line planning and timetabling. However, due to the complexity and variety of OD passenger flows types, general prediction models have difficulty in capturing the features of different OD passenger flows, which in turn leads to poor prediction performance. To address this issue, we propose an integrated framework that combines clustering and prediction methods. First, an unsupervised deep learning model is devised to automatically cluster OD flow types by capturing shape characteristics. Second, three types of features are created to enhance training efficiency, including static features, time-dependent observed features, and time-dependent known features. Based on the clustering of OD passenger flow, a weighted adaptive passenger flow prediction model is developed. The study employs a temporal fusion transformers model to enable multitype OD passenger flow prediction. In the numerical experiments, the model was applied to the urban rail transit in South China, and the model clustered 15,168 OD pairs into 4 types for prediction. The findings show that this approach enhanced the prediction accuracy by 2.0%–9.6% compared to the LSTM model and by 1.6%–4.3% compared to the Graph WaveNet. Moreover, the model can accurately assess the various features for diverse types of OD flows.

To meet the evolving demands of land use and transport interaction (LUTI) assessment within China’s national territory spatial planning (NTSP) system, this paper introduces the level of development (LoD) and the level of matching (LoM) evaluation models, based on the traffic facility and land use factors. The LoD model, founded on the modified node-place model, provides a comprehensive assessment of the traffic facility and land use development scales. Conversely, the LoM model, grounded in data envelopment analysis methods, evaluates the relative relationship between transport services and the travel demand generated by land use. The integrated use of LoD and LoM can both reflect the development scale and matching status between traffic services and travel activities, which are crucial contents in the planning works, especially within the NTSP framework. The proposed models are tested in the city of Guangzhou, and the LoD values exhibit peaks in central urban zones, suburban towns, and areas adjacent to railway transit, with a decline observed in rural farmland and ecological regions. In contrast, the LoM distribution performs a distinct pattern, highlighting numerous underperforming areas with congestion or idle problems in urban centers, alongside well-coordinated regions showcasing a balance between traffic facilities and land uses in rural regions. Furthermore, the LoM scores revealed frequent instances of facility crowding in urban regions and intensive occurrences of facility idleness in rural areas. By marking regions with low LoD scores, the LoD model finds suitable application in determining the urban development border, essential for restricting land development and preserving farmland and ecological areas. Meanwhile, LoM aids in improving urban renewal efforts by assessing and optimizing the balance between intensive land uses and limited traffic facilities. Validated against the existing metrics, the combined use of LoD and LoM efficiently captures the most details of the LUTI process at the lowest computational cost.

Connected vehicles (CVs) leverage the vehicle-to-everything (V2X) function to interact with various mobility systems. In China, regulations mandate that CVs must possess automatic recording capabilities as stipulated by the “Management Specification for Road Tests of Intelligent CVs.” Yet, public perception of these functionalities and their impact on the acceptance of V2X technology remains unclear. This study explores the acceptance of V2X in China by augmenting the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Utilizing a survey of 567 Chinese drivers, we employed structural equation modeling (SEM) and Multiple Indicators Multiple Causes (MIMIC) analysis to dissect the factors influencing the behavioral intention (BI) to use V2X. Our findings reveal that social influence (SI), facilitating conditions (FC), and effort expectancy (EE) significantly predict the intention to adopt V2X. Interestingly, while trust (T) does not exert a direct influence, its overall effect on BI surpasses those of the previously mentioned factors. Moreover, the MIMIC models highlight that individuals’ understanding of V2X significantly shapes their acceptance attitudes. These insights underscore the importance of enhancing T, particularly in the data security aspects of V2X, to bolster its acceptance in China. By addressing these concerns, stakeholders can pave the way for wider adoption of this pivotal technology.

Accurately understanding the travel demand of urban rail transit (URT) systems is crucial for effective operational management. Despite the recognition that the diversity in human activity patterns results in different travel demands, few studies have thoroughly investigated the heterogeneity among passengers and its impact on URT ridership. This study utilizes smart card data collected from the Beijing Subway to categorize passengers into four groups: tourist passengers, flexible commuters, regular commuters, and life-oriented passengers, based on their spatiotemporal travel patterns. Furthermore, a Multiscale Geographically Weighted Regression (MGWR) model is employed to examine the relationship between station-level ridership of URT and its determinants, including the built environment and station properties, for each passenger group. The results indicate that the influence of these determinants on station-level ridership varies across passenger groups and spatial scales. For instance, regular commuters exhibit lower sensitivity to accessibility on workdays, whereas those unfamiliar with the URT network are more concerned about the bus accessibility in pedestrian- or bicycle-unfriendly areas. Notably, for tourist and life-oriented passengers, the stations significantly affected by population density are concentrated in areas with a higher proportion of elderly individuals. Conversely, for flexible and regular commuters, these stations are predominantly situated in areas associated with internet technology and scientific research. These findings are valuable for policymakers in designing strategies tailored to different passenger groups to balance trip demand and capacity, thereby improving URT services and promoting a sustainable urban environment.

Transit signal priority (TSP) is a traffic control strategy aiming at prioritising public transit vehicles at signalised intersections. The emergence of connected vehicles (CVs) provides the opportunity to enhance TSP operation, mitigating challenges such as the negative impact on nontransit users and the management of conflicting priority requests. Furthermore, traffic control policies produce environmental impacts, whilst TSP strategies are typically evaluated based on common traffic flow indicators, such as average vehicle speed, delay and/or the number of stops. In light of the recent progress made in CV technology, we propose and assess two user-based TSP strategies. The first approach aims to minimise total user delay at a signalised intersection, whilst the second considers both reducing bus schedule delay and total user delay. We also measure the environmental effects of these TSP strategies. A microscopic simulation environment is used to compare the proposed methods’ performance against a conventional TSP ring-and-barrier controller in a case study involving two adjacent signalised intersections in Helsinki, Finland. The findings indicate that implementing the proposed strategies effectively enhances TSP performance whilst also lowering adverse environmental impacts.

Accurate prediction of bus arrival time is essential to achieve efficient bus dispatch and improve bus trip sharing rate. This article proposes using the improved whale optimization algorithm–long short-term memory (IWOA–LSTM) model to predict bus arrival times and improving the whale algorithm by optimizing the hyperparameters of the LSTM model, so that the advantages and disadvantages of the whale algorithm and the LSTM model can complement each other, thus enhancing the robustness of the model. Initially, the bus arrival process and its associated influencing factors are analyzed, with certain factors being quantified to serve as input features for the prediction model. After processing the GPS data of the No. 220 bus in Nanchang, Jiangxi, China, the proposed prediction model is analyzed and validated using an example and compared with other prediction models. The results show that the IWOA–LSTM prediction model has the best-fitting effect between the predicted values and actual values in all time periods. Its MAPE, RMSE, and MAE have been reduced by at least 9.47%, 12.77%, and 8.93%, respectively, and the overall R2 has been improved by at least 10.65%. These results indicate that the model has the best predictive performance.

The cascading reliability problem of international railway freight network is becoming noticeable due to the limitation of node transportation capacity with increases in the transport volume of the international railway freight trains. We discuss this problem in this study, thereby focusing on the failure process of the international railway freight network. As the first step, we consider three factors of node degree, node betweenness, and edge betweenness based on the complex network theory, and establish the node model using coupled map lattice method. Next, we select three indicators to evaluate the reliability characteristics of the network and evaluate the robustness of the network with the maximum effective graph and the network efficiency. Finally, we apply the model to the China Railway Express freight network and consider two situations: cascading failures and noncascading failures that are corresponding to two strategies: redistributing cargoes and disbanding cargoes. The results show that the cascading reliability of the China Railway Express freight network is not high. The indicators decrease less than 10% under noncascading failure, while more than 40% under cascading failure, so the network is more reliable under noncascading failure. Our research provides a new way to test the cascading reliability of the international railway freight network and provide different strategies for improving reliability.

Hazardous materials pose significant dangers during transportation due to their flammable and explosive properties. The consequences of accidents involving such materials are often severe and irreparable. A well-designed hazardous materials transportation network can mitigate these risks. However, designing such a network presents two major challenges: quantifying the risk associated with hazardous materials transportation and addressing the hierarchical relationship between government and companies. To address these challenges, we enhance the accuracy of accident probability estimates and the comprehensiveness of accident consequence assessments, incorporating the uncertainty of accident outcomes. We propose a comprehensive risk assessment model and develop a bilevel programming model to reflect the hierarchical relationship. In this model, the government at the upper level aims to minimize the total risk, while companies at the lower level seek to minimize their total costs. The model is transformed using chance-constrained programming and solved using heuristic algorithms. We apply the model to the highway network in Anhui province, China, to verify its validity. The results demonstrate that the model effectively manages the hierarchical relationship between government and companies, reduces the risk of hazardous materials transportation, and enhances the stability and safety of the transportation network.

Vehicle interactions with different driving behaviors in mixed traffic conditions, in which autonomous vehicles (AVs) and manual vehicles (MVs) coexist, would result in unstable traffic flow leading to a potential crash risk. A proactive traffic management strategy is required to enhance both safety and mobility by preventing hazardous events in connected environments. The purpose of this study is to develop a Proactive Lane-changE Assistant Strategy for Automated iNnovative Transportation (PLEASANT) to enhance traffic safety. PLEASANT is a strategy for providing lane change assistance information to vehicles approaching risky situations such as crashes, broken vehicles, and upcoming hazardous obstacles. In addition, this study proposed a comprehensive simulation framework that incorporates driving simulation and traffic simulation to evaluate the performance of PLEASANT when dealing with mixed traffic. To characterize vehicle interactions between AVs and MVs, this study analyzes driving behavior in mixed car-following situations based on multiagent driving simulation (MADS), which is able to synchronize the space and time domains on the road by connecting two driving simulators. The characteristics of vehicle interactions between AVs and MVs were incorporated into microscopic traffic simulations. The effectiveness of PLEASANT was evaluated based on the crash potential index from the perspective of safety. The results showed that PLEASANT was capable of enhancing traffic safety by approximately 21%. PLEASANT is expected to be useful as a novel management strategy for enhancing traffic safety in mixed-traffic environments.

The primary objective of this paper is to minimize the overall travel costs for passengers while simultaneously maximizing the operational revenue for the transportation company. This is achieved through the optimization and adjustment of various factors, such as the intervals between regular bus and subway services, the duration of vehicle stops at each station, and the pricing structure for subway and shared bicycle usage. By enhancing the efficiency of passenger travel, we have successfully bolstered the company’s operational profits. In contrast to prior research, this paper comprehensively considers the dual uncertainties associated with both bus operations and shared bicycle operations within a cooperative system. By establishing a coordinated dual-level optimization model for regular bus, subway, and bike-sharing networks under dual uncertainty conditions, we employed convex combination techniques to unify the dual uncertain variables into a single objective, which was then incorporated into a chance-constrained bilevel programming model. Ultimately, we utilized KKT conditions to transform the model from a bilevel to a single level for resolution. This paper centers its research on the collaborative system comprising the Nanchang Metro Line 1, Bus Route 520, Bus Route 211, and the adjacent region hosting a cluster of shared bicycles. By leveraging Python programming, optimization models, empirical data on traffic flow and stoppage times, and OD data, we conducted an optimization analysis to solve the problem at hand. According to the optimization results, passenger waiting time, passenger transfer time, and passenger on board time are effectively reduced by 6.81%, 18.29%, and 23.92%. At a confidence level of 95%, the resulting time level results in a 12.44% reduction in total travel time. The average subway fare increased by 18.12%, the average shared bicycle fare decreased by 19.12%, and the total cost of travel expenses increased by 16.68%. The final total cost of travel was reduced by 4.06%, and the business operating income was increased by 13.10%. The comprehensive optimization results have effectively fulfilled the objectives of the bilevel optimization model, thereby confirming the rationality and practicality of the optimization approach. The research outcomes hold significant practical implications for facilitating the efficient and cooperative development of urban transportation networks, ultimately enhancing the convenience of residents’ travel experiences.

A dedicated bus lane is designed to give priority to buses over private cars on the road. However, this approach might waste road traffic capacity when bus demand is low and private car demand is high. Thus, it is essential to utilize bus lanes more effectively. Previous work focuses on allowing private cars to use bus lanes which may cause bus delays. To strike a balance, some approaches involve mandating private cars to leave bus lanes and limiting the number of private cars entering bus lanes. However, these highly rely on transportation infrastructure and emerging technologies. Hence, we propose a simple method of a partially space-shared bus lane, which allows private cars to merge into the bus lane at specific locations. Lane-changing decision is modeled as a result of speed difference between adjacent lanes. Traffic flow is simulated by the LWR model in the Lagrangian coordinate system. Two scenarios are set up—dedicated bus lane and partially space-shared bus lane—to evaluate the partially space-shared system. The only optimization variable is the location that allows private cars to conduct lane-changing. We use the genetic algorithms to optimize the system. Finally, simulation results show that the partially space-shared bus lane reduces private-car delays and total delays of the system. The buses would experience an increase of delays, inevitably. But the bus delay increase could be limited in a small range, which maintains the bus priority. In addition, a multiple lane-changing position strategy is better than the single position one. This approach is suitable for traffic situations with low bus demand and high private car demand. Our work is expected to contribute to the design of future urban bus lanes and improve overall traffic operations.

India initiated its ethanol fuel blending program (EBP) two decades ago to enhance energy security, reduce crude imports, and promote low-carbon transportation. However, despite government initiatives and policies, the EBP has made slower progress than anticipated. For the long-term adoption and success of the EBP, the following critical areas must be analyzed: integrated ethanol production from multiple feedstocks, demand and linkage to industries requiring ethanol, impact on the environment and revenue prospects, and evaluation of the policy measures adopted. This study addresses these topics by analyzing the interaction between various industries (demand) and ethanol production from multiple sources (supply) using system dynamics modeling. Simulation and scenario analysis have been used to evaluate the environmental and economic performance of ethanol blends under the influence of various policy parameters. The findings indicate that, contrary to the conventional belief, the production of ethanol directly from sugarcane juice does not significantly threaten food security. Higher blending ratios yield enhanced environmental benefits and revenues in the short term, but these are outweighed by the long-term benefits of lower blending ratios. The findings also indicate that encouraging second-generation ethanol production from rice stalks and increasing the blending ratios will reduce CO2 emissions. However, the goals set for blending cannot be achieved until measures to diversify feedstocks and improve the infrastructure for ethanol production are scaled up.

Transit performance is greatly influenced by its accessibility, which considers the spatial distribution of transit facilities with different periods of operation. The present study analyzes the spatiotemporal variation in transit accessibility and proposes a modification to enhance the evaluation process. The proposed modification involves assigning weighted indexing to the public transport coverage index (PTCI) using the CRITIC (criteria importance through intercriteria correlation) MCDM technique. The indicators exhibit temporal and spatial variations based on network and operational characteristics, with temporal variations relying on the number of scheduled transits and spatial variations influenced by the network and other operational attributes. The case study conducted in Surat, India, reveals that areas such as the city center and inner fringe have a higher concentration of scheduled transits and bus stops. However, demand fulfillment, measured by the offered seat capacity per population, is relatively low in most zones. To prioritize areas for resource allocation and policy implementation, the use of “RAdial REferenced Scatter QUAdRant (RARE SQUARE) Performance” charts are developed, which provide a straightforward tool to validate findings. The study highlights a low relative transit demand in the city, resulting in a mode share of approximately 2.5%.

Real-time automatic displacement monitoring of metro tunnels is vital for ensuring operational safety and contributes to carbon reduction goals by improving system efficiency. This study focuses on key monitoring elements such as displacement, settlement, convergence, and cracking. Through the analysis of continuous monitoring data, a real-time displacement monitoring model for metro tunnels based on robotic total stations is proposed. This model can timely identify potential risks, thereby ensuring the safe operation of tunnels and reducing carbon emissions from unnecessary maintenance operations, thereby reducing the carbon footprint of metro operations. This article takes the Jinan Metro Tunnel Displacement Real-time Monitoring Project in China as a case study and constructs a comprehensive monitoring framework using robotic total stations, intelligent automated deformation monitoring data collectors, and cloud servers. The implementation details of the project, displacement monitoring principles, monitoring system construction, and data analysis processes are elaborated in detail. Taking the monitoring data of Jinan Metro Line 2 from April 1, 2022, to May 31, 2023, as an example, the results show that the tunnel displacement is within the safe range, verifying the practical application value of the method proposed in this paper. It can effectively ensure the safe operation of the metro and promote sustainable development and low-carbon metro construction.

Travel Time Reliability (TTR) plays a pivotal role in commuting. Nevertheless, existing measurement methods are not specifically designed for commuting scenarios, and their direct application to assess TTR for commuting may yield results incongruent with actual commuting conditions, as they overly rely on measures like mean and percentiles. Drawing on the cyclical characteristics of commuting, the study has established a TTR measurement model based on information entropy and standard deviation, tailored to individual commuters. By selecting commuting data from extensive travel datasets and applying both this model and conventional measurement methods, the focus is on quantitatively analyzing TTR for metro commuters and car commuters under various feature conditions, with a particular emphasis on commuting to work. The objective is to verify the feasibility and advantages of the proposed model. The research indicates that, compared to typical measurement methods, this model more accurately reflects TTR for commuting purposes. The results underscore a significantly superior TTR for metro commuters over car commuters. Distance and departure time exert a substantial impact on the TTR of car commuters, while distance and transfer times moderately influence the TTR of metro commuters. These findings serve as a crucial foundation for enhancing the quality of commuting experiences.

Accurate and timely forecasting of critical components is pivotal in intelligent transportation systems and traffic management, crucially mitigating congestion and enhancing safety. This paper aims to comprehensively review deep learning algorithms and classical models employed in traffic forecasting. Spanning diverse traffic datasets, the study encompasses various scenarios, offering a nuanced understanding of traffic forecasting methods. Reviewing 111 seminal research works since the 1980s, encompassing both deep learning and classical models, the paper begins by detailing the data sources utilized in transportation systems. Subsequently, it delves into the theoretical underpinnings of prevalent deep learning algorithms and classical models prevalent in traffic forecasting. Furthermore, it investigates the application of these algorithms and models in forecasting key traffic characteristics, informed by their utility in transport and traffic analyses. Finally, the study elucidates the merits and drawbacks of proposed models through applied research in traffic forecasting. Findings indicate that while deep learning algorithms and classic models serve as valuable tools, their suitability varies across contexts, necessitating careful consideration in future studies. The study underscores research opportunities in road traffic forecasting, providing a comprehensive guide for future endeavors in this domain.

The ability to change lanes safely, efficiently, and comfortably is an important prerequisite for the application of Connected-Automated Vehicles (CAVs). Based on the five-order polynomial trajectory planning for CAVs, the 2D-Action Asynchronous Lane Change (AALC) trajectory planning model is constructed by further considering the longitudinal and lateral driving action execution time parameters. This is done to improve the applicability of the lane change model and increase the CAV lane change success rate. The continuous collision space algorithm is constructed by determining the continuity condition of collision trajectory parameter solution space through the monotonicity of trajectory curve parameters and collision form classification. AALC trajectory safety judgment is realized through this algorithm. A cooperative lane change trajectory evaluation objective function is constructed, considering multivehicle comfort and efficiency. Finally, the AALC model is solved in the continuous collision space according to the optimal objective function, and the lane change is divided into free, cooperative, and refused according to the optimization. The results indicate that the AALC model achieves the transfer of collision space between lanes through asynchronous process of behavior execution time window, thereby reducing the possibility of vehicle collision. The AALC model reduces the degree of change of cooperative lane change parameters by asynchronous process of behavior, increasing the number of free lane change trajectories by about 17%, effectively reducing the occurrence of lane change refusal, improving the successful rate of lane change, and enhancing the overall evaluation of the lane change. The AALC model realizes the reallocation of collision space between different lanes through asynchronous process, making it more suitable for environments with large differences in vehicle gaps such as ramp merging. The collision-based trajectory optimization algorithm can quickly obtain the corresponding safety space and optimal trajectory. The maximum calculation time for a single cooperative lane change is 0.073 s, thus enabling real-time trajectory planning.

With the emergence of Level 4 automated vehicles, it is necessary to investigate the impact of these vehicles on mode choice. Previous studies have looked at the potential benefits and drawbacks of automated vehicles, but there has been little research done on how these vehicles will impact individuals’ travel behaviors. This paper proposes a needs-based approach to study the impact of Level 4 automated vehicles on mode choice. The approach takes into consideration the travel needs of different individuals and their willingness to adopt new technologies. Through a stated preference survey in China, the data on travel preferences and the perceived safety levels of automated vehicles can be collected. Then, a model is built to simulate the adoption of Level 4 automated vehicles and estimate the mode split for different scenarios. The results indicated that private AV modes are preferred, and business and nonwork trips may be the targeted market for all AV modes. Overall, value of automation increases with income for private modes, with large variance. Furthermore, Pro-AV attitude has a positive effect on value of automation, especially for self-owned AV and AV subscription. Accordingly, the needs-based approach demonstrates a promising method to study the impact of new technologies on travel behaviors and provides insights for policy makers to promote more sustainable transportation systems.

The advent of automated vehicles (AVs) and connected automated vehicles (CAVs) creates significant uncertainties in infrastructure planning due to many unknowns, such as performance variability and user adaptation. As technologies are still emerging with low market penetration, limited observational data hinder validation and escalate prediction uncertainty. This study addresses these gaps by employing diverse vehicle models and wide performance ranges in Aimsun microsimulations. It involved three AV/CAV car-following models with the default Gipps human-driven vehicle (HDV) model. We evaluated the performance of a mixed fleet in three well-calibrated real-world corridor models, including two highways and one freeway. Vehicle parameters in Aimsun are commonly drawn from a corresponding truncated normal distribution with fixed mean, min, and max values. However, to account for future uncertainty and heterogeneity, our AV/CAV models were given truncated normal distributions with variable means for important parameters to incorporate broader performance ranges. The variable means are drawn from intervals with uniform probability, and some of the interval extended below HDV values to account for scenarios where riders opt for smoother rides at the cost of traffic flow. Recognizing that precise future prediction is unattainable, we aimed to establish traffic performance boundaries that define best- and worst-case scenarios in a mixed-fleet environment. Enumerating all possible combinations is impractical, so a refined optimization algorithm was employed to expedite solution discovery. Our findings suggest that AVs/CAVs, even with conservative performance parameters, can improve traffic operations by reducing peak delays and enhancing travel time reliability. Freeways benefited more than arterial roads, especially with full CAV penetration, although the authors speculate this could create bottlenecks at off-ramps. The added capacity may induce traffic demand that is difficult to estimate. Instead, we conducted a demand sensitivity analysis to gauge additional traffic accommodation without worsening delays. Compared to point predictions, establishing the range of possibilities can help us future-proof infrastructure by considering uncertainties in the planning process. Our framework can be adopted to test alternative models or scenarios as more data becomes available.

Safety and efficiency of autonomous driving behavior are a tradeoff. Behaviors that are too focused on safety can reduce road operation efficiency, while those that are too efficient can compromise passengers’ safety beyond their tolerance. Therefore, it is important to understand people’s characteristics and maintain a balance between safety and efficiency. Overtaking, which involves passing the preceding vehicle and improving road capacity, requires complex interaction as collisions with opposing vehicles must be avoided on a two-lane, two-way road. Overtaking to increase road capacity can induce unnecessary deceleration in oncoming vehicles, harming oncoming traffic flow. To address these concerns, a diverse dataset of natural overtaking behavior is a priority. We conduct experiments using a network connection between two multiagent driving simulators to collect a human behavior-based overtaking dataset and develop driving behavior models engaged in overtaking situations using the Extra Trees model. The behavior models are embedded in microsimulation to generate human behavior-based datasets under different conditions using a dynamic link library and component object model interfaces. To understand the interaction in an overtaking scenario by the generated datasets, we used a K-means clustering technique to analyze the different reaction behaviors between the oncoming and overtaking vehicles. The threshold for achieving a balanced combination of safety and efficiency is established using XGboost. Finally, safe overtaking behavior is analyzed using a combination of the classified driving styles and thresholds. The results show that the overtaking vehicle can safely start overtaking without endangering oncoming vehicles when both speed and distance conditions are met simultaneously; the speed is lower than 44.29 km/h and it is 407 m away from oncoming vehicles.

This paper proposes an optimization model for the train operation scheme of the Airport Express Line (AEL) based on the expected arrival time of passengers by the introduction of the train departure time to cope with the time-dependent passenger flow and provide better prompt train service according to passengers’ demand. Considering factors such as train sections, station arrangement, passenger capacity, departure time windows, passenger flow conservation, and boarding and disembarkation processes, this paper also aims to find the optimal combination of the passengers’ total travel time and the train operation cost. A set of alternative train options is introduced to simplify the model and convert integer variables related to train pairs into 0-1 variables. The elaborately designed simulated annealing algorithm mainly focuses on the key elements of strategies like initial solution generation, neighborhood solution construction, and the allocation of passenger flows, tailored to the model’s unique features and the time-dependent passenger flow. Neighborhood solution strategies include the increase or haut of train operations and the adjustment of the number of stops, which refines the solution space and boosts the process efficiency of the heuristic algorithm. Additionally, the model and algorithm proposed in this paper are practiced during the peak hour of Nanjing Metro Line S1 for empirical validation. The research findings demonstrate that the optimized train operation scheme is better synchronized with the fluctuating number of time-dependent passenger flows and exhibits notable improvement in computational efficiency and convergence.

More highway built in the mountains in recent years, the driving risk in the tunnel group is becoming a new issue. This paper analyzed the driving risk in the mountain highway tunnel group from the perspective of visual load. Based on vehicle test in the Pengshui-Xiantang tunnel group in China, the evolution characteristics of MTPA were quantitatively analyzed, and the random forest model was constructed to discuss the effect factors of the maximum transient velocity value of the pupil area (MTPA) in different sections. The results are as follows: (1) The MTPA frequently presents a tendency of steep rise and fall in the tunnel group. MTPA in the second tunnel is significantly higher than the first tunnel. (2) The mountain tunnel group can be divided into nine sections; the velocity, design luminance, measured luminance, and location have different effects on MTPA in each section. Due to the complex terrain conditions, the location has a more significant impact on MTPA in the second tunnel. (3) The first tunnel entrance, the first tunnel exit to the second tunnel entrance, and the second tunnel exit are the areas with more significant pressure on drivers in the tunnel group. The visual load of drivers in the exit section of the last tunnel is the greatest. The driving risk reduction recommendations include improving the transition lighting design of the second tunnel, clarifying the tunnel group identification, and adding safety features at the tunnel connection section, in order to clarify the driver’s expectations and reduce the fear of the unknown mountain environment.

The Dedicated Bus Lane (DBL) is often adopted to ensure transit priority. This is because transit priority can effectively mitigate congestion at the signalized intersection. However, the DBL would cause heavy sacrifices from general vehicles when the frequency of buses is low. To address this issue, many studies were proposed to reduce general vehicles’ sacrifice by converting DBLs into Bus-Priority Lanes (BPLs). Such BPLs can be intermittently open to general vehicles. However, these studies cannot ensure absolute transit priority when general vehicles access BPLs. With the advance of Connected Automated Vehicle (CAV) technology, this paper proposes a Trajectory-Based Control (TBC) method for connected automated traffic to approach signalized intersections considering absolute transit priority. A TBC controller is designed to control general vehicles’ trajectories to access BPLs without interference with buses. The TBC controller can balance the multiple cost factors and ensure absolute bus priority. The proposed TBC controller is evaluated against the noncontrol baseline and the state-of-the-art TBC. Sensitivity analysis is conducted under four different congestion levels. The results demonstrate that the proposed TBC method outperforms and has benefits in improving throughputs and fuel efficiency and reducing delays.

Traffic accident data-based portrait plays a vital role in accident cause investigation, relationship reasoning, prevention, and control. The traffic accident data tend to be multisourced with increasingly hidden and complicated accident relationships. The existing reported research focus more on traffic drivers’ measurement of penalty, the relationship among drivers, cars, and dates, etc. How to use multisource data based on deep learning, especially based on the Chinese recent unstructured data and structured data to establish accident portrait for individual and groups of accident drivers, still lacks. Moreover, how to perform multisource accident data label extraction, identity, and relationship extraction are still challenging problems. This paper proposes a multisource accident datasets-driven deep learning-based traffic accident portrait method. Our multisource accident datasets-driven deep learning model is composed of the following three submodels: (1) the structured data accident model using our accident feature-driven bidirectional long short-term memory (Bi-LSTM) and accident feature-driven bidirectional conditional random field (Bi-CRF) model to extract labels, (2) the unstructured traffic accident data model using our accident feature-driven piecewise convolutional neural network (PCNN) model to identify the extracted labels, and (3) the semistructured traffic accident data processing model. Moreover, to solve the problem of how to construct hidden relationship among the multisource accident data, a multisource accident data visualization method based on traffic accident knowledge graph where the accident relational inference algorithm is to complete the hidden relationship between traffic accident data labels is used and then data are visualized using the traffic accident knowledge graph. This paper uses the NER dataset of the People’s Daily and a manually labeled dataset to test the Bi-LSTM + Bi-CRF model, and it acquires the highest scores of 0.9562 and 0.9779 compared with several other models. This paper uses the DuIE dataset and a manually labeled dataset to test the PCNN model, and it acquires the highest scores of 0.9674 and 0.9108 compared with several other models. Experiments verified our model’s merits than other models in regards to accident label extraction, accident identity identification, and accident relationship extraction.