Vehicle dynamics models are widely used in many areas of the automotive industry. The usability of each model depends on how well it is able to mimic the behavior of the real vehicle. Each simulation model must go through a thorough investigation process first, which is called model validation. Although, vehicle dynamics simulation models and methodology for computational model validation are well established fields, to the best of the authors' knowledge a general framework for vehicle dynamics model validation is still lacking. The research aims to develop a comprehensive methodological framework for vehicle dynamics model validation. In this paper the aim is to present the high level layout of the proposed framework, introducing the main blocks and the tasks related, also addressing some critical issues regarding vehicle dynamics model validation such as validation metrics and vehicle parameter measurement and estimation. An important part of the proposed methodology is a sophisticated vehicle dynamics measurement system, which gives the opportunity to estimate a bunch of vehicle parameters during dynamic testing, which can be useful for several reasons, e.g. fine-tuning the parameters of the Pacejka Magic formula. As a case study some vehicle dynamics test based parameter estimations are shown to justify the raison d'être and investigate possible applications. INDEX TERMS Vehicle model validation, vehicle dynamics, vehicle parameter identification, methodological framework.

Road traffic congestion has become an everyday phenomenon in today's cities all around the world. The reason is clear: at peak hours, the road network operates at full capacity. In this way, growing traffic demand cannot be satisfied, not even with traffic-responsive signal plans. The external impacts of traffic congestion come with a serious socio-economic cost: air pollution, increased travel times and fuel consumption, stress, as well as higher risk of accidents. To tackle these problems, a number of European cities have implemented reduced speed limit measures. Similarly, a general urban speed limit measure is in preparatory phase in Budapest, Hungary. In this context, a complex preliminary impact assessment is needed using a simulated environment. Two typical network parts of Budapest were analyzed with microscopic traffic simulations. The results revealed that speed limits can affect traffic differently in diverse network types indicating that thorough examination and preparation works are needed prior to the introduction of speed limit reduction.

Road traffic monitoring at intersections is important in traffic engineering practice. Measured data together with traffic estimation represents base input information for traffic management or for road infrastructure development. Measuring turning rates is problematic in roundabouts due to their special geometry. This needs laborious manual traffic counts or other special methods such as automatic number-plate recognition by image processing or Bluetooth sensing, which are more costly compared to the simple automatic cross-sectional detection. As a cost-effective solution to this problem, a hybrid solution is suggested, i.e. using cross-sectional detection combined with advanced estimation. The paper investigated different methods. Traditional iteration based approach as well as estimators adopted from control theory were benchmarked and validated on real-world traffic data as well as via microscopic traffic simulation. Considering different error metrics, it is shown that constrained Kalman Filter and Moving Horizon Estimation provide reliable solution to the turning rate estimation problem in roundabouts.