This paper presents a model to simulate non-signalized pedestrian crosswalks. Principal scope is to develop a tool to be used by decision-makers to evaluate the necessity of introducing a new crosswalk and/or switching to a traffic light and estimate the potential benefits of such a measure in terms of Level of Service. The model is based on empirical evidence gained during an observation of a non-signalized crosswalk. Pedestrian motion is simulated using a Cellular Automata model which is capable to simulate pedestrian dynamics at low density conditions, as observed in the considered scenario. Vehicles use a continuous car following model inspired on Gipps equations in which driver’s reaction time is considered. Pedestrian’s decision-making process on crossing attempt and model parameters are directly obtained from the analysis of pedestrian-vehicle interactions observed in reality. The model developed employs small time steps, thus allowing the consideration of different pedestrian speeds (intrinsically considering pedestrians with reduced mobility, such as elderlies) and smoothly reproducing car-pedestrian interactions. This aspect required the definition of distinct behavioral rules for vehicles and pedestrians that, in their dynamic interaction, implement an ad-hoc coordination model. In order to validate the model, delays (or waiting times) measured for both pedestrians and drivers were compared with simulated values. Results show a good agreement between empirically obtained time delay and values computed in the simulation.

Feliciani, C., Crociani, L., Gorrini, A., Vizzari, G., Bandini, S., Nishinari, K. (2017). A simulation model for non-signalized pedestrian crosswalks based on evidence from on field observation. INTELLIGENZA ARTIFICIALE, 11(2), 117-138 [10.3233/IA-170110].

A simulation model for non-signalized pedestrian crosswalks based on evidence from on field observation

Crociani, L
Membro del Collaboration Group
;
Gorrini, A
Membro del Collaboration Group
;
Vizzari, G
Membro del Collaboration Group
;
Bandini, S
Membro del Collaboration Group
;
2017

Abstract

This paper presents a model to simulate non-signalized pedestrian crosswalks. Principal scope is to develop a tool to be used by decision-makers to evaluate the necessity of introducing a new crosswalk and/or switching to a traffic light and estimate the potential benefits of such a measure in terms of Level of Service. The model is based on empirical evidence gained during an observation of a non-signalized crosswalk. Pedestrian motion is simulated using a Cellular Automata model which is capable to simulate pedestrian dynamics at low density conditions, as observed in the considered scenario. Vehicles use a continuous car following model inspired on Gipps equations in which driver’s reaction time is considered. Pedestrian’s decision-making process on crossing attempt and model parameters are directly obtained from the analysis of pedestrian-vehicle interactions observed in reality. The model developed employs small time steps, thus allowing the consideration of different pedestrian speeds (intrinsically considering pedestrians with reduced mobility, such as elderlies) and smoothly reproducing car-pedestrian interactions. This aspect required the definition of distinct behavioral rules for vehicles and pedestrians that, in their dynamic interaction, implement an ad-hoc coordination model. In order to validate the model, delays (or waiting times) measured for both pedestrians and drivers were compared with simulated values. Results show a good agreement between empirically obtained time delay and values computed in the simulation.
Articolo in rivista - Articolo scientifico
Non-signalized crosswalk, vehicular traffic, pedestrian traffic, traffic simulation, pedestrian-vehicle interaction
Italian
2017
11
2
117
138
open
Feliciani, C., Crociani, L., Gorrini, A., Vizzari, G., Bandini, S., Nishinari, K. (2017). A simulation model for non-signalized pedestrian crosswalks based on evidence from on field observation. INTELLIGENZA ARTIFICIALE, 11(2), 117-138 [10.3233/IA-170110].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/186124
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