Real-time bus route state forecasting using particle filter and mesoscopic modeling
place - north america, mode - bus, infrastructure - traffic signals, technology - geographic information systems, technology - intelligent transport systems, operations - coordination, operations - traffic
Bus route, Stochastic bus model, Probabilistic distributions, Portland transit data, Particle filter, Forecast
In the absence of system control strategies, it is common to observe bus bunching in transit operations. A transit operator would benefit from an accurate forecast of bus operations in order to control the system before it becomes too disrupted to be restored to a stable condition. To accomplish this, we present a general bus prediction framework. This framework relies on a stochastic and event-based bus operation model that provides sets of possible bus trajectories based on the observation of current bus positions, available via global positioning system (GPS) data. The median of the set of possible trajectories, called a particle, is used as the prediction. In particular, this enables the anticipation of irregularities between buses. Several bus models are proposed depending on the dwell and inter-stop running time representations. These models are calibrated and applied to a real case study thanks to the high quality data provided by TriMet (the Portland, Oregon, USA transit district). Predictions are finally evaluated by ana posteriori comparison with the real trajectories. The results highlight that only bus models accounting for the bus load can provide valid forecasts of a bus route over a large prediction horizon, especially for headway variations. Accounting for traffic signal timings and actual traffic flows does not significantly improves the prediction. Such a framework paves the way for further development of refined dynamic control strategies for bus operations.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Hans, E., Chiabaut, N., Leclercq, L., & Bertini, R.L. (2015). Real-time bus route state forecasting using particle filter and mesoscopic modeling. Transportation Research Part C: Emerging Technologies, Vol. 61, pp. 121–140.