Efficient Traffic Flow Research Articles

Traffic Aware Data Gathering Protocol for VANETs

Vehicular Ad-Hoc Networks (VANETs) are a challenging yet active research area. It offers a wide range of applications, including Intelligent Transport System (ITS), effective road traffic monitoring, efficient traffic flow and road safety applications. During real-time data gathering for emergency scenarios, the fixed silent segments cause a problem for smooth communication. Moreover, the critical ITS operations may be delayed due to this problem. This paper proposes a Real-Time Traffic-Aware Data Gathering Protocol (TDG) where the dynamic segmentation switching is adopted to handle the communication limitations. TDG is lightweight and dynamically designed for collecting and forwarding data packets based on current and rapid evolving traffic conditions. The primary objective is to reduce network and data communication overhead to incorporate real-time data collection time constraints. TDG implements a data aggregation scheme for data analysis to fetch information based on location, speed, vehicle id and neighbour count. Moreover, a data extraction scheme is implemented to increase data retrieval and data utilization effectiveness in an intelligent way at the base station. Extensive simulation and evaluation results validate that our proposed solution outperforms existing data gathering protocols in effectiveness, efficiency, delay, communication overhead and data transmission rate.

Decentralized Platoon Join-in-Middle Protocol Considering Communication Delay for Connected and Automated Vehicle.

Cooperative driving is an essential component of intelligent transport systems (ITSs). It promises greater safety, reduced accidents, efficient traffic flow, and fuel consumption reduction. Vehicle platooning is a representative service model for ITS. The principal sub-systems of platooning systems for connected and automated vehicles (CAVs) are cooperative adaptive cruise control (CACC) systems and platoon management systems. Based on vehicle state information received through vehicle-to-vehicle (V2V) communication, the CACC system allows platoon vehicles to maintain a narrower safety distance. In addition, the platoon management system using V2V communications allows vehicles to perform platoon maneuvers reliably and accurately. In this paper, we propose a CACC system with a variable time headway and a decentralized platoon join-in-middle maneuver protocol with a trajectory planning system considering the V2V communication delay for CAVs. The platoon join-in-middle maneuver is a challenging research subject as the research must consider the requirement of a more precise management protocol and lateral control for platoon safety and string stability. These CACC systems and protocols are implemented on a simulator for a connected and automated vehicle system, PreScan, and we validated our approach using a realistic control system and V2V communication system provided by PreScan.

Performance analysis of LSTM model with multi-step ahead strategies for a short-term traffic flow prediction

In this study, the effect of direct and recursive multi-step forecasting strategies on the short-term traffic flow forecast performance of the Long Short-Term Memory (LSTM) model is investigated. To increase the reliability of the results, analyses are carried out with various traffic flow data sets. In addition, databases are clustered using the k-means++ algorithm to reduce the number of experiments. Analyses are performed for different time periods. Thus, the contribution of strategies to LSTM was examined in detail. The results of the recursive based strategy performances are not satisfactory. However, different versions of the direct strategy performed better at different time periods. This research makes an important contribution to clarifying the compatibility of LSTM and forecasting strategies. Thus, more efficient traffic flow prediction models will be developed and systems such as Intelligent Transportation System (ITS) will work more efficiently. A practical implication for researchers that forecasting strategies should be selected based on time periods.

Design of Lighting Intelligent Control System Based on OpenCV Image Processing Technology

The high growth of vehicular travel in urban areas, in particular, requires a traffic control system that optimizes traffic flow efficiency. Traffic congestion can also occur by large de-lays in Red Light etc. The delay in lighting is difficult to code and does not rely on real traffic density. It follows that traffic controls are simulated and configured to better meet this rising demand. So, in order to avoid the traffic control problem, the Adaptive Intelligent Traffic Light control system (AITLCS) has been proposed based on OpenCV and Image processing technique. The system proposed is designed to ensure smooth and efficient traffic flow for daily life as well as emergency and public transportation safety. Based on the road density instead of the levels set the proposed system provides the timing for the traffic light signal so that a highly loaded side switched on over long periods compared with the other lanes. It can also be used at an intersection with traffic signs, which controls the traffic light signal at the intersection. If timers are smart to predict the exact time, the system is more efficient because it reduces the time spent on unintended green signal significantly. With the help of OpenCV software, this paper aims to have a signal management SMART solution that will be cost-effective at the end. The system consists of a camera facing a lane taking pictures of the route we want to travel and then the density of the pedestrian and vehicle is taken and compared with each image employing image processing. Such images are processed effectively to learn the density of traffic.

Optimal Topology Formation and Adaptation of Integrated Access and Backhaul Networks

With the increasing densification of cellular networks, it has become exceedingly difficult to provide traditional fiber backhaul access to each cell site, which is especially true for small cell base stations (SBSs). The increasing maturity of millimeter wave (mmWave) communication coupled with multiple-input-multiple-output (MIMO) and beamforming technologies has opened up the possibility of providing high-speed wireless backhaul to such cell sites. The third-generation partnership project (3GPP) is defining an integrated access and backhaul (IAB) architecture for the fifth-generation (5G) cellular networks, in which the same infrastructure and spectral resources are used for both the access and the backhaul. In IAB networks, SBSs, so-called IAB nodes, act either as relay nodes carrying the traffic through multiple hops from a macrocell to an end user and vice versa or as access points to serve user equipments (UEs) in their proximity. To this end, the topology of such IAB networks is essential to enable efficient traffic flow and minimize congestion or increase robustness to backhaul link failure. In this paper, we propose a topology formation algorithm together with methodologies to implement it in real networks and compare it with a standard random sequence approach as well as with an optimal topology obtained using dynamic programming. Our simulation results demonstrate that the proposed algorithm outperforms the random sequence approach by 26% on average in terms of lower bound of the network capacity and is up to 99.7% close to the optimal solution, while being significantly less complex.

AoA-Based Localization System Using a Single IoT Gateway: An Application for Smart Pedestrian Crossing

During the last decade, the concept of smart cities is gaining popularity, and many cities all around the globe are adopting it. Lately, the Smart Pedestrian Crossing (SPC) idea has become an important part of the smart city concept. The main objective of the SPC is to achieve a safe and smooth traffic flow of pedestrians and vehicles in the smart city. In this paper, we present a simple yet efficient localization system which is suitable for locating people over a pedestrian crossing. By adopting the proposed localization system, the people flow over the pedestrian crossing can be observed. Consequently, a safe and efficient traffic flow in the smart city can be achieved. The proposed system utilizes Internet of Things (IoT) transceivers that are carried by the pedestrians or embedded in the pedestrians’ smart devices. These transceivers can be activated to transmit a signal every few seconds when the pedestrian passes over a pedestrian crossing. These periodic signals will be received by a single Angle of Arrival (AoA) estimation system to estimate the direction of the pedestrian. Furthermore, the pedestrian direction and the environmental features of the pedestrian crossings are utilized in a 1D particle filter to estimate accurately the pedestrian’s location. The proposed system has been validated experimentally in the smart zone of Antwerp city. The experimental results reveal that the proposed localization system can provide an accurate, cost effective and reliable localization solution to this specific problem without violating the pedestrian privacy.

Tensor Decomposition for Spatial-Temporal Traffic Flow Prediction with Sparse Data.

Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance.

Short-term traffic flow prediction based on 1DCNN-LSTM neural network structure

In the past decade, the number of cars in China has significantly raised, but the traffic jam spree problem has brought great inconvenience to people’s travel. Accurate and efficient traffic flow prediction, as the core of Intelligent Traffic System (ITS), can effectively solve the problems of traffic travel and management. The existing short-term traffic flow prediction researches mainly use the shallow model method, so they cannot fully reflect the traffic flow characteristics. Therefore, this paper proposed a short-term traffic flow prediction method based on one-dimensional convolution neural network and long short-term memory (1DCNN-LSTM). The spatial information in traffic data is obtained by 1DCNN, and then the time information in traffic data is obtained by LSTM. After that, the space-time features of the traffic flow are used as regression predictions, which are input into the Fully-Connected Layer. In the end, the corresponding prediction results of the current input are calculated. In the past, most of the researches are based on survey data or virtual data, lacking authenticity. In this paper, real data will be used for research. The data are provided by OpenITS open data platform. Finally, the proposed method is compared with other road forecasting models. The results show that the structure of 1DCNN-LSTM can further improve the prediction accuracy.

IAB Topology Design: A Graph Embedding and Deep Reinforcement Learning Approach

As the density of cellular networks grows, it becomes exceedingly difficult to provide traditional fiber backhaul access to each cell site. Millimeter wave communication coupled with beamforming can be used to provide high-speed wireless backhaul to such cell sites. Therefore, the 3rd generation partnership project (3GPP) defines an integrated access and backhaul (IAB) architecture, in which the same infrastructure and spectral resources are shared to provide access and backhaul. However, this complicates the design of topologies in such networks as they need to enable efficient traffic flow and minimize congestion or increase robustness to backhaul link failure. We formulate this problem as a graph optimization problem that maximizes the lower bound of the network capacity and propose a topology formation approach based on a combination of deep reinforcement learning and graph embedding. Our proposed approach is significantly less complex, more scalable, and yields very close performance compared to the optimal dynamic programming approach and significant gains when compared with baseline approaches.

Machine Learning-based traffic prediction models for Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) have attracted an increasing amount of attention in recent years. Thanks to the fast development of vehicular computing hardware, vehicular sensors and citywide infrastructures, many impressive applications have been proposed under the topic of ITS, such as Vehicular Cloud (VC), intelligent traffic controls, etc. These applications can bring us a safer, more efficient, and also more enjoyable transportation environment. However, an accurate and efficient traffic flow prediction system is needed to achieve these applications, which creates an opportunity for applications under ITS to deal with the possible road situation in advance. To achieve better traffic flow prediction performance, many prediction methods have been proposed, such as mathematical modeling methods, parametric methods, and non-parametric methods. Among the non-parametric methods, the one of the most famous methods today is the Machine Learning-based (ML) method. It needs less prior knowledge about the relationship among different traffic patterns, less restriction on prediction tasks, and can better fit non-linear features in traffic data. There are several sub-classes under the ML method, such as regression model, kernel-based model, etc. For all these models, it is of vital importance that we choose an appropriate type of ML model before building up a prediction system. To do this, we should have a clear view of different ML methods; we investigate not only the accuracy of different models, but the applicable scenario and sometimes the specific type of problem the model was designed for. Therefore, in this paper, we are trying to build up a clear and thorough review of different ML models, and analyze the advantages and disadvantages of these ML models. In order to do this, different ML models will be categorized based on the ML theory they use. In each category, we will first give a short introduction of the ML theory they use, and we will focus on the specific changes made to the model when applied to different prediction problems. Meanwhile, we will also compare among different categories, which will help us to have a macro overview of what types of ML methods are good at what types of prediction tasks according to their unique model features. Furthermore, we review the useful add-ons used in traffic prediction, and last but not least, we discuss the open challenges in the traffic prediction field.

Providing personal information to the benefit of others

Personal information is a precious resource, not only for commercial interests but also for the public benefit. Reporting personal location data, for example, may aid efficient traffic flows and sharing one's health status may be a crucial instrument of disease management. We experimentally study individuals' willingness to contribute personal information to information-based public goods. Our data provide evidence that-compared to monetary contributions to public goods-information may be substantially under-provided. We show that the degree of information provision is strongly correlated to the information's implicit (emotional and cognitive) costs. Individual's reluctance to share personal information with high implicit, in particular emotional costs, may seriously limit the effectiveness of information-based public goods.

DeepGuard: Efficient Anomaly Detection in SDN With Fine-Grained Traffic Flow Monitoring

Software-Defined Networking (SDN) leverages the implementation of reliable, flexible and efficient network security mechanisms which make use of novel techniques such as artificial intelligence (AI) and machine learning (ML). In particular, these techniques - together with SDN - are the key enablers for the design of anomaly detection methods which are based on efficient traffic flow monitoring. In this paper, we tackle this problem by proposing an efficient anomaly detection framework, denoted as DeepGuard, which improves the detection performance of cyberattacks in SDN based networks by adopting a fine-grained traffic flow monitoring mechanism. Specifically, the proposed framework utilizes a deep reinforcement learning technique, i.e., Double Deep ${Q}$ -Network (DDQN), to learn traffic flow matching strategies maximizing the traffic flow granularity while proactively protecting the SDN data plane from being overloaded. Afterwards, by implementing the learned optimal traffic flow matching control policy, the most beneficial traffic information for anomaly detection is acquired at runtime—thereby improving the cyberattack detection performance. The performance of the proposed framework is validated by extensive experiments, and the results show that DeepGuard yields significant performance improvements compared to existing traffic flow matching mechanisms regarding the level of traffic flow granularity. In the case of distributed denial-of-service (DDoS) attacks, DeepGuard achieves a remarkable attack detection performance while effectively preventing forwarding performance degradation in the SDN data plane.

Prioritizing Safety or Traffic Flow? Qualitative Study on Highly Automated Vehicles’ Potential to Prevent Pedestrian Crashes with Two Different Ambitions

Interaction between drivers and pedestrians enables pedestrians to cross the street without conflicts. When highly automated vehicles (HAVs) become prevalent, interaction will change. Although HAVs manage to identify pedestrians, they may not be able to assess pedestrians’ intentions. This study discusses two different ambitions: Prioritizing pedestrian safety and prioritizing efficient traffic flow; and how these two affect the possibilities to avoid fatal crashes between pedestrians and passenger cars. HAVs’ hypothetical possibilities to avoid different crash scenarios are evaluated based on 40 in-depth investigated fatal pedestrian crashes, which occurred with manually-driven cars in Finland in 2014–2016. When HAVs prioritize pedestrian safety, they decrease speed near pedestrians as a precaution which affects traffic flow due to frequent decelerations. When HAVs prioritize efficient traffic flow, they only decelerate, when pedestrians are in a collision course. The study shows that neither of these approaches can be applied in all traffic environments, and all of the studied crashes would not likely be avoidable with HAVs even when prioritizing pedestrian safety. The high expectations of HAVs’ safety benefits may not be realized, and in addition to safety and traffic flow, there are many other objectives in traffic which need to be considered.

Modeling and Assessing Connectivity Services Performance in a Sandbox Domain

The automation of Network Services (NS) consisting of virtual functions connected through a multilayer packet-over-optical network requires predictable Quality of Service (QoS) performance, measured in terms of throughput and latency, to allow making proactive decisions. QoS is typically guaranteed by overprovisioning capacity dedicated to the NS, which increases costs for customers and network operators, especially when the traffic generated by the users and/or the virtual functions highly varies over the time. This article presents the PILOT methodology for modeling the performance of connectivity services during commissioning testing in terms of throughput and latency. Benefits are double: first, an accurate per-connection model allows operators to better operate their networks and reduce the need for overprovisioning; and second, customers can tune their applications to the performance characteristics of the connectivity. PILOT runs in a sandbox domain and constructs a scenario where an efficient traffic flow simulation environment, based on the CURSA-SQ model, is used to generate large amounts of data for Machine Learning (ML) model training and validation. The simulation scenario is tuned using real measurements of the connection (including throughput and latency) obtained from a set of active probes in the operator network. PILOT has been experimentally validated on a distributed testbed connecting UPC and Telefonica premises.

Optimal Control for Connected and Autonomous Vehicles at Signal-Free Intersections

Abstract The development of connected and autonomous vehicles (CAVs) is one of the central aspects in the pathway towards future intelligent mobility systems. This paper addresses the problem of coordinating CAVs crossing an uncontrolled intersection so as to maintain safe and efficient traffic flow. The proposed control strategy is based on an optimal control framework that is formulated to minimize a weighted sum of total energy consumption and travel time of all CAVs by finding the optimal velocity trajectory of each vehicle. The design procedure starts with a proper formulation of the autonomous intersection crossing problem for CAVs, with various cases of energy recovery capability by the CAVs considered, to also investigate the influence of powertrain electrification on the intersection crossing problem. This yields an optimal control problem (OCP) with nonlinear and nonconvex dynamics and constraints. In order to ensure a rapid solution search and a unique global optimum, the OCP is reformulated via convex modeling techniques. Numerical results validate the effectiveness of the proposed approaches, while the trade-off between energy consumption and travel time is illustrated by Pareto optimal solutions.

Scalable Emulated Framework for IoT Devices in Smart Logistics Based Cyber-Physical Systems: Bonded Coverage and Connectivity Analysis

In this research, scalable framework for Smart Logistics based Cyber-Physical System (SLCPS) is emulated for stable coverage and connectivity of Internet of Things (IoT) devices. This work is modern manifestation of three laws of computing. Moore's and Koomey's laws recommend performance gain and energy efficiency whereas Metcalfe's law imply network scalability. Combination of these laws suggests the research proposition that development of scalable and performance efficient IoT networks is inevitable. Although IoT has improved specific logistics modules considerably, but incorporation of IoT in complete supply chain of food and random placement of IoT devices due to which unstable coverage and connectivity occurred are major challenges in logistics. The proposed SLCPS framework is designed firstly, to develop apt IoT protocol stack for logistics. Secondly, for bonded connectivity and coverage, mathematical models are proposed instead of random placement and coverage map is based on binary coverage model. Thirdly, for scalability supply chain of food for smart logistics process is designed in terms of container, storehouse and warehouse comprising of varying number of IoT devices. The architecture of SLCPS framework has three modules i.e. internal IoT network, border router and external network, emulated in Cooja simulator. The contikimac protocol is used for efficient traffic flow and power consumption. Single hop, multiple hops and random IoT devices placement scenarios are used for results comparison and validation. The performance evaluation results, i.e. throughput, network convergence time, packet delivery ratio, average latency, power consumption and timeline investigation validated utilization of proposed framework in terms of enhanced network performance. Significance of proposed SLCPS framework results in cost minimization, reducing communication and computation overhead, resilience to IoT device failures and an interference free network connectivity and coverage. Coverage and connectivity are measure of quality of service in IoT network. Therefore, this research provided bonded coverage and connectivity in smart logistics using mathematical models. In addition, a baseline framework is provided for extended research in CPS and IoT applications.

Traffic Routing Algorithm for Road Network

Abstract Nobody in this world wants to be held hostage in a traffic jam for hours and hours. The need to have efficient traffic flow in our roads cannot be overemphasized. It is in this understanding that this paper delves into the study and implementation of a suitable algorithm for traffic routing. The implementation is based on graph theory, where a road network map is first converted to a graph with nodes and edges where the edges represent the roads and the nodes represent the various road intersections. The roads are given various weights depending on their distance and traffic density, and the time a motorist would take to move from one point to another is estimated. A suitable routing algorithm, which in this case is the Dijkstra’s algorithm is applied on the weighted graph.

Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges

The promising advancements in the telecommunications and automotive sectors over the years have empowered drivers with highly innovative communication and sensing capabilities, in turn paving the way for the next-generation connected and autonomous vehicles. Today, vehicles communicate wirelessly with other vehicles and vulnerable pedestrians in their immediate vicinity to share timely safety-critical information primarily for collision mitigation. Furthermore, vehicles connect with the traffic management entities via their supporting network infrastructure to become more aware of any potential hazards on the roads and for guidance pertinent to their current and anticipated speeds and travelling course to ensure more efficient traffic flows. Therefore, a secure and low-latency communication is highly indispensable in order to meet the stringent performance requirements of such safety-critical vehicular applications. However, the heterogeneity of diverse radio access technologies and inflexibility in their deployment results in network fragmentation and inefficient resource utilization, and these, therefore, act as bottlenecks in realizing the aims for a highly efficient vehicular networking architecture. In order to overcome such sorts of bottlenecks, this article brings forth the current state-of-the-art in the context of intelligent transportation systems (ITS) and subsequently proposes a software-defined heterogeneous vehicular networking (SDHVNet) architecture for ensuring a highly agile networking infrastructure to ensure rapid network innovation on-demand. Finally, a number of potential architectural challenges and their probable solutions are discussed.

Evaluating the safety impact of connected and autonomous vehicles on motorways

Recent technological advancements bring the Connected and Autonomous Vehicles (CAVs) era closer to reality. CAVs have the potential to vastly improve road safety by taking the human driver out of the driving task. However, the evaluation of their safety impacts has been a major challenge due to the lack of real-world CAV exposure data. Studies that attempt to simulate CAVs by using either a single or integrating multiple simulation platforms have limitations, and in most cases, consider a small element of a network (e.g. a junction) and do not perform safety evaluations due to inherent complexity. This paper addresses this problem by developing a decision-making CAV control algorithm in the simulation software VISSIM, using its External Driver Model Application Programming Interface. More specifically, the developed CAV control algorithm allows a CAV, for the first time, to have longitudinal control, search adjacent vehicles, identify nearby CAVs and make lateral decisions based on a ruleset associated with motorway traffic operations. A motorway corridor within M1 in England is designed in VISSIM and employed to implement the CAV control algorithm. Five simulation models are created, one for each weekday. The baseline models (i.e. CAV market penetration: 0%) are calibrated and validated using real-world minute-level inductive loop detector data and also data collected from a radar-equipped vehicle. The safety evaluation of the proposed algorithm is conducted using the Surrogate Safety Assessment Model (SSAM). The results show that CAVs bring about compelling benefit to road safety as traffic conflicts significantly reduce even at relatively low market penetration rates. Specifically, estimated traffic conflicts were reduced by 12–47%, 50–80%, 82–92% and 90–94% for 25%, 50%, 75% and 100% CAV penetration rates respectively. Finally, the results indicate that the presence of CAVs ensured efficient traffic flow.

Vehicular traffic optimisation and even distribution using ant colony in smart city environment

For a few years, route optimisation and efficient traffic flow are a big challenge, especially in a current situation when 54.5% population is living in the urban environment all over the world. At peak hours, the traffic jams in urban areas are frequent. Lots of works have been done in finding the shortest path to optimise the route to the destination in minimum time. However, moving vehicles toward the shorter paths causes a severe traffic jam in the city. Therefore, in this study, the authors proposed a framework to enhance the efficiency of the ant colony optimisation (ACO) algorithm to optimise the vehicular traffic, i.e. named as smart traffic distribution ACO. It helps to optimise the route and city traffic efficiently while avoiding congestion in all circumstances using up-to-date city traffic data. Their proposed framework finds the optimal path in such a way that the traffic flow on each road remains normal. The detection of congestion on the road at an early stage and even distribution of traffic on all roads helps to achieve maximum flow, speed, and optimum density of the roads.