Safety Of Transportation Networks Research Articles

Understanding integration issues in intelligent transportation systems with IoT platforms, cloud computing, and connected vehicles

<p>The fast improvements in information and communication technology have altered the transportation business, resulting in intelligent transportation systems (ITS) that aim to improve transportation network safety, efficiency, and sustainability. Cloud computing, networked automobiles, and IoT platforms are driving this shift. Infrastructure for current civilization must include transport. The effectiveness of transport infrastructure is vital for all countries’ individual mobility, trade, and economic development. Traffic congestion, fuel price increases, and CO<sub>2</sub> emissions have all increased in recent years, posing problems for modern civilization. It is crucial to improve transportation efficiency and safety. To develop a sustainable, intelligent transportation system, it is necessary to seamlessly integrate and interoperate with cutting-edge technologies like linked cars, the Internet of Things (IoT), and cloud computing. To respond to difficulties facing the transportation industry, which include rising fuel costs, high CO<sub>2</sub> emissions, expanding congestion in traffic, and greater dangers on the highway, integration concerns must be concluded.</p>

Modeling the Impact of COVID-19 Interventions on Interstate Crash Rates Using Comparative Interrupted Time Series

In the early stage of the severe acute respiratory syndrome (SARS)-CoV-2 coronavirus (COVID-19) outbreak in March 2020, a series of restrictions were released to minimize the exposure and spread of the communicable virus. These restrictions had significant impacts on the mobility and safety of transportation networks. The objective of this study is to assess the causal effects of the COVID-19 restrictions on traffic crashes on interstate highways. Specifically, compared with previous years, the study is to understand whether the COVID-19 outbreak has increased the rate of traffic crashes on Interstate 80 in Nebraska. A comparative interrupted time series (CITS) model was established to examine the week-to-week differences in traffic crashes before, during, and after the implementation of the COVID restrictive measures in Nebraska. Traffic crash data and traffic count data on Interstate 80 for 33 weeks, from Day 1 to Day 183, of 2020 were used. Meanwhile, the same periods of data in 2018 and 2019 were also included as a control group in the models. Results show that the COVID-19 restrictive measures were associated with significant, immediate, positive effects on crashes (i.e., crash reduction or mitigation) with no or minor injuries, crashes with single or two vehicle(s), and crashes with one injury involved. The findings contribute to the understanding of the impact of COVID-19 interventions on interstate highway safety when future outbreaks of COVID-19 or other pandemics become possible.

System-level impacts of en-route information sharing considering adaptive routing

Connected and automated vehicles (CAVs) and infrastructure-to-vehicle (I2V) communication have great potential to improve traffic safety and mobility at intersection and road segment levels. However, since transportation is an interconnected network, local transportation state change could lead to broader impacts by rerouting. In this study, we focus on investigating the impacts of en-route locational information updates on the system-level safety and mobility considering adaptive routing of CAVs. We propose a novel two-stage stochastic traffic equilibrium model to characterize the equilibrium traffic patterns considering adaptive routing behavior when en-route traffic information is provided. The proposed methodology is tested using the Orlando transportation network with real traffic and accident data. Different I2V information sharing strategies are analyzed to better understand the impacts of information sharing locations of I2V on transportation network safety and mobility, through which we generate insights on the optimal design of traffic information sharing systems.

Unraveling the Decision-making Process Interpretable Deep Learning IDS for Transportation Network Security

The growing ubiquity of IoT-enabled devices in recent years emphasizes the critical need to strengthen transportation network safety and dependability. Intrusion detection systems (IDS) are crucial in preventing attacks on transport networks that rely on the Internet of Things (IoT). However, understanding the rationale behind deep learning-based IDS models may be challenging because they do not explain their findings. We offer an interpretable deep learning system that may be used to improve transportation network safety using IoT. To develop naturally accessible explanations for IDS projections, we integrate deep learning models with the Shapley Additive Reasons (SHAP) approach. By adding weight to distinct elements of the input data needed to develop the model, we increase the readability of so-called black box processes. We use the ToN_IoT dataset, which provides statistics on the volume of network traffic created by IoT-enabled transport systems, to assess the success of our strategy. We use a tool called CICFlowMeter to create network flows and collect data. The regularity of the flows, as well as their correlation with specific assaults, has been documented, allowing us to train and evaluate the IDS model. The experiment findings show that our explainable deep learning system is extremely accurate at detecting and categorising intrusions in IoT-enabled transportation networks. By examining data using the SHAP approach, cybersecurity specialists may learn more about the IDS's decision-making process. This enables the development of robust solutions, which improves the overall security of the Internet of Things. Aside from simplifying IDS predictions, the proposed technique provides useful recommendations for strengthening the resilience of IoT-enabled transportation systems against cyberattacks. The usefulness of IDS in defending mission critical IoT infrastructure has been questioned by security experts in the Internet of Vehicles (IoV) industry. The IoV is the primary research object in this case. Deep learning algorithms' versatility in processing many forms of data has contributed to their growing prominence in the field of anomaly detection in intrusion detection systems. Although machine learning models may be highly useful, they frequently yield false positives, and the path they follow to their conclusions is not always obvious to humans. Cybersecurity experts who want to evaluate the performance of a system or design more secure solutions need to understand the thinking process behind an IDS's results. The SHAP approach is employed in our proposed framework to give greater insight into the decisions made by IDSs that depend on deep learning. As a result, IoT network security is strengthened, and more cyber-resilient systems are developed. We demonstrate the effectiveness of our technique by comparing it to other credible methods and utilising the ToN_IoT dataset. Our framework has the best success rate when compared to other frameworks, as evidenced by testing results showing an F1 score of 98.83 percent and an accuracy of 99.15 percent. These findings demonstrate that the architecture successfully resists a variety of destructive assaults on IoT networks. By integrating deep learning and methodologies with an emphasis on explainability, our approach significantly enhances network security in IoT use scenarios. The ability to assess and grasp IDS options provides the path for cybersecurity experts to design and construct more secure IoT systems.

Point cloud semantic segmentation of complex railway environments using deep learning

Safety of transportation networks is of utmost importance for our society. With the emergency of digitalization, the railway sector is accelerating the automation in inventory and inspection procedures. Mobile mapping systems allow capturing three-dimensional point clouds of the infrastructure in short periods of time. In this paper, a deep learning methodology for semantic segmentation of railway infrastructures is presented. The methodology segments both linear and punctual elements from railway infrastructure, and it is tested in four scenarios: i) 90 km-long railway; ii) 2 km-long low-quality point clouds; iii) 400 m-long high-quality point clouds; iv) 1.4 km-long railway recoded with aerial mapping system. The longest one is used for training and testing, obtaining mean accuracy greater than 90%. The other scenarios are used only for testing, and qualitative results are discussed, proving that the method can be applied to new scenarios that significantly differ in terms of data quality and resolution.

Variational Graph Neural Networks for Road Traffic Prediction in Intelligent Transportation Systems

As one of the most important applications of industrial Internet of Things, intelligent transportation system aims to improve the efficiency and safety of transportation networks. In this article, we propose a novel Bayesian framework entitled variational graph recurrent attention neural networks (VGRAN) for robust traffic forecasting. It captures time-varying road-sensor readings through dynamic graph convolution operations and is capable of learning latent variables regarding the sensor representation and traffic sequences. The proposed probabilistic method is a more flexible generative model considering the stochasticity of sensor attributes and temporal traffic correlations. Moreover, it enables efficient variational inference and faithful modeling of implicit posteriors of traffic data, which are usually irregular, spatial correlated, and multiple temporal dependents. Extensive experiments conducted on two real-world traffic datasets demonstrate that the proposed VGRAN model outperforms state-of-the-art approaches while capturing innate ambiguity of the predicted results.

Dedicated Short-Range Wireless Communications for Intelligent Transportation System Applications: State of the Art

In efforts to reduce traffic accidents and improve the efficient utilization of existing transportation networks, increasing attention is being given to the use of wireless communications between vehicles and roadside equipment to develop new intelligent transportation system (ITS) applications that can help improve the safety of transportation networks and reduce urban congestion. Of particular interest for the development of such applications are the currently proposed dedicated short-range communication (DSRC) wireless standards. This paper aims to provide transportation researchers and engineers with an introduction to these standards. The paper presents the current mobile wireless communication applications and standards, the various DSRC standards that have been proposed, their potential use in ITS applications, their envisioned implementation benefits, and the remaining research needs.

Dedicated Short-Range Wireless Communications for Intelligent Transportation System Applications

In efforts to reduce traffic accidents and improve the efficient utilization of existing transportation networks, increasing attention is being given to the use of wireless communications between vehicles and roadside equipment to develop new intelligent transportation system (ITS) applications that can help improve the safety of transportation networks and reduce urban congestion. Of particular interest for the development of such applications are the currently proposed dedicated short-range communication (DSRC) wireless standards. This paper aims to provide transportation researchers and engineers with an introduction to these standards. The paper presents the current mobile wireless communication applications and standards, the various DSRC standards that have been proposed, their potential use in ITS applications, their envisioned implementation benefits, and the remaining research needs.

Challenges and Strategies for Local Road Safety Training and Technology Transfer

A significant increase in the safety of transportation networks in the United States and throughout the world will require the direct involvement of local jurisdictions (e.g., counties, townships, villages, and cities). In 1998, approximately 4 out of 10 fatalities in the United States occurred on collector and local roadways. Local road agencies need to have the skills to identify, evaluate, and mitigate safety concerns. Effective local road safety training, technology transfer, and resources are essential. A number of challenges need to be met to complete these tasks, but current local road safety training and technology transfer programs and strategies also offer opportunities. Some of these challenges are identified and discussed, and several current and potential programs and strategies for local road safety training and technology transfer are described. Relevant results from a recent FHWA and AASHTO international scan tour on the management and organization of comprehensive highway safety programs are also presented and used to support the suggestions provided. A synopsis of the relevant discussions from a recent workshop on highway safety workforce development, sponsored by FHWA, AASHTO, TRB, and ITE, is also offered. The objective is to initiate discussion about the need for and development of a comprehensive and cost-effective local safety training and technology transfer program.