Mobile Crowd Sensing (MCS) leverages the widespread availability of smart devices to collect and analyze environmental and social data. While MCS has been widely applied in smart cities to optimize vehicle traffic and safety, the needs of pedestrians remain largely unaddressed. To address this need, we propose CrossTime, a sensing application designed to estimate waiting times at pedestrian crossings. Using GPS data, accelerometer readings, and open-source intersection location data, CrossTime autonomously detects when a user is waiting at an intersection. To evaluate its feasibility and accuracy, we conducted a test case on three routes in an urban environment, comparing system-detected waiting times with manually recorded values. Our results show that CrossTime effectively captures pedestrian waiting behavior, although there are some discrepancies due to sensor limitations and environmental factors.
Ciabattini, L., Esposito, A., Moghbelan, Y., Forlesi, M., Bruno, J., Zyrianoff, I., et al. (2025). CrossTime: A Mobile Application for Smarter Pedestrian Navigation and Traffic Light Awareness [10.1109/MDM65600.2025.00054].
CrossTime: A Mobile Application for Smarter Pedestrian Navigation and Traffic Light Awareness
Ciabattini L.;Moghbelan Y.;Forlesi M.;Zyrianoff I.;Bononi L.
2025
Abstract
Mobile Crowd Sensing (MCS) leverages the widespread availability of smart devices to collect and analyze environmental and social data. While MCS has been widely applied in smart cities to optimize vehicle traffic and safety, the needs of pedestrians remain largely unaddressed. To address this need, we propose CrossTime, a sensing application designed to estimate waiting times at pedestrian crossings. Using GPS data, accelerometer readings, and open-source intersection location data, CrossTime autonomously detects when a user is waiting at an intersection. To evaluate its feasibility and accuracy, we conducted a test case on three routes in an urban environment, comparing system-detected waiting times with manually recorded values. Our results show that CrossTime effectively captures pedestrian waiting behavior, although there are some discrepancies due to sensor limitations and environmental factors.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
