Contents of Volume 36 (2026)

1/2026 2/2026 3/2026 4/2026 5/2026

1/2026

  • [1] Cevik H., Pereira A.M., Pribyl O., (CZ)
    Bridging Agent- and Trip-Based Demand Models for Multimodal OD Estimation: A Review and Conceptual IDE Framework, pp. 1-32

      Full text         https://doi.org/10.14311/NNW.2026.36.001

    Abstract: Understanding multi-modal travel demand is essential for traffic management and transport planning. However, estimating time-dependent origindestination (OD) flows remains challenging, when direct observations are limited and different transport modes (vehicles, cyclists, and pedestrians) influence each other. While numerous OD estimation methods exist, most treat modes separately and rely heavily on traditional surveys or historical matrices, often unavailable or incomplete. This paper presents a non-systematic review of existing approaches to offline dynamic OD estimation and proposes the Integrated Demand Estimation (IDE) framework for multi-modal offline dynamic OD. This framework integrates the disaggregate behaviour of agent-based models with the stability and computational efficiency of trip-based demand models. The framework is designed to generate an initial multi-modal OD matrix using synthetic population methods and iteratively adjusts it using observed traffic counts, travel times, link popularity, terrain characteristics, and GTFS data. By explicitly accounting for the interactions between motorised and vulnerable road users, the framework aims to improve the consistency and realism of OD estimates for vehicles, cyclists, and pedestrians. It addresses literature gaps regarding limited non-motorised data and fragmented multi-modal treatment. The framework is intended to support urban digital twins, provide a basis for multimodal simulation inputs, and help planners understand multimodal travel patterns for sustainable mobility and infrastructure design.

  • [2] Cenkner M., Tylova N., Michel-Schneider U., Bouchner P. , (CZ)
    Operator Control Strategy in Virtual Reality Crane Training, pp. 33-52

      Full text         https://doi.org/10.14311/NNW.2026.36.002

    Abstract: Performance assessment in simulator-based crane training commonly relies on task completion, task duration, and errors such as collisions. These measures describe task outcomes but provide limited insight into the operators control strategy. Coordinated use of both hands and simultaneous control of multiple crane functions are widely recognized as key challenges in crane operator training. This study investigates whether interaction-based metrics derived directly from joystick signals can describe bimanual joystick control and multi-axis coordination during virtual reality mobile crane operation. Principal component analysis was used to identify the main patterns of variability in these metrics and to explore differences in control patterns across ten operators and their task executions. The observed distribution in the PCA space indicated differences among operators in the combined patterns of individual axis activation and simultaneous control of multiple crane functions.