Dynamic Traffic Routing Research Articles

A Review of the Advances in Artificial Intelligence in Transportation System Development

In modern times, the rapid expansion of urban populations has intensified the urgency to optimize transportation systems, which has become an alarming issue in the face of urbanization and traffic congestion. This paper reviews the latest applications of Artificial Intelligence (AI) in the transport sector. It explores various AI methodologies, including Artificial Neural Networks (ANN), Genetic Algorithms (GA), Simulated Annealing (SA), Ant Colony Optimizer (ACO), Bee Colony Optimization (BCO), disruptive urban mobility, Fuzzy Logic Models (FLM), automated incident detection systems, and drones, which improve dynamic traffic management and route optimization. The study reveals that integrating these AI techniques with real-time data analytics improves traffic flow, automated incident management, and overall transportation efficiency. The results demonstrate that AI-driven systems, such as drones equipped with advanced sensors and AI algorithms, are increasingly capable of autonomous navigation, real-time monitoring, and predictive traffic management. These advancements in technologies, such as electric Vertical Take-off and Landing (eVTOL) aircraft, Hyperloop Transportation Technologies (HTT), Mobility-as-a-Service (MaaS) and autonomous delivery robots, contribute to smarter urban mobility solutions. However, it is important to focus on refining AI models for better performance, addressing challenges such as computational complexity and privacy concerns, and continuing to innovate in AI to improve the economic efficiency and reliability of transportation systems. Furthermore, to promote sustainability development in this sector, ethical considerations such as the protection of user information and the integration of the concepts of informed consent and human autonomy with community engagement programs should also be considered.

Advanced Data Science Applications in Vehicles: A Comprehensive Review

Purpose: This research paper aims to explore the transformative impact of data science applications within the automotive industry, with a focus on the evolution of vehicles into intelligent, connected, and autonomous entities. Through an extensive literature review and analysis of recent developments, the paper seeks to provide a comprehensive understanding of the current state and future prospects of data science in vehicles.
 Methodology: Advancements in sensor technologies, such as LiDAR, HD maps, radars, location tracking and cameras, are discussed, highlighting their role in data collection for applications like advanced driver assistance systems (ADAS) and autonomous driving. Relevant citations are provided. This subsection covers the collection and transmission of real-time data through telematics systems, showcasing their importance for predictive maintenance, fleet management, and personalized insurance programs. Citations support the presented information. The deployment of edge computing for real-time data processing in vehicles is discussed, emphasizing its significance for safety-critical applications like collision avoidance. Citations are provided to support the information. This section explores the application of machine learning algorithms to predict vehicle failures, optimize fuel efficiency, and analyze driver behavior. The importance of leveraging historical data to create accurate models is highlighted with supporting citations.
 Findings: The pivotal role of data science in enabling autonomous vehicles to navigate complex environments is discussed, emphasizing the use of machine learning models for real-time decision-making. Citations support the presented information. This subsection explores the integration of data science in traffic management systems, covering dynamic traffic signal control, congestion prediction, and route optimization. Citations support the findings related to traffic management applications. The paper discusses challenges associated with widespread implementation, including data privacy concerns, cybersecurity risks, and the need for standardized communication protocols. Additionally, potential future directions are outlined, such as the integration of blockchain for secure data sharing and the development of advanced human-machine interfaces.
 Unique Contribution to Theory, Practice and Policy: This research paper provides a well-rounded contribution by seamlessly integrating theoretical concepts, practical applications, and policy considerations in the realm of advanced data science applications in vehicles.

A Novel Hybrid Protocol in Achieving QoS Regarding Data Aggregation and Dynamic Traffic Routing in IoT WSNs

A Novel Hybrid Protocol in Achieving QoS Regarding Data Aggregation and Dynamic Traffic Routing in IoT WSNs

Multi-scale convolutional networks for traffic forecasting with spatial-temporal attention

• Capturing complex spatial-temporal correlations for both short and long term traffic prediction. • Applying soft thresholding fusion to eliminate unimportant spatial-temporal features. • Designing a trainable module to automatically determine the spatial and temporal thresholds. • Constructing multiple convolutions networks and forming groups of units to extract multi-scale features. Traffic forecasting is a classical spatial-temporal data prediction problem with extensive applications, such as dynamic traffic management, route planning and traffic congestion alleviation. However, accurate traffic forecasting is challenging due to complex correlations of traffic data. Few approaches are satisfied with both long-term and short-term prediction tasks. In this paper, we propose a novel Multi-Scale Convolutional Networks (MSCN), an end-to-end solution to solve traffic forecasting problem. MSCN first employs an encoder with spatial-temporal attention mechanism to model both spatial and temporal correlations. Then, the decoder utilizes multiple convolutions and form groups of units to to extract spatial-temporal features for different resolutions. In particular, we propose a soft thresholding fusion mechanism to adaptively adjust the conditions of spatial and temporal correlations. Experiments on two real traffic datasets demonstrate that the proposed MSCN obtains improvements over the state-of-the-art baselines.

Long-term prediction of multiple types of time-varying network traffic using chunk-based ensemble learning

With the development of networking technologies, global Internet traffic is constantly increasing. Moreover, various traffic types associated with a variety of network services and applications co-exist in the network, having diverse volume and seasonality patterns. The knowledge of future patterns of these heterogeneous traffic types is beneficial for the network operators, as it enables proper adjustment of available network resources in near real-time, and allow performing periodical network reconfigurations. In this paper, we propose a chunk-based ensemble learning method for a long-term prediction of multiple network traffic types. Our developed prediction method combines two popular machine learning (ML) approaches: chunk-based learning on data streams and ensemble learning. The proposed method does not require large volumes of training data and is resilient to changes in traffic characteristics. Finally, we create a custom metric called allocation outside blocking threshold (AOBT), which links the problem of network traffic prediction to bandwidth blocking probability in dynamic traffic routing. We compare the performance of our online method to a number of baselines, using the root mean square percentage error (RMSPE) and the proposed AOBT metrics. According to conducted experiments, the proposed streaming approach outperforms the reference ones in RMSPE and AOBT. Depending on the metric and model, chunk-based ensemble learning achieves 5%–34% lower prediction errors across traffic types. However, the choice of a specific model depends on the investigated traffic type and applied metric.

Ant colony optimization-based traffic routing with intersection negotiation for connected vehicles

This study presents a decentralized traffic management system that integrates dynamic traffic routing and signal-free intersection traffic control for connected vehicles (CV). In particular, we apply the ant colony optimization algorithm and the proposed concept of colored CV to solve the dynamic traffic routing problem with multi-source multi-destination traffic flows. Each CV, modeled as an ant, deposits pheromones on its path, while road sensors aggregate the pheromone information representing traffic conditions and transfer the information to the incoming CV to perform routing. Using the colored CV concept, the CV with the same destination deposit the corresponding pheromone called colored pheromone, and only these CV can interact with that pheromone. Thus, we can distinguish multi-source multi-destination traffic flows in the routing process. For implementing signal-free intersection traffic control, we propose a decentralized intersection negotiation protocol that allows the CV to pass the intersections by exchanging their trajectory information, thereby significantly reducing the time required by CV to pass the intersections. We performed extensive simulations to evaluate our system using Simulation of Urban Mobility, a microscopic traffic simulator. The results show improvement of our proposed system over the conventional traffic management systems in terms of the mean trip time and the number of running vehicles.

Swarm intelligence-based green optimization framework for sustainable transportation

Traffic congestion is one of the most critical issues in developing sustainable transportation in smart cities. As the Internet of Things evolves, connected vehicle technology has arisen as an essential research topic in smart, sustainable transportation. This study investigates a decentralized green traffic optimization framework by pushing swarm intelligence into connected vehicles to mitigate traffic congestion. We present a dynamic traffic routing method based on ant species’ swarm intelligence for connected vehicles so that they can communicate with each other and their surrounding environment via digital pheromones to perform routing decision-making in a decentralized manner. Traditional pheromones attract other vehicles to move to the optimal path, which will soon be congested if many vehicles travel on that path concurrently. To overcome this limitation, we propose the concept of repelling pheromone, which generates a repulsive force among vehicles so that their travel paths are distributed throughout a road network, resulting in a congestion-free road network. The proposed method is validated in the Simulation of Urban Mobility platform. Simulation findings reveal that the proposed method outperforms baseline methods in mitigating traffic congestion, reducing average fuel consumption and emissions by 13–19% and the average trip duration by 19–28%.

Dynamic VNF placement, resource allocation and traffic routing in 5G

5G networks are going to support a variety of vertical services, with a diverse set of key performance indicators (KPIs), by using enabling technologies such as software-defined networking and network function virtualization. It is the responsibility of the network operator to efficiently allocate the available resources to the service requests in such a way to honor KPI requirements, while accounting for the limited quantity of available resources and their cost. A critical challenge is that requests may be highly varying over time, requiring a solution that accounts for their dynamic generation and termination. With this motivation, we seek to make joint decisions for request admission, resource activation, VNF placement, resource allocation, and traffic routing. We do so by considering real-world aspects such as the setup times of virtual machines, with the goal of maximizing the mobile network operator profit. To this end, first, we formulate a one-shot optimization problem which can attain the optimum solution for small size problems given the complete knowledge of arrival and departure times of requests over the entire system lifespan. We then propose an efficient and practical heuristic solution that only requires this knowledge for the next time period and works for realistically-sized scenarios. Finally, we evaluate the performance of these solutions using real-world services and large-scale network topologies. Results demonstrate that our heuristic solution performs better than a state-of-the-art online approach and close to the optimum.

The effects of dynamic traffic conditions, route characteristics and environmental conditions on trip-based electricity consumption prediction of electric bus

As prediction of trip-based electricity consumption has become an prerequisite for the deployment of large-scale EB fleets, this study has established random forest-based models to systematically investigate the impacts of environmental conditions, route characteristics, and dynamic traffic conditions. The models have been performed on real-world data collected from 1024 EBs over five consecutive months in Shenzhen, China. The results show that considering all the influencing variables can significantly enhance the prediction performance, but comparatively speaking, the route characteristics contribute the most among the three categories and involving more variables demonstrates greater advantages within the trip length under 20 km. It is also found that the trip length, the number of bus stops and the number of the traffic lights passed rank the top three most influencing factors, while the wet-dry condition is the least one. In addition, the variations under five operation scenarios show similar trend. The trip length and average travel speed are inversely proportional to the specific electricity consumption, while the number of bus stops visited, traffic lights passed, and ambient temperature exhibit a gentle proportional relationship. Moreover, it is suggested to plan the new bus line over 10 km in terms of reducing electricity consumption per kilometre.

Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles

In this study, we focus on dynamic traffic routing of connected vehicles with various origins and destinations; this is referred to as a multi-source multi-destination traffic routing problem. Ant colony optimization (ACO)-based routing method, together with the idea of coloring ants, is proposed to solve the defined problem in a distributed manner. Using the concept of coloring ants, traffic flows of connected vehicles to different destinations can be distinguished. To evaluate the performance of the proposed method, we perform simulations on the multi-agent NetLogo platform. The simulation results indicate that the ACO-based routing method outperforms the shortest path-based routing method (i.e., given the same simulation period, the average travel time decreases by 8% on average and by 11% in the best case, whereas the total number of arrived vehicles increases by 13% on average and by 23% in the best case).

Dynamic Routing and Wavelength Assignment Using Cost Based Heuristics in WDM Optical Networks

Dynamic routing and wavelength assignment problem in optical networks is a two-step problem that is influenced by the choice of a successful optimal path selection and wavelength assignment. Proper selection techniques reduce the number of wavelengths required in the network and thereby improves traffic grooming. Heuristic algorithms and integer linear programming models help in selection of route and wavelength separately. Hence, the computation time is large which makes the system slow. A cost function is computed which uses independent parameters in the network for the selection of route and wavelength for a call. The heuristic reduces computation time by combining the search of route and wavelength to be assigned. In addition, the network performance is analyzed with and without alternate routing along with proposed heuristics. The selection of proper route and wavelength finding technique is very essential since it improves the grooming factor of the network thereby allowing more traffic support by the network. Our objective is to investigate and propose a cost based heuristics for dynamic traffic routing and wavelength Assignment in WDM optical networks. For this we plan to develop cost functions and heuristics to compute the route and wavelength assignment strategy. Here, our objective is to reduce the computation time for selection of route and wavelength assignment strategy by weighted cost function. The function has to include network parameters for its processing. Our work provides an overview about DRWA by applying cost based heuristics in WDM networks. This paper explains the proposed cost function and its applications in line with selection of independent parameters. The details of other functions like cost function formulation, hop-based route assignment, available wavelength based route assignment, mathematical analysis of proposed cost function are also explained. Results and discussions based on the findings are presented.

Linearly Solvable Mean-Field Traffic Routing Games

We consider a dynamic traffic routing game over an urban road network involving a large number of drivers in which each driver selecting a particular route is subject to a penalty that is affine in the logarithm of the number of drivers selecting the same route. We show that the mean-field approximation of such a game leads to the so-called linearly solvable Markov decision process, implying that its mean-field equilibrium (MFE) can be found simply by solving a finite-dimensional linear system backward in time. Based on this backward-only characterization, it is further shown that the obtained MFE has the notable property of strong time-consistency. A connection between the obtained MFE and a particular class of fictitious play is also discussed.

Search for parking: A dynamic parking and route guidance system for efficient parking and traffic management

Parking space is a scarce resource in today’s cities. Travelers often spend a sizable amount of time searching for available parking spaces. This increases travelers’ overall travel cost and causes additional congestion on the road. Without a good parking information guidance system, parking search remains a trial and error process. In this article, we propose a dynamic parking guidance system that integrates parking destination switching and real-time traffic routing. Drivers can switch their parking destinations and routes during their trip to minimize their expected travel costs. The probability that a driver is willing to switch to a new parking garage is taken to be proportional to the cost difference of the current parking destination and the new one. Microscopic simulation using SUMO and OmNet++ is designed to test the proposed dynamic parking and route guidance system. The simulation results show that with the proposed joint dynamic parking destination switching and dynamic traffic routing, the average overall cost in the network can be reduced. From the parking resources management point of view, parking garage occupancy can be more balanced across all the parking garages with the proposed system, which indicates that the system can help better coordinate available parking resources and avoid potential over-utilization or under-utilization of parking resources.

An integrated approach for dynamic traffic routing and ramp metering using sliding mode control

The problem of designing integrated traffic control strategies for highway networks with the use of route guidance, ramp metering is considered. The highway network is simulated using a first order macroscopic model called LWR model which is a mathematical traffic flow model that formulates the relationships among traffic flow characteristics in terms of density, flow, and mean speed of the traffic stream. An integrated control algorithm is designed to solve the proposed problem, based on the inverse control technique and variable structure control (super twisting sliding mode). Three case studies have been tested in the presence of an on-ramp at each alternate route and where there is a capacity constraint in the network. In the first case study, there is no capacity constraint at either upstream or downstream of the alternate routes and the function of the proposed algorithm is only to balance the traffic flow on the alternate routes. In the second case study, there is capacity constraint at downstream of alternate routes. The proposed algorithm aims to avoid congestion on the main road and balance the traffic flow on the alternate routes. In the last case study, there is capacity constraint at upstream of alternate routes. The objective of proposed algorithm is to avoid congestion on the main road and to balance the traffic flow on the alternate routes. The obtained results show that the proposed algorithms can establish user equilibrium between two alternate routes even when the on-ramps, located at alternate routes, have different traffic demands.

Dynamic traffic routing in a network with adaptive signal control

In real traffic networks, travellers’ route choice is affected by traffic control strategies. In this research, we capture the interaction between travellers’ route choice and traffic signal control in a coherent framework. For travellers’ route choice, a VANET (Vehicular Ad hoc NETwork) is considered, where travellers have access to the real-time traffic information through V2V/V2I (Vehicle to Vehicle/Vehicle to Infrastructure) infrastructures and make route choice decisions at each intersection using hyper-path trees. We test our algorithm and control strategy by simulation in OmNet++ (A network communication simulator) and SUMO (Simulation of Urban MObility) under several scenarios. The simulation results show that with the proposed dynamic routing, the overall travel cost significantly decreases. It is also shown that the proposed adaptive signal control reduces the average delay effectively, as well as reduces the fluctuation of the average speed within the whole network.

Algorithmic Support of Optimization of Multicast Data Transmission in Networks with Dynamic Routing

This article describes the optimization process for the multicast tree in the networks with dynamic traffic routing, which is based on the channel state and evaluates each channel. The problem of constructing a multicast connections tree on graphs is formalized. The possibility of improving the existing algorithms by selecting certain rendezvous points, as well as the algorithm for reducing the cost of the multicast routing tree are analysed. Simulation of tree construction was made in accordance with the family of Protocol-Independent Multicast protocols and the proposed algorithm. A critical analysis of the simulation results and comparative evaluation of uniformity of loading data channel at multicast routing are carried out.

Ant Colony Routing algorithm for freeway networks

Dynamic traffic routing refers to the process of (re)directing vehicles at junctions in a traffic network according to the evolving traffic conditions. The traffic management center can determine desired routes for drivers in order to optimize the performance of the traffic network by dynamic traffic routing. However, a traffic network may have thousands of links and nodes, resulting in a large-scale and computationally complex non-linear, non-convex optimization problem. To solve this problem, Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. ACO is implemented online to determine the control signal – i.e., the splitting rates at each node. However, using standard ACO for traffic routing is characterized by four main disadvantages: 1. traffic flows for different origins and destinations cannot be distinguished; 2. all ants may converge to one route, causing congestion; 3. constraints cannot be taken into account; and 4. neither can dynamic link costs. These problems are addressed by adopting a novel ACO algorithm with stench pheromone and with colored ants, called Ant Colony Routing (ACR). Using the stench pheromone, the ACR algorithm can distribute the vehicles over the traffic network with less or no traffic congestion, as well as reduce the number of vehicles near some sensitive zones, such as hospitals and schools. With colored ants, the traffic flows for multiple origins and destinations can be represented. The proposed approach is also implemented in a simulation-based case study in the Walcheren area, the Netherlands, illustrating the effectiveness of the approach.

THE ANALYSIS OF TELECOM OPERATOR'S REVENUE DEPENDENCE ON DIFFERENT ROUTING AND INTERCONNECTION CHARGING METHODS

Telecommunication operators endeavour to offer their users numerous services and, at the same time, to be competitive with other operators at the market while accomplishing high revenue. The only way to achieve all those goals is to make better usage of their network resources and realise high network efficiency. In this paper, we tested the assumption that a proper combination of the different dynamic telecommunication traffic routing methods and interconnection charging methods can provide higher operators revenue. In order to analyze the effects of combinations of four different routing and two interconnection charging methods on operator's revenue, we developed new software for simulation of network operations. This software is used for simulation of the three interconnected networks operators. Results considering the dependence of operator's revenue on routing and interconnection charging methods combination are analyzed and discussed.

Short-Term Traffic State Forecasting for Dynamic Traffic Routing System

The paper researches the problem of how to forecast traffic information in one minute interval. A kalman filter algorithm for short-term traffic forecasting based on the construction of dynamic traffic routing system of Nanning city of China was proposed in this paper, and selected National Road as field calibration, which compared with the real traffic conditions, and the error of predicted results is less than 10%. The results demonstrate the effectiveness of the proposed forecasting model in paper, and the research has a significant contribution for data process of Dynamic Traffic Routing System.

Threshold based AntNet algorithm for dynamic traffic routing of road networks

Dynamic routing algorithms play an important role in road traffic routing to avoid congestion and to direct vehicles to better routes. AntNet routing algorithms have been applied, extensively and successfully, in data communication network. However, its application for dynamic routing on road networks is still considerably limited. This paper presents a modified version of the AntNet routing algorithm, called “Threshold based AntNet”, that has the ability to efficiently utilize a priori information of dynamic traffic routing, especially, for road networks. The modification exploits the practical and pre-known information for most road traffic networks, namely, the good travel times between sources and destinations. The values of those good travel times are manipulated as threshold values. This approach has proven to conserve tracking of good routes. According to the dynamic nature of the problem, the presented approach guards the agility of rediscovering a good route. Attaining the thresholds (good reported travel times), of a given source to destination route, permits for a better utilization of the computational resources, that, leads to better accommodation for the network changes. The presented algorithm introduces a new type of ants called “check ants”. It assists in preserving good routes and, better yet, exposes and discards the degraded ones. The threshold AntNet algorithm presents a new strategy for updating the routing information, supported by the backward ants.