Traffic Management Approach Research Articles

Analysis of Traffic Accident Patterns Using Association Rule Mining

This study analyzed the levels of minor, moderate, and severe traffic accidents in the Palembang Police area from 2015 to 2020 using association rule mining and the apriori algorithm. The study established valuable insights into accident trends and contributing factors by leveraging traffic accident data and determining variable relationships. With a minimum support threshold of 0.05 and a confidence value of 0.5, the processed data revealed 349 total incidents, categorized as follows: 58 minor accidents (16.62%), 168 moderate accidents (48.14%), and 123 severe accidents (35.24%). The findings highlight that moderate-level accidents form the majority, underlining the need for targeted interventions in this category. The application of the apriori algorithm facilitated the identification of frequent itemsets and rules that reveal patterns across accident variables, such as road conditions, road functions, accident types, weather conditions, and victim statuses. This study also demonstrated the practicality of the apriori algorithm in analyzing extensive datasets to extract actionable insights. The processed rules can be a foundation for developing predictive models or decision-making tools to mitigate accident risks. For example, analyzing variables at different accident levels allows policymakers to identify critical factors contributing to accidents, implement tailored safety measures, and prioritize infrastructure improvements. Furthermore, the study emphasizes the potential of data-driven traffic management and accident prevention approaches. By incorporating modern data mining techniques, stakeholders can transition from traditional data recapitulation to predictive analytics, enabling proactive measures for public safety. Future research can build upon this work by integrating real-time data sources, such as IoTbased traffic monitoring systems, to enhance the prediction accuracy and scope of analysis. Further exploration of mid- and low-confidence rules may provide insights into rare but critical patterns, offering a more comprehensive understanding of accident dynamics. Overall, this research is crucial to leveraging advanced computational methods for public safety and traffic accident reduction, aligning with global efforts to improve road safety and minimize fatalities.

AI for Traffic Prediction and Management

Urban traffic congestion is a growing problem in cities across the globe, contributing to long delays, higher fuel consumption, environmental degradation, and economic losses. Conventional traffic management systems often depend on static data and rule-based approaches, which fall short in dealing with the complexity and variability of modern traffic. This paper introduces an AI-based traffic management approach that utilizes machine learning models to provide real-time traffic forecasts. By integrating historical data, live sensor inputs, and machine learning techniques, this system aims to enhance traffic flow, alleviate congestion, and improve travel efficiency. The model is compared against existing systems, demonstrating improved accuracy, flexibility, and scalability. Results indicate that the AI-based system offers significant advantages in managing urban traffic, surpassing traditional methods. The study further elaborates on how AI-powered traffic management can cut travel times by ensuring efficiency and safety, therefore playing a part in environmental sustainability through emission reduction, and the opportunities and challenges it brings to the table in the implementation of AI in traffic systems.

Revolutionizing emergency vehicle response: a smart traffic management approach with adaptive CNN and hybrid deep learning

Revolutionizing emergency vehicle response: a smart traffic management approach with adaptive CNN and hybrid deep learning

Exploring Trends in Transportation Management: A Review of Scientific Papers on Dimension AI Database from 2020-2024

This qualitative study explores contemporary trends and advancements in sustainable transportation management practices, focusing on articles published between 2020 and 2024 and sourced from sustainability journals. A comprehensive search strategy using the Dimension AI database with the keywords "Transportation" and "Management". Articles were first screened by their title, abstract, and keywords, from which 63 manuscripts were chosen for thematic analysis. The key findings were the importance of sustainable traffic management systems, including congestion measures, infrastructure resilience, and environmental impact assessment. Another strand that emerged was data-driven approaches for smart city traffic management, which included novel techniques for effective decision-making. The study has especially highlighted the requirements of strategies that would set up the innovation in modeling techniques and resilience frameworks for transportation management. This study offers significant insight into modern trends in sustainable transportation management, which should be of immense value to policymakers, urban planners, and transportation professionals in seeking ways to overcome the problems associated with modern urban mobility while ensuring environmental sustainability.

Impact of traffic simulation parameters on the estimation of noise exposure in an urban environment

Since road traffic is the most impactful noise source in the European cities, the evaluation of citizens' exposure is crucial. Although the noise map accuracy is affected by uncertainties in traffic volume, to perform extensive traffic monitoring is not practical and expensive, even in small cities. Traffic simulation software uses routing algorithms to suggest the fastest path to distribute vehicles from an origin through a network to a desired destination which interacts within the urban environment. Therefore, it could represent an important tool for noise mapping in the critical task to fill the data gaps in traffic volumes, also for implementing action plans using a suitable traffic management approach. However, there is a lack of evidence on how the choice of traffic simulation parameters influences noise estimation. In this work, we design a simulation framework to evaluate the impact of routing algorithms on the estimation of population noise exposure in an urban area. Using an open-source pipeline based on public databases and open-source software SUMO and NoiseModelling, we evaluated the implication on the traffic distribution and resulting noise exposure for a set of traffic model key parameters.

Revolutionizing Urban Mobility: Smart Transportation & Vehicle-to-Infrastructure Communication – A Review

The burgeoning popularity of IoT technology has significantly altered transportation, with an extensive variety of repercussions. Intelligent Transportation Systems (ITS) are at the vanguard of this change, which has the potential to fundamentally change urban transportation for both individuals and businesses. ITS has expanded considerably in recent years, owing to global urbanization and industrialization advancements. It is regarded as a game-changing technology that improves road safety, improves traffic flow, and improves the entire driving experience. Despite many research efforts on ITS applications and simulation platforms, formal semantic descriptions for intelligent transportation systems have been noticeably lacking. Rising rates of accidents suggest the necessity for a robust Smart Transport System (STS) that prioritizes improving mobility, and reliability, as well as reducing carbon dioxide and methane emissions from automobiles beyond dispersing gains throughout everywhere. Multiple traffic signal management approaches have been designed to enhance real-time traffic movement at intersections, but none have achieved seamless, congestion-free traffic movements. The creation of an interconnected unified framework that integrates and links vehicles with VANET, IoT, and AI technologies will significantly affect the establishment of an innovative reliable mobility network. Internet of Vehicles (IoV) systems will connect several automobiles to share vital information through an IoT-enabled network. Vehicle-to-infrastructure (V2I) communication is essential in developing intelligent transportation and rail traffic management systems, significantly improving road traffic safety and efficiency. A V2I system integrates transmission and analysis capabilities into vehicles and corresponding infrastructure. Wireless sensors are strategically positioned along highways or driving zones in this system. The significance of safe and effective V2X communication between automobiles and facilities expands substantially as intelligent modes of transport progress.

Urban traffic signal control optimization through Deep Q Learning and double Deep Q Learning: a novel approach for efficient traffic management

Urban traffic signal control optimization through Deep Q Learning and double Deep Q Learning: a novel approach for efficient traffic management

DCoMA: A dynamic coordinative merging assistant strategy for on-ramp vehicles with mixed traffic conditions

Merging sections on highways are identified as traffic bottlenecks, leading to congestion and accidents. The emergence of Connected and Autonomous Vehicles (CAVs) technology promises an optimized solution to on-ramp merging issues. Existing cooperative merging strategies typically focus on determining the merging sequence of vehicles in specific merging areas, overlooking the influence of the macroscopic traffic flow conditions on the merging process. In this paper, we innovatively introduce the Dynamic Cooperative Merging Assistance (DCoMA) strategy, a traffic management approach designed to enhance merging operations under variable traffic demands. At the macroscopic level, DCoMA employs the fundamental diagram of traffic flow to develop a platoon formation algorithm for mainline vehicles, tailored to the dynamic macroscopic states of traffic. The algorithm adaptively adjusts the size of platoons based on the volumes of both the mainline and the on-ramp. Subsequently, the spatio-temporal dynamics of these platoons function as the ’red phase’ of traffic signals, with the intervals between platoons analogous to the ’green phase’. This information is then converted into a time-series format and transmitted to all vehicles on the on-ramp. At the microscopic level, vehicles on the on-ramp alter their driving strategies in response to this time-series data, ensuring a seamless merging into the mainline flow without causing halting. Through simulations and comparative analysis with three existing strategies (i.e., CoopMA, ALINEA, and X-ALINEAQ), the proposed DCoMA strategy exhibits considerable potential for application across diverse traffic volumes and CAV penetration rates. The results indicate that the proposed approach can significantly improve the efficiency of the mainline and on-ramp, achieving a maximum improvement of 18.32%. More importantly, the DCoMA strategy effectively enlarges merging gaps and, coupled with the speed control capabilities of CAVs, significantly mitigates the risk of accidents and reduces exhaust emissions.

Combining multi-agent deep deterministic policy gradient and rerouting technique to improve traffic network performance under mixed traffic conditions

In the future, mixed traffic flow will include two types of vehicles: connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs). CAVs emerge as new solutions to disrupt the traditional transportation system. This new solution shares real-time data with each other and the roadside units (RSU) for network management. Reinforcement learning (RL) is a promising approach for traffic signal management in complex urban areas by leveraging information gathered from CAVs. In particular, coordinating signal management at many intersections is a critical challenge in multi-agent reinforcement learning (MARL). According to this vision, we propose an approach that combines an actor–critic network–based multi-agent deep deterministic policy gradient (MADDPG) model and a rerouting technique (RT) to increase traffic performance in vehicular networks. This algorithm overcomes the inherent non-stationary of Q-learning and the high variance of policy gradient (PG) algorithms. Based on centralized learning with decentralized execution, the MADDPG model employs one actor and one critic for each agent. The actor network uses local information to execute actions, while the critic network is trained with extra information, including the states and actions of other agents. Through a centralized learning process, agents can coordinate with each other, diminishing the influence of an unstable environment. Unlike previous studies, we not only manage traffic light systems but also consider the effect of platooning vehicles on increasing throughput. Experimental results show that our model outperforms other models in terms of traffic performance in different scenarios.

An Intelligent Bayesian Inference Based Learning Automaton Approach for Traffic Management in Radio Access Network

An Intelligent Bayesian Inference Based Learning Automaton Approach for Traffic Management in Radio Access Network

Traffic management approaches using machine learning and deep learning techniques: A survey

Traffic management is improved in cutting-edge smart cities using technologies such as machine learning and deep learning to streamline daily tasks and boost productivity. However, traffic management still suffers from challenging issues such as poor traffic congestion prediction, lack of traffic flow management, public transportation optimization, and emergency management. In this article, we provide a thorough understanding of the benefits, drawbacks, and practical implications of leveraging machine learning and deep learning in Traffic Management Systems (TMSs) by methodically reviewing and critically analysing various traffic management techniques. We also present a generic traffic management architecture that uses a set of assessment criteria to evaluate 20 recently proposed research prototypes (i.e., published since 2019). Finally, we highlight the ongoing challenges and prospective trajectories within the rapidly evolving domain of traffic management, underscoring the need to address emerging issues and directions in its dynamic development. This survey article offers insights that might help in efficiently tackling the issues posed by traffic management while maximizing the potential of machine learning and deep learning techniques. This survey can be a significant resource for researchers, policymakers, and practitioners.

Data-Driven Optimization for Air Traffic Flow Management with Trajectory Preferences

In this paper, we present a novel data-driven optimization approach for trajectory-based air traffic flow management (ATFM). A key aspect of the proposed approach is the inclusion of airspace users’ trajectory preferences, which are computed from traffic data by combining clustering and classification techniques. Machine learning is also used to extract consistent trajectory options, whereas optimization is applied to resolve demand-capacity imbalances by means of a mathematical programming model that judiciously assigns a feasible four-dimensional trajectory and a possible ground delay to each flight. The methodology has been tested on instances extracted from the Eurocontrol data repository. With more than 32,000 flights considered, we solve the largest instances of the ATFM problem available in the literature in short computational times that are reasonable from the practical point of view. As a by-product, we highlight the trade-off between preferences and delays as well as the potential benefits. Indeed, computing efficient solutions to the problem facilitates a consensus between the network manager and airspace users. In view of the level of accuracy of the solutions and the excellent computational performance, we are optimistic that the proposed approach can make a significant contribution to the development of the next generation of air traffic flow management tools.

A real-time traffic rescheduling approach for the metro system

This paper addresses timetable disruptions in metro systems and their impact on passenger service quality. To mitigate these effects, the study introduces a time-space network-based traffic management approach with a two-layer network. The first layer focuses on timetable rescheduling, using an integer program for modeling and a Lagrangian relaxation-based heuristic for solution. The second layer addresses rolling stock duty rescheduling, employing a greedy heuristic for train set duties. The proposed approach is applied to a real-world problem presented in the INFORMS railway application competition (2022). Results demonstrate that, with topological vertex orders, the approach achieves schedule amendments within 3–6 minutes for various disruption scenarios. This rapid response highlights the method's efficacy and advantage in real-time applications, showcasing its potential for practical implementation in metro systems.

A Real-Time Railway Traffic Management Approach Preserving Passenger Connections

A Real-Time Railway Traffic Management Approach Preserving Passenger Connections

Meteorologically normalized spatial and temporal variations investigation using a machine learning-random forest model in criteria pollutants across Tehran, Iran

Investigating road traffic contribution to urban air pollution is helpful to determine traffic management approaches and environmental intervention policies that focus on better air quality. To this end, hourly average values of air pollutants collected from 2015 to 2021 from 20 monitoring sites including 10 traffic (TS) and 10 urban background (UB) sites in Tehran, Iran were retrieved. A machine learning-random forest (RF) model was applied to decouple the meteorology effects of the observed air pollutant values. The meteorologically normalized concentrations of air quality data were used for calculating the traffic increment and trend analysis. The seasonal component showed the greatest importance for predicting PM2.5 and PM10 and boundary layer heights (BLH) was the dominant explanatory variable for the predictions of CO, NO, NO2, and O3 concentrations. The overall annual average value of traffic increment during study period was found as PM10 (10.5 μg m−3), PM2.5 (0.19 μg m−3), NO (9.24 ppb), NO2 (3.67 ppb), NOx (15.6 ppb), and SO2 (0.33 ppb). Ozone indicates a traffic decrement in concentration (−0.70 ppb). The Theil-Sen estimated slopes showed meteorological normalized negative trends for PM2.5, PM10, CO, NO, NOx, and SO2, while meteorological normalized positive trends were observed for NO2 and O3. The PM10, CO, NO, NOx, and SO2 concentrations trends at the traffic sites that experience more anthropogenic emissions revealed greater reductions when emission interventions or control policies are used. The higher increase of NO2 at the UB sites than the traffic sites can be due to the weaker control policies, high traffic volume, higher number of diesel-fueled vehicles, and a higher NO to NO2 conversion because of the relatively high levels of temperature and O3. This quantitative summary provides useful information for decision-makers for evaluating the intervention policies related to road traffic pollutants.

Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration.

Traffic management is a critical task in software-defined IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic management approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and multi-arm bandit algorithms to dynamically optimize traffic management policies based on real-time network traffic patterns. Specifically, our approach uses a GNN model to learn and predict network traffic patterns and a multi-arm bandit algorithm to optimize traffic management policies based on these predictions. We evaluate the proposed approach on three different datasets, including a simulated corporate network (KDD Cup 1999), a collection of network traffic traces (CAIDA), and a simulated network environment with both normal and malicious traffic (NSL-KDD). The results demonstrate that our approach outperforms other state-of-the-art traffic management methods, achieving higher throughput, lower packet loss, and lower delay, while effectively detecting anomalous traffic patterns. The proposed approach offers a promising solution to traffic management in SDNs, enabling efficient resource management and QoS assurance.

Multiple UAVs collaborative traffic monitoring with intention-based communication

This article proposes an innovative ground traffic management approach that utilizes the mobility, flexibility, and collaborative capabilities of multiple Unmanned Aerial Vehicles (UAVs). The objective is to enhance the navigation and driving experience of self-driving vehicles by employing UAVs to cover blind areas that cannot be observed by ground monitoring equipment, thereby facilitating the avoidance of congested routes. This study focuses on determining the optimal number of UAVs and enhancing the scheduling strategy and task assignment method by introducing novel UAV communication collaboration techniques. The research faces challenges such as a limited quantity of UAVs, rapid response and feedback, variations in traffic conditions and tasks, and system complexity. Furthermore, the difficulty of communication collaboration is exacerbated by the incomplete connectivity of UAV networks. To address these challenges, this article introduces a method that utilizes intention information as the message for communication between UAVs, thereby enhancing their collaborative capabilities. Using the Multi-Agent Reinforcement Learning (MARL) neural network, this method generates intention information and utilizes the intention information generated by other UAVs to optimize the flight control and traffic monitoring task allocation strategy. This method effectively addresses the challenge of UAV collaboration in efficiently managing a large volume of stochastic traffic monitoring tasks. Experimental results demonstrate that the proposed approach significantly improves vehicle navigation accuracy and achieves a more balanced distribution of workload among multiple UAVs. Moreover, by sharing road conditions detected in the cloud, it reduces operational costs and opens up possibilities for the practical implementation of UAVs in traffic management.

Intelligent traffic light systems using edge flow predictions

In this paper, we propose a novel graph-based semi-supervised learning approach for traffic light management in multiple intersections. Specifically, the basic premise behind our paper is that if we know some of the occupied roads and predict which roads will be congested, we can dynamically change traffic lights at the intersections that are connected to the roads anticipated to be congested. Comparative performance evaluations show that the proposed approach can produce comparable average vehicle waiting time and reduce the training/learning time of learning adequate traffic light configurations for all intersections within a few seconds, while a deep learning-based approach can be trained in a few days for learning similar light configurations.

Real-time traffic, accident, and potholes detection by deep learning techniques: a modern approach for traffic management

Real-time traffic, accident, and potholes detection by deep learning techniques: a modern approach for traffic management

A game theory approach for smart traffic management

The rapid increase in population and transportation resources presents numerous challenges, including traffic congestion and accidents. This paper proposes a smart traffic management (STM) framework that combines the Internet of Vehicles (IoV) and game theory to manage traffic loads at road intersections. The intersection is considered a non-cooperative game, where traffic flow for each route is determined by the Nash Equilibrium (NE) to ensure that no individual can improve their performance by changing their strategy. In severe congestion, many players/vehicles significantly affect the strategy selection process. To address this, we adopt an agent-representative approach, using spectral clustering to group players with the same strategies and payoff. The proposed STM is compared with other schemes, i.e., the conventional approach without NE and SmartRoute. Our STM significantly outperformed existing protocols, reducing intersection traffic intensity by 30% and average waiting time by 40%, demonstrating its effectiveness in managing traffic loads at uncontrolled intersections.