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1. Kernel Ridge Regression in Predicting Railway Crossing Accidents

   https://doi.org/10.1115/JRC2024-130036  

   Villalobos, Ethan; Tarawneh, Constantine; Chen, Jia; Papalexakis, Evangelos E; Xu, Ping (May 2024, American Society of Mechanical Engineers)

   Abstract Expanding on the insights from our initial investigation into railway accident patterns, this paper delves deeper into the predictive capabilities of machine learning to forecast potential accident trends in railway crossings. Focusing on critical factors such as “Highway User Position” and “Equipment Involved,” we integrate Kernel Ridge Regression (KRR) models tailored to distinct clusters, as well as a global model for the entire dataset. These models, trained on historical data, discern patterns and correlations that might elude traditional statistical methods. Our findings are compelling: certain clusters, despite limited data points, showcase remarkably Root Mean Squared Error (RMSE) values between predictions and real data, indicating superior model performance. However, certain clusters hint at potential overfitting, given the disparities between model predictions and actual data. Conversely, clusters with vast datasets underperform compared to the global model, suggesting intricate interactions within the data that might challenge the model’s capabilities. The performance nuances across clusters emphasize the value of specialized, cluster-specific models in capturing the intricacies of each dataset segment. This study underscores the efficacy of KRR in predicting future railway crossing incidents, fostering the implementation of data-driven strategies in public safety. 

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2. Local hypergraph clustering using capacity releasing diffusion

   https://doi.org/10.1371/journal.pone.0243485  

   Ibrahim, Rania; Gleich, David F (December 2020, PLOS ONE)

   Sendiña-Nadal, Irene (Ed.)

   Local graph clustering is an important machine learning task that aims to find a well-connected cluster near a set of seed nodes. Recent results have revealed that incorporating higher order information significantly enhances the results of graph clustering techniques. The majority of existing research in this area focuses on spectral graph theory-based techniques. However, an alternative perspective on local graph clustering arises from using max-flow and min-cut on the objectives, which offer distinctly different guarantees. For instance, a new method called capacity releasing diffusion (CRD) was recently proposed and shown to preserve local structure around the seeds better than spectral methods. The method was also the first local clustering technique that is not subject to the quadratic Cheeger inequality by assuming a good cluster near the seed nodes. In this paper, we propose a local hypergraph clustering technique called hypergraph CRD (HG-CRD) by extending the CRD process to cluster based on higher order patterns, encoded as hyperedges of a hypergraph. Moreover, we theoretically show that HG-CRD gives results about a quantity called motif conductance, rather than a biased version used in previous experiments. Experimental results on synthetic datasets and real world graphs show that HG-CRD enhances the clustering quality. 

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3. Enhancing Intersection Safety at Uncontrolled Three‐Legged Intersections Through Assessment of Risk

   https://doi.org/10.1049/itr2.70062  

   Bhatt, Khushbu; Shah, Jiten; Bell, Margaret C; Dissanayake, Dilum (January 2025, IET Intelligent Transport Systems)

   ABSTRACT The accepted gap—the time or distance a driver deems sufficient to enter or cross an intersection—is a key indicator of traffic risk, particularly at uncontrolled three‐legged intersections. Smaller accepted gaps are linked to higher risk due to an increased chance of vehicle conflicts. This study investigates the relationship between accepted gaps and risk and proposes a method to quantify the level of risk and severity (LORS) to guide targeted safety interventions. Data on vehicle speed, accepted gap and critical gap were collected from six rural intersections in India. Using a binary logit regression model and clustering techniques, the LORS was estimated and validated against actual accident data, yielding a predictive accuracy of up to 83%. The significance of this study lies in its novel data‐driven approach to safety assessment using parameters easily measured in the field. Designed for heterogeneous traffic conditions, the method provides traffic engineers and planners with a practical tool to assess intersection safety, recommend specific remedial measures and prioritise interventions based on risk and severity levels. With potential for automation and scalability, this research contributes to the development of safer road systems, particularly in low‐resource settings where conventional crash data is limited or unavailable. 

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4. The relationship between freight train length and the risk of derailment

   https://doi.org/10.1111/risa.14312  

   Madsen, Peter M; Dillon, Robin L; Triantis, Konstantinos P; Bradley, Joseph A (November 2024, Risk Analysis)

   Abstract In recent years, longer and heavier trains have become more common, primarily driven by efficiency and cost‐saving measures in the railroad industry. Regulation of train length is currently under consideration in the United States at both the federal and state levels, because of concerns that longer trains may have a higher risk of derailment, but the relationship between train length and risk of derailment is not yet well understood. In this study, we use data on freight train accidents during the 2013–2022 period from the Federal Railroad Administration (FRA) Rail Equipment Accident and Highway‐Rail Grade Crossing Accident databases to estimate the relationship between freight train length and the risk of derailment. We determine that longer trains do have a greater risk of derailment. Based on our analysis, running 100‐car trains is associated with 1.11 (95% confidence interval: 1.10–1.12) times the derailment odds of running 50‐car trains (or a 11% increase), even accounting for the fact that only half as many 100‐car trains would need to run. For 200‐car trains, the odds increase by 24% (odds ratio 1.24, 95% confidence interval: 1.20–1.28), again accounting for the need for fewer trains. Understanding derailment risk is an important component for evaluating the overall safety of the rail system and for the future development and regulation of freight rail transportation. Given the limitations of the current data on freight train length, this study provides an important step toward such an understanding. 

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5. Cooperative Driving of Connected Autonomous Vehicles using Responsibility Sensitive Safety Rules: A Control Barrier Functions Approach

   MOHAMMAD KHAYATIAN, VECNA ROBOTICS MOHAMMADREZA (June 2024, ACM transactions on cyberphysical systems)

   Connected Autonomous Vehicles (CAVs) are expected to enable reliable, efficient, and intelligent transportation systems. Most motion planning algorithms for multi-agent systems implicitly assume that all vehicles/agents will execute the expected plan with a small error and evaluate their safety constraints based on this fact. This assumption, however, is hard to keep for CAVs since they may have to change their plan (e.g., to yield to another vehicle) or are forced to stop (e.g., A CAV may break down). While it is desired that a CAV never gets involved in an accident, it may be hit by other vehicles and sometimes, preventing the accident is impossible (e.g., getting hit from behind while waiting behind the red light). Responsibility-Sensitive Safety (RSS) is a set of safety rules that defines the objective of CAV to blame, instead of safety. Thus, instead of developing a CAV algorithm that will avoid any accident, it ensures that the ego vehicle will not be blamed for any accident it is a part of. Original RSS rules, however, are hard to evaluate for merge, intersection, and unstructured road scenarios, plus RSS rules do not prevent deadlock situations among vehicles. In this paper, we propose a new formulation for RSS rules that can be applied to any driving scenario. We integrate the proposed RSS rules with the CAV’s motion planning algorithm to enable cooperative driving of CAVs. We use Control Barrier Functions to enforce safety constraints and compute the energy optimal trajectory for the ego CAV. Finally, to ensure liveness, our approach detects and resolves deadlocks in a decentralized manner. We have conducted different experiments to verify that the ego CAV does not cause an accident no matter when other CAVs slow down or stop. We also showcase our deadlock detection and resolution mechanism using our simulator. Finally, we compare the average velocity and fuel consumption of vehicles when they drive autonomously with the case that they are autonomous and connected. 

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