Personal mobile devices are widespread and carried by their users most of the time over the day. Thanks to the integrated sensors they can report about visited places, movement types and speed of the users. However, efficient stopping event detection on public transport vehicles is still a challenge. These events, associated with the coordinates of real stations, can be useful to update public transit timetables according to real-time traffic. In field tests we evaluated the most commonly available suitable sensors' precision and efficiency and developed our Stopping Event Detection Algorithm (SEDA), which utilizes only the accelerometer to find potential stopping times and the Wi-Fi sensor to validate or discard them by a novel localization method. Wi-Fi is used only 6.66% of the time of actual traveling on public vehicles. Our algorithm is shown to recognize properly 82.9-89.47% of public traffic stations while consuming daily only 13% the capacity of an average smartphone's battery.