Modelling cyclist queue formation using a two-layer framework for operational cycling behaviour

Gavriilidou, Alexandra; Daamen, Winnie; Yuan, Yufei; Hoogendoorn, Serge P.

doi:10.1016/j.trc.2019.06.012

Cited by 31 publications

(10 citation statements)

References 17 publications

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“…In recent, Mohammed et al [31] presented the solution of the agentbased model for investigating the continuous trajectories of two-wheelers. The generative adversarial imitation learning approach in this model requires high-quality and large-amount trajectory data [62], though there is still a challenge of collecting enough two-wheeler trajectory data [21]. Meanwhile, the nature of this black box model also leads to its lack of ability to explain the process of two-wheeler interactions.…”

Section: Related Workmentioning

confidence: 99%

“…This is because the bicycle is one of the typical two-wheelers. According to the definition of behaviour for bicycles made by Gavriilidou et al [21], tasks of interaction during riding belong to the same operational level and should be simultaneously modelled from both mental (decisions) and physical (actions) processes. For decades, several research efforts have been made to capture the two-wheeler (mainly related to cyclists) interactions, however, they cannot well represent the two-wheeler behaviour to some extent.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, most mainstream models, namely Cellular Automata (CA), deal with the movements and interactions using the principle of spatial-discrete assumptions [23]. The one-on-one interaction between cells and lanes has not been adjusted to fit the operations of two-wheelers in reality [21,24]. Even recent simulations using force-based models [25] can reflect the contact with several road users on the two-dimensional space, they are still regarded essentially as the pure dynamics control of the operational physical process that is influenced by the interactive forces from adjacent agents [26].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An operational simulation framework for modelling the multi-interaction of two-wheelers on mixed-traffic road segments

Ying

Yuan

et al. 2023

Physica A: Statistical Mechanics and its Applications

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An operational simulation framework for modelling the multi-interaction of two-wheelers on mixed-traffic road segments

Ying

Yuan

et al. 2023

Physica A: Statistical Mechanics and its Applications

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“…Pedestrian and cyclist dynamics in cities can be described and predicted at three behavioral levels: strategic, tactical and operational [15], [18]. Several modelling approaches at three behavioral levels have been proposed in the past, both micro-and macroscopically.…”

Section: A Different Data For Different Active Mode Researchmentioning

confidence: 99%

AMSense: How Mobile Sensing Platforms Capture Pedestrian/Cyclist Spatiotemporal Properties in Cities

Vial

Daamen

Ding

et al. 2022

IEEE Intell. Transport. Syst. Mag.

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We present a design for a novel mobile sensing system (AMSense) that uses vehicles as mobile sensing nodes in a network to capture spatiotemporal properties of pedestrians and cyclists (active modes) in urban environments. In this dynamic, multi-sensor approach, real-time data, algorithms, and models are fused to estimate presence, positions and movements of active modes with information generated by a fleet of mobile sensing platforms. AMSense offers a number of advantages over the traditional methods using stationary sensor systems or more recently crowd-sourced data from mobile and wearable devices, as it represents a scalable system that provides answers to spatiotemporal resolution, intrusiveness, and dynamic network conditions. In this paper, we motivate the need and show the potential of such a sensing paradigm, which supports a host of new research and application development, and illustrate this with a practical urban sensing example. We propose a first design, elaborate on a variety of requirements along with functional challenges, and outline the research to be performed with the generated data.

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“…us, researchers embedded one multilayered process framework in the SF model to simulate bicycles' complex motion at intersections. For instance, Gavriilidou et al [20] designed a two-layered microscopic model to reproduce the formation of bicycle queue, where the SF model is proposed in the second layer to determine a feasible trajectory. Li et al [21] embedded the decision process in the SF model and designed a behavior force to explore the lateral dispersion phenomenon of high density through bicycle flow at an intersection.…”

Section: Introductionmentioning

confidence: 99%

An Improved Social Force Model for Bicycle Flow in Groups

Rui

Tang

Zhang

2021

Journal of Advanced Transportation

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Bicycle flow widely has group behavior (i.e., cyclists have a tendency to ride in groups), which may have some significant effects on the bicycle’s motion. However, the existing studies on bicycle flow rarely consider this factor. Generally, bicycle flow has two kinds of group behaviors, i.e., shoulder group behavior and following group behavior. In this paper, we propose an improved social force (SF) model to describe the two kinds of group behaviors. Then, we use the improved SF model to, respectively, explore the effects of the two kinds of group behaviors on the bicycle’s motion from the simulation perspective. The numerical results show that (i) shoulder group behavior has some negative impacts on the bicycle’s motion, i.e., the critical density (where the through capacity can reach the maximum value), the jam density, and the through capacity will be reduced; (ii) following group behavior has some positive impacts on the bicycle’s motion, i.e., the critical density, the jam density, and the through capacity will be enhanced; (iii) the impacts of coexistence of shoulder and following group behavior are related to the density. Besides, increasing group size and group probability will enlarge the negative impacts of shoulder group behavior and alleviate the positive impacts of following group behavior. These results can guide administrators to better manage bicycle flow (especially reasonably control the negative impacts of group behaviors).

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Modelling cyclist queue formation using a two-layer framework for operational cycling behaviour

Cited by 31 publications

References 17 publications

An operational simulation framework for modelling the multi-interaction of two-wheelers on mixed-traffic road segments

An operational simulation framework for modelling the multi-interaction of two-wheelers on mixed-traffic road segments

AMSense: How Mobile Sensing Platforms Capture Pedestrian/Cyclist Spatiotemporal Properties in Cities

An Improved Social Force Model for Bicycle Flow in Groups

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