A Low-Cost IoT Platform for Crowd Density Detection in Jakarta Commuter Line

The increasing number of Commuter Line passengers calls for innovation as to how the crowd density across the carriages of Commuter Line trains can be better distributed. We develop an IoT system to detect the crowd density of Commuter Line trains so that (incoming) passengers can be better informed regarding which carriage is best to get in, hence ameliorating the train density distribution. We investigate two different approaches for density detection: CNN and YOLO+KNN. Moreover, we also analyze the impact of different single-board computers, that is, Raspberry Pi 3B and NVIDIA Jetson Nano, and that of different camera angle settings. In total, there are 20 different scenario combinations. We comparatively evaluate the density detection performance as well as the business value for each scenario.