Field Of Intelligent Transportation Systems Research Articles

Implementation of Smart Vehicle Assistant using Computer vision and Deep Learning

bstract: Autonomous cars are a popular research topic in the field of intelligent transportation systems because they have the potential to greatly reduce traffic and improve travel efficiency. Scene classification is a key technology for autonomous cars because it forms the basis of these vehicles' decision-making. In recent years, deep learning-based methods have demonstrated promise in resolving the scene classification issue. However, further research is needed on a few difficulties related to scene classification methodologies, such as how to treat similarities and contrasts within the same category. To tackle these problems, this research suggests an improved deep network-based scene classification method. Autonomous cars are a popular research topic in the field of intelligent transportation systems because they have the potential to greatly reduce traffic and improve travel efficiency.

Detection of black hole attacks in vehicle-to-vehicle communications using ad hoc networks and on demand protocols

PurposeThe potential of vehicle ad hoc networks (VANETs) to improve driver and passenger safety and security has made them a hot topic in the field of intelligent transportation systems (ITSs). VANETs have different characteristics and system architectures from mobile ad hoc networks (MANETs), with a primary focus on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. But protecting VANETs from malicious assaults is crucial because they can undermine network security and safety.Design/methodology/approachThe black hole attack is a well-known danger to VANETs. It occurs when a hostile node introduces phony routing tables into the network, potentially damaging it and interfering with communication. A safe ad hoc on-demand distance vector (AODV) routing protocol has been created in response to this issue. By adding cryptographic features for source and target node verification to the route request (RREQ) and route reply (RREP) packets, this protocol improves upon the original AODV routing system.FindingsThrough the use of cryptographic-based encryption and decryption techniques, the suggested method fortifies the VANET connection. In addition, other network metrics are taken into account to assess the effectiveness of the secure AODV routing protocol under black hole attacks, including packet loss, end-to-end latency, packet delivery ratio (PDR) and routing request overhead. Results from simulations using an NS-2.33 simulator show how well the suggested fix works to enhance system performance and lessen the effects of black hole assaults on VANETs.Originality/valueAll things considered, the safe AODV routing protocol provides a strong method for improving security and dependability in VANET systems, protecting against malevolent attacks and guaranteeing smooth communication between cars and infrastructure.

Emergency Vehicle Detection and Traffic Prevention Using Open CV

The proposed project focuses on the development of an innovative system for "Emergency Vehicle Detection and Traffic Prevention using OpenCV." The project aims to enhance road safety and expedite emergency response by leveraging computer vision techniques implemented through the OpenCV framework. The primary objective is to design a robust algorithm that can accurately identify emergency vehicles in real-time based on distinct visual features such as color, shape, and motion patterns. OpenCV, a powerful open-source computer vision library, will serve as the foundation for image processing and analysis. Upon successful detection of an emergency vehicle, the system will employ dynamic traffic prevention measures. This includes the activation of intelligent traffic signal control to ensure the smooth and rapid passage of the emergency vehicle through intersections. The system may also interface with connected infrastructure, such as smart traffic lights, to optimize overall traffic flow and minimize delays. The project aims to contribute to public safety and emergency response efficiency by providing a reliable and automated solution for prioritizing emergency vehicles on the road. Through the integration of OpenCV, the system will offer a versatile and scalable approach to emergency vehicle detection and traffic prevention, making it a valuable contribution to the field of intelligent transportation systems. Index terms Emergency Vehicle Detection, Traffic Prevention, OpenCV, Computer Vision Techniques, Real-time Vehicle Identification, Intelligent Traffic Signal Control, Dynamic Traffic Management, Road Safety Enhancement, Emergency Response Efficiency, Public Safety, Automated Traffic Prioritization, Intelligent Transportation Systems, Visual Feature Analysis, Smart Traffic Lights Integration, Automated Traffic Flow Optimization

Intelligent Control of Electric Vehicle Drives using Swarm Robotics

This study investigates the incorporation of swarm robotics into the control mechanism of electric vehicles (EVs), introducing an innovative intelligent control framework that utilizes the concepts of decentralized decision-making. The research entails a methodical inquiry that encompasses the design of system architecture, the creation of a model for swarm robotics, the modeling of electric vehicle drive, the integration of swarm robotics with EV control, the development of algorithms for intelligent control, and the execution of real-world tests. The fleet of electric cars, propelled by a collective of independent robotic entities, displayed remarkable flexibility in adjusting to fluctuating surroundings. Findings demonstrated disparities in operating duration, distance traversed, mean speed, and energy expenditure during several iterations, highlighting the system’s adeptness in promptly reacting to instantaneous inputs. Significantly, the swarm-propelled electric cars successfully attained varied operating durations, showcasing the system’s adaptability in accommodating environmental dynamics. The swarm-driven system demonstrated its navigation effectiveness by effectively covering various distances, highlighting its versatility and extensive coverage capabilities. The system’s ability to effectively balance energy economy and performance is shown by the collective regulation of average velocity. The energy consumption study demonstrated the system’s efficacy in optimizing energy use, with certain experiments showing significant savings. Percentage change studies have yielded valuable insights into the comparative enhancements or difficulties seen in each indicator, so illustrating the influence of decentralized decision-making on operational results. This study is a valuable contribution to the ever-changing field of intelligent transportation systems, providing insight into the immense potential of swarm-driven electric cars to completely transform sustainable and adaptable transportation. The results highlight the remarkable flexibility and optimization skills of swarm robotics in the management of electric vehicles, paving the way for future advancements in the quest for intelligent, energyefficient, and dynamically responsive transportation solutions.

Efficient Communications in V2V Networks with Two-Way Lanes Based on Random Linear Network Coding.

Vehicle-to-vehicle (V2V) communication has gained significant attention in the field of intelligent transportation systems. In this paper, we focus on communication scenarios involving vehicles moving in the same and opposite directions. Specifically, we model a V2V network as a dynamic multi-source single-sink network with two-way lanes. To address rapid changes in network topology, we employ random linear network coding (RLNC), which eliminates the need for knowledge of the network topology. We begin by deriving the lower bound for the generation probability. Through simulations, we analyzed the probability distribution and cumulative probability distribution of latency under varying packet loss rates and batch sizes. Our results demonstrated that our RLNC scheme significantly reduced the communication latency, even under challenging channel conditions, when compared to the non-coding case.

A visual-based toolkit to support mobility data analytics

The Knowledge Discovery from Data (KDD) process is widely used across various domains to get valuable insights from data. Many platforms, like KNIME or RapidMiner, offer effective tools for KDD analysts, allowing them to perform data analytics tasks in a visual fashion, without writing code. In recent years, the increasing availability of mobility data has led to a surge in KDD-based initiatives from both industry and academia in the Intelligent Transportation Systems (ITS) domain. Still, KDD platforms lack comprehensive support for some typical mobility data manipulation tasks. As a result, mobility data analysis still requires a significant coding phase, with reduced productivity and hindered replicability of results.To address this gap, this paper presents a novel solution aimed at supporting ITS data analysts in defining KDD processes more efficiently. More in detail, we extended the KNIME platform by introducing a collection of new components explicitly tailored to facilitate some peculiar KDD tasks from mobility data. These components encompass critical functionalities such as map coverage analysis, trajectory partitioning and map-matching.To showcase the effectiveness of the proposed solution, we used it to replicate a study published in the ITS data analytics domain. Thanks to our proposal, such replication can be accomplished in a few minutes and with just a few clicks, without any manual coding, resulting in a pipeline that is easier to understand, distribute and re-execute, also for domain experts with no programming experience.Our solution is open-source and freely downloadable from the Knime Hub. In this way, we aim to foster data-driven research and practice in the ITS field, by providing researchers and practitioners with more effective analytics tools to handle mobility data.

Adaptive traffic lights based on traffic flow prediction using machine learning models

<span lang="EN-US">Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.</span>

Optimizing Traffic Control with Real-time Data: A HERE API-Based Approach

Traffic congestion is a pervasive issue in urban areas, leading to increased commute times, environmental pollution, and reduced overall quality of life. To address this challenge, this paper introduces a dynamic traffic control system that leverages the HERE API for real-time traffic data. The system provides a web- based interface with user authentication and allows users to input the address of a signal they wish to control. Utilizing the HERE API, it fetches live traffic information, including jam factor and free flow speed, for all four roads at the specified signal. This data serves as the foundation for an intelligent traffic management system that dynamically allocates green signal times based on the intensity of traffic, with roads experiencing higher congestion receiving longer green time. The system's backend is built using Python and the Flask framework, while the frontend features HTML and CSS for a user-friendly interface. Authentication ensures that only authorized users can access and utilize the system. Geocoding is employed to convert user-provided addresses into geographic coordinates, enabling precise data retrieval from the HERE API. Once the traffic data is collected, an analysis is performed to identify the road with the highest traffic intensity. The dynamic allocation of green signal times is a fundamental feature, allowing for real-time traffic optimization and a reduction in congestion. The paper explores the development, implementation, and testing of this dynamic traffic control system, emphasizing its potential to significantly improve urban mobility. Furthermore, considerations for scalability, security, and maintenance are addressed to ensure the system's practicality in real-world scenarios. This project contributes to the growing field of intelligent transportation systems by showcasing the benefits of data-driven decision-making in traffic signal control. By utilizing the HERE API, this system not only provides a solution to mitigate traffic congestion but also represents a step towards more sustainable and efficient urban transportation systems. Key Words: Traffic control, HERE API, Dynamic signal allocation, Real-time traffic data, Urban mobility, Traffic congestion, Traffic optimization, Intelligent transportation systems, Geocoding, User authentication, Python backend, Flask framework, User interface, Green signal time, Scalability, Security, Maintenance, Sustainable transportation.

Multisource fusion of exogenous inputs based NARXs neural network for vehicle speed prediction between urban road intersections

The economy and safety of passages through the urban road intersection environment is an important research topic in the field of intelligent transportation systems, but vehicle speed prediction as its subtopic is still under-researched, and its prediction accuracy is unsatisfactory. Therefore, a model for vehicle speed prediction based on the nonlinear autoregressive model with multisource exogenous inputs (NARXs) neural network is proposed. The model combines the human-vehicle-road model with the NARXs neural network to perform speed prediction between urban road intersections. First, multisource features, including the variables of driving behavior characteristics, vehicle responses, and road conditions, are extracted to construct the human-vehicle-road model. Then, the model is introduced into the NARXs neural network. Finally, the advantages of the proposed model are verified from two perspectives by evaluation indices such as mean absolute error ( MAE), mean absolute percentage error ( MAPE), root mean square error ( RMSE), Theil index ( Theil ic), and goodness-of-fit ( R2) compared with several other models. On the one hand, the analysis results show that the proposed model has higher prediction accuracy than the other comparative models for different prediction durations and has the best performance in 30 s duration backward prediction. On the other hand, the curves of each evaluation index of the proposed model are horizontal, which indicates that the prediction performance of the model hardly varies with the length of the training dataset. These positive results demonstrate the higher accuracy and outstanding characteristics of the proposed model in the subject of vehicle speed prediction.

A Tribute to Women in the Field of Intelligent Transportation Systems [President’s Message

A Tribute to Women in the Field of Intelligent Transportation Systems [President’s Message

Artificial Intelligence in Transportation Systems A Critical Review

Purpose: The purpose of the research is to investigate the role of machine learning (ML) and artificial intelligence (AI) in the growth of smart cities. It aims to understand how these technologies are being used to manage expanding metropolitan areas, boost economies, reduce energy consumption, and improve the living standards of residents. The study also aims to analyze the information flow associated with ICT in smart cities.
 Methodology: The methodology involves conducting a survey to identify the typical technologies used to support communication in smart cities. It also involves a systematic evaluation of current patterns in publications related to ICT in smart cities. The research utilizes ML and AI techniques to analyze and interpret the collected data.
 Findings: The findings of the study indicate that ML and AI play a significant role in various aspects of smart cities, particularly in the field of intelligent transportation systems. These technologies are utilized for tasks such as modeling and simulation, dynamic routing and congestion management, and intelligent traffic control. The research also reveals the application of ML and AI in other forms of transportation like air, rail, and road travel.
 Recommendations: Based on the findings, the study suggests that the agent computing paradigm is a powerful technology for the development of large-scale distributed systems, particularly in the context of geographically dispersed and dynamic transport systems. The research emphasizes the interoperability, flexibility, and extendibility of agent-based traffic control and management systems. It concludes by suggesting potential future research directions to effectively integrate agent technology into traffic and transportation systems.

Valuable Resources and Opportunities for IEEE ITSS Members [President’s Message

The field of intelligent transportation systems (ITS) is rapidly growing and has had a significant impact on society as it aims to make transportation safer, more efficient, and more sustainable. The strong leadership of the IEEE ITS Society (ITSS), made up of a dedicated and experienced group of volunteers who are committed to advancing the field of ITS, is working to provide valuable resources and opportunities for members through its Chapters. The Society’s Chapters provide a local network of professionals and students, who are interested in the field of ITS. ITSS Chapters provide a place for members to connect with others in their area, share ideas, and stay current with the latest developments in their area of expertise.

Longitudinal Control-Oriented Spectrum Sharing Based on C-V2X for Vehicle Platoons

Combined with the development of wireless communication, the vehicle platoon will play a key role in the field of intelligent transportation system, due to its significant advantages in improving traffic safety, increasing road capacity, decreasing energy consumption, etc. In a vehicle platoon, the driving-related information is collected through vehicle-to-vehicle (V2V) communication to guarantee safety and improve control performance. However, it has been proved that the delay and reliability of V2V communication greatly affect the platoon stability. In this article, we investigate the spectrum-sharing problem for a vehicle platoon satisfying longitudinal control requirements. Particularly, we derive the communication delay constraints that guarantee plant stability and string stability. Then, using the obtained delay constraint, the wireless resource management, including the optimal power allocation and selection of spectrum sharing of the involved vehicles, is carried out in consideration of intracell interference and heterogeneous communication requirements. Finally, simulation results demonstrated the feasibility and effectiveness of the proposed scheme in vehicle-following control and wireless resource allocation.

Limi-TFP: Citywide Traffic Flow Prediction With Limited Road Status Information

Citywide traffic flow prediction is a basic but challenging task in the field of Intelligent Transportation Systems (ITS). Most existing traffic flow prediction methods require historical traffic data of all the target roads in both training and application stages. However, it is not easy, and usually costly, to collect real-time traffic data across the entire road network. Therefore, many existing methods have to narrow down the scope of their prediction on the parts of road networks. In this paper, we propose a deep-learning model, named Limi-TFP, which has the ability to identify a limited number of monitored roads, and achieves citywide traffic flow prediction by using the historical traffic data of these selected roads. Specifically, an embedding module is proposed to capture the spatial context (road topology structure) and the attributes (e.g., road type, length, lanes, etc.) of each road by embedding all road segments into vectors. A road ranking method is then developed for selecting a limited number of roads to be monitored. A group of multi-head attention mechanisms is exploited for capturing the dynamic correlations with each monitored road. Meanwhile, the fusion with external factors, including the point of interests (POIs) and weather data, is also taken into account to further improve the prediction accuracy. Extensive experiments on the real-world datasets demonstrate that the proposed method achieves the superior performance and is robust with the presence of noise compared to the state-of-the-art baselines, with only 5% roads being monitored.

Traffic Flow Forecasting in the COVID-19: A Deep Spatial-temporal Model Based on Discrete Wavelet Transformation

Traffic flow prediction has always been the focus of research in the field of Intelligent Transportation Systems, which is conducive to the more reasonable allocation of basic transportation resources and formulation of transportation policies. The spread of COVID-19 has seriously affected the normal order in the transportation sector. With the increase in the number of infected people and the government's anti-epidemic policy, human outgoing activities have gradually decreased, resulting in increasingly obvious discreteness and irregularities in traffic flow data. This article proposes a deep-space time traffic flow prediction model based on discrete wavelet transform (DSTM-DWT) to overcome the highly discrete and irregular nature of the new crown epidemic. First, DSTM-DWT decomposes traffic flow into discrete attributes, such as flow trend, discrete amplitude, and discrete baseline. Second, we design the spatial relationship of the transportation network as a graph and integrate the new crown pneumonia epidemic data into the characteristics of each transportation node. Then, we use the graph convolutional network to calculate the spatial correlation of each node, and the temporal convolutional network to calculate the temporal correlation of the data. In order to solve the problem of high discreteness of traffic flow data during the epidemic, this article proposes a graph memory network (GMN), which is used to convert discrete magnitudes separated by discrete wavelet transform into high-dimensional discrete features. Finally, use DWT to segment the predicted traffic data, and then perform the inverse discrete wavelet transform between the newly segmented traffic trend and discrete baseline and the discrete model predicted by GMN to obtain the final traffic flow prediction result. In simulation experiments, this work was compared with the existing advanced baselines to verify the superiority of DSTM-DWT.

Spatial–Temporal Complex Graph Convolution Network for Traffic Flow Prediction

Traffic flow prediction remains an ongoing hot topic in the field of Intelligent Transportation System. The state-of-the-art traffic flow prediction models can effectively extract both spatial and temporal features of traffic flow data, but ignore the correlation and external interference between traffic nodes. To this end, this paper proposes a novel method based on Spatial–Temporal Complex Graph Convolution Network (ST-CGCN) for traffic flow prediction. Specifically, we first constructs the distance matrix, the data correlation matrix, and the comfort measurement matrix according to the geographical locations, the historical data record, and the external interference between traffic nodes. Then, these three matrices are fused into a complex correlation matrix by introducing self-learning dynamic weights to improve the joint modeling ability of spatial–temporal features and external factors. Next, a spatial feature extraction module and a temporal feature extraction module are designed to characterize dynamic spatial–temporal features. The spatial feature extraction module consists of a graph convolution operator with a proposed complex correlation matrix and a residual unit. The temporal feature extraction module consists of a 3D convolution operator and a Long Short-Term Memory (LSTM). Experiments constructed on five real-world datasets demonstrate that the new proposed ST-CGCN is more effective than several existing deep learning based traffic flow prediction models. The key source code and data are available at https://github.com/Bounger2/ST-CGCN.

Self-Similar Growth and Synergistic Link Prediction in Technology-Convergence Networks: The Case of Intelligent Transportation Systems

Self-similar growth and fractality are important properties found in many real-world networks, which could guide the modeling of network evolution and the anticipation of new links. However, in technology-convergence networks, such characteristics have not yet received much attention. This study provides empirical evidence for self-similar growth and fractality of the technology-convergence network in the field of intelligent transportation systems. This study further investigates the implications of such fractal properties for link prediction via partial information decomposition. It is discovered that two different scales of the network (i.e., the micro-scale structure measured by local similarity indices and the scaled-down structure measured by community-based indices) have significant synergistic effects on link prediction. Finally, we design a synergistic link prediction (SLP) approach which enhances local similarity indices by considering the probability of link existence conditional on the joint distribution of two scales. Experimental results show that SLP outperforms the benchmark local similarity indices in most cases, which could further validate the existence and usefulness of the synergistic effect between two scales on link prediction.

An Optimal Security Framework Based on Driver Authentication for Intelligent Transportation Systems

Abstract Security and safety have been a critical issue in the field of Intelligent Transportation Systems. Security of both people and vehicles is a matter of concern. In this paper, an authentication-based security framework is designed to serve the purpose. Only authorized users are provided access to the vehicle. The drivers are first registered for driving the vehicle from an authorized and valid source. The next step is the authentication check before one can be given access to the vehicle. There are two alternative methods provided for the authentication check in the solution depending upon the situation the vehicle is in. The solution is an optimally viable solution from the security perspective of the real-time Intelligent Transportation System environment. The results have shown that the security solution fits into the domain very well and performs as per expectations. The solution has been tested on a Vehicle Tracking Device connected to a vehicle. The same solution can be extended to a fleet of vehicles.

Выбор и оценка ARIMA-модели для прогнозирования пассажиропотока

Short-term forecasting of transport traffic flow is one of the main areas of research in the field of intelligent transport systems. In recent years, a large number of studies on traffic forecasting have been conducted. However, it is difficult to pick the most appropriate traffic forecasting method for a particular application. Naive methods are widely used due to their low computational costs and simplicity of implementation. But the accuracy of these methods is usually quite low. Clustering methods average out the traffic variables within a certain group of days based on similar traffic models. Macroscopic simulation models take into account only such global variables of the road network as traffic density, average speeds and flows. The advantage of nonparametric models lies in the fact that they enable us to model complex, dynamic and nonlinear processes occurring in road traffic. One of the forecasting options for passenger traffic is the forecasting on the basis of time series processing, which allows obtaining rather reliable results. Currently, considerable attention is given to the spatiotemporal forecasting. ARIMA processes are flexible linear models which can accurately describe a wide range of various time series behavior, including medium-term peaks and troughs. Despite the fact that these basic models are quite simple to describe, their evaluation requires extensive computer calculations. This paper investigates the problem of seasonality modeling when studying the time distribution of passenger traffic flow in road transport. The basic information for predicting passenger traffic flow is the data obtained during the daily parsing of the online service for searching traveling car companions (edem.rf (едем.рф)) for 12 months (February 2022 - January 2023).

Awakening Worldwide Interest in the Field of Intelligent Transportation Systems [President’s message

The research, development, and commercialization of products and strategies relating to intelligent transportation systems (ITSs) increase safety and help to reduce increasing traffic emissions and congestion problems. By applying information and communication technologies to all modes of passenger and freight, transport can be made more efficient and sustainable. For this reason, the ITS field is gaining more interest and is being included in many government plans.