Abstract

ISTS 2014 - 4th International Symposium of Transport Simulation, Ajaccio, FRANCE, 01-/06/2014 - 04/06/2014; Buses on the same route tend to bunch when the system is uncontrolled. This lack of regularity leads to an increase in the average passenger waiting time, increases delays and makes travel times uncertain. A wide variety of solutions have been proposed to maintain accurate bus system performance. Unfortunately, if a strategy is applied permanently, it could detract from the entire transport system efficiency. That is why a transit operator needs an accurate forecast of the route in order to intervene before the bus route is too disrupted to be restored to regularity. This paper aims to predict critical situations in real-time forecasting of a bus route state. To accomplish this, we propose to take advantage of both theoretical and empirical information (model and data) using data assimilation (a particle filter). On one hand, a stochastic dynamic bus model forecasts future bus route states. On the other hand, archived data calibrates the model parameters while real-time data provides information about the actual route state. The methodology is applied to a real case study thanks to the quality data provided by TriMet (the Portland, Oregon transit district). Predictions are finally evaluated by an a posteriori comparison with real data. The results highlight that the method leads to a valid forecast of a bus route state with a 8 minutes time window. This duration is sufficient to predict critical situations, especially bus bunching. Further research would have to consider deterministic travel times from a traffic model instead of the distributions in order to maintain correlation between travel times on links. In that case, the assimilation process would focus on the surrounding traffic flow, also potentially available in the Portland data.

Abstract

The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 321132, project TRAMAN21. Summarization: First-order traffic flow models of the LWR (Lighthill-Whitham-Richards) type are known for their simplicity and computational efficiency and have, for this reason, been widely used for various traffic engineering tasks. However, these first-order models are not able to reproduce significant traffic phenomena of great interest, such as the capacity drop and stop-and-go waves. This paper presents an overview of modeling approaches, which introduce the ability to reflect the capacity-drop phenomenon into discretized LWR-type first order traffic flow models; and also proposes a new approach. The background and main characteristics of each approach are analyzed with particular emphasis on the practical applicability of such models for traffic simulation, management and control. The presented modeling approaches are tested and validated using real data from a motorway network in the U.K. Presented on: Transportation Research Part B: Methodological