Online calibration for microscopic traffic simulation and dynamic multi-step prediction of traffic speed. (July 2016)

• Record Type: Journal Article
• Authors: Papathanasopoulou, Vasileia; Markou, Ioulia; Antoniou, Constantinos
• Is Part Of: Transportation research. Volume 68(2016)
• Page Start: 144
• Page End: 159
• Publication Date: 2016-07
• Subjects: Speed prediction -- Traffic simulation -- Car-following models -- Dynamic calibration; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2016.04.006
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Model parameters vary in multiple dimensions, across drivers, spatially and temporally. A methodology for online calibration of microscopic simulation for dynamic multi-step prediction. The methodology is validated using real trajectory data. The dynamic model outperforms the static calibrated model in all cases. Less than 10% error in speed prediction even for ten steps into the future. Abstract: Simulating driving behavior in high accuracy allows short-term prediction of traffic parameters, such as speeds and travel times, which are basic components of Advanced Traveler Information Systems (ATIS). Models with static parameters are often unable to respond to varying traffic conditions and simulate effectively the corresponding driving behavior. It has therefore been widely accepted that the model parameters vary in multiple dimensions, including across individual drivers, but also spatially across the network and temporally. While typically on-line, predictive models are macroscopic or mesoscopic, due to computational and data considerations, nowadays microscopic models are becoming increasingly practical for dynamic applications. In this research, we develop a methodology for online calibration of microscopic traffic simulation models for dynamic multi-step prediction of traffic measures, and apply it to car-following models, one of the key models in microscopic traffic simulation models. The methodology is illustrated using real trajectory data available from an experiment conducted in Naples, using a well-established car-following model. The performance of the application with the dynamic model parameters consistently outperforms the corresponding static calibrated model in all cases, and leads to less than 10% error in speed prediction even for ten steps into the future, in all considered data-sets.