Real-time forecasting the bus route state by data assimilation process

Buses of a same route are known to be naturally unstable: they tend to bunch when they are uncontrolled. Consequently, the bus route turns to be unreliable. Many solutions exist to maintain accurate bus performance. However, these strategies must be applied at the right moment to be efficient. To this end, we propose to predict critical situations in forecasting the bus route state. A dynamical bus model describes bus behavior and forecasts bus trajectories. Moreover, data from ITS allow calibrating the bus model and give access in real-time to the bus route state. Data assimilation takes advantage of both model and data information to estimate the likeliest position of buses every time step. This methodology is presented and applied to bus systems. Then the data assimilation process is applied in a controlled framework. Simulations show the inability of the model to forecast synthetic bus trajectories in some cases. That speaks in favor of developing a model more based on physical phenomena. Finally the contribution of real-time data is evaluated.

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