@incollection{TalluriWeidl2024, author = {Talluri, Kranthi Kumar and Weidl, Galia}, title = {Impact of Accidents on Traffic Congestions: A Bayesian Network Approach Using Real City Data}, series = {Advances in Transdisciplinary Engineering}, booktitle = {Advances in Transdisciplinary Engineering}, number = {63}, publisher = {IOS Press}, isbn = {9781643685601}, issn = {2352-751X}, doi = {10.3233/ATDE241181}, pages = {64 -- 78}, year = {2024}, abstract = {Traffic congestion has been a major concern in urban areas due to its strong impact on various social, economic, and human safety sectors. Understanding the relationship and analyzing the trends and patterns between congestion and accidents can strengthen the strategy for reducing traffic congestion. Research on causes of accidents and their impact on congestion has recently been explored on a greater scale, but there is still a lot of scope for vast areas of improvement. To tackle this issue, we built a Bayesian Network (BN) model for analyzing and predicting congestion probability that can occur due to accidents. In this work, the complexity of handling real data obtained from Darmstadt city is described in detail. The accidents and congestion are correlated by introducing a novel threshold-based approach, which identifies congestion based on the change in vehicle density immediately following an accident. Different thresholds are explored to determine the most reliable measure of congestion, with the T4 threshold emerging as the optimal choice. Moreover, the proposed BN model is evaluated against several machine learning models, demonstrating competitive performance and its ability to understand the root cause of traffic congestion.}, subject = {Verkehrsstau}, language = {en} } @inproceedings{TalluriStangWeidl2025, author = {Talluri, Kranthi Kumar and Stang, Christopher and Weidl, Galia}, title = {Green Wave as an Integral Part for the Optimization of Traffic Efficiency and Safety: A Survey}, series = {2025 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2025 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, doi = {10.1109/IV64158.2025.11097706}, pages = {2422 -- 2429}, year = {2025}, abstract = {Green Wave provides practical and advanced solutions to improve traffic efficiency and safety through network coordination. Nevertheless, the complete potential of Green Wave systems has yet to be explored. Utilizing emerging technologies and advanced algorithms, such as AI or V2X, would aid in achieving more robust traffic management strategies, especially when integrated with Green Wave. This work comprehensively surveys existing traffic control strategies that enable Green Waves and analyzes their impact on future traffic management systems and urban infrastructure. Understanding previous research on traffic management and its effect on traffic efficiency and safety helps explore the integration of Green Wave solutions with smart city initiatives for effective traffic signal coordination. This paper also discusses the advantages of using Green Wave strategies for emission reduction and considers road safety issues for vulnerable road users, such as pedestrians and cyclists. Finally, the existing challenges and research gaps in building robust and successful Green Wave systems are discussed to articulate explicitly the future requirement of sustainable urban transport.}, subject = {Verkehrsregelung}, language = {en} } @inproceedings{TalluriWeidlKasuluru2025, author = {Talluri, Kranthi Kumar and Weidl, Galia and Kasuluru, Vaishnavi}, title = {Accident-Driven Congestion Prediction and Simulation: An Explainable Framework Using Advanced Clustering and Bayesian Networks}, series = {IEEE 102nd Vehicular Technology Conference}, booktitle = {IEEE 102nd Vehicular Technology Conference}, doi = {10.48550/arXiv.2507.22529}, year = {2025}, abstract = {Traffic congestion due to uncertainties, such as accidents, is a significant issue in urban areas, as the ripple effect of accidents causes longer delays, increased emissions, and safety concerns. To address this issue, we propose a robust framework for predicting the impact of accidents on congestion. We implement Automated Machine Learning (AutoML)-enhanced Deep Embedding Clustering (DEC) to assign congestion labels to accident data and predict congestion probability using a Bayesian Network (BN). The Simulation of Urban Mobility (SUMO) simulation is utilized to evaluate the correctness of BN predictions using evidence-based scenarios. Results demonstrate that the AutoML-enhanced DEC has outperformed traditional clustering approaches. The performance of the proposed BN model achieved an overall accuracy of 95.6\%, indicating its ability to understand the complex relationship of accidents causing congestion. Validation in SUMO with evidence-based scenarios demonstrated that the BN model's prediction of congestion states closely matches those of SUMO, indicating the high reliability of the proposed BN model in ensuring smooth urban mobility.}, subject = {Verkehrsstau}, language = {en} } @inproceedings{TalluriMadsenWeidl2025, author = {Talluri, Kranthi Kumar and Madsen, Anders L. and Weidl, Galia}, title = {Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks}, series = {2025 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2025 IEEE Intelligent Vehicles Symposium (IV)}, doi = {10.48550/arXiv.2505.02050}, year = {2025}, abstract = {Cut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions, necessitating safe and efficient lane change strategies. We propose a Dynamic Bayesian Network (DBN) framework to integrate lateral evidence with safety assessment models, thereby predicting lane changes and ensuring safe cut-in maneuvers effectively. Our proposed framework comprises three key probabilistic hypotheses (lateral evidence, lateral safety, and longitudinal safety) that facilitate the decision-making process through dynamic data processing and assessments of vehicle positions, lateral velocities, relative distance, and Time-to-Collision (TTC) computations. The DBN model's performance compared with other conventional approaches demonstrates superior performance in crash reduction, especially in critical high-speed scenarios, while maintaining a competitive performance in low-speed scenarios. This paves the way for robust, scalable, and efficient safety validation in automated driving systems.}, subject = {Unfallverh{\"u}tung}, language = {en} }