@conference {cMartinez25, title = {Smart Bus Stops: AI-Driven Public Transport Optimization}, booktitle = {12th International Congress on Transportation Research}, year = {2025}, month = {10/2025}, address = {Thessaloniki, Greece}, abstract = {

The digitalization of public transport offers new opportunities to improve service efficiency, particularly in managing peak hours and reducing delays. A key contributor to transport inefficiency is crowd congestion at bus stops. Monitoring bus stops enables a better understanding of passenger flow and waiting times, supporting more effective planning and faster real-time response. However, current data on activity at these locations is scarce due to privacy concerns and the practical difficulties of collecting data in urban settings. To address this gap, this paper proposes a solution to monitor bus stops by equipping them with depth cameras, which capture anonymized data by only recording distance and leveraging computer vision techniques to perform people and bus detection. Results show the viability of the proposed solution to optimize bus networks and its potential replicability in other urban environments.

}, keywords = {AI, Bus, computer Vision, Object detection, Public Transport}, author = {Mart{\'\i}nez, H. and Davins, M. and C{\'a}rdenas, I. and L{\'o}pez Romero, M. and L{\'o}pez Mar{\'\i}n, A. and Barelles, F. and Morros, J.R.} } @conference {cMorros, title = {Real-time lane classification and accident detection for safer micromobility}, booktitle = {11th Internationa Congress on Transportation Research}, year = {2023}, month = {09/2023}, address = {Heraklion, Crete}, abstract = {

The lack of knowledge of the micromobility regulations by e-scooter users is an important factor behind some of\ the accidents involving these vehicles. We present two modules that can increase the safety of the users and\ pedestrians: First, a computer vision model that analyses the video feed captured with a smartphone attached to\ the e-scooter, and predicts in real-time the type of lane in which the user is riding. This knowledge is used by an\ application which combines this information with GSNN location information and a database of mobility\ regulations, and informs the user when he/she is not complying with these regulations. Second, an accident\ detection system, using the smartphone accelerometer, that detects if there is a fall during the riding, so that the\ app can contact the authorities to determine the appropriate response. The experimental results show excellent\ results for both modules.

}, keywords = {accident detection, computer Vision, deep learning, lane classification, Micromobility, RideSafeUM}, author = {Morros, J.R. and Broquetas, A. and Mateo, A. and Puig, J. and Davins, M.} }