Evaluation of Pedestrians’ Behavior and Walking Infrastructure Based on Simulation

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Abstract

Sustainable mobility mainly refers to cycling, walking, e-scooters, public transport etc. The increase of the usage of these means of transport against the use of motorised vehicles can improve the overall quality of the urban environment. Pedestrian streets play a significant role towards the direction of sustainable mobility and the facilitation of environment friendly daily trips. This research concerns one of the most important pedestrian streets in the centre of the city of Thessaloniki, Greece. The evaluation of the existing situation in the pedestrian street together with the evaluation of various scenarios concerning changes in the behaviour and direction of movement of pedestrians due to incidents is examined in the framework of this paper. The evaluation took place by using the pedestrian simulation software PTV Viswalk and four different scenarios were tested. The first scenario is the base scenario referring to the existing situation. The second scenario deals with an increase in the pedestrian flow due to unexpected events. The other two scenarios refer to evacuation phenomenon, thus preventing access to part of the pedestrian street. The examination concerns the pedestrian Level of Service (LOS) and the identification of critical segments of the pedestrian street in terms of pedestrians’ flow and composition. The simulation results show that even in the case of doubling of pedestrian flow, this will not cause an overall significant drop in LOS, except for specific sections of the pedestrian street. These results can assist the authorities so to minimize walking difficulties in the pedestrian street and at the same time to take care for waiting areas and the provision of optimum evacuation routes.

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Campisi, T., Basbas, S., Tesoriere, G., Canale, A., Vaitsis, P., Zeglis, D., & Andronis, C. (2020). Evaluation of Pedestrians’ Behavior and Walking Infrastructure Based on Simulation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12250 LNCS, pp. 741–753). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58802-1_53

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