Traffic Circumstances Research Articles

Choice-based macroscopic lane-change prediction model for weaving areas

This paper introduces two macroscopic lane change models, separately for small vehicles (passenger cars and vans) as well as heavy vehicles. The model is structured using a Nested Logit discrete choice framework. The model was estimated and validated using unique trajectory data collected from a busy weaving section in Antwerp, Belgium. The models exhibit an average and maximum rate of inaccurate prediction results, amounting to 11% and 16%, respectively. Additionally, the model underestimates the total number of lane change maneuvers, with a margin of no more than 4.1–7.7%. This study aims to fill the gap in macroscopic lane change models, which mainly depend on theoretical underpinnings, by including empirical data from the analysis of more than 31,000 observed maneuvers. Furthermore, it differentiates among three maneuver intents, a unique alternative-specific discrete choice framework. This differentiation allows for the consideration of drivers’ systematic taste variations. In contrast to other models that utilize empirical datasets and are heavily dependent on expensive video-based data, including the entire traffic flow, this study introduces a novel approach for estimating the position and frequency of maneuvers by using just a small fraction (1–2%) of vehicle trajectories, supplemented by loop detector data, therefore eliminating the need for information from surrounding vehicles. Additionally, the work’s uniqueness is its validation methods extending beyond just numerical evaluations. The practical usability and usefulness of the model are shown in real-world traffic circumstances. Finally, the model is integrated in the context of a macroscopic simulation that represents a highly consistent lane balance based on the Scalable Quality Value statistics.

AN EFFECTIVE FRAMEWORK TO IMPROVE PERFORMANCE AND SECURITY IN INTELLECTUAL VANET

Vehicular Ad Hoc Networks, also known as VANETs, are an essential component in the development of intelligent transportation systems. They hold the promise of improving commuters' safety, efficiency, and convenience. The dynamic and decentralised character of VANETs, on the other hand, presents a number of issues, notably with regard to performance and security. a complete framework that is aimed to enhance the operation of VANETs and address the difficulties that have been identified. Intelligent routing algorithms, collaborationbased collision avoidance methods, and robust security protocols are the three primary components that make up the system. In the first place, intelligent routing algorithms make use of machine learning techniques in order to dynamically adapt to shifting traffic circumstances. This increases the efficiency of data transmission while simultaneously reducing delays. Whendetermining how to properly route data packets over the network, these algorithms take into consideration a variety of parameters, including the density of vehicles, traffic patterns, and congestion on the network.

Artificial Intelligence Based CCTV-Accident Detection System

Urban infrastructure and public safety are severely hampered by traffic congestion and accidents. This research suggests an enhanced traffic light control system that makes use of deep learning methods and IoT integration to address these problems. The system computes vehicle density in real-time and indicates smart light conditions at traffic signals by combining the object identification algorithm of YOLOv3 with Internet of Things sensors. The secret to the system is the use of YOLOv3 for precise vehicle identification and categorization in video feeds recorded by security cameras. The technology can effectively identify cars, collisions, and violent incidents on the road by utilising YOLOv3. In order to enable quick emergency response, the system sends notification alerts via GSM connection to neighbouring police stations and hospitals upon detection. In addition, the suggested system uses Internet of Things devices to calculate the number of vehicles at crossings. Traffic signal timings can be dynamically adjusted thanks to this real-time data on vehicle flow, which improves traffic flow and lessens congestion. IoT integration also makes it possible for traffic signals to have smart light indication, which adjusts signal timings in response to current traffic circumstances to improve efficiency and safety.

An Architecture and Review of Intelligence Based Traffic Control System for Smart Cities

City traffic congestion can be reduced with the help of adaptable traffic signal control system. The technique improves the efficiency of traffic operations on urban road networks by quickly adjusting the timing of signal values to account for seasonal variations and brief turns in traffic demand. This study looks into how adaptive signal control systems have evolved over time, their technical features, the state of adaptive control research today, and Control solutions for diverse traffic flows composed of linked and autonomous vehicles. This paper finally came to the conclusion that the ability of smart cities to generate vast volumes of information, Artificial Intelligence (AI) approaches that have recently been developed are of interest because they have the power to transform unstructured data into meaningful information to support decision-making (For instance, using current traffic information to adjust traffic lights based on actual traffic circumstances). It will demand a lot of processing power and is not easy to construct these AI applications. Unique computer hardware/technologies are required since some smart city applications require quick responses. In order to achieve the greatest energy savings and QoS, it focuses on the deployment of virtual machines in software-defined data centers. Review of the accuracy vs. latency trade-off for deep learning-based service decisions regarding offloading while providing the best QoS at the edge using compression techniques. During the past, computationally demanding tasks have been handled by cloud computing infrastructures. A promising computer infrastructure is already available and thanks to the new edge computing advancement, which is capable of meeting the needs of tomorrow's smart cities.

DIB - A Novel Optimized VANET Traffic Management Using a Deep Neural Network

The advancement of the Internet of Things (IoT) establishes the development of the Internet of Vehicles (IoV) and Intelligent Transportation Systems (ITS). An integral part of ITS is the vehicular ad hoc network (VANET) with smart vehicles (SV). In this research, a dynamic method of traffic regulation in VANET is presented using Deep Neural Networks (DNN) and Bat Algorithms (BA). With a reduced average delay, the former (DNN) is utilized to guide vehicles across very crowded routes to increase efficiency. In order to examine the traffic congestion status between network nodes, BA is integrated with the IoT and moved over VANETs. Experiments were conducted to test the effectiveness of the proposed method with various parameters such as average latency, packet delivery ratio (PDR) and throughput and the performance were compared with different machine learning (ML) algorithms. The simulation outputs proved that the proposed technique supports real-time traffic circumstances with less energy usage and delay than existing methods.

On-demand route allocation: A smart transportation scheme

For effective crowd mobility, smart cities must have smart transportation. Smart cities need a transportation system that is intelligent, simple, and effective. Here is a creative idea for modernizing the current public transit system. Hardware modules based on the Internet of Things (IoT) that are required for implementation are discussed in this work. Traffic circumstances and extra elements have been added to Dijkstra’s shortest path method. The proposed algorithm has been tested in a number of circumstances, and the outcomes have been displayed accordingly. At a cheaper cost, the suggested solution can upgrade the current infrastructure. With various request counts, the algorithm performs well.

On-Demand Route Allocation: A Smart Transportation Scheme

For effective crowd mobility, smart cities must have smart transportation. Smart cities need a transportation system that is intelligent, simple, and effective. Here is a creative idea for modernizing the current public transit system. Hardware modules based on the Internet of Things (IoT) that are required for implementation are discussed in this work. Traffic circumstances and extra elements have been added to Dijkstra’s shortest path method. The proposed algorithm has been tested in a number of circumstances, and the outcomes have been displayed accordingly. At a cheaper cost, the suggested solution can upgrade the current infrastructure. With various request counts, the algorithm performs well.

DMGAN: Dynamic Multi-Hop Graph Attention Network for Traffic Forecasting

In the intelligent transportation system, traffic forecasting, which is generally characterized as a graph spatial-temporal prediction task, plays a crucial role. It is challenging to generate reliable forecast results due to the complexity of traffic topological information and the inherent uncertainty of road traffic circumstances. Existing works generally focus on modeling spatial dependency on static graph structures, but ignore dynamic relations between road segments and cannot extract long-range traffic dependencies in spatial-temporal domains. To bridge the above gaps, we present a novel framework, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynamic Multi-Hop Graph Attention Network</i> (DMGAN). Specifically, we leverage dynamic graph modeling to capture time-varying relations across road sections and introduce the multi-hop operation in each message propagation layer to extract long-range spatial dependency. Meanwhile, we develop a fusion-attention module, preserving both local and global hidden layer outputs of the encoder, to capture both long- and short-term temporal dependencies jointly. In this way, our method can fully model complex time-varying traffic topology information and capture the internal patterns of traffic series by integrating dynamic graph structure and temporal attention component. DGMAN achieves state-of-the-art performance in three metrics, as demonstrated by experimental findings on four real-world public traffic datasets, METR-LA, PEMS-BAY, PEMS03, and PEMS07. This code and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/EEHITer/2022-TKDE-DMGAN-Pytorch/tree/main</uri> for reproducibility and further studies.

An Evolutionary Game of Perception-Based Stochastic User-Equilibrium in a Parallel Network

In recent years, advanced information communication technology (such as beyond 5G and 6G) will enable convenient end-to-end communication. This would allow collections of individual preferences as predictions of travellers’ perceptions, and thus support data analysis and traffic control in a dynamic circumstance. Therefore, analysis of nonlinearly modelled perceptions and perception-based user equilibrium are the necessary problem we encounter. This research explores the existence characteristics of perception-based stochastic user equilibrium (P-SUE), a type of stochastic user equilibrium where travelers make route choices according to their perceptive traffic circumstances instead of actual ones. A nonlinear perception model with stochasticity is developed and the traffic arrivals are approximately transformed from Poisson-distributed to Normal-distributed. From a static perspective, we develop a P-SUE model with a heterogeneous traveler community and discuss the interaction among travelers’ attitude, grouping and static perception-based stochastic user equilibrium (P-SSUE). After embedding a day-to-day learning process into the P-SSUE model, we further explore evolutionary dynamics of the equilibrium. Several scenarios are provided to investigate effects and sensitivity of traveler-related and road-related factors. It is found that learning produces perturbation and makes the existence condition of P-SSUE more complicated; the convergence processes of P-SSUEs are significantly shortened as the learning rate increases to higher than 0.1.

Traffic Safety Pyramid Based on Visual Navigation Distraction: A Naturalistic Study on App-Taxis

Evidence suggests that mobile phone navigation applications (MPNAs) may reduce road traffic safety by causing a visual distraction. This study aimed to (i) identify a list of road safety-related events (RSREs) that could occur while looking at the MPNA, (ii) examine the effect of app-taxFi drivers’ glances at MPNA on missing of RSREs, (iii) develop any potential new indicator in road traffic safety, and (iv) develop a pyramid of traffic incidents. This study investigated 36 app-taxi drivers in real road traffic circumstances. Data were collected by video recording using a double-lens camera. The events that occurred while the app-taxi driver was looking at the MPNA were extracted, recorded, and classified. A time span of less than one second and a new indicator, namely “seeing the event BEFORE visual distraction” (seBvd), were examined. The data were analyzed using descriptive statistics and non-parametric tests with a significance level of 0.05 in SPSS 18. A list of 23 events was identified. The most frequent event was “presence of pedestrians across the road” (including crossing the road and beside the road 36%). The number of glances at the MPNA had a significant correlation with time span ≤1 s ( p = 0.001, r &lt; 0.6). The total time of looking at the MPNA had a significant correlation with time span &gt;1 s ( p = 0.001, r &lt; 0.6). A pyramid of traffic incidents was developed based on the results. The new seBvd indicator is a binary (positive; negative) surrogate safety measure that can be useful in evaluating the effect of visual distraction on road traffic safety. The introduced pyramid of traffic incidents seems useful in traffic safety.

Implementation on IoT Based Traffic Management System for Emergency Vehicles

Abstract: In many Indian cities along with those in other nations, congestion caused by traffic is a serious issue. Traffic congestion is caused by signal failure, ineffective law enforcement, and poor traffic administration. The economy, the environment, and general quality of life are all negatively impacted by traffic congestion. Therefore, it is imperative that the traffic congestion issue be managed efficiently. The proposed system's goal is to suggest a smart traffic control and management system that makes use of the Internet of Things, a decentralised strategy and algorithms to handle all traffic circumstances more precisely. The shortcomings of the existing traffic control systems will be fixed by the proposed system. To reduce traffic congestion, a forecast of upcoming traffic density will be made using an algorithm. The proposed method enables an emergency vehicle to travel directly to its location by turning all red lights on its route into green ones, thereby reducing traffic congestion. The system manages traffic signals and reduces wait times during emergencies. This makes it an endeavour that can save a life.

Driver Drowsiness Detection System Using Machine Learning

Driver sleepiness has been one of the major causes of accidents in recent years all around the world. Many road incidents frequently have driver weariness as a primary contributing factor. As a result, methods that can identify and alert a motorist to their poor psychophysiological condition are needed, which might greatly minimise the incidence of incidents involving exhaustion. Nevertheless, there are several challenges in the development of such systems that are connected to the quick and accurate identification of a driver's tiredness symptoms. The employment of a vision-based technique is one of the technological options for implementing driver drowsiness detection systems. Driver Drowsiness is the condition of being very drowsy or exhausted when driving. Several things, including lack of sleep, working long hours, certain medicines, sleep problems, and drug or alcohol use, might contribute to it. The risk of accidents might rise because of drowsiness, which can affect a driver's ability to pay attention to the road, make wise decisions, and respond swiftly to changes in traffic circumstances. Driver sleepiness is characterised by yawning, frequent blinking, lane wandering, difficulties maintaining a steady pace, and difficulty keeping the head up. Drivers must be aware of the symptoms of tiredness and take action to treat them, such as taking a break, obtaining additional sleep, or, if necessary, switching drivers.

Medium access in cloud-based for the internet of things based on mobile vehicular infrastructure

Smart cities are made up of a large number of smart, intelligent gadgets that can sense, compute, act, and communicate. Focusing on how data is transferred between sensory devices and applications in the internet of things (IoT), and cyber-physical systems have led to 5G/IoT integration. This paper proposes a revolutionary architecture for mobile vehicular cloud infrastructure that takes variable weather, road, and traffic circumstances into consideration. It proposes a dynamic speed management system for smart cities. To optimize system flexibility and reduce costs, the system makes advantage of the most recent advancements in wireless communication and utilizes current telecommunication infrastructures utilized in data streaming, sound, and video. The study presents an internet protocol (IP) real-time subsystem-network-based framework for requesting bandwidth from free wireless channel resources using the channell quality indicator channel.

A Link Real-time Travel Time Estimation on Speed-time Field Traversing Incorporating LSTM Neural Network

A travel time estimation method based on Speed-time field traversal including LSTM neural network was proposed to increase the real-time and accuracy of travel time estimation. The node departure speed of the traditional piecewise truncated quadratic speed trajectory model was optimized by the road node arrival speed, considering the impact of road conditions on travel time. The node arrival speed was modeled as time series and predicted in the short-term future by combining the LSTM neural network model to construct a spatiotemporally continuous speed trajectory, to estimate the link’s travel time. The method was tested on an actual road and gave considerably improved estimates and results compared to the original method, using LSTM neural network to predict node departure speed and the technique using node arrival speed. According to experimental results, the proposed method performs well in both smooth and crowded traffic circumstances, serving as a benchmark for precise real-time travel time estimation on urban roads.

Vehicle health monitoring and accident avoidance system based on IoT model

The field of self-driving cars is one that is rapidly growing in popularity. The goal of autonomous vehicles has always been to avoid accidents. It has long been argued that human errors while driving are the primary cause of traffic accidents, and autonomous cars have the potential to remove this. An intelligent transportation system based on the Internet of Things (IoT) is required at some point for the vehicle to make an instant choice to evade accidents, regardless of the competence of a decent driver Mishaps on the road and in the weather are those that occur due to unfavourable weather circumstances such as fog, gusts, snow, rain, slick pavement, sleet, etc. There are many factors that might cause a vehicle to lose control, including speed, weight, momentum, poor fleet maintenance. It has the potential to lessen the number of collisions caused by poor weather and deteriorating road circumstances. An IoT-based intelligent accident escaping system for poor weather and traffic circumstances is presented here. A variety of sensors are used to check the health of the vehicle. Data from sensors is processed by a microcontroller and displayed on the dashboard of a car after it has been received. The proposed model combines both an IoT system that monitors weather and road conditions and an intelligent system based on deep learning that learns the adverse variables that impact an accident in order to anticipate and prescribe a harmless speed to the driver. The experimental results show that the proposed deep learning technique achieved 94% of accuracy, where the existing LeNet model achieved 80% of accuracy for the prediction process. The proposed ResNet is more effective than LeNet, because identity mapping is used to solve the vanishing gradient problems.

Implementing the Maximum Likelihood Method for Critical Gap Estimation under Heterogeneous Traffic Conditions

Gap acceptance analysis is crucial for determining capacity and delay at uncontrolled intersections. The probability of a driver accepting an adequate gap changes over time, and in different intersection types and traffic circumstances. The majority of previous studies in this regard have assumed homogeneous traffic conditions, and applying them directly to heterogeneous traffic conditions may produce biased results. Moreover, driver behavior concerning critical gap acceptance or rejection in traffic also varies from one location to another. The current research focused on the estimation of critical gaps considering different vehicle types (cars, and two- and three-wheelers) under heterogenous traffic conditions at uncontrolled crossings in the city of Peshawar, Pakistan. A four-legged uncontrolled intersection in the study area was used to investigate drivers’ gap acceptance behavior. The gaps were investigated for various vehicle types: two-wheelers, three-wheelers, and cars. For data collection, a video recording method was used, and Avidemux video editing software was used for data investigation. The study investigated the applicability of the maximum likelihood (MLM) method to analyzing a vehicle’s critical gap. MLM estimation results indicate that the essential critical gap values for car drivers are in the range from 7.45 to 4.6 s; for two-wheelers, the critical gap was in the range from 6.78 to 4.7 s; and for three-wheelers, the values were in the range from 6.3 to 4.9 s. At an uncontrolled intersection, the proposed method’s results can assist in distinguishing between different road user groups. This study’s findings are intended to be useful to both researchers and practitioners, particularly in developing countries with similar traffic patterns and vehicle adherence patterns at unsignalized intersections.

Enhancement of license plate recognition performance using Xception with Mish activation function.

The current breakthroughs in the highway research sector have resulted in a greater awareness and focus on the construction of an effective Intelligent Transportation System (ITS). One of the most actively researched areas is Vehicle Licence Plate Recognition (VLPR), concerned with determining the characters contained in a vehicle’s Licence Plate (LP). Many existing methods have been used to deal with different environmental complexity factors but are limited to motion deblurring. The aim of our research is to provide an effective and robust solution for recognizing characters present in license plates in complex environmental conditions. Our proposed approach is capable of handling not only the motion-blurred LPs but also recognizing the characters present in different types of low resolution and blurred license plates, illegible vehicle plates, license plates present in different weather and light conditions, and various traffic circumstances, as well as high-speed vehicles. Our research provides a series of different approaches to execute different steps in the character recognition process. The proposed approach presents the concept of Generative Adversarial Networks (GAN) with Discrete Cosine Transform (DCT) Discriminator (DCTGAN), a joint image super resolution and deblurring approach that uses a discrete cosine transform with low computational complexity to remove various types of blur and complexities from licence plates. License Plates (LPs) are detected using the Improved Bernsen Algorithm (IBA) with Connected Component Analysis(CCA). Finally, with the aid of the proposed Xception model with transfer learning, the characters in LPs are recognised. Here we have not used any segmentation technique to split the characters. Four benchmark datasets such as Stanford Cars, FZU Cars, HumAIn 2019 Challenge datasets, and Application-Oriented License Plate (AOLP) dataset, as well as our own collected dataset, were used for the validation of our proposed algorithm. This dataset includes the images of vehicles captured in different lighting and weather conditions such as sunny, rainy, cloudy, blurred, low illumination, foggy, and night. The suggested strategy does better than the current best practices in both numbers and quality.

Performance of Anti-Lock Braking Systems Based on Adaptive and Intelligent Control Methodologies

Automobiles of today must constantly change their speeds in reaction to changing road and traffic circumstances as the pace and density of road traffic increases. In sophisticated automobiles, the Anti-lock Braking System (ABS) is a vehicle safety system that enhances the vehicle's stability and steering capabilities by varying the torque to maintain the slip ratio at a safe level. This paper analyzes the performance of classical control, model reference adaptive control (MRAC), and intelligent control for controlling the (ABS). The ABS controller's goal is to keep the wheel slip ratio, which includes nonlinearities, parametric uncertainties, and disturbances as close to an optimal slip value as possible. This will decrease the stopping distance and guarantee safe vehicle operation during braking. A Bang-bang controller, PID, PID based Model Reference Adaptive Control (PID-MRAD), Fuzzy Logic Control (FLC), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controller are used to control the vehicle model. The car was tested on a dry asphalt and ice road with only straight-line braking. Based on slip ratio, vehicle speed, angular velocity, and stopping time, comparisons are performed between all control strategies. To analyze braking characteristics, the simulation changes the road surface condition, vehicle weight, and control methods. The simulation results revealed that our objectives were met. The simulation results clearly show that the ANFIS provides more flexibility and improves system-tracking precision in control action compared to the Bang-bang, PID, PID-MRAC, and FLC.

ФОРМИРОВАНИЕ ТРЕХМЕРНОЙ ПРОСТРАНСТВЕННО-СЛЕДОВОЙ БАЗЫ ИСХОДНЫХ ДАННЫХ С ИСПОЛЬЗОВАНИЕМ СКАНИРУЮЩИХ БЕСПИЛОТНЫХ ЛЕТАТЕЛЬНЫХ АППАРАТОВ И ЕЕ ИНТЕГРАЦИЯ В МОДЕЛЬНО-ОРИЕНТИРОВАННУЮ РЕКОНСТРУКЦИЮ ДОРОЖНО-ТРАНСПОРТНЫХ ПРОИСШЕСТВИЙ

The paper is devoted to an up-to-date problem connected with consequences of accident rate on roads and ways to increase a quality of carrying out an expertise of traffic accidents. The authors have carried out studies on the use of unmanned aerial vehicles (UAV) with the 3D-scanning technology that provides a high-quality and highly efficient collection of initial data for carrying out the expertise of traffic accidents. They have developed an algorithm of UAV application for the registration of traffic accident circumstances. For the verification of the proposed method the authors have simulated a traffic accident and created a 3D-model of a scene of the accident with the use of UAV with the Lidar technology. Results of the study confirmed that the use of UAV with the 3D-scanning technology provides the collection of comprehensive information needed for the expertise of a traffic accident.

Privacy-Aware Data Forensics of VRUs Using Machine Learning and Big Data Analytics

The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results.