Traffic Complexity Research Articles

Traffic complexity assessment on the malacca strait with traffic zone matrix based on AIS data

This study proposes a novel and feasible framework for assessing maritime traffic complexity, utilising historical Automatic Identification System data to extract dynamic traffic characteristics of sea waters and quantify the corresponding maritime traffic complexity. The processed historical statistics are initially segmented into subsets according to different geographic traffic cells based on traffic zones. Subsequently, a Radial Basis Function regression model with a Gaussian kernel is employed to extract dynamic traffic parameters at the central coordinates of each geographic traffic zone based on subsets. The dynamic parameters derived from nonlinear regression and complexity sub-models are ultimately applied in maritime traffic complexity identification. The effectiveness of the proposed framework is validated through complexity assessments in selected areas of Malacca Strait. Empirical results are significant for maritime shipping research on data-driven traffic complexity monitoring and digitalised decision support.

Complexity analysis of air traffic based on complex networks under TBO mode

Since the complex network constructed according to the unified separation standard does not take into account the differences in the operation of aircraft types, it cannot meet the refined requirements of air traffic complexity analysis under trajectory based operation (TBO). To solve this problem, an air traffic complexity analysis model based on complex networks to distinguish different aircraft types is proposed. Firstly, a calculation model for lateral flight safety intervals of different aircraft types is established, an aircraft precise protection zone is constructed, and the basis for determining the aircraft connection in the flight conflict network is optimized. On the basis of considering information such as aircraft heading and speed, flight conflict judgment pays attention to the different performance and status of aircraft, so that the flight conflict network can be closer to the operation mode of TBO. Through experimental simulation of the TBO operation environment and verification using radar data from Gaoqi Airport, the results show that compared with the previous flight conflict network, the model can refine the horizontal separation standards between aircraft, reduce the complexity of the airspace, reduce the workload of controllers, and improve the operation efficiency of the airspace, and provide more space for aircraft to autonomously select the optimal trajectory.

Traffic advisory for ship encounter situation based on linear dynamic system

Enhancing Situation Awareness (SA) is crucial for maritime traffic safety. Various indicators have been developed to assess risks in encounter situations and support the SA of Vessel Traffic Service Operators (VTSOs) and Officers on Watch (OOW), including collision risk and traffic complexity. Despite the widespread use of these navigational aids, ship collision incidents have not been effectively reduced. This paper abstracts ship encounter situations as linear dynamic systems to enhance the understanding of traffic situations. A traffic advisory framework based on the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) is proposed by integrating complexity metrics with risk indicators. The proposed method is validated through simulations of head-on, overtaking, and crossing scenarios, demonstrating its ability to accurately assess encounter complexity and issue advisories for free navigation, complexity, and resolution. Finally, we discuss the practical application of the proposed method through real-world experiments conducted in the waters of Qiongzhou Strait. The results indicate that the proposed method effectively quantifies the complexity of ship encounter situations and identifies high-collision-risk vessels from a microscopic perspective while providing insights into maritime traffic surveillance from a macro perspective.

Probabilistic Air Traffic Complexity Analysis Considering Prediction Uncertainties in Traffic Scenarios

This article presents a methodology for analyzing probabilistic air traffic complexity by integrating prediction uncertainties in convective weather scenarios. With the Performance Review Unit (PRU) model as a base, this method modifies the original framework by incorporating a weather-related complexity indicator. The approach was tested in Austrian airspace using ensemble weather forecasts and historical flight plan data. The results demonstrated that a probabilistic model effectively assesses traffic complexity and captures trends in complexity over time, providing greater reliability in high-complexity sectors. Validation revealed a strong alignment between simulator complexity values and probabilistic complexity, especially in sectors characterized by dense data distributions. In contrast, sectors with more elongated distributions tended to overestimate complexity. Quantitative analysis indicated that the error between the probabilistic mean complexity and the simulator complexity values ranged from 12% to 23%, with higher errors in sectors with lower complexity. This validation confirmed the model’s ability to predict complexity trends, thereby assisting flow manager positions (FMPs) in traffic flow and airspace management. Overall, this study demonstrated that probabilistic complexity assessment provides a deeper understanding of traffic behaviour, facilitating more effective air traffic flow management in uncertain and dynamic conditions.

Complexity Analysis Using Graph Models for Conflict Resolution for Autonomous Ships in Complex Situations

Abstract Maritime autonomous surface ships (MASSs) will reshape the fast-evolving ecosystem for their attractive socio-economic benefits and potential to improve safety. However, their new systems and technology need thorough verifications to identify unintended components of risk. The interaction between MASS cyber-physical systems and the existing regulatory framework is currently unpredictable; artificial intelligence-powered intelligent situation awareness and autonomous navigation algorithms must safely and efficiently adhere to the regulations which are only designed for human interpretation without MASSs consideration. This paper contributes to algorithmic regulations and particularly algorithmic COLREGs in real-world MASS applications. It focuses on codifying COLREGs into a machine-executable system applicable to MASSs, then analyzing their performance in dynamic and mixed interactions between multiple vessels in complex scenarios. Based on fullest pairwise COLREGs criteria, this paper considers decision-making (DM) and complexity analysis in multi-collision-conflict scenarios. Complexity influential factors are an interplay between the characteristics of COLREGs, traffic scenarios, MASS interactions, and the environment. Participant vessels are the decision-makers forming a decentralized uncertain DM process, casted into a multi-participant multi-conflict multicriteria DM problem. This is tackled through the technique of graph models for conflict resolution, using risk graph models and fuzzy preferences over alternative collision-avoidance states. This paper conducts a comprehensive analysis of DM and navigational complexity; we develop novel complexity estimation scores, tools for complexity monitoring for human intervention, and spatial analysis of traffic complexity for geo-intelligent MASSs deployment and operation planning. The presented work is validated on a database of historical scenarios extracted from multiple data sources.

An improved maritime traffic situation complexity model for intelligent maritime management in the inland ferry area

In increasingly busy inland ferry waterways, traditional maritime traffic situation awareness mainly relies on the experience of Officers on Watch (OOW) and Vessel Traffic Service Operators (VTSOs). Although advancements in science and technology can provide more navigational data to OOW and VTSOs, they often find it difficult to intuitively perceive navigation risks from the massive amounts of heterogeneous data. Current maritime traffic situation models mostly used in open waters make it hard to objectively grasp and understand the overall complexity of ferry water area traffic and to capture the most critical ship targets in local areas. Therefore, this study proposes an improved complexity model for maritime traffic situations in inland waters. It selects dynamic traffic density factor, ship approaching factor, and conflict area to assess the local complexity between ships. Then, the global complexity of traffic in the ferry water areas can be evaluated using the entropy weighting method. These promising results suggest that assessing maritime traffic complexity on different levels can help OOW and VTSOs manage traffic in ferry water areas and identify target vessels that require traffic services and control. The application of this method supports the construction of smart maritime management, enhancing the efficiency and automation of ferry water areas.

Whole exome sequencing identified a homozygous novel variant in DOP1A gene in the Pakistan family with neurodevelopmental disabilities: case report and literature review.

Hereditary neurodevelopmental disorders (NDDs) are prevalent in poorly prognostic pediatric diseases, but the pathogenesis of NDDs is still unclear. Irregular myelination could be one of the possible causes of NDDs. Here, whole exome sequencing was carried out for a consanguineous Pakistani family with NDDs to identify disease-associated variants. The co-segregation of candidate variants in the family was validated using Sanger sequencing. The potential impact of the gene on NDDs has been supported by conservation analysis, protein prediction, and expression analysis. A novel homozygous variant DOP1A(NM_001385863.1):c.2561A>G was identified. It was concluded that the missense variant might affect the protein-protein binding sites of the critical MEC interaction region of DOP1A, and DOP1A-MON2 may cause stability deficits in Golgi-endosome protein traffic. Proteolipid protein (PLP) and myelin-associate glycoprotein (MAG) could be targets of the DOP1A-MON2 Golgi-endosome traffic complex, especially during the fetal stage and the early developmental stages. This further supports the perspective that disorganized myelinogenesis due to congenital DOP1A deficiency might cause neurodevelopmental disorders (NDDs). Our case study revealed the potential pathway of myelinogenesis-relevant NDDs and identified DOP1A as a potential NDDs-relevant gene in humans.

Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship

The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident’s probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers.

Defining Terminal Airspace Air Traffic Complexity Indicators Based on Air Traffic Controller Tasks

This paper focuses on terminal air traffic complexity indicators. By thorough analysis of previous research, the benefits and limitations of the existing terminal complexity models are identified. According to these findings, a new approach for determining terminal air traffic complexity indicators is proposed which assumes that terminal complexity could be determined based on approach air traffic controller (ATCO) tasks. The comprehensive list of general approach ATCO tasks was defined using a literature review and observation of training exercises, forming the basis for subsequent expert group workshops which enabled the acquisition of ATCOs’ knowledge data. Through these workshops, new approach ATCO tasks were additionally identified, and terminal complexity indicators were defined with airspace and traffic parameters. These new tasks and indicators present a novelty in this field of research since they incorporate ATCOs’ knowledge as the data input and consider various traffic scenarios, all types of traffic, weather conditions, and off-nominal situations.

Different smartphone tasks and traffic complexity affect pedestrian awareness of co-existing road objects and cerebral oxygenation during shared space walking

Distraction, primarily owing to the widespread use of cell phones and mobile devices while walking, is a contributing factor to pedestrian injuries. Pedestrians distracted by smartphones exhibit decreased situational awareness, influenced by the nature of concurrent tasks. However, little is known about the neural correlations between smartphone use and pedestrian responses to safety–critical events in a walking environment. This study recruited 30 young adults to investigate pedestrians’ ability to selectively respond to road objects while using smartphones (texting and reading) while walking in an immersive virtual environment system. The level of traffic complexity for the road conditions was manipulated by varying the number of safety–critical road objects coexisting in the environment at any given time and the number of required responses to the number of objects. During each 7-min task condition, cerebral oxygenation was measured using functional near-infrared spectroscopy. Furthermore, smartphone task performance, response times to road objects, and perceived workload were also measured. The participants exhibited reduced texting speed and reading performance when responding sequentially to coexisting road objects. Compared with other road conditions, those requiring two responses to four objects resulted in an increased workload and longer object response times. Participants perceived a higher workload in conditions involving tasks with smartphones than those without smartphones. More importantly, increased concentrations of oxygenated hemoglobin in the prefrontal cortex were observed when participants were concurrently performing smartphone and pedestrian tasks. Compared to smartphone reading, smartphone texting induced greater cerebral oxygenation in the bilateral frontopolar cortex. These results suggest that continuously texting while walking may demand additional cognitive resources for executive function. The findings of this study represent an initial step towards elucidating the cognitive mechanisms underlying attentional impairments experienced during smartphone distraction and working towards a viable solution to mitigate pedestrian risk.

When Do Users Prefer Voice Control Systems in Vehicles? A Survey of Chinese Drivers

Voice control systems (VCSs) are becoming increasingly common in modern vehicles, but concerns are still expressed by drivers about adopting them in smart cockpits. Previous research on privacy and recognition accuracy may not fully address users’ needs with regard to VCSs. In addition, studies on the safety impacts of different interaction modalities may not reveal drivers’ preference for interaction modalities, given that driver choice may not be solely based on interaction safety. Thus, to better understand and optimize VCSs design, an online survey was conducted in China to investigate drivers’ choices between VCSs and manual interaction. In total, we analyzed 168 drivers’ preferred interaction modality (i.e., either VCSs or manual interaction) in different scenarios defined by passenger presence (i.e., present versus absent), traffic complexity (i.e., low versus high traffic density), roadway type (i.e., urban road versus highway), and task characteristics (i.e., six in-vehicle interaction tasks). Compared with manual interactions, drivers preferred VCSs when driving alone, driving on highways, and driving in complex traffic. Further, drivers preferred VCSs when in-vehicle tasks were more distracting and time-demanding (e.g., compiling a text message). The preference for interaction modalities could also be affected by the characteristics of drivers: those with lower driving frequencies and higher VCSs familiarity and those who perceived higher VCSs usability tended to choose VCSs as an interaction modality for in-vehicle interaction tasks. These findings offer insights for adaptive interface design and future optimization of VCSs.

How Has the Concept of Air Traffic Complexity Evolved? Review and Analysis of the State of the Art of Air Traffic Complexity

Air traffic complexity is an indicator that allows air traffic controllers to understand the airspace situation. Controllers need support tools to reduce their workload. For this reason, complexity is a parameter that is being studied more and more, as it makes it possible to know a large amount of information about air traffic. In this article, we perform a bibliometric analysis in the field of air traffic complexity. Through Web of Science (WoS), a collection of complexity-related articles from 2001 to 2022 is compiled. Subsequently, the bibliometric analysis itself is performed. Then, a summary of five main contributions is presented, identifying the strengths and weaknesses of the contributions, and thus the topic. The results of the bibliometric analysis show that future air traffic complexity indicators should consider aircraft trajectories but also take into account other aspects, such as regulations. In addition, future complexity indicators should introduce artificial intelligence predictions to foresee areas of conflict in airspace but taking into account the main limitations, such as uncertainty of the air traffic trajectories. This study helps in the study of complexity due to being able to know previous studies in a summarised form and being able to draw conclusions on future lines.

Identification of Key Risk Hotspots in Mega-Airport Surface Based on Monte Carlo Simulation

The complex layout of the airport surface, coupled with interrelated vehicle behaviors and densely mixed traffic flows, frequently leads to operational conflict risks. To address this issue, research was conducted on the recognition of characteristics and risk assessment for airport surface operations in mixed traffic flows. Firstly, a surface topological network model was established based on the analysis of the physical structure features of the airport surface. Based on the Monte Carlo simulation method, the simulation framework for airport surface traffic operations was proposed, enabling the simulation of mixed traffic flows involving aircraft and vehicles. Secondly, from various perspectives, including topological structural characteristics, network vulnerabilities, and traffic complexity, a comprehensive system for feature indices and their measurement methods was developed to identify risk hotspots in mixed traffic flows on the airport surface, which facilitated the extraction of comprehensive risk elements for any node’s operation. Finally, a weighting rule for risk hotspot feature indices based on the CRITIC–entropy method was designed, and a risk assessment method for surface operations based on TOPSIS–gray relational analysis was proposed. This method accurately measured risk indices for airport surface operations hotspots. Simulations conducted at Shenzhen Bao’an International Airport demonstrate that the proposed methods achieve high simulation accuracy. The identified surface risk hotspots closely matched actual conflict areas, resulting in a 20% improvement in the accuracy of direct risk hotspot identification compared to simulation experiments. Additionally, 10.9% of nodes in the airport surface network were identified as risk hotspots, including 3 nodes with potential conflicts between aircraft and ground vehicles and 21 nodes with potential conflicts between aircraft. The proposed methods can effectively provide guidance for identifying potential “aircraft–vehicle” conflicts in complex airport surface layouts and scientifically support informed decisions in airport surface operation safety management.

Identification of multi-ship maritime traffic situation based on ship traffic complexity measurement model

Maritime Traffic Situation (MTS) is crucial for enhancing the efficiency and safety of maritime navigation, with the complexity of ship traffic serving as a key aspect of identifying and managing maritime risks. This paper proposes a ship traffic complexity measurement model for MTS. The proposed model quantifies the complexity between ships by evaluating factors such as density, proximity factor, and mitigation index, and then integrates these evaluations into multiple ships interaction scenarios to enable a comprehensive assessment of ship traffic complexity. The results of case study have fully demonstrated the model's capability in identifying potential collision risks during the interaction processes of multiple ships situation within a navigational sea. Research on the ship traffic complexity measurement model can provide strong theoretical and practical support for maritime traffic management and navigational safety.

Investigation of driver preference for a user-centred design of decision systems in autonomous vehicles, part I: preferences for binary self-driving modes

As autonomous vehicles (AV) are becoming more pervasive in transportation, it is important to consider drivers’ perceptions of these vehicles. The existing research has investigated taking over AV control, its safety and acceptance. However, the preferences for self-driving in multiple traffic situations have not been extensively investigated. In Part I, we aim to bridge these gaps by investigating such preferences in high and low traffic complexities. Eighty-eight participants in North America were recruited. They viewed video recordings of driving in the city of Toronto, the regional municipality of Waterloo and highways to answer survey questions. Their responses regarding perceptions and preferences were simply analysed using descriptive statistics and Chi-square test at various traffic situations with two traffic complexities. It showed strong preferences for self-driving in most low complexity situations and certain situations in both complexities. These findings can suggest a few applicable design principles of AV decision system regarding traffic situation-based and biased perceptions-based user preferences. In Part II, we extend our analyses to user preferences for multiple two-stage actions of AVs and suggest additional design principles of the system with a more-in-depth insights.

The Influence of Agent Transparency and Complexity on Situation Awareness, Mental Workload, and Task Performance

Transparency is a design principle intended to make the inner workings of autonomous agents visible to end-users such that humans can evaluate the reasoning behind its decisions and actions. To test the effect of agent transparency on situation awareness, mental workload, and task performance, an experiment was performed where 34 nautical navigators were tasked with interpreting the information provided by an autonomous collision and grounding avoidance system. Sixteen traffic situations were created with two levels of complexity. Four levels of transparency varied the amount and type of information in terms of the system’s decisions, planned actions, reasoning, and input parameters. The results show that increased transparency improves SA without increasing mental workload. However, the time to comprehend the system’s decisions and planned actions increased when its reasoning was depicted. Traffic complexity impaired SA, mental workload, and time-to-comprehension regardless of transparency level. However, for level 2 SA, transparency was found to negate the influence of complexity, resulting in improved comprehension of the agent’s reasoning despite high traffic complexity. These outcomes demonstrate the merits of agent transparency as a design principle in supporting human supervision of autonomous agents. However, developers should take care when extending these principles to time-critical applications.

Bayesian network modelling for navigation status control of cargo ships in the Three Gorges Waterway

This paper proposes a Bayesian network-based navigation status control model to estimate the probability of cargo ships being allowed to navigate in the waterway between the Three Gorges Dam and the Gezhouba Dam. The objective of the study is to develop a model to decide the navigation status of cargo ships by considering their attribute characteristics and the navigation risk factors related to the channel flow. Therefore, environmental condition, ship condition, and traffic complexity three modules are comprehensively considered in the proposed model. Specifically, the historical navigation data of cargo ships collected from the Three Gorges Navigation Administration, together with the navigation regulations of this waterway, are used to analyze the influencing factors of the navigation status and to construct the graphical structure of the Bayesian network. Moreover, the conditional probabilities of some nodes are determined using the Noisy-OR gate and the IF-THEN method. Finally, the navigation records are used to verify the feasibility of the navigation status control model for cargo ships, and the estimated average navigable probability of cargo ships is 89 %. The results indicate that the proposed model can reasonably estimate the navigation probability of cargo ships and assist maritime supervisors in controlling the navigation of cargo ships in the Three Gorges Waterway.

Impact of Aging on Driver Preferences for Self-Driving Modes and Behaviors in Two Traffic Complexities

Impact of Aging on Driver Preferences for Self-Driving Modes and Behaviors in Two Traffic Complexities

Graph-based ship traffic partitioning for intelligent maritime surveillance in complex port waters

Maritime Situational Awareness (MSA) is a critical component of intelligent maritime traffic surveillance. However, it becomes increasingly challenging to gain MSA accurately given the growing complexity of ship traffic patterns due to multi-ship interactions possibly involving classical manned ships and emerging autonomous ships. This study proposes a new traffic partitioning methodology to realise the optimal maritime traffic partition in complex waters. The methodology combines conflict criticality and spatial distance to generate conflict-connected and spatially compact traffic clusters, thereby improving the interpretability of traffic patterns and supporting ship anti-collision risk management. First, a composite similarity measure is designed using a probabilistic conflict detection approach and a newly formulated maritime traffic route network learned through maritime knowledge mining. Then, an extended graph-based clustering framework is used to produce balanced traffic clusters with high intra-connections but low inter-connections. The proposed methodology is thoroughly demonstrated and tested using Automatic Identification System (AIS) trajectory data in the Ningbo-Zhoushan Port. The experimental results show that the proposed methodology 1) has effective performance in decomposing the traffic complexity, 2) can assist in identifying high-risk/density traffic clusters, and 3) is sufficiently generic to handle various traffic scenarios in complex geographical waters. Therefore, this study makes significant contributions to intelligent maritime surveillance and provides a theoretical foundation for promoting maritime anti-collision risk management for the future mixed traffic of both manned and autonomous ships.

Traffic Volume Forecast of Parking Lot Entrance and Exit Roads

As people's living standards improve, commercial plaza development also more and more quickly, commercial plaza and the surrounding areas of transport vehicles, in the parking lot entrances and exits of highway road traffic conditions, the parking lot entrances and exits of low traffic capacity and traffic organization chaos (motor vehicles, electric vehicles and the status of the bicycle traffic complex). It is necessary to predict the traffic volume of the entrance and exit roads of the parking lot. In ningyang county lingyun building as an example, through direct observation records investigation parking lot parking condition and facilities, analysis of traffic organization form of the parking lot entrances, investigate the status of the parking lot entrance road and surrounding roads, using artificial paper counting method to collect parking entrance road traffic flow, speed and other data, The time series moving average and exponential smoothing are used to predict the traffic volume at the entrance and exit of the parking lot. The prediction results show that the two methods are suitable for the parking lot entrance traffic volume prediction, and the moving average method is more accurate than the exponential smoothing method, which is closer to the actual value.