arXiv subjects

Serge P. Hoogendoorn

Publications and source records attributed to Serge P. Hoogendoorn.

7 recordsLinked to original sources

arXiv2026

On Traffic Interactions for Unmanned Aerial Vehicles: Traffic Flow Applied to Three Dimensional Space

Unmanned aerial vehicles (UAVs, or drones) are likely to significantly increase the amount of air traffic. If the skies are full of UAVs, they need to interact with each other, for instance by yielding or other evasive maneuvers. The aggregated movements of drones will create traffic patterns. Just like in current road traffic, the interactions will be very frequent, so a centralized computer managing these interactions is expected not to be possible. There is a long history of traffic flow theory and modeling for 1 dimensional (road) traffic; this has been expanded to 2 dimensional traffic (pedestrians). It is unclear how traffic flow theory works for 3 dimensional traffic. In this paper we show how drone traffic can interact in a decentralized way. For the microscopic description, we add asymmetric interaction rules. We show that without centralized control, we can have efficient and safe traffic. Moreover, we provide a framework that directly links microscopic interactions to macroscopic properties. For the macroscopic description, we formulate and apply a numerical scheme that integrates the competition of space by UAVs for multiple classes, directions and dimensions. We apply both the microscopic and macroscopic descriptions to analyze (emerging) patterns which may arise in 3D traffic flow. The current paper provides background to develop interaction rules for drone traffic. Currently, the drone traffic is taking its first steps, but once the aeronautic technique takes off, the legislation regarding drone interactions should be ready. To support so, and be able to assess traffic consequences of decisions, the traffic flow theory framework developed here is essential.

physics.soc-ph↗

arXiv2024

Driving pattern interpretation based on action phases clustering

Current approaches to identifying driving heterogeneity face challenges in comprehending fundamental patterns from the perspective of underlying driving behavior mechanisms. The concept of Action phases was proposed in our previous work, capturing the diversity of driving characteristics with physical meanings. This study presents a novel framework to further interpret driving patterns by classifying Action phases in an unsupervised manner. In this framework, a Resampling and Downsampling Method (RDM) is first applied to standardize the length of Action phases. Then the clustering calibration procedure including ''Feature Selection'', ''Clustering Analysis'', ''Difference/Similarity Evaluation'', and ''Action phases Re-extraction'' is iteratively applied until all differences among clusters and similarities within clusters reach the pre-determined criteria. Application of the framework using real-world datasets revealed six driving patterns in the I80 dataset, labeled as ''Catch up'', ''Keep away'', and ''Maintain distance'', with both ''Stable'' and ''Unstable'' states. Notably, Unstable patterns are more numerous than Stable ones. ''Maintain distance'' is the most common among Stable patterns. These observations align with the dynamic nature of driving. Two patterns ''Stable keep away'' and ''Unstable catch up'' are missing in the US101 dataset, which is in line with our expectations as this dataset was previously shown to have less heterogeneity. This demonstrates the potential of driving patterns in describing driving heterogeneity. The proposed framework promises advantages in addressing label scarcity in supervised learning and enhancing tasks such as driving behavior modeling and driving trajectory prediction.

cs.AI↗

arXiv2024

Performance Comparison of Deep RL Algorithms for Mixed Traffic Cooperative Lane-Changing

Lane-changing (LC) is a challenging scenario for connected and automated vehicles (CAVs) because of the complex dynamics and high uncertainty of the traffic environment. This challenge can be handled by deep reinforcement learning (DRL) approaches, leveraging their data-driven and model-free nature. Our previous work proposed a cooperative lane-changing in mixed traffic (CLCMT) mechanism based on TD3 to facilitate an optimal lane-changing strategy. This study enhances the current CLCMT mechanism by considering both the uncertainty of the human-driven vehicles (HVs) and the microscopic interactions between HVs and CAVs. The state-of-the-art (SOTA) DRL algorithms including DDPG, TD3, SAC, and PPO are utilized to deal with the formulated MDP with continuous actions. Performance comparison among the four DRL algorithms demonstrates that DDPG, TD3, and PPO algorithms can deal with uncertainty in traffic environments and learn well-performed LC strategies in terms of safety, efficiency, comfort, and ecology. The PPO algorithm outperforms the other three algorithms, regarding a higher reward, fewer exploration mistakes and crashes, and a more comfortable and ecology LC strategy. The improvements promise CLCMT mechanism greater advantages in the LC motion planning of CAVs.

cs.RO↗

arXiv2023

Identification of Driving Heterogeneity using Action-chains

Current approaches to identifying driving heterogeneity face challenges in capturing the diversity of driving characteristics and understanding the fundamental patterns from a driving behaviour mechanism standpoint. This study introduces a comprehensive framework for identifying driving heterogeneity from an Action-chain perspective. First, a rule-based segmentation technique that considers the physical meanings of driving behaviour is proposed. Next, an Action phase Library including descriptions of various driving behaviour patterns is created based on the segmentation findings. The Action-chain concept is then introduced by implementing Action phase transition probability, followed by a method for evaluating driving heterogeneity. Employing real-world datasets for evaluation, our approach effectively identifies driving heterogeneity for both individual drivers and traffic flow while providing clear interpretations. These insights can aid the development of accurate driving behaviour theory and traffic flow models, ultimately benefiting traffic performance, and potentially leading to aspects such as improved road capacity and safety.

cs.LG↗

arXiv2019

How many people can simultaneously move through a pedestrian space? The impact of complex flow situations on the shape of the fundamental diagram

Pedestrian crowding occurs more frequent. As a result of the increasing pedestrian demand in public space, the limits of pedestrian spaces are of increasing interest. Some research on the maximum demand that can flow through a cross-section has been presented, which mainly features simple movement base cases, low-density situations and/or a homogeneous crowd. Consequently, it is currently unclear to what extent their findings apply to heterogeneous high-density crowds, which are often encountered during real-world scenarios. The CrowdLimits experiment attempted to reproduce crowd movement dynamics of heterogeneous crowds experiencing higher densities than have been recorded up to this moment. Here, the aim was to study the impact of the three important differences between the current laboratory studies and real-world crowd dynamics in crowded pedestrian spaces simultaneously, namely crowd heterogeneity, high densities movements, and (more) complex movement base cases. This study shows that there are substantial differences in the maximum sustainable flow rate, and the maximum local and global sustainable density for distinct movement base cases and flow ratios. Moreover, the results provide evidence of that continuation of flow under very high densities can be recreated under laboratory conditions using a heterogeneous population of pedestrians. Besides, the experimental results indicate that the maximum global flow rate decreases when the scenario becomes more difficult (i.e. bidirectional to intersecting) and the collision avoidance opportunities decrease (i.e. 80-20 to 50-50 flow ratio). Thus, this paper concludes that the maximum flow rate of pedestrian infrastructures decreases with increasing complexity.

physics.soc-ph↗

arXiv2014

The Lagrangian coordinate system and what it means for two-dimensional crowd flow models

A continuum crowd flow model is reformulated in the Lagrangian coordinate system. The system has proven to give computational advantages over the traditional Eulerian coordinate system for (one-dimensional) road traffic flow. Our extension of the model and simulation method to (two-dimensional) crowd flow paves the way to explore whether the advantages also hold in two dimensions. This paper provides a first exploration and shows that a model and simulation method for two-dimensional crowd flow can be developed in the Lagrangian coordinate system and that is leads to promising results.

math.NA↗

arXiv2014

Modelling supported driving as an optimal control cycle: Framework and model characteristics

Driver assistance systems support drivers in operating vehicles in a safe, comfortable and efficient way, and thus may induce changes in traffic flow characteristics. This paper puts forward a receding horizon control framework to model driver assistance and cooperative systems. The accelerations of automated vehicles are controlled to optimise a cost function, assuming other vehicles driving at stationary conditions over a prediction horizon. The flexibility of the framework is demonstrated with controller design of Adaptive Cruise Control (ACC) and Cooperative ACC (C-ACC) systems. The proposed ACC and C-ACC model characteristics are investigated analytically, with focus on equilibrium solutions and stability properties. The proposed ACC model produces plausible human car-following behaviour and is unconditionally locally stable. By careful tuning of parameters, the ACC model generates similar stability characteristics as human driver models. The proposed C-ACC model results in convective downstream and absolute string instability, but not convective upstream string instability observed in human-driven traffic and in the ACC model. The control framework and analytical results provide insights into the influences of ACC and C-ACC systems on traffic flow operations.

physics.soc-ph↗