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The study of pedestrian dynamics has become in the latest years an increasing field of research. A relevant number of technicians\ud have been looking for improving technologies able to detect walking people in various conditions. Several researchers have\ud dedicated their works to model walking dynamics and general laws. Many studiers have developed interesting software to simulate\ud pedestrian behavior in all sorts of situations and environments. Nevertheless, till nowadays, no research has been carried out to\ud analyze all the three over-mentioned aspects. The remarked lack in literature of a complete research, pointing out the fundamental\ud features of pedestrian detection techniques, pedestrian modelling and simulation and their tight relationships, motivates the draft\ud of this paper.\ud Aim of the paper is, first, to provide a schematic summary of each topic. Secondly, a more detailed description of the subjects is\ud displayed, pointing out the advantages and disadvantages of each detection technology, the working logic of each model, outlining\ud the inputs and the provided outputs, and the main features of the simulation software. Finally, the obtained results are summarized\ud and discussed, in order to outline the correlation among the three explained themes

Walking is the original form of transportation, and pedestrians have always made up a significant share of transportation system users. In contrast to motorized traffic, which has to move on precisely defined lanes and follow strict rules, pedestrian traffic is not heavily regulated. Moreover, pedestrians have specific characteristics—in terms of size and protection—which make them much more vulnerable than drivers. In addition, the difference in speed between pedestrians and motorized vehicles increases their vulnerability. All these characteristics, together with the large number of pedestrians on the road, lead to many safety problems that professionals have to deal with. One way to tackle them is to model pedestrian behavior using microsimulation tools. Of course, modeling also raises questions of reliability, and this is also the focus of this paper. The aim of the present research is to contribute to improving the reliability of microsimulation models for pedestrians by testing the possibility of applying neural networks in the model calibration process. Pedestrian behavior is culturally conditioned and the adaptation of the model to local specifics in the calibration process is a prerequisite for realistic modeling results. A neural network is formulated, trained and validated in order to link not-directly measurable model parameters to pedestrian crossing time, which is given as output by the microsimulation tool. The crossing time of pedestrians passing the road on a roundabout entry leg has been both simulated and calculated by the network, and the results were compared. A correlation of 94% was achieved after both training and validation steps. Finally, tests were performed to identify the main parameters that influence the estimated crossing time.

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Smartphones have become an integral part of our everyday lives and keep us busy while doing other primary activities such as driving, cycling or walking in traffic. The problem of digital distraction among drivers has been largely addressed, and interest is growing also on vulnerable road users as well. In fact, high percentages of pedestrians and cyclists are accustomed to checking their devices while moving in traffic. This research links to the presented theme and aims to investigate the extent to which digital distraction in the form of social media app checking influences pedestrian behavior. The focus of the study is specifically on signalized intersections. An outdoor, eye-tracking experiment was conducted on a specific route consisting of various elements typical of urban areas. Participants were asked to walk the predefined route twice, encountering three signalized intersections: the first time they were asked to walk with their smartphone in hand, the second time without. The recordings of each participant’s route were then analyzed, examining reaction time, crossing time and speed, fixations and gaze paths. The results show a clear impact of digital devices on pedestrians’ attention by increasing their reaction and crossing times and decreasing crossing speeds. In addition, the analysis of fixations found that 82.54% of the time was devoted to the smartphone, while interest in other street elements decreased from 16.64% to 4.03%.