Road Traffic Information Research Articles

The Effect of the COVID-19 Pandemic on Traffic Flow Characteristics, Emissions Production and Fuel Consumption at a Selected Intersection in Slovakia

The surveyof traffic intensity is used to obtain information on the number of vehicles on roads during the day. Subsequently, it is possible to derive from this the daily, weekly, and other road traffic intensity information. This survey represents the basis for the calculation of the annual average daily traffic volume and the basic characteristics of traffic flow. The COVID-19 pandemic has caused extensive economic and social damage around the world. These damages have also affected traffic. Changes in traffic behavior have mainly affected the reduction in traffic intensity on road networks. Thanks to the reduction in the demand for transport, there has also been a significant reduction in traffic delays, fuel consumption and emissions. An examination of changes in traffic intensity took place at a selected intersection in 2019, 2020 and 2021. This paper describes the effects of reducing the traffic intensity, fuel consumption and emissions obtained by microsimulation. The results obtained confirmed the reduction in traffic, which also contributed to a significant reduction in vehicle delays.

Prediction of full-path operating conditions based on the characteristics of the driving environment

ABSTRACT The prediction of driving conditions is an important basis for the energy management systems of new energy vehicles. Accurate predictions of driving conditions can help reduce the use of on-board energies such as gasoline and electricity, and improve the efficiency of energy use. Aiming at the limitations of existing driving condition prediction, this paper proposes a full-path driving condition prediction based on driving environment characteristics. BP neural network optimized by GA algorithm is used to establish a prediction model of driving condition characteristic parameters based on driving environment characteristics, and use road traffic environment information to predict driving condition characteristic parameters. And use the predicted characteristic parameters to perform fuzzy matching to predict the type of driving conditions of the vehicle for a period of time in the future. In this paper, some roads in Nanjing are used as an example to verify the prediction model. The results show that the prediction accuracy has reached 94.18%. When it is applied to energy consumption prediction, the prediction error is only 5.82%. This method provides a more accurate and reliable driving condition prediction method for new energy vehicles, which is conducive to the design of more reliable and efficient energy management strategies.

Connected Vehicles: V2V and V2I Road Weather and Traffic Communication Using Cellular Technologies.

There is a continuous need to design and develop wireless technologies to meet the increasing demands for high-speed wireless data transfer to incorporate advanced intelligent transport systems. Different wireless technologies are continuously evolving including short-range and long-range (WiMAX, LTE, and 5G) cellular standards. These emerging technologies can considerably enhance the operational performance of communication between vehicles and road-side infrastructure. This paper analyzes the performance of cellular-based long-term evolution (LTE) and 5GTN (5G Test Network) in pilot field measurements (i.e., vehicle-to-vehicle and vehicle-to-infrastructure) when delivering road weather and traffic information in real-time environments. Measurements were conducted on a test track operated and owned by the Finnish Meteorological Institute (FMI), Finland. The results showed that 5GTN outperformed LTE when exchanging road weather and traffic data messages in V2V and V2I scenarios. This comparison was made by mainly considering bandwidth, throughput, packet loss, and latency. The safety critical messages were transmitted at a transmission frequency of 10 Hz. The performance of both compared technologies (i.e., LTE and 5GTN) fulfilled the minimum requirements of the ITS-Assisted Road weather and traffic platform to offer reliable communication for enhanced road traffic safety. The field measurement results also illustrate the advantage of cellular networks (LTE and 5GTN) with a clear potential to use it heterogeneously in future field tests with short-range protocols, e.g., IEEE 802.11p.

Target Tracking and 3D Trajectory Reconstruction Based on Multicamera Calibration

In traffic scenarios, vehicle trajectories can provide almost all the dynamic information of moving vehicles. Analyzing the vehicle trajectory in the monitoring scene can grasp the dynamic road traffic information. Cross-camera association of vehicle trajectories in multiple cameras can break the isolation of target information between single cameras and obtain the overall road operation conditions in a large-scale video surveillance area, which helps road traffic managers to conduct traffic analysis, prediction, and control. Based on the framework of DBT automatic target detection, this paper proposes a cross-camera vehicle trajectory correlation matching method based on the Euclidean distance metric correlation of trajectory points. For the multitarget vehicle trajectory acquired in a single camera, we first perform 3D trajectory reconstruction based on the combined camera calibration in the overlapping area and then complete the similarity association between the cross-camera trajectories and the cross-camera trajectory update, and complete the trajectory transfer of the vehicle between adjacent cameras. Experiments show that the method in this paper can well solve the problem that the current tracking technology is difficult to match the vehicle trajectory under different cameras in complex traffic scenes and essentially achieves long-term and long-distance continuous tracking and trajectory acquisition of multiple targets across cameras.

A Quality Aware Multiunit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting

Crowdsourcing with the intelligent agents carrying smart devices is becoming increasingly popular in recent years. It has opened up meeting an extensive list of real-life applications such as measuring air pollution levels, road traffic information, etc. In literature, this is known as <i>mobile crowdsourcing</i> or <i>mobile crowdsensing</i>. In this paper, the discussed set-up consists of multiple task requesters (or task providers) and multiple IoT devices (as <i>task executors</i>), where each of the task providers has multiple homogeneous sensing tasks. Each task requester reports a bid and the number of homogeneous sensing tasks to the platform. On the other side, multiple IoT devices report the ask (the charge for imparting its services) and the number of sensing tasks they can execute. The valuations of task requesters and IoT devices are <i>private</i> information, and both might act <i>strategically</i>. One assumption that is made in this paper is that the bids and asks of the agents (<i>task providers</i> and <i>IoT devices</i>) follow <i>decreasing marginal returns</i> criteria. Given the above-discussed scenario, the objectives are: (1) to determine a set of quality IoT devices for each of the tasks held by the task requesters, and (2) to select a subset of quality IoT devices from among the available quality IoT devices for each of the sensing tasks. In this paper, a truthful mechanism is proposed for allocating the IoT devices to the sensing tasks carried by task requesters that also keep into account the quality of IoT devices. Through theoretical analysis, it is shown that the mechanism is <i>truthful</i>, <i>budget balanced</i>, <i>individually rational</i>, <i>computationally efficient</i>, <i>correct</i>, and <i>prior-free</i>. Further, probabilistic analysis is carried out to estimate the average number of tasks that get executed for any task requester. The simulations are carried out to measure the performance of the proposed mechanism against the benchmark mechanisms on the ground of <i>truthfulness</i>, <i>budget balance</i>, <i>satisfaction level</i>, <i>average incentive</i>, and <i>computational efficiency</i>.

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

In implementing the Intelligent Traffic Monitoring System (ITMS), timely and effective access to road traffic information is an essential link. It requires an effective traffic Information Acquisition System (IAS) to collect real-time data and transmit the collected information to the background for processing. Therefore, this paper studies on-road vehicle information recognition based on Deep Learning (DL). Firstly, a framework of traffic IAS is proposed. Then, an improved MT-GooGleNet model based on Convolutional Neural Network (CNN) is proposed to locate and recognize vehicles in traffic images. Finally, the performance of the model is analyzed by simulation. The experimental results of vehicle position recognition show that the classification accuracy of Multi-Task (MT)-GooGleNet after fine-tuning is 99.5%. Compared with other models, the MT-GooGleNet model proposed is the best in vehicle position recognition, and its positioning accuracy is very high. The results of vehicle identification show that after data enhancement and pre-training, the testing set accuracy of the MT-GooGleNet model is 79.96%. The results show that the model&#x2019;s accuracy has been dramatically improved after processing. The research provides a reference for establishing IAS in the future.

The influence of smart equipment Smart Hub over highway traffic in the Romanian border points

The pandemic event introduces some changes in the control procedures from Romanian highway network border points. The cargo flow is controlled on the output and input direction using a set of goods scales. This process generates a very long queue on Romanian Highway Network for cargo trucks with a negative impact for users and highway infrastructure. The use of data provided by road traffic information collection equipment, located on the highway and the implementation of smart equipment for their management and control of traffic management equipment can lead to the elimination of these negative aspects. To prove the need to implement this system a simulation model was developed, and a series of results were generated using real data collected from the highway for different operating scenarios.

A Survey on IoT-Based Smart Cars, their Functionalities and Challenges

The Internet of Things (IoT) is a modern technology buzzword that makes everyday objects smarter. The concept of the Internet of Things extends beyond smartphones and computers to make vehicles, homes and communities intelligent. The most pressing concern in relation to growing vehicle use is the rising frequency of traffic accidents. The issue with modern automobiles is that they do not offer us safety, which might lead to tragic accidents. The concept of IoT in vehicles was developed to ensure the safety and efficiency of road conditions. It is a dynamic mobile communication system between cars and humans that is supposed to deliver real-time road traffic information, safeguard journey comfort, and increase transport efficiency. The paper reviews the evolution of smart cars throughout the 6 stages of automation and compares the various methodologies that use IoT.

Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning

Rough pavements cause ride discomfort and energy inefficiency for road vehicles. Existing methods to address these problems are time-consuming and not adaptive to changing driving conditions on rough pavements. With the development of sensor and communication technologies, crowdsourced road and dynamic traffic information become available for enhancing driving performance, particularly addressing the discomfort and inefficiency issues by controlling driving speeds. This study proposes a speed control framework on rough pavements, envisioning the operation of autonomous vehicles based on the crowdsourced data. We suggest the concept of ‘maximum comfortable speed’ for representing the vertical ride comfort of oncoming roads. A deep reinforcement learning (DRL) algorithm is designed to learn comfortable and energy-efficient speed control strategies. The DRL-based speed control model is trained using real-world rough pavement data in Shanghai, China. The experimental results show that the vertical ride comfort, energy efficiency, and computation efficiency increase by 8.22%, 24.37%, and 94.38%, respectively, compared to an optimization-based speed control model. The results indicate that the proposed framework is effective for real-time speed controls of autonomous vehicles on rough pavements.

Enhancing the Reliability of Communication between Vehicle and Everything (V2X) Based on Deep Learning for Providing Efficient Road Traffic Information

Developing efficient communication between vehicles and everything (V2X) is a challenging task, mainly due to the characteristics of vehicular networks, which include rapid topology changes, large-scale sizes, and frequent link disconnections. This article proposes a deep learning model to enhance V2X communication. Various channel conditions such as interference, channel noise, and path loss affect the communication between a vehicle (V) and everything (X). Thus, the proposed model aims to determine the required optimum interference power to enhance connectivity, comply with the quality of service (QoS) constraints, and improve the communication link reliability. The proposed model fulfills the best QoS in terms of four metrics, namely, achievable data rate (Rb), packet delivery ratio (PDR), packet loss rate (PLR), and average end-to-end delay (E2E). The factors to be considered are the distribution and density of vehicles, average length, and minimum safety distance between vehicles. A mathematical formulation of the optimum required interference power is presented to achieve the given objectives as a constrained optimization problem, and accordingly, the proposed deep learning model is trained. The obtained results show the ability of the proposed model to enhance the connectivity between V2X for improving road traffic information efficiency and increasing road traffic safety.

Analysis of collaborative wireless vehicular technologies under realistic conditions

Reliable communications are essential to provide intelligent services to connected cars. For operational services, connected vehicles in VANET (Vehicle Ad hoc Networks) regularly transfer large amounts of data related to vehicular safety. Similarly, V2X (Vehicle-to-Everything) communications include vehicles exchanging information with each other and with infrastructure that is, Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V). This paper has analyzed the performance of IEEE 802.11p and 5G test network in a collaborative manner under realistic conditions. For performance analysis the exchange of safety-critical road weather and traffic information has been performed to enhance the traffic safety and traffic efficiency in the domain of intelligent transportation system (ITS). The vehicular connectivity is provided in V2V and V2I scenarios by utilizing short-range IEEE 802.11 standard or cellular approaches, such as the 5G network. Here, we consider combining these technologies in a cooperative manner to exploit jointly their advantages. In this cooperative heterogeneous network, the IEEE 802.11p supports safety-related pilot use cases while the provision of non-safety-related pilot use cases are supported by the 5G test network. The performance analysis revealed that the IEEE 802.11p performs quite reasonably well with restricted mobility in contrast of 5G test network in a collaborative manner to avoid road accidents.

3D Digital Twin of Intelligent Transportation System based on Road-Side Sensing

Cooperative vehicle-infrastructure is one of the most import developing direction of future intelligent transportation system, while digital twin system can record, reproduce, and even deduce the physical system, which could be helpful for the development of cooperative vehicle-infrastructure. In this study, we proposed a 3D digital twin platform of intelligent transportation system based on road-side sensing, a core component of cooperative vehicle-infrastructure system. This platform consists of real road-side sensing unit,3D virtual environment, and the ROS bridge between them, by receiving the sensing results of physical world in real-time, the virtual world can reproduce the compatible road traffic information, such as the type,3D position and orientation of traffic participants.

Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles

Abstract Internet of Vehicles (IoV) is a network of vehicles communicating with each other by exchanging road traffic information via radio access technologies. Two potential technologies of V2X that have gained attention over the past years are DSRC and cellular networks such as 4G LTE and 5G. DSRC is suitable for low latency communications, however provides a shorter coverage range whereas, 4G LTE offers a wide coverage range but has high transmission time intervals. In contrast, 5G offers higher data rates, low latencies but prone to blockages. Single technology might not fully accommodate the requirements of vehicular communications. Hence, it is required to interwork with more than one radio access network to satisfy the requirements of safety vehicular applications. One issue identified when working with multiple radio access networks is the selection of the most appropriate network for vertical handover. Usually, in the previous works, the network is selected directly or will be connected to the available network due to which the handover had to take place frequently resulting in unnecessary handovers. Hence, in the existing state-of-the-art, the need for handover is not validated. In this paper, we have proposed a dynamic Q-learning algorithm to validate the need for handover, and then, appropriate selection of network would take place by using a fuzzy convolutional neural network. Besides, a modified jellyfish optimization algorithm is proposed to select the shortest paths by forming V2V pairs that take into account channel metrics, vehicle metrics, and vehicle performance metrics. The proposed algorithms are then evaluated using OMNET++ and compared with the existing state-of-the-art concerning mean handover, HO failure, throughput, delay, and packet loss as the performance metrics.

Performance evaluation of IEEE 802.11p, LTE and 5G in connected vehicles for cooperative awareness

AbstractIn recent years, researchers and scientists are continuously striving to design and develop advance wireless technologies for vehicular communication. Advanced short‐range (IEEE‐802.11p/ITS‐G5) and long‐range (LTE, LTE‐A, and 5G) technologies are indispensable for Vehicular Ad hoc networking (VANETs). Meanwhile, exploiting technologies for cooperative awareness of connected vehicles is important for seamless network connectivity in hazardous situations. For seamless network availability, there is a need to integrate short‐range and long‐range technologies together. In this article, we design a pilot platform based on IEEE‐802.11p, LTE and 5G test network (5GTN) utilizing road traffic and weather information. The use of real‐time and accurate road traffic and weather information together with road condition information is crucial to enhance road safety. To deliver updated road weather and traffic information, we have used a combination of networks. In this article, a comparison of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) pilot scenarios was carried out considering the mentioned networks. The used IEEE 802.11p acts as a primary vehicular communication channel, assisted by other cellular‐based LTE and 5G networks. Network performance was analyzed by considering latency, packet loss, and throughput in V2V and V2I scenarios. These pilot measurements revealed that the state‐of‐the‐art 5G test network performs better in contrast of IEEE‐802.11p and LTE network by delivering real‐time road weather and road traffic observation data. These pilots filed measurements show the potential of combining short‐ and long‐range technologies for enhanced road safety.

Optimal speed planning and collision avoidance control for electric vehicles considering traffic lights

<p indent=0mm>To reduce the energy consumption and avoid the risk of rear-end collisions for intelligent electric vehicles (EVs) during cruising under urban conditions, a hierarchical control system for energy saving and collision avoidance for EVs considering the information of traffic lights is proposed. The upper layer of the system plans the vehicle speed based on the aforementioned traffic information and a vehicle dynamic model and obtains the reference speed sequence with the minimum energy consumption through online rolling optimization; the bottom layer calculates the optimal acceleration of the vehicle in real-time along with the reference speed sequence and multiobjective function and performs collision avoidance control. Finally, the simulation model of the control system was built based on the Matlab/Simulink platform, and the effectiveness of the proposed control system was verified based on conditions of the presence and absence of human-driven vehicles in front of the EV. The upper layer can realize the optimal reference speed in vehicle speed planning according to the change in road traffic information and improve the energy consumption characteristics of the EV; the collision avoidance control by the lower layer can calculate the optimal acceleration control input by combining the optimal reference speed and the multiobjective function evaluation results. Thus, it is clear that the designed system can ensure energy-efficient, safe, and stable driving of EVs.

A Pairing‐Free Certificateless Signcryption Scheme for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) create an vital platform for communication between vehicles, which can realize accident warning, auxiliary driving, road traffic information query, passenger communication and other applications. While providing convenient services for people, VANETs also bring some security risks. Security and privacy are the primary issues in the research of VANETs. Signcryption is an ideal way to transfer messages in a logical step in a secure and authenticated way. We design a Certificateless signcryption (CLSC) scheme to provide confidentiality, authentication, integrity, non-repudiation and user privacy preservation for the information transmitted between vehicle communication units. We demonstrate the confidentiality and unforgeability of the proposed scheme in the random oracle model. Moreover, compared with the existing CLSC schemes, ours realizes the perfect combination of efficiency, security and privacy, and it is particularly well adapted to the secure communication of vehicle networks.

Basic Design Architecture of Congestion Notification System: A Real Time Road Traffic Information Enabler

Road traffic congestion is a prominent challenge of today’s urban center. As a push factor of urban centers, it impact negatively on socio-economic well-being of cities. However, contemporary innovative transport technology of Intelligent Transport System (ITS) is bridging the traveler information gaps, through installation and deployment of smart transport infrastructure such as Congestion Notification System at critical traffic intersections and points that aggravate road traffic congestion. This paper therefore provides a detailed explanation on the configuration and basic architecture of a primary Congestion Notification System (CNS) stating its working principles in providing real time road traffic congestion level information to motorist, prior entering the congestion zones or section of the road. Thus, engendering informed decision by motorists on alternative routes rather than the congested route.

Study on DID Application Methods for Blockchain-Based Traffic Forensic Data

Traffic accident investigation has been used to reconstruct the cause and condition of the accident by using images and driving record data that introduce IT technology, and to analyze both sides of the accident, skid marks, and vehicle damage to identify the perpetrators and victims. However, level 3 self-driving vehicles are the most important factor in determining the cause and imputation of the accident by the driver or manufacturer with control information at the time of the accident. It is also developing into a network and connected vehicle with various restrictions such as the burden of the price of sensors equipped with self-driving cars and climate and rapidly changing road traffic information. In addition, network and connected vehicle driving data are stored on the outside, or various devices and sensors are installed to store information on the outside for convenience in operation, and efforts to enact laws are continuing. This paper attempts to propose a traffic investigation digital framework using digital data generated by these devices and sensors.

Decentralized Autonomous Navigation of a UAV Network for Road Traffic Monitoring

With the increase of population and the fast growth of private vehicle ownership in urban areas, many roads have become more congested than ever before. The road traffic information plays a crucial role in traffic management. The currently available traffic information collection systems mainly rely on static road-side units, which passively record the traffic information. This article considers an alternative option, i.e., the usage of unmanned aerial vehicles (UAVs) for aerial surveillance, which is more efficient than the conventional ground-based strategies. We focus on deploying a UAV network to monitor the traffic on a road. We propose a decentralized navigation scheme that enables the UAV network to detect blockage and then gather to the blocked area to have better views of the ground vehicles. This scheme only requires the UAVs to share some light measurement information and positions, and it can be implemented in real time.

Density waves in car-following model for autonomous vehicles with backward looking effect

Autonomous vehicles can obtain abundant road traffic information and communicate with each other using intelligent transportation system. For the purpose of detecting the influence of backward looking effect on the traffic flow of autonomous vehicles and better providing driving decisions for autonomous vehicles, in this study, a novel car-following model is presented accounting for the backward looking effect on the basis of the two-velocity difference model. The stability condition of this novel model in autonomous vehicles flow is established by incorporating the linear stability theory. The Burgers equation, Korteweg-de Vries equation, and modified Korteweg-de Vries equation are inferred based on nonlinear analysis to describe triangular wave, soliton wave, and kink-antikink wave, corresponding to the stable, metastable, and unstable regions of autonomous vehicles flow separately. Subsequently, numerical simulation is performed, exhibiting that this novel model can strengthen the traffic flow stability of autonomous vehicles and simulate the above three different density waves of traffic flow. In addition, the numerical simulation also further verifies the accuracy of the theoretical analysis.