Bayesian calibration of dynamic traffic simulations

We present an operational framework for the calibration of demand models for dynamic traffic simulations. Our focus is on disaggregate simulators that represent every traveler individually. We calibrate, at a likewise individual level, arbitrary choice dimensions within a Bayesian framework, where the analyst's prior knowledge is represented by the dynamic traffic simulator itself and the measurements are comprised of sensor data such as traffic counts. The approach is equally applicable to an equilibrium-based planning model and to a telematics model of spontaneous and imperfectly informed drivers. It is based on consistent mathematical arguments, yet applicable in a purely simulation-based environment, and, as our experimental results show, capable of estimating practically relevant scenarios in real-time

An integrated method for short-term prediction of road traffic conditions for intelligent transportation systems applications

The paper deals with the short-term prediction of road traffic conditions within Intelligent Transportation Systems applications. First, the problem of traffic modeling and the potential of different traffic monitoring technologies are discussed. Then, an integrated method for short-term traffic prediction is presented, which integrates an Artificial Neural Network predictor that forecasts future states in standard conditions, an anomaly detection module that exploits floating car data to individuate possible occurrences of anomalous traffic conditions, and a macroscopic traffic model that predicts speeds and queue progressions in case of anomalies. Results of offline applications on a primary Italian motorway are presented

Disaggregate path flow estimation in an iterated DTA microsimulation

This text describes the first application of a novel path flow and origin/destination (OD) matrix estimator for iterated dynamic traffic assignment (DTA) microsimulations. The presented approach, which operates on a trip-based demand representation, is derived from an agent-based DTA calibration methodology that relies on an activity-based demand model (Flötteröd et al., 2011a). The objective of this work is to demonstrate the transferability of the agent-based approach to the more widely used OD matrix-based demand representation. The calibration (i) operates at the same disaggregate level as the microsimulation and (ii) has drastic computational advantages over conventional OD matrix estimators in that the demand adjustments are conducted within the iterative loop of the DTA microsimulation, which results in a running time of the calibration that is in the same order of magnitude as a plain simulation. We describe an application of this methodology to the trip-based DRACULA microsimulation and present an illustrative example that clarifies its capabilities

Calibration of choice model parameters in a transport scenario with heterogeneous traffic conditions and income dependency

By raising the issue of data requirements for the purpose of modal development, validation and application, this study proposes an approach to calibrate choice model parameters in heterogeneous traffic condition using minimal empirical data. For this, a real-world scenario of Patna, India is chosen. For the calibration, a Bayesian framework-based calibration technique (CaDyTS: Calibration of Dynamic Traffic Simulations) is used. Commonly available, mode-specific, hourly-classified traffic counts are used to generate full day plans of agents and their initially unknown activity locations. While the proposed approach implements location choice implicitly, the approach can be applied to a variety of other problems. Further, the effect of household income is included in the utility function to incorporate the effect of income in the decision-making process of individual travelers and to filter out inconsistencies in the daily plans, which originate from the survey data

Integrating CEMDAP and MATSIM to Increase the Transferability of Transport Demand Models

At the time of publication C.R. Bhat was at the University of Texas at Austin, while D. Ziemke and K. Nagel were at the University of Berlin.An activity-based approach to transport demand modeling is considered the most behaviorally sound procedure to assess the impacts of transport policies. In this paper, it is investigated whether it is possible to transfer an estimated model for activity generation from elsewhere (the estimation context) and use local area (application context) traffic counts to develop a local area activity-based transport demand representation. Here, the estimation context is the Dallas-Fort Worth area, and the application context is Berlin, Germany. Results in this paper suggest that such a transfer approach is feasible, based on comparison with a Berlin travel survey. Additional studies in the future need to be undertaken to examine the stability of the results obtained in this paper.Civil, Architectural, and Environmental Engineerin