Online calibration for microscopic traffic simulation and dynamic multi-step prediction of traffic speed☆
Introduction
Developments of Advanced Traveler Information Systems (ATIS) rely significantly on the capability to perform accurate estimates of the current traffic state and short-term predictions of driving behavior and traffic characteristics, such as speed (Vlahogianni et al., 2005, van Lint et al., 2005, Vlahogianni et al., 2008). Due to a number of practical, data and computational considerations, during the past two decades, ATIS applications have been mostly supported by mesoscopic or macroscopic traffic simulation models. Data collection and computational advances are making it possible to consider more detailed, microscopic models for this kind of applications. Naturally, such models introduce a number of complications, and therefore their adoption should be clearly motivated and justified.
While such systems have been around for decades, current developments, such as the increasing interest in Active Traffic Management, make them more relevant (Kurzhanskiy and Varaiya, 2010). Indeed, ATIS can be effective in supporting active traffic management policies by Real-Time Decision Support Systems, whose core engine is a real-time traffic simulation model. The real-time requirements bring to the forefront the limitations of static calibration, and accelerate the need for procedures like the ones discussed in this research. An example of such applications is the Integrated Corridor Management initiative in the US (Miller and Skabardonis, 2010).
Simulation models do not always adequately reflect field conditions outside of the time period for which they have been calibrated (Balakrishna et al., 2007, Daamen et al., 2014, Henclewood et al., 2012). Microscopic models often comprise different detailed models, including car-following, lane-changing and gap-acceptance models. In most cases, the parameters of these models are assumed to be stable, both across space and time, and also across drivers. The online calibration of car-following models is a promising approach to capture the heterogeneity of driver behavior and traffic conditions. By continuously supplying a car-following model with surveillance data, an online calibration process could be applied in order to adapt model parameters to the current traffic state. In this view, the use of richer data, such as real-time Floating Car Data (FCD), based on traces of Global Navigation Satellite Systems (GNSS), could be leveraged as a reliable and cost-effective way to gather accurate traffic data (De Fabritiis et al., 2008, Antoniou et al., 2011).
Calibration of car-following models (Brackstone and McDonald, 1999) has been an issue for a long time (Aycin and Benekohal, 1999), but nowadays it has received a new boost (Hoogendoorn and Hoogendoorn, 2010, Monteil et al., 2014), in light of new data-collection techniques, mostly related to the increasing availability of trajectory data (Kesting and Treiber, 2008, Punzo et al., 2005, Papathanasopoulou and Antoniou, 2015), which of course introduce other challenges (Punzo et al., 2012).
Online calibration has been used in many macroscopic and mesoscopic modeling approaches (Papageorgiou et al., 1989, Kim, 2002, Antoniou et al., 2005, Fei et al., 2011). The use of the Kalman Filter (and its extensions) for online parameter calibration has shown encouraging results (Antoniou et al., 2007). However, in recent years there has been an increasing interest in online applications of microscopic traffic models. Moreover, Henclewood et al. (2012) suggest that a real-time calibration algorithm should be included in online, data-driven microscopic traffic simulation tools.
The objective of this paper is to motivate, develop and demonstrate with real data a practical approach for the online calibration of microscopic traffic simulation models, which considers dynamic parameters for individual drivers, in time and space. At each time instance, the dynamically obtained model parameters are being used for short-term prediction (up to ten steps into the future), and the performance of this prediction is compared with the reference case of static model parameters.
This paper presents an alternative methodology for microscopic online calibration and multiple step prediction and is organized as follows. Firstly, a literature review is presented in the following section. Then, the overall methodological framework is presented. A case study setup to demonstrate the feasibility and superiority of the approach, over previous techniques, is then presented, followed by the presentation of the dynamic calibration procedure and an analysis of the results. A discussion of the results follows in the concluding section, and future prospects are proposed.
Section snippets
Literature review
Reliable representation of driving behavior is a crucial issue for traffic simulation. Appropriate simulation models are chosen according to the requirements of each application; when considering the modeling detail, traffic simulation models can be divided into microscopic, mesoscopic and macroscopic. Microscopic models provide the highest level of detail for advanced transport applications (Antoniou and Koutsopoulos, 2006). However, the traditional static calibration approach may not allow
Methodology
In this research, a methodological framework for the dynamic calibration of car-following models using real-time data is proposed. The approach has two main steps, an estimation phase and a prediction phase. The estimation phase relies on a constrained global optimization algorithm. Once an optimal set of parameters is identified for each individual time-instance (and each individual driver), multi-step prediction is performed. In each time step, prediction is achieved using the estimated
Case study setup
The methodology is applied to a car-following model, which is arguably the most critical component of microscopic traffic simulation models. In particular, Gipps’ model (used e.g. in the widely used Aimsun traffic simulation model) is calibrated using available data from an experiment conducted in Naples (Punzo et al., 2005). A static calibration is also performed in order to be used as a reference benchmark. The main difference from dynamic calibration is that the parameter values are constant
Dynamic calibration and results analysis
Gipps’ model is calibrated dynamically in order to simulate the speed of the third vehicle ((t + τ)). Gipps’ model requires as input data and the appropriate parameter values. The superiority of this calibration over the static calibration presented before is demonstrated both for estimation and also for multiple step prediction of traffic speeds.
Conclusions and future prospects
The findings of this research suggest that dynamic calibration for microscopic traffic models could be promising and should be further studied. In this research, the prediction of the dynamic parameters was simple, in the sense that the dynamically calibrated parameters were assumed as the best available estimate for the short-term values of these parameters. Further research could consider secondary models that would actually aim at predicting the evolution of these parameters, as well, e.g.
Acknowledgments
Research supported by the Action: ARISTEIA-II (Actions Beneficiary: General Secretariat for Research and Technology), co-financed by the European Union (European Social Fund ESF) and Greek national funds. The authors would like to thank Prof. Vincenzo Punzo from the University of Napoli – Federico II for kindly providing the data used in this research.
The first author is thankful for a scholarship by the Alexander S. Onassis Foundation.
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This article belongs to the Virtual Special Issue on Recent Advances in Transportation Management and Control.