Serge Hoogendoorn

Panchamy Krishnakumari, Sascha Hoogendoorn-Lanser, Jeroen Steenbakkers et al.

This paper presents novel technology and methodology aimed at enhancing crowd management in both the planning and operational phases. The approach encompasses innovative data collection techniques, data integration, and visualization using a 3D Digital Twin, along with the incorporation of artificial intelligence (AI) tools for risk identification. The paper introduces the Bowtie model, a comprehensive framework designed to assess and predict risk levels. The model combines objective estimations and predictions, such as traffic flow operations and crowdedness levels, with various aggravating factors like weather conditions, sentiments, and the purpose of visitors, to evaluate the expected risk of incidents. The proposed framework is applied to the Crowd Safety Manager project in Scheveningen, where the DigiTwin is developed based on a wealth of real-time data sources. One noteworthy data source is Resono, offering insights into the number of visitors and their movements, leveraging a mobile phone panel of over 2 million users in the Netherlands. Particular attention is given to the left-hand side of the Bowtie, which includes state estimation, prediction, and forecasting. Notably, the focus is on generating multi-day ahead forecasts for event-planning purposes using Resono data. Advanced machine learning techniques, including the XGBoost framework, are compared, with XGBoost demonstrating the most accurate forecasts. The results indicate that the predictions are adequately accurate. However, certain locations may benefit from additional input data to further enhance prediction quality. Despite these limitations, this work contributes to a more effective crowd management system and opens avenues for further advancements in this critical field.

Ting Gao, Stavros Orfanoudakis, Nan Lin et al.

Diffusion-based policies have gained significant popularity in Reinforcement Learning (RL) due to their ability to represent complex, non-Gaussian distributions. Stochastic Differential Equation (SDE)-based diffusion policies often rely on indirect entropy control due to the intractability of the exact entropy, while also suffering from computationally prohibitive policy gradients through the iterative denoising chain. To overcome these issues, we propose Flow Matching Policy with Entropy Regularization (FMER), an Ordinary Differential Equation (ODE)-based online RL framework. FMER parameterizes the policy via flow matching and samples actions along a straight probability path, motivated by optimal transport. FMER leverages the model's generative nature to construct an advantage-weighted target velocity field from a candidate set, steering policy updates toward high-value regions. By deriving a tractable entropy objective, FMER enables principled maximum-entropy optimization for enhanced exploration. Experiments on sparse multi-goal FrankaKitchen benchmarks demonstrate that FMER outperforms state-of-the-art methods, while remaining competitive on standard MuJoco benchmarks. Moreover, FMER reduces training time by 7x compared to heavy diffusion baselines (QVPO) and 10-15% relative to efficient variants.

Elif Arslan, Jacobus G. M. van der Linden, Serge Hoogendoorn et al.

Sparse decision tree learning provides accurate and interpretable predictive models that are ideal for high-stakes applications by finding the single most accurate tree within a (soft) size limit. Rather than relying on a single "best" tree, Rashomon sets-trees with similar performance but varying structures-can be used to enhance variable importance analysis, enrich explanations, and enable users to choose simpler trees or those that satisfy stakeholder preferences (e.g., fairness) without hard-coding such criteria into the objective function. However, because finding the optimal tree is NP-hard, enumerating the Rashomon set is inherently challenging. Therefore, we introduce SORTD, a novel framework that improves scalability and enumerates trees in the Rashomon set in order of the objective value, thus offering anytime behavior. Our experiments show that SORTD reduces runtime by up to two orders of magnitude compared with the state of the art. Moreover, SORTD can compute Rashomon sets for any separable and totally ordered objective and supports post-evaluating the set using other separable (and partially ordered) objectives. Together, these advances make exploring Rashomon sets more practical in real-world applications.

Ting Gao, Elvin Isufi, Winnie Daamen et al.

Estimating queue lengths at signalized intersections remains a challenge in traffic management, especially under partially observed conditions where vehicle flows are not fully captured. This paper introduces Q-Net, a data-efficient and interpretable framework for queue length estimation that performs robustly even when traffic conservation assumptions are violated. Q-Net integrates two widely available and privacy-friendly data sources: (i) vehicle counts from loop detectors near stop lines, and (ii) aggregated floating car data (aFCD), which divides each road section into segments and provides segment-wise average speed measurements. These data sources often differ in spatial and temporal resolution, creating fusion challenges. Q-Net addresses this by employing a tailored state-space model and an AI-augmented Kalman filter, KalmanNet, which learns the Kalman gain from data without requiring prior knowledge of noise covariances or full system dynamics. We build on the vanilla KalmanNet pipeline to decouple measurement dimensionality from section length, enabling spatial transferability across road segments. Unlike black-box models, Q-Net maintains physical interpretability, with internal variables linked to real-world traffic dynamics. Evaluations on main roads in Rotterdam, the Netherlands, demonstrate that Q-Net outperforms baseline methods by over 60\% in Root Mean Square Error (RMSE), accurately tracking queue formation and dissipation while correcting aFCD-induced delays. Q-Net also demonstrates strong spatial and temporal transferability, enabling deployment without costly sensing infrastructure like cameras or radar. Additionally, we propose a real-time variant of Q-Net, highlighting its potential for integration into dynamic, queue-based traffic control systems.

Yan Feng, Dorine Duives, Serge Hoogendoorn

Although route and exit choice in complex buildings are important aspects of pedestrian behaviour, studies predominantly investigated pedestrian movement in a single level. This paper presents an innovative VR tool that was designed to investigate pedestrian route and exit choice in a multi-story building. This tool supports free navigation and collects pedestrian walking trajectories, head movements and gaze points automatically. An experiment was conducted to evaluate the VR tool from objective standpoints (i.e., pedestrian behaviour) and subjective standpoints (i.e., the feeling of presence, system usability, simulation sickness). The results show that the VR tool allows for accurate collection of pedestrian behavioural data in the complex building. Moreover, the results of the questionnaire report high realism of the virtual environment, high immersive feeling, high usability, and low simulator sickness. This paper contributes by showcasing an innovative approach of applying VR technologies to study pedestrian behaviour in complex and realistic environments.