Walking Secure: Safe Routing Planning Algorithm and Pedestrian’s Crossing Intention Detector Based on Fuzzy Logic App

Abstract

Improving road safety through artificial intelligence is now crucial to achieving more secure smart cities. With this objective, a mobile app based on the integration of the smartphone sensors and a fuzzy logic strategy to determine the pedestrian’s crossing intention around crosswalks is presented. The app developed also allows the calculation, tracing and guidance of safe routes thanks to an optimization algorithm that includes pedestrian areas on the paths generated over the whole city through a cloud database (i.e., zebra crossings, pedestrian streets and walkways). The experimentation carried out consisted in testing the fuzzy logic strategy with a total of 31 volunteers crossing and walking around a crosswalk. For that, the fuzzy logic approach was subjected to a total of 3120 samples generated by the volunteers. It has been proven that a smartphone can be successfully used as a crossing intention detector system with an accuracy of 98.63%, obtaining a true positive rate of 98.27% and a specificity of 99.39% according to a receiver operating characteristic analysis. Finally, a total of 30 routes were calculated by the proposed algorithm and compared with Google Maps considering the values of time, distance and safety along the routes. As a result, the routes generated by the proposed algorithm were safer than the routes obtained with Google Maps, achieving an increase in the use of safe pedestrian areas of at least 183%.

A Survey of Human Gait-Based Artificial Intelligence Applications

“…SG extends beyond the context of smart home and will play a major role in the future smart cities ( Lozano Domínguez and Mateo Sanguino Tde, 2021 ), smart factories ( Pech et al, 2021 ), smart retail stores ( Zhang et al, 2019a ), smart rehabilitation labs ( Sessoms et al, 2015 ), and smart devices ( Lozano Domínguez and Mateo Sanguino Tde, 2021 )…”

Application of EMGB to Study Impacts of Public Green Space on Active Transport Behavior: Evidence from South Korea

“…Research on active transport has found that an MGB greatly explains pedestrian and cyclist behaviors [ 19 , 20 ]. However, the nature of walking and cycling is changing, with there being greater use of smart app technology; consequently, research on smart apps for active transport has found that they can influence walkers’ and cyclists’ perceptions in a variety of ways [ 21 , 22 , 23 , 24 ]. Nevertheless, despite the importance of green space and motivation for engagement in active transport, research on public green space and extrinsic/intrinsic motivation has been neglected in terms of the role of smart apps for walking and cycling based on goal-directed behavior theory.…”

“…iPhone apps enabled reliable evaluations of walking activities [ 23 ]. Users of Google Maps and other apps are able to obtain safer routes for walking and crossing in urban areas [ 21 ]. These results suggest that individuals with high perceived usefulness of smart app for walking and cycling are potentially likely to possess different characteristics from individuals with low perceived usefulness of smart apps for active transport.…”

Feasibility of Using a MEMS Microphone Array for Pedestrian Detection in an Autonomous Emergency Braking System

“…On the one hand, among the different actions to reduce the number of pedestrian accidents that are being carried out, some of these actions focus on studies related to the analysis of pedestrian behavior at crosswalks [ 2 , 3 , 4 , 5 ], to try to anticipate their movements, and take them into account when making decisions about braking the vehicle or slowing down its speed in areas likely to be at risk, due to the presence of pedestrians, who could become potential victims. This information is very valuable in terms of paying attention to pedestrian detection and tracking [ 4 ] and also in terms of designing routes to avoid potential risk areas as much as possible [ 5 ].…”