Intelligent Transportation System Infrastructure Research Articles

Intelligent Transportation System Technologies, Challenges and Security

Intelligent Transportation Systems (ITS) first appeared in 1868 with traffic lights. With developing technology, the need to bring a smart approach to transportation applications within the scope of speed and environmental protection has emerged. Protecting ITS infrastructure against cyber attacks has become a matter of reputation for states. It is essential to provide the necessary technological infrastructure for the integrated operation of the systems used in ITS, especially geographical location, communication, and mapping. These technological developments bring cyber attacks, risks, and many dangers that should be avoided, especially on the systems used. This study examines ITS architecture, applications, communication technologies, and new trend technologies in detail. This study includes contributing to studies in the field of ITS and preventing attacks and incidents that may occur in terms of cyber security. The most important cyber attacks that may occur in ITS applications are included. In addition, the minimum security requirements that can be taken in ITS applications and infrastructures against these attacks are included.

Implementation of blockchain technology in integrated IoT networks for constructing scalable ITS systems in India

The implementation of blockchain technology in integrated IoT networks for constructing scalable Intelligent Transportation Systems (ITSs) in India has the potential to revolutionize the way we approach transportation. By leveraging the power of IoT and blockchain, we can create a highly secure, transparent, and efficient system that can transform the way we move people and goods. India, one of the world’s most populous countries, has a highly congested and inefficient transportation system that often leads to delays, accidents, and waste of time and resources. The integration of IoT and blockchain can help address these issues by enabling real-time monitoring, tracking, and optimization of traffic flows, thereby reducing congestion, improving safety, and increasing the overall efficiency of the transportation system. This paper explores the potential of blockchain technology in the context of integrated IoT networks for constructing scalable ITS systems in India. The methodology followed is to develop a proof-of-concept blockchain-based application for ITS, implement the blockchain solution into the existing ITS infrastructure, and ensure proper integration and compatibility with other systems. Conduct thorough research and maintenance to ensure the reliability and sustainability of such blockchain-based systems. This research discusses the various benefits and challenges of this approach and the various applications of this technology in the transportation sector, including the green sustainability concept. The results find various ways in which such implementations of blockchain and IoT-Machine Learning (IoT-ML) can revolutionize transportation systems.

DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system

Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially catastrophic accidents. In order to anticipate and handle unusual driving behavior in Intelligent Transportation Systems (ITS), this research presents a unique Driver Decision Support System (DDSS). A reliable driving behavior prediction system is used by the suggested DDSS to categorize drivers as displaying normal or abnormal behavior. In order to prevent accidents in ITS scenarios, the system reliably detects anomalous driving patterns and advises nearby vehicles to change lanes or alter speed. The driver behavior prediction algorithm efficiently groups drivers into behavior categories using the K-Means clustering method. In order to evaluate the algorithm's efficacy, a comparative analysis is conducted by comparing its outcomes against those of Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbours (KNN), Logistic Regression, and Naïve Bayes. The integration of the Driver Decision Support System into the Intelligent Transportation System infrastructure serves to augment endeavours in accident prevention. Monitoring and analysis of driver behavior enable timely interventions, promoting safer driving practices and reducing accident risks. This research helps to create a more effective transportation system by reducing the number of accidents brought on by reckless driving. Because of its novel method to anticipating and controlling driver behavior, the proposed DDSS has promise for improving road safety and preventing accidents. The efficacy and the dependability of the driver behavior prediction algorithm are confirmed by the experimental assessment.

Digital Twins-Based Automated Pilot for Energy-Efficiency Assessment of Intelligent Transportation Infrastructure

To realize the great potential of the intelligent transportation infrastructure, the investment in the transportation infrastructure in the intelligent transportation system should be rationally planned. Firstly, the application status of cutting-edge Data Envelopment Analysis (DEA) model in transportation infrastructure efficiency evaluation is analyzed, and based on this, a DEA model of transportation infrastructure efficiency evaluation under Digital Twins technology is established. Secondly, with the transportation infrastructure of 12 prefecture-level cities in Jiangsu Province from 2005 to 2020 as the research object, the Digital Twins DEA model and the traditional Stochastic Frontier Approach (SFA) model are used to estimate the efficiency of transportation infrastructure in 12 cities. Finally, the traffic flow data of a certain road section in Zhenjiang City (J11 City) is simulated and predicted by using the Long Short-term Memory (LSTM) traffic flow prediction model. The results show that the average efficiency of the 12 cities estimated by the DEA model based on the Digital Twins is 0.7083, the average efficiency of the 12 cities estimated by the SFA model is 0.6445, and there are significant differences in the efficiency rankings of the cities. Compared with the actual efficiency, the established Digital Twins DEA model is more reasonable for the calculation of transportation infrastructure efficiency. The results of the LSTM traffic flow prediction model show that the Mean Absolute Error (MAE) of the LSTM model is 24.29, the Root Mean Square Error (RSME) is 0.1186, and the Mean Absolute Perce (MAPE) is 17.78, which are all lower than other models. Compared with other models, the proposed LSTM-based traffic flow prediction model is more accurate in traffic flow prediction. Hence, the research content provides a reference for the investment planning of intelligent transportation system infrastructure.

Parametric Evaluation of Internet of Things Applications to Freight Transportation Using Model-Based Systems Engineering

The intended purpose of an intelligent transportation systems (ITS) project is to automate operations through device-to-device connectivity. These devices generally represent stakeholder’s endpoints and expose interfaces to automated operations. Current trends in communication allow more applications and devices to perform functions traditionally allocated to the transportation ITS infrastructure. This connected environment of industrial internet of things presents design challenges because of the diversity of stakeholders, interfaces, and the messages among them. This may require new ways in which ITS planning can handle the scale and complexity of these highly connected systems. This work focuses on the modeling of the architecture of a freight-focused ITS application, including maritime ports. The proposed model integrates stakeholders, behaviors, and messages using the Systems Modeling Language (SysML). The key contribution of this work is to demonstrate the creation of an executable SysML model for an ITS application without sacrificing the typical Systems Engineering Management Plan artifacts (e.g., requirements traceability matrices and interface control documents). At the same time, the proposed model provides a re-usable pattern to support parametric analysis of candidate architectures with respect to any measure of effectiveness. This allows establishing a single source of architecture definition and having multiple architecture specializations depending on the measure of effectiveness being evaluated. Recommendations for implementation and integration with existing ITS tools are provided.

BIM-Based Description of Intelligent Transportation Systems for Roads

Intelligent transportation systems (ITS) provide safer, greener, and more convenient mobility, while reducing the impact on the environment. In recent years, simulation platforms have been employed to study ITS applications, mostly focusing on traffic-related simulations. Despite several research studies on ITS applications and simulation platforms, formal semantic descriptions of intelligent transportation systems have not been addressed yet. In this paper, a semantic model describing intelligent transportation systems for roads is proposed. The semantic model is devised to provide a basis for designing ITS simulation platforms. Building upon the semantic model, an extension to an open building information modeling (BIM) standard, i.e., the Industry Foundation Classes (IFC) schema, is presented. The IFC schema extension is verified and validated using a BIM-based simulation scenario of ITS for roads. It is shown that the proposed IFC-compliant description of ITS for roads provides a formal basis for generating BIM-based simulations and hence facilitates ITS infrastructure modeling in BIM projects. It is concluded that the present work represents a cornerstone for designing BIM-based ITS simulation platforms. In future endeavors, potential standardization and formalization efforts may be discussed.

Intelligent Transportation System Security: Impact-Oriented Risk Assessment of in-Vehicle Networks

The concept of Intelligent Transportation Systems (ITS) was developed in the 1980s when then upcoming technologies and cognitive information were incorporated into the road infrastructures and vehicles. The ITS infrastructure is critical yet not immune to both physical and cyber threats. Vehicles are cyber-physical systems which are a core component of ITS, can be either a target or a launching point for an attack on the ITS network. Unknown vehicle security vulnerabilities trigger a race among adversaries to exploit the weaknesses and security experts to mitigate the vulnerability. In this study, we identified opportunities for adversaries to take control of the in-vehicle network, which can compromise the safety, privacy, reliability, efficiency, and security of the transportation system. This study contributes in three ways to the literature of ITS security and resiliency. First, we aggregate individual risks that are associated with hacking the in-vehicle network to determine system-level risk. Second, we employ a risk-based model to conduct a qualitative vulnerability-oriented risk assessment. Third, we identify the consequences of hacking the in-vehicle network through a risk-based approach, using an impact-likelihood matrix. The qualitative assessment communicates risk outcomes for policy analysis. The outcome of this study would be of interest and usefulness to policymakers and engineers concerned with the potential vulnerabilities of such critical infrastructures.

OneVFC—A Vehicular Fog Computation Platform for Artificial Intelligence in Internet of Vehicles

We are witnessing the evolution from Internet of Things (IoT) to Internet of Vehicles (IoV). Internet connected vehicles can sense, communicate, analyze and make decisions. Rich vehicle-related data collection allows to apply artificial intelligence (AI) such as machine learning and deep learning (DL) to develop advanced services in Intelligent Transportation Systems (ITS). However, AI/DL-based ITS applications require intensive computation, both for model training and deployment. The exploitation of the huge computational power obtained through aggregation of resources present in individual vehicles and ITS infrastructure brings an efficient solution. In this work, oneVFC , a tangible vehicular fog computing (VFC) platform based on oneM2M is proposed. It benefits from the oneM2M standard to facilitate interoperability as well as hierarchical resource organization. oneVFC manages the distributed resources, orchestrates information flows and computing tasks on vehicle fog nodes and feeds back results to the application users. On a lab scale model consisting of Raspberry Pi modules and laptops, we demonstrate how oneVFC manages the AI-driven applications running on various machines and how it succeeds in significantly reducing application processing time, especially in cases with high workload or with requests arriving at high pace. We also show how oneVFC facilitates the deployment of AI model training in Federated Learning (FL), an advanced privacy preserving and communication saving training approach. Our experiments deployed in an outdoor environment with mobile fog nodes participating in the computation jobs confirm the feasibility of oneVFC for IoV environments whenever the communication links among fog nodes are guaranteed by V2X technology.

Multi-Sensor Multi-Vehicle (MSMV) Localization and Mobility Tracking for Autonomous Driving

Vehicle localization and mobility tracking are important tasks in autonomous driving. Traditional methods either have insufficient accuracy or rely on additional facilities to reach the desired accuracy for autonomous driving. In this paper, a multi-sensor multi-vehicle localization and mobility tracking framework is developed for autonomous vehicles equipped with GPS, inertial measurement unit (IMU), and an integrated sensing system. Our algorithm fuse the information from local onboard sensors as well as the observations of other vehicles or existing intelligent transportation system infrastructure such as road side units (RSU) to improve the precision and stability of localization and mobility tracking. Specifically, this framework incorporates the dynamic model of vehicles to achieve better localization and tracking performance. The communication delays during the information sharing process are explicitly taken into account in our algorithm development. Simulations manifest that not only the accuracy of localization and mobility tracking could be greatly enhanced in general, but also the robustness can be guaranteed under circumstances where traditional localization and tracking devices fail.

A Blockchain-Assisted Intelligent Transportation System Promoting Data Services with Privacy Protection.

Being able to obtain various environmental and driving data from vehicles is becoming more and more important for current and future intelligent transportation systems (ITSs) to operate efficiently and economically. However, the limitations of privacy protection and security of the current ITSs are hindering users and vehicles from providing data. In this paper, we propose a new ITS architecture by using blockchain technology solving the privacy protection and security problems, and promoting users and vehicles to provide data to ITSs. The proposed architecture uses blockchain as a trust infrastructure to protect users’ privacy and provide trustworthy services to users. It is also compatible with the legacy ITS infrastructure and services. In addition, the hierarchical organization of chains enables the scalability of the system, and the use of smart contracts provides a flexible way for introducing new services in the ITS. The proposed architecture is demonstrated by a proof of concept implementation based on Ethereum. The test results show that the proposed architecture is feasible.

Competing Risks Models for the Assessment of Intelligent Transportation Systems Devices: A Case Study for Connected and Autonomous Vehicle Applications

Intelligent transportation system (ITS) has become a crucial section of transportation and traffic management systems in the past decades. As a result, transportation agencies keep improving the quality of transportation infrastructure management information for accessibility and security of transportation networks. The goal of this paper is to evaluate the impact of two competing risks: “natural deterioration” of ITS devices and hurricane-induced failure of the same components. The major devices employed in the architecture of this paper include closed circuit television (CCTV) cameras, automatic vehicle identification (AVI) systems, dynamic message signals (DMS), wireless communication systems and DMS towers. From the findings, it was evident that as ITS infrastructure devices age, the contribution of Hurricane Category 3 as a competing failure risk is higher and significant compared to the natural deterioration of devices. Hurricane Category 3 failure vs. natural deterioration indicated an average hazard ratio of 1.5 for CCTV, AVI and wireless communications systems and an average hazard ratio of 2.3 for DMS, DMS towers and portable DMS. The proportional hazard ratios of the Hurricane Category 1 compared to the devices was estimated as <0.001 and that of Hurricane Category 2 < 0.5, demonstrating the lesser impact of the Hurricane Categories 1 and 2. It is expedient to envisage and forecast the impact of hurricanes on the failure of wireless communication networks, vehicle detection systems and other message signals, in order to prevent vehicle to infrastructure connection disruption, especially for autonomous and connected vehicle systems.

A Generalized Dynamic Planning Framework for Green UAV-Assisted Intelligent Transportation System Infrastructure

Roadside unit (RSU) planning is vital for the operation of an intelligent transportation system (ITS). RSUs provide ground coverage limited by obstacles. Unmanned aerial vehicles (UAVs) can complement RSU coverage by providing flexible connectivity capable of adapting coverage for traffic fluctuations, energy consumption, and budgetary constraints that all have effects on ITS operations. This article proposes a general RSU/UAV joint planning solution, where complex dynamic parameters are investigated. The objective is to maximize the effective coverage of placed RSUs and UAV docks given: a budget comprised of periodic operating expenses and capital expenditures, limitations of the ground transceivers and UAVs, and use of renewable energy to offset the on-grid electricity cost. We formulate a mixed-integer quadratically constrained problem that can determine the optimal placement of RSUs and UAV stations, RSU activation schedules, if solar panels are attached, and their coverage during each time period. Due to NP-hard complexity of such a planning problem, we design a heuristic algorithm that produces suboptimal solutions in less time. Afterward, we perform a sensitivity analysis and show that changes to the parameters lead to logical shifts in infrastructure coverage. Additionally, we visualize the algorithm's performance on a large setting—Manhattan Island.

Method of early pedestrian warning in developing intelligent transportation system infrastructure

The development of highly automated and driverless vehicles keeps gaining pace, which creates a need for ensuring pedestrian safety in an environment with a much lower noise level. This issue is further exacerbated by the popularity of various gadgets, which pedestrians may use to listen to music tracks while crossing the road. One of the advanced features of modern headphones (headsets) is active noise control, which cancels out any ambient noises and makes warning pedestrians through sound signals all the more challenging.Intelligent transportation systems offer an option to control the media system of the pedestrian’s personal gadget. However, it is not efficient to move the standard sound signal thesaurus into the personal space centered around the pedestrian’s headphones, as the pedestrian may be unable to identify the signals quickly and correctly. This calls for a special context-oriented solution.This paper outlines the architecture of an early warning system, proposes a methodology for creating a sonification thesaurus, and summarizes the results.

Future UAV-Based ITS: A Comprehensive Scheduling Framework

Multi-rotor unmanned aerial vehicles (UAVs) have been recently recognized as one of the top emerging technologies to be utilized in various smart city domains such as intelligent transportation systems (ITS). They represent an innovative mean to complement existing technologies to surveil transportation network, control traffic, and monitor incidents. The UAVs usually operate for time-limited missions due to their limited battery capacity. Hence, they need to frequently return to their docking stations to recharge their batteries, which handicaps their mission coverage and performance. When designing a UAV-based ITS infrastructure, it is crucial to leverage the UAV fleet effectively. In this paper, a generic management framework of UAVs for ITS applications is developed. The problem of docking/charging station placement is first investigated to find optimized locations for a given number of stations to be installed by the ITS operator. To this end, two fundamental criteria are taken into account: i) the flying time required by the UAV to reach the mission/incident location and ii) the risk of battery failure during the UAV operation. The two algorithms, namely a penalized weighted k-means algorithm and the particle swarm optimization algorithm, are designed for this purpose. Once the docking stations are placed, a UAV scheduling program is formulated to optimally cover the pre-known missions while minimizing the total energy consumption of the fleet and respecting a coverage efficiency targeted by the ITS operator. The proposed proactive scheduling approach employs multiple UAVs in sequential and parallel ways to cover spatially and temporally distributed events in the geographical area of interest over a long period.

A Low-Cost Real-World Planning Strategy for Deploying a Dedicated Short-Range Communications Roadside Unit on a Highway Off-Ramp

The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment, such as a traffic light’s signal phase and timing, using vehicle-to-infrastructure communication. Several scholarly papers exist on planning strategies for DSRC RSU deployments using simulation without accounting for wireless communication constraints and environmental changes. This paper proposes an empirical-based planning strategy for a highway off-ramp in a real-world environment. The research goal focuses on developing a low-cost and structured deployment plan for DSRC RSUs with the following objectives: use free planning tools; apply the deployment strategy in a real-world environment; utilize publicly available DSRC RSU data measurements; and leverage existing intelligent transportation systems infrastructure when possible. The proposed planning strategy includes three steps: (1) conduct a virtual site survey, (2) gather baseline performance data for the DSRC RSU equipment, and (3) generate a predictive radio frequency signal. The planning strategy was successfully applied on a highway off-ramp at exit 19A of the Capital Beltway, which encircles Washington, DC.

Fine-grained vehicle emission management using intelligent transportation system data

The increasing adoption of intelligent transportation system (ITS) data in smart-city initiatives worldwide has offered unprecedented opportunities for improving transportation air quality management. In this paper, we demonstrate the effective use of ITS and other traffic data to develop a link-level and hourly-based dynamic vehicle emission inventory. Our work takes advantage of the extensive ITS infrastructure deployed in Nanjing, China (6600 km2) that offers high-resolution, multi-source traffic data of the road network. Improved than conventional emission inventories, the ITS data empower the strength of revealing significantly temporal and spatial heterogeneity of traffic dynamics that pronouncedly impacts traffic emission patterns. Four urban districts account for only 4% of the area but approximately 30%–40% of vehicular emissions (e.g., CO2 and air pollutants). Owing to the detailed resolution of road network traffic, two types of emission hotspots are captured by the dynamic emission inventory: those in the urban area dominated by urban passenger traffic, and those along outlying highway corridors reflecting inter-city freight transportation (especially in terms of NOX). Fine-grained quantification of emissions reductions from traffic restriction scenarios is explored. ITS data-driven emission management systems coupled with atmospheric models offer the potential for dynamic air quality management in the future.

Multi-Modal Design of an Intelligent Transportation System

This paper proposes a novel intelligent transportation system (ITS) using the cellular network, GPS probes, and limited ITS infrastructure for edge-level speed estimation under heterogeneous traffic condition. The erroneous vehicle position data taken from cellular network are processed in real time to compute edge level vehicle flow, space occupancy, and congestion with a mean error of less than 10%. For edge-level speed estimation, two models of ITS infrastructure deployment are proposed: the COngestion COverage MOdel (COCOMO) and the Edge COverage MOdel (ECOMO). The GPS Probes’ speed data are used to extrapolate speed estimations from an infrastructure edge to the associated infrastructureless edge(s). The infrastructure requirement of COCOMO is constant, whereas that of ECOMO depends upon diversity in the congestion profile of edges. The COCOMO and ECOMO permit edge-level speed estimation with the 90 percentile error of 10%–22% and 10%–13%, respectively. The communication and storage requirement of the proposed ITS and the utility of generated traffic information are analyzed.

A Study Of Cost And Emissions Considering Speed Optimization In Trans Eurasia Transportation Based On Intelligent Transportation

Abstract People attach much importance to greenhouse gas emissions, and there is an increasing concern on cost and emissions on account of speed reduction for international shipping. Low steaming encountered these challenges of time constraint and technical requirements, while the cost and emissions of railway transportation distinctly differentiate from international shipping and it is a good substitute for international shipping to some extent. Intelligent transportation system (ITS) has proven to be a useful tool in providing efficient, environmentally friendly and safe transportation systems within inland and shipping. The paper focus on the selection and cooperative process between the shipping and railway, and discusses the cost and emissions by speed optimization for international shipping and railway transport aiming to select effective and environmental transportation mode based on ITS infrastructure. Firstly this paper reviews ITS and investigates the relationship of cost and speed along with the relationship of energy consumption and speed respectively for shipping and railway transportation, and then presents the speed optimization both shipping and railway transportation based on ITS so as to minimize cost and emissions. Subsequently it compared and analyzed the cost and emissions for mode selections, and it found that it is more effective and environmental to employ railway transport to some route leg. Finally A numerical case is presented and the results showed that it is necessary to shift model from sea to land on some section

Intelligent Transportation Systems-A Frontier for Breaking Boundaries of Traditional Academic Engineering Disciplines [Education

Introduction evolution of the modern transportation system has been instrumental in the prosperity of human civilization. Over the centuries, technological innovations have improved the performance and efficiency of transportation systems, which has in turn led us to the creation of the modern intelligent transportation system (ITS). Nonetheless, innovations in sensing, computing, analytics and communication technologies over the last few decades have vastly changed how we consume transportation services. According to the US National Academy of Engineering's report The Engineer of 2020: Visions of Engineering in the New Century, information and communication technologies will be integrated in every process and structure in future, and these technologies will be critical to the seamless performance of every engineered product [1]. Auto makers are expected to market several vehicle models with vehicle-tovehicle (V2V) connectivity within a few years with a subsequent presence of autonomous vehicles on highways within a decade. Such breakthroughs will create new challenges for ITS professionals concerning the design and development of ITS infrastructure. Although multidisciplinary skills are essential for ITS professionals, the current pedagogy of training ITS engineers is inadequate for this new era of smart and connected transportation systems. Consequently, there is a great urgency to train a new generation of mobility professionals in traditional traffic engineering skills as well in cyberphysical systems. focus of this article is to establish the importance of a new educational focus area in ITS at the undergraduate and graduate levels.

Smart Event Traffic Management

In 2008 the City of Orlando, Florida, embarked on construction of a new Amway Center to replace the existing Amway Arena. The new center was designed to host a variety of events such as concerts, arena football, graduation ceremonies, National Collegiate Athletic Association basketball, and the National Basketball Association's (NBA's) Orlando Magic home games. The Amway Center, which opened in October 2010, had a capacity of 19,000 patrons for NBA games. The Amway Center was adjacent to Interstate 4 and State Road 408 in the downtown core. To prepare for the Amway Center's event management challenges, the City of Orlando's Transportation Engineering Department utilized smart event traffic management techniques, which integrated the following system and plan components: intelligent transportation system infrastructure (software and hardware), traffic control device upgrades, regional planning efforts, and state-of-the-art safety and security protocols. These components also helped minimize the environmental impacts of traffic congestion. When designing smart event plans, it is important to consider high-threshold events such as the NBA All-Star Game for system performance and to understand the balance required for operational efficiency and security in today's global climate. In addition, infrastructure performance is critical during high-profile events so that the local economy can benefit and the municipality can stay competitive to attract more of these types of events. Finally, the resultant operational efficiency is vital for reducing the carbon footprint and negative societal impacts.