Field Of Intelligent Transportation Systems Research Articles

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.

The role of intelligent transportation systems in solving traffic problems and reducing environmental negative impact of urban transport

Information technology has produced many modern systems in the field of intelligent transportation systems that aim to integrate existing and new systems for the management of transportation and traffic networks to respond to changing (dynamic) traffic conditions. These systems have been applied in many Western countries for several purposes, the most important of which is traffic safety and the reduction of losses resulting from poor planning of these networks. Many transport systems in developing countries continue to suffer from many problems, such as high accident rates, traffic congestion, and pollution from car exhaust. This paper aims to use multi-criteria decision-making methods to help select the best technology to overcome the traffic problems in Libya. A hybrid model of Analytic Hierarchical Process (AHP) and the Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS) method was used. The AHP method was used to determine the weights of six technologies for intelligent systems, with the results showing that the most appropriate technology was surveillance cameras, with a weight of 28%. The resulting weights were used to rank the impact of these technologies on safety, environment and efficiency. This study can help decision makers on selection of the best systems that can be used to overcome the current problems faced by the transportation system.

Strategic Transit Route Recommendation Considering Multi-Trip Feature Desirability Using Logit Model with Optimal Travel Time Analysis

Route recommendation continues to manifest noteworthy contributions to the intelligent transportation system field of research as it evolves through time. Early related studies helped passengers and tourists experience a more convenient travel. At the same time, these helped transport planners analyze people’s trip preferences and its correlation with the region-specific economic status in a more time-relevant data. Majority, however, require historical data and heavy data collection methods. For user quantified metrics such as route cost in terms of travel time and distance, the complexity and sparsity of preferences between travelers are persistent challenges. The strategic transit route recommendation proposed in this study takes into account multiple trip features (both quantitative and qualitative) desirability using logit model and the optimal travel time with respect to a given road traffic condition, headway, and passenger demand. The chosen area of study is the Western Visayas region of the Philippines specific to the public utility bus (PUB) and jeepney (PUJ) transit routes. The results of the research exhibited the feasibility of an optimal and strategic recommendation of public transportation route for passengers considering present time relevant trip conditions rather than relying on the historical data which are difficult to obtain, or worse, non-existent.

Blockchain-Based Intelligent Transportation Systems: The Human Use of Cyberphysical–Social Transportation Systems [President’s Message

The interconnected objects, infrastructure, and data that form the base of cyberphysical systems use a broad range of electronic technologies to improve the usage of civil infrastructure and make transportation systems safe, efficient, reliable, and environmentally friendly. These existing relationships have established a strong relevance for potential future developments in automation and data exchange in the field of intelligent transportation systems (ITS). Collaborative platforms to acquire data; advanced driver assistance systems; or other tools, such as simulation platforms with connectivity and automation capabilities, are examples of the possibilities for the systemic coverage of many aspects of the automotive, transport, and mobility system, and they make it possible to address interdisciplinary holistic problems that include aspects from social sciences, transport, planning, robotics, and communication technologies.

Development of a cyber-physical-system perspective based simulation platform for optimizing connected automated vehicles dedicated lanes

Recently, many studies have shown that during the transition period of the coexistence of autonomous vehicles (AVs)/connected automated vehicles (CAVs) and human-driven vehicles (HDVs), a dedicated lane (DL) for AVs/CAVs can improve traffic throughput and lower collision risk, which has become a hot topic in the field of intelligent transportation systems (ITS). This study presents an integrated simulation platform for DL optimization from cyber-physical-system (CPS) perspective. The developed simulation platform can be used to qualitatively and quantitatively evaluate the design parameters, operation modes, contingency plan, etc. of DL in a “What You See Is What You Get (Wysiwyg)” style. First, we introduce the architecture and features of the proposed platform, which comprises of SUMO, OMNeT++, and Unity3D. We implemented all experimental scripts for DL optimization in SUMO, including the configuration of the DLs, car-following models, lane-changing models, vehicle departure models, origin–destination demands, and 2D visualization of traffic flow simulation. We used OMNeT++ for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Unity3D is a 3D visualization platform for simulations synchronized with SUMO. Connected with the driving simulator and paired with a virtual reality (VR) helmet, Unity3D can offer a high-fidelity immersive environment for all types of users. We developed a specific communication protocol to achieve data sharing and synchronization of all components of the proposed simulation platform. Second, this paper depicts the functions of the proposed simulation platform in detail, including the modeling of road networks, vehicles, traffic flow, optimal design, and evaluation indicators of DL. Finally, by using the developed simulation platform, we demonstrated and analyzed three cases: (1) without DL vs with DL, (2) inner lane vs outer lane, and (3) without V2I vs with V2I. The experimental results confirm the feasibility of the developed simulation platform, and yield some interesting findings, such as (1) market penetration rates (MPRs) are an important factor in deciding whether to deploy DLs, (2) the number of lane changes when the vehicle enters/leaves the designated lane is the key criterion for determining the placement of the DLs, and (3) the cooperative vehicle-infrastructure system can offer AVs/CAVs with better over-the-horizon perception capabilities, helping to improve the smoothness of traffic flow in the case of abnormal events. The developed simulation platform is expected to offer a new tool for optimal design and evaluation of DLs for AVs/CAvs.

"A Machine Intelligent Cloud Based Framework to Monitor Driving Behavior of Vehicles Using Internet of Things"

Monitoring driving behavior of vehicles is a very important area of research in the field of Intelligent Transportation Systems (ITSs). As vehicles are rising tremendously on the roads of cities, it is very much essential to track the driving behavior of a vehicle to reduce the accidents in the cities. The drivers who are not driving properly, for example if a driver is continuously performing sudden acceleration, sudden deceleration, sudden left turn, sudden right turn, etc. then these types of vehicles need to be monitored and detected. In this work, a machine intelligent cloud-based framework is proposed to monitor the driving behavior of vehicles in the city using internet of things (IoTs). The vehicles are installed with an on-board unit which is responsible for collecting all readings from the sensors and sending these readings to cloud using IoT for detection of driving patterns. Here, the cloud is deployed with a supervised machine intelligent model that is responsible for identifying the driving pattern after receiving the readings from a vehicle. The model is selected by conducting training and testing over a standard dataset. The performance of the framework is tested using Python tool. From the results, it is found that Random Forest (RF) model performs better in identifying the accurate vehicle behavior with highest classification accuracy (CA).

Traffic sign detection and recognition based on convolutional neural network

Traffic sign detection is one of the critical technologies in the field of intelligent transportation systems (ITS). The difficulty of traffic sign detection mainly lies in detecting small objects in a wide and complex traffic scene quickly and accurately. To develop an efficient traffic sign detection and recognition that can detect and categorize traffic signal into different classes in real-time with the help of Deep Learning techniques. In this project, regard traffic sign detection as a region classification problem and propose a two-stage R- CNN-based approach to solve it. To build a deep neural Network model that can classify traffic sign present in images into different categories. The training process takes predefined traffic signals and has them 'learn' into a model. With this model, people are able to read and understand traffic signs which are a very important task for all autonomous vehicles. A visual traffic sign recognition system can be integrated into the automobile with the objective of detecting and recognizing all emerging traffic signs. In case the driver refuses to heed traffic signs, the system will trigger an alarm.

Traffic sign detection and recognition based on convolutional neural network

Traffic sign detection is one of the critical technologies in the field of intelligent transportation systems (ITS). The difficulty of traffic sign detection mainly lies in detecting small objects in a wide and complex traffic scene quickly and accurately. To develop an efficient traffic sign detection and recognition that can detect and categorize traffic signal into different classes in real-time with the help of Deep Learning techniques. In this project, regard traffic sign detection as a region classification problem and propose a two-stage R- CNN-based approach to solve it. To build a deep neural Network model that can classify traffic sign present in images into different categories. The training process takes predefined traffic signals and has them 'learn' into a model. With this model, people are able to read and understand traffic signs which are a very important task for all autonomous vehicles. A visual traffic sign recognition system can be integrated into the automobile with the objective of detecting and recognizing all emerging traffic signs. In case the driver refuses to heed traffic signs, the system will trigger an alarm.

A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection

In the field of intelligent transportation systems (ITS), video surveillance is a hot research topic; this surveillance is used in a variety of applications, such as detecting the cause of an accident, tracking down a specific vehicle, and discovering routes between major locations. Object detection and shadow elimination are the main tasks in this area. Object detection in computer vision is a critical and vital part of object and scene recognition, and its applications are vast in the fields of surveillance and artificial intelligence. Additionally, other challenges arise in regard to video surveillance, including the recognition of text. Based on shadow elevation, we present in this work an inner-outer outline profile (IOOPL) algorithm for detecting the three levels of object boundaries. A system of video surveillance monitoring of traffic can be incorporated into this method. It is essential to identify the type of detected objects in intelligent transportation systems (ITS) to track safely and estimate traffic parameters correctly. This work addresses the problem of not recognizing object shadows as part of the object itself in-vehicle image segmentation. This paper proposes an approach for detecting and segmenting vehicles by eliminating their shadow counterparts using the delta learning algorithm (Widrow-Hoff learning rule), where the system is trained with various types of vehicles according to their appearance, colors, and build types. An essential aspect of the intelligent transportation system is recognizing the type of the detected object so that it can be tracked reliably and the traffic parameters can be estimated correctly. Furthermore, we propose to classify vehicles using a machine learning algorithm consisting of artificial neural networks trained using the delta learning algorithm, a high-performance machine learning algorithm, to obtain information regarding their travels. The paper also presents a method for recognizing the number plate using text correlation and edge dilation techniques. In regard to video text recognition, number plate recognition is a challenging task.

Achieving Climate Neutrality Through the Technological Aspects of Transportation [President’s Message

Transport-related decisions can make a significant contribution to achieving climate neutrality, the adaptation of transport systems being not only a factor for transport efficiency but also part of the technological aspects of the digital transformation. In this context, the application of digital technologies for use in automation, sensors, and communication technologies as they pertain to the field of intelligent transportation systems (ITS) makes it possible to respond to transportation needs through sustainable solutions. The IEEE Intelligent Transportation Systems Society promotes research for cleaner and more efficient transportation that helps to mitigate the consequences that transport has for climate change.