PTV Group

Karlsruhe, Germany

PTV Group

Karlsruhe, Germany
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Kretz T.,PTV Group
Physica A: Statistical Mechanics and its Applications | Year: 2015

The Social Force Model is one of the most prominent models of pedestrian dynamics. As such naturally much discussion and criticism have spawned around it, some of which concerns the existence of oscillations in the movement of pedestrians. This contribution is investigating under which circumstances, parameter choices, and model variants oscillations do occur and how this can be prevented. It is shown that oscillations can be excluded if the model parameters fulfill certain relations. The fact that with some parameter choices oscillations occur and with some not is exploited to verify a specific computer implementation of the model. © 2015 Elsevier B.V. All rights reserved.

Kretz T.,PTV Group | Lehmann K.,PTV Group | Hofsass I.,PTV Group
Advances in Complex Systems | Year: 2014

For the simulation of pedestrians, a method is introduced to find routing alternatives from any origin position to a given destination area in a given geometry composed of walking areas and obstacles. The method includes a parameter which sets a threshold for the approximate minimum size of obstacles to generate routing alternatives. The resulting data structure for navigation is constructed such that it does not introduce artifacts to the movement of simulated pedestrians and locally pedestrians prefer to walk on the shortest path. The generated set of routes can be used with iterating static or dynamic assignment methods. © 2014 World Scientific Publishing Company.

Kara D.,PTV Group | Koesling S.,PTV Group | Kretz T.,PTV Group | Laugel Y.,Head of traffic management Center | And 2 more authors.
Proceedings of the Institution of Civil Engineers: Civil Engineering | Year: 2014

Planning of urban spaces and mobility has become a demanding task. All modes of transport have to be granted their very own right of way. Stakeholders of particular modes, tourists and commuters, old and young have their own needs and ideas and they all demand to be involved in the planning process of transport projects without having to dig into the words and numbers produced by traffic engineers. Microsimulation is a tool which accommodates both: the requirements that are set by the complexity of such projects, as well as the needs to produce easy-to-understand information and communication material. This paper demonstrates the capability of microsimulation through a French case study, including the planning and design of transport schemes for a busy city centre and urban area where all modes of transport are being used and there is a strong focus of public attention. © ICE Publishing: All rights reserved.

PubMed | Northumbria University and PTV GROUP
Type: Journal Article | Journal: Accident; analysis and prevention | Year: 2016

In this paper, we propose a Bayesian hierarchical model for predicting accident counts in future years at sites within a pool of potential road safety hotspots. The aim is to inform road safety practitioners of the location of likely future hotspots to enable a proactive, rather than reactive, approach to road safety scheme implementation. A feature of our model is the ability to rank sites according to their potential to exceed, in some future time period, a threshold accident count which may be used as a criterion for scheme implementation. Our model specification enables the classical empirical Bayes formulation - commonly used in before-and-after studies, wherein accident counts from a single before period are used to estimate counterfactual counts in the after period - to be extended to incorporate counts from multiple time periods. This allows site-specific variations in historical accident counts (e.g. locally-observed trends) to offset estimates of safety generated by a global accident prediction model (APM), which itself is used to help account for the effects of global trend and regression-to-mean (RTM). The Bayesian posterior predictive distribution is exploited to formulate predictions and to properly quantify our uncertainty in these predictions. The main contributions of our model include (i) the ability to allow accident counts from multiple time-points to inform predictions, with counts in more recent years lending more weight to predictions than counts from time-points further in the past; (ii) where appropriate, the ability to offset global estimates of trend by variations in accident counts observed locally, at a site-specific level; and (iii) the ability to account for unknown/unobserved site-specific factors which may affect accident counts. We illustrate our model with an application to accident counts at 734 potential hotspots in the German city of Halle; we also propose some simple diagnostics to validate the predictive capability of our model. We conclude that our model accurately predicts future accident counts, with point estimates from the predictive distribution matching observed counts extremely well.

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