Traffic Congestion Control Research Articles

IoT based traffic congestion control for environmental applications

Recently, the demands of the Internet of Things (IoT) have been steadily growing .The main idea of the Internet of Things (IoT) is to revolutionize the current Internet by enriching it with a large number of intelligent objects that communicate with each other. Heterogeneous communication technologies are integrated into the Internet to achieve the IoT application. The function of these objects is to collect data from sensors, process it, and communicate it. Therefore, sensors are main features of IoT. The Heterogeneous Wireless sensor network (HWSN) is a key technology element of the Internet of Things (IoT), which is considered as the future evolution of the Internet. The integration of HWSNs into IoT makes communication with any type of object possible and opens the gates to a multitude of application areas. We have proposed: (Load-Balancing based Clustering Multipath routing protocol for Heterogeneous sensor networks). After having detailed the functioning of our protocol, we present the realization of the simulations by the NS3 simulator, whose objective is to allow us to evaluate the performances of the proposed (Load-Balancing based Clustering Multipath routing protocol for HWSN) by comparing the results with the two protocols: (Weight based clustering in wireless sensor networks) and (Distributed Energy efficient Adaptive Clustering Protocol). We thus take into account several metrics for the performance of evaluation. We also note that the proposed protocol is 47% better than DEACP with respect to fist node die, and 35% better than WBCHN with respect to last node die while maintaining the average data transmission delay. We were able to show through the simulation results obtained that the objectives have been reached.

Evolution of road traffic congestion control: A survey from perspective of sensing, communication, and computation

Road traffic congestion can inevitably degrade road infrastructure and decrease travel efficiency in urban traffic networks, which can be relieved by employing appropriate congestion control. According to different developmental driving forces, in this paper, the evolution of road traffic congestion control is divided into two stages. The ever-growing number of advanced sensing techniques can be seen as the key driving force of the first stage, called the sensing stage, in which congestion control strategies experienced rapid growth owing to the accessibility of traffic data. At the second stage, i.e., the communication stage, communication and computation capability can be regarded as the identifying symbols for this stage, where the ability of collecting finer-grained insight into transportation and mobility reality improves dramatically with advances in vehicular networks, Big Data, and artificial intelligence. Specifically, as the pre-requisite for congestion control, in this paper, existing congestion detection techniques are first elaborated and classified. Then, a comprehensive survey of the recent advances for current congestion control strategies with a focus on traffic signal control, vehicle route guidance, and their combined techniques is provided. In this regard, the evolution of these strategies with continuous development of sensing, communication, and computation capability are also introduced. Finally, the paper concludes with several research challenges and trends to fully promote the integration of advanced techniques for traffic congestion mitigation in transportation systems.

Traffic Efficiency Models for Urban Traffic Management Using Mobile Crowd Sensing: A Survey

The population is increasing rapidly, due to which the number of vehicles has increased, but the transportation system has not yet developed as development occurred in technologies. Currently, the lowest capacity and old infrastructure of roads do not support the amount of vehicles flow which cause traffic congestion. The purpose of this survey is to present the literature and propose such a realistic traffic efficiency model to collect vehicular traffic data without roadside sensor deployment and manage traffic dynamically. Today’s urban traffic congestion is one of the core problems to be solved by such a traffic management scheme. Due to traffic congestion, static control systems may stop emergency vehicles during congestion. In daily routine, there are two-time slots in which the traffic is at peak level, which causes traffic congestion to occur in an urban transportation environment. Traffic congestion mostly occurs in peak hours from 8 a.m. to 10 a.m. when people go to offices and students go to educational institutes and when they come back home from 4 p.m. to 8 p.m. The main purpose of this survey is to provide a taxonomy of different traffic management schemes for avoiding traffic congestion. The available literature categorized and classified traffic congestion in urban areas by devising a taxonomy based on the model type, sensor technology, data gathering techniques, selected road infrastructure, traffic flow model, and result verification approaches. Consider the existing urban traffic management schemes to avoid congestion and to provide an alternate path, and lay the foundation for further research based on the IoT using a Mobile crowd sensing-based traffic congestion control model. Mobile crowdsensing has attracted increasing attention in traffic prediction. In mobile crowdsensing, the vehicular traffic data are collected at a very low cost without any special sensor network infrastructure deployment. Mobile crowdsensing is very popular because it can transmit information faster, collect vehicle traffic data at a very low cost by using motorists’ smartphone or GPS vehicular embedded sensor, and it is easy to install, requires no special network deployment, has less maintenance, is compact, and is cheaper compared to other network options.

An Analysis on Contemporary MAC Layer Protocols in Vehicular Networks: State-of-the-Art and Future Directions

Traffic density around the globe is increasing on a day-to-day basis, resulting in more accidents, congestion, and pollution. The dynamic vehicular environment induces challenges in designing an efficient and reliable protocol for communication. Timely delivery of safety and non-safety messages is necessary for traffic congestion control and for avoiding road mishaps. For efficient resource sharing and optimized channel utilization, the media access control (MAC) protocol plays a vital role. An efficient MAC protocol design can provide fair channel access and can delay constraint safety message dissemination, improving road safety. This paper reviews the applications, characteristics, and challenges faced in the design of MAC protocols. A classification of the MAC protocol is presented based on contention mechanisms and channel access. The classification based on contention is oriented as contention-based, contention-free, and hybrid, whereas the classification based on channel access is categorized as distributed, centralized, cluster-based, cooperative, token-based, and random access. These are further sub-classified as single-channel and multi-channel, based on the type of channel resources they utilize. This paper gives an analysis of the objectives, mechanisms, advantages/disadvantages, and simulators used in specified protocols. Finally, the paper concludes with a discussion on the future scope and open challenges for improving the MAC protocol design.

AIoT Enabled Traffic Congestion Control System Using Deep Neural Network

With rapid population growth in cities, to allow full use of modern technology, transportation networks need to be developed efficiently and sustainability. A significant problem in the traffic motion barrier is dynamic traffic flow. To manage traffic congestion problems, this paper provides a metho

Mobile crowd sensing based dynamic traffic efficiency framework for urban traffic congestion control

Mobile Crowd Sensing (MCS) is a developing category of Internet of Things applications that are used in Intelligent Transportation System to improve the transport system. In today’s metropolitan life, traffic congestion in busy hours is the main problem due to which many commuters are unable to reach their destination on time, motorist time and extra fuel are wasted. In this paper, the MCS-based Dynamic Traffic Efficiency Framework (MCS-DTEF) for traffic congestion control is presented. The basic characteristics of MCS-DTEF are: traffic detection can be performed without deploying sensor devices on both sides of the road. The traffic data can be collected in real time, traffic flow can be predicted and managed according to current situations which reducing fuel consumption and driving time. Simulation of Urban Mobility simulator is used for evaluation. Meaningful and reliable statistical data is obtained by the traffic flow which is built according to Normal Traffic Conditions (NTC’s) in the first scenario. In the second simulation scenario, the MCS-DTEF mechanism is applied to assign a time slot to a requested vehicle and dynamically allocate the fastest path to each vehicle which is introduced to avoid traffic congestion. To validate the results of the proposed framework technique by comparing with NTC’s and real-world traffic flow in three different situations morning, afternoon and evening are examined and tested to explore the traffic usage parameter. The results show that the MCS-DTEF strategy demonstrates significantly improved traffic flow performance in terms of reducing time to destination, fuel consumption and increased vehicle average speed.

IoT-based traffic prediction and traffic signal control system for smart city

Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of ‘5’ phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods.

Optimization Model and Method of Variable Speed Limit for Urban Expressway

The urban expressway network is the main part of the urban traffic network carrying most of the city’s traffic pressure for its continuity and rapidity, but the control method of the traffic flow was too simple to other control methods in application in addition to the ramp control and the fixed speed control. In this paper, the theory of variable speed limit (VSL) was used to develop an optimal control model based on the improved traffic flow simulation model according to the characteristics of urban expressway traffic flow. The objective of the proposed model is to minimize the delay and maximize the traffic flow. It can adjust the traffic flow on the network in space time so that the whole network is in a state of equilibrium which not only is conducive to the control of the local traffic congestion and avoids the spread of congestion but also improves the traffic safety. The SPSA-based solution algorithm was proposed by taking into account the needs of real-time online applications. It can not only ensure the accuracy of the solution but also meet the requirements of the simulation time. The simulation results show that the variable speed limit can be optimized in moderate demand, and the proposed model and algorithm are effective and feasible in this paper. The conclusions are useful to help the traffic management department to formulate reasonable traffic control strategies.

Video Analytics based Intelligent Transport System for passenger flow forecast and Social Distancing Indication

Crowd density management in the transport sector is still one of the ongoing research problems. Intelligent Transportation System (ITS) is one of the branches of smart cities that aim to achieve better traffic efficiency. The intelligent transport system optimizes the traffic congestion control by acquiring real time data. Optimized traffic congestion control demands a robust system that could count the number of people inside a carrier for taking optimized decisions. In this paper we proposed an intelligent algorithm named Modified Intelligent Centroid Tracker and Counter (MICTC) that could detect, count, and measure the distance between humans in a closed and controlled environment. The proposed algorithm is vision based and the scope of the work is to optimize the congestion control inside the passenger carrier and supports countless use cases like smart transport, buildings, and other demography where social distancing is enforced. MICTC algorithm not only offers visual indication with a bounding box but also generates metadata which gives a clear picture to the concerned operational or administrative head regarding the current passenger count status. The work deployed in the public transport sector as a candid spot to operate. The algorithm delivers an adequate transport facility to the public, as it gathers information on crowd density in a public transport medium to the commuters of every region. The work gathers crowd density information and provides commuters a suggestion on availability of seats in the carrier, which then saves time, avoids catching the crowded carriers, ensures social distancing, and standardizes the public transportation system which has practical significance. On experimental analysis we could infer that the proposed approach works with accuracy of 0.81,0.83, 0.85, 0.88, 0.82, 0.89 on VISOR, Kaggle, CALTECH, Penn-Fudan, Daimler Mono and INRIA respectively

Optimized Feedback-based Traffic Congestion Pricing and Control for Improved Return on Investment (ROI)

Traffic congestion is a serious problem in any developing society. One of the approaches used in addressing this problem is congestion pricing. In this paper, the effects of social behavior on congestion pricing and control were considered and a scenario of a 1 x 2 traffic tolling system is used. Also, this work considers the rate of return on investment (RoI) of toll facilities in order to justify the worthiness of the design to investors. In earlier works on feedback-based traffic congestion pricing, the traffic parameters in the logit expression were selected arbitrarily and this made it difficult for traffic designers to arrive at optimum parameters within a reasonable amount of time. In order to address this challenge, the traffic parameter problem is formulated into a traffic congestion control optimization problem whose goal is to maximize the congestion price. The constraints are boundaries for the traffic parameters and the investment boundary conditions. The fitness of the formulated optimization problem was determined using genetic algorithm (GA). A number of simulations were performed by considering different multiplication factors and results were obtained for each multiplication factor (m.f). The simulation results justify the exactness of the formulated optimization problem and the superior performance of this work over the one that involves manually selection of traffic parameters.

Cloud-Assisted Mobile Crowd Sensing for Route and Congestion Monitoring

Accurate and reliable real-time urban traffic management can benefit urban citizens’ daily life by reducing stress, travel time and carbon footprint. The provision of reliable and accurate traffic information has however proven to be a major challenge in intelligent transportation systems. Citizens carrying smartphones can be exploited as an important provider of traffic information and the mobile crowd sensing paradigm can be used as a solution to this challenge. In this paper, an urban traffic monitoring system which exploits the power of participatory sensing and cloud messaging is proposed. Crowd intelligence that is used to estimate traffic congestion levels, arrival times, and average road speed is harvested from crowd sensed data. Traffic congestion control at route level is implemented with a route guidance system. Proactive warnings or recommendations to drivers in the vicinity of or on the route to reported events are provided. The drivers can also report short-term traffic events and physical road conditions for road monitoring. Real-world experiments are conducted with a prototype implementation and the results demonstrates both system feasibility and traffic estimation accuracy.

Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-Based Traffic Congestion Control Systems

Recent work has shown that the introduction of autonomous vehicles (AVs) in traffic could help reduce traffic jams. Deep reinforcement learning methods demonstrate good performance in complex control problems, including autonomous vehicle control, and have been used in state-of-the-art AV controllers. However, deep neural networks (DNNs) render automated driving vulnerable to machine learning-based attacks. In this work, we explore the backdooring/trojanning of DRL-based AV controllers. We develop a trigger design methodology that is based on well-established principles of traffic physics. The malicious actions include vehicle deceleration and acceleration to cause stop-and-go traffic waves to emerge (congestion attacks) or AV acceleration resulting in the AV crashing into the vehicle in front (insurance attack). We test our attack on single-lane and two-lane circuits. Our experimental results show that the backdoored model does not compromise normal operation performance, with the maximum decrease in cumulative rewards being 1%. Still, it can be maliciously activated to cause a crash or congestion when the corresponding triggers appear.

Traffic Congestion Control Synchronizing and Rerouting Using LoRa

Recent growth of Automobile users in big cities leads to traffic congestion. Traffic congestion leads to more waiting time for the vehicle users to reach destination. To avoid this road congestion, Cognitive Radio Networks (CRN) with proper allocation of spectrum, Bandwidth helps to divert the traffic at ease for the GPS enabled vehicle by applying Deep learning techniques. The proposed work introduces synchronization of two traffic signals using the Long Range (LoRa) module and concept of time division algorithm, that gives information about traffic by rerouting the vehicle to reach their destination with the shortest duration. Multi-level Traffic Monitoring Control (TMC) has the facility of sensing the information from the vehicle through transceivers (has the ability of gathering information and capturing the image of the road) receives the data from the vehicle and communicates to the server. The server processes captured image and communicates to the TMC. The TMC alerts vehicle users to divert their path by studying the multi-level TMC. The proposed concept helps vehicle users to take alternate direction by avoiding the congested traffic during peak hours. GPS enabled vehicle communicates the source and destination with live traffic to TMC, in turn receives the information with traffic free shortest route to reach destination.

Research on an intelligent traffic congestion control model

This paper summarizes the status quo of intelligent traffic congestion control and vehicle following on traffic road, puts forward the key technology model and its content of intelligent traffic control, elaborates the model and content in detail, and summarizes the research done, hoping to provide reference for the related research on intelligent traffic congestion control.

Output feedback control of two-lane traffic congestion

This paper develops output feedback boundary control to mitigate traffic congestion of an unidirectional two-lane freeway segment. The macroscopic traffic dynamics are described by the Aw–Rascle–Zhang (ARZ) model respectively for both the fast and slow lanes. The traffic density and velocity of each of the two lanes are governed by coupled 2 × 2 nonlinear hyperbolic partial differential equations (PDEs). Lane-changing interactions between the two lanes lead to exchanging source terms between the two pairs second-order PDEs. Therefore, we are dealing with 4 × 4 nonlinear coupled hyperbolic PDEs. Based on driver’s preference for the slow and fast lanes, a reference system of lane-specific uniform steady states in congested traffic is chosen. To stabilize traffic densities and velocities of both lanes to the steady states, two distinct variable speed limits (VSLs) are applied at outlet boundary, controlling the traffic velocity of each lane. Using backstepping transformation, we map the coupled heterodirectional hyperbolic PDE system into a cascade target system, in which the traffic oscillations are damped out through actuation of the velocities at the downstream boundary. Two full-state feedback boundary control laws are developed. We also design a collocated boundary observer for state estimation with sensing of the densities at the outlet. Output feedback boundary controllers are obtained by combining the collocated observer and full-state feedback controllers. The finite time convergence to equilibrium is achieved and a performance improvement in fuel consumption, drivers’ comfort and total travel time is demonstrated with the proposed output feedback controllers for some parameter choice, compared with the open-loop system. The proposed output feedback design is also validated for different congested traffic scenarios and compared with one-lane backstepping and PI control designs.

Deep learning-based urban big data fusion in smart cities: Towards traffic monitoring and flow-preserving fusion

ObjectiveIn the last few years, several techniques and models are used for retrieving significant information from urban big data of smart cities. This research work aims at developing a data fusion-based traffic congestion control system in smart cities using a deep learning model. MethodologyA hybrid model based on the convolution neural network (CNN) and long short term memory (LSTM) architectures are used for region-based traffic flow predictions in smart cities. CNN is used for the classification of spatial data while LSTM for temporal data. ConclusionThe experiments used the CityPulse Traffic and CityPulse Pollution datasets, and measured root mean square error (RMSE), time consumption and accuracy. A small RMSE value of 49 and highest accuracy of 92.3% compared to other baseline models depicts the applicability of the proposed model in the region-based traffic flow prediction problems in the smart cities.

Extremum Seeking for Traffic Congestion Control With a Downstream Bottleneck

Abstract This paper develops boundary control for freeway traffic with a downstream bottleneck. Traffic on a freeway segment with capacity drop at outlet of the segment is a common phenomenon that leads to traffic bottleneck problem. The capacity drop can be caused by lane-drop, hills, tunnel, bridge, or curvature on the road. If incoming traffic flow remains unchanged, traffic congestion forms upstream of the bottleneck since the upstream traffic demand exceeds its capacity. Therefore, it is important to regulate the incoming traffic flow of the segment to avoid overloading the bottleneck area. Traffic densities on the freeway segment are described with the Lighthill–Whitham–Richards (LWR) macroscopic partial differential equation (PDE) model. The incoming flow at the inlet of the freeway segment is controlled so that the optimal density that maximizes the outgoing flow is reached and the traffic congestion upstream of the bottleneck is mitigated. The density and traffic flow relation at the bottleneck area, usually described with fundamental diagram, is considered to be unknown. We tackle this problem using extremum seeking (ES) control with delay compensation for the LWR PDE. ES control, a nonmodel-based approach for real-time optimization, is adopted to find the optimal density for the unknown fundamental diagram. A predictor feedback control design is proposed to compensate the delay effect of traffic dynamics in the freeway segment. In the end, simulation results are obtained to validate a desired performance of the controller on the nonlinear LWR model with an unknown fundamental diagram.

Research and Strategy of Urban Traffic Congestion Control

<p class="MsoNormal" style="margin-bottom: 0.0000pt; text-indent: 0.0000pt; mso-layout-grid-align: none; line-height: 15.0000pt; mso-line-height-rule: exactly;"><span style="mso-spacerun: 'yes'; font-family: 'Times New Roman'; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0000pt;">With the acceleration of urbanization, urban public transportation has been developed and improved for a long time as well. Currently, China’s traditional and single ground transportation system has been transformed into a multi-functional and compound multi-transportation one. However, the congestion problem in cities has become increasingly serious. Cities in different countries should take different measures to implement the accumulation pole. They also should focus on energy source consumption, environmental pollution and health care brought by traffic congestion. The practice and research countermeasures of relieving urban traffic congestion can be divided into developmental, managerial and restrictive measures. Urban traffic congestion is a systematic problem, which needs to be treated by comprehensive measures, and given priority to the use of developmental measures in order to improve urban traffic supply capacity. It is necessary to strive to enhance urban traffic management level, practice administrative measures. With historical basis, development level and fairness of urban development in China need to be taken into account, and carefully consider the use of restrictive measures.</span></p><p class="MsoNormal" style="margin-top: 70pt; margin-bottom: 8pt; text-indent: 0pt;"><strong><span style="font-family: 'Times New Roman'; font-size: 16pt;">Research and Strategy of Urban Traffic Congestion Control</span></strong><strong></strong></p>

UAV-Assisted Hybrid Scheme for Urban Road Safety Based on VANETs

Traffic congestion control is becoming a popular field of research due to the rapid development of the automotive market. Vehicular ad hoc networks (VANETs) have become the core research technology for numerous application possibilities related to road safety. Road congestions have been a serious issue of all time since the nodes have high mobility and transmission range is limited, resulting in an interruption of communication. One of the significant technical challenges faced in implementing VANET is the design of the routing protocol, providing adequate information and a reliable source for the destination. We proposed a novel mechanism unmanned aerial vehicle (UAV)-assisted ad hoc on-demand distance vector (AODV) routing protocol (UAVa) for current-time traffic information accumulation and sharing to the entire traffic network and to control congestions before it happens. The UAV-assisted (UAVa) protocol is dedicated to urban environments, and its primary goal is to enhance the performance of routing protocols based on intersections. We compared the overall performance of existing routing protocols, namely ad hoc on-demand distance vector. The simulations were done by using OpenStreetMap (OSM), Network Simulator (NS-2.35), Simulation of Urban Mobility (SUMO), and VanetMobiSim. Furthermore, we compared the simulation results with AODV, and it shows that UAV-assisted (UAVa) AODV has significantly enhanced the packet delivery ratio, reduced the end-to-end delay, improved the average and instant throughput, and saved more energy. The results show that the UAVa is more robust and effective and we can conclude that UAVa is more suitable for VANETs.

Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems

In recent years, the Intelligent transportations system (ITS) has received considerable attention, due to higher demands for road safety and efficiency in highly interconnected road networks. As an essential part of ITS, traffic prediction can provide support in many aspects, such as road routing, traffic congestion control, etc. To provide a more comprehensive overview of the role of traffic forecasting in ITS systems, we will first introduce the corresponding ITS applications and discuss how traffic forecasting can improve the performance of these applications. Next, we will introduce the general prediction procedure as well as some basic concepts of traffic flow prediction, followed by a description of a general framework for implementing the traffic flow prediction. In this survey, mainly two sorts of prediction methods are focused, statistics-based and machine learning (ML)-based. These two types of approaches are more used in ITS traffic flow predictions these years, and service for different contexts. Generally speaking, the statistics-based models have better model interpretability, but the rigorous model structure limits the adaptability, while ML-based models are more flexible. Accordingly, we will introduce the characteristics of these two types of methods through specific examples of state-of-the-art approaches. Last but not least, some potential and meaningful development directions corresponding to this domain are introduced to do a great favor for future research.