Calibration and validation have long been a significant topic in traffic model development. In fact, when moving to Dynamic Traffic Assignment (DTA) models, the need to dynamically update the demand and supply component creates a considerable burden on the existing calibration algorithms, often rendering them impractical. These calibration approaches are mostly restricted either due to non-linearity or increasing problem dimensionality. Simultaneous Perturbation Stochastic Approximation (SPSA) has been proposed for DTA model calibration, with encouraging results, for more than a decade. However, it often fails to converge reasonably with the increase in problem size and complexity. In this research, we combine SPSA with Principal Components Analysis (PCA), to form a new algorithm we call PC-SPSA. PCA limits the search area of SPSA within the structural relationships captured from historical estimates in lower dimensions, reducing the problem size and complexity. We formulate the algorithm, demonstrate its operation and explore its performance using an urban network of Vitoria, Spain. Practical issues that emerge from the scale of different variables and bounding their values are also analyzed through a sensitivity analysis using a non-linear synthetic function.

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Nowadays, the growth of traffic congestion and emissions has led to the emergence of an innovative and sustainable transportation service, called dynamic vanpooling. The main aim of this study is to identify factors affecting the travel behavior of passengers due to the introduction of dynamic vanpooling in the transportation system. A web-based mode choice survey was designed and implemented for this scope. The stated-preference experiments offered respondents binary hypothetical scenarios with an ordered choice between dynamic vanpool and the conventional modes of transport, private car and public transportation. In-vehicle travel time, total travel cost and walking and waiting time or searching time for parking varies across the choice scenarios. An ordered probit model, a multinomial logit model and two binary logit models were specified. The model estimation results indicate that respondents who are aged between 26 and 35 years old, commute with PT or are members of bike-sharing services were significantly more likely to choose dynamic vanpool or PT than private car. Moreover, respondents who are worried about climate change and are willing to spend more for environmentally friendly products are significantly more likely to use dynamic vanpool in comparison with private cars. Finally, to indicate the model estimation results for dynamic vanpool, the value of in-vehicle travel time is found to be 12.2€ per hour (13.4€ for Munich subsample).

Time-dependent Origin-Destination (OD) demand flows are fundamental inputs for Dynamic Traffic Assignment (DTA) systems and real-time traffic management. This work introduces a novel state-space framework to estimate these demand flows in an online context. Specifically, we propose to explicitly include trip-chaining behavior within the state-space formulation, which is solved using the well-established Kalman Filtering technique. While existing works already consider structural information and recursive behavior within the online demand estimation problem, this information has been always considered at the OD level. In this study, we introduce this structural information by explicitly representing trip-chaining within the estimation framework. The advantage is twofold. First, all trips belonging to the same tour can be jointly calibrated. Second, given the estimation during a certain time interval, a prediction of the structural deviation over the whole day can be obtained without the need to run additional simulations. The effectiveness of the proposed methodology is demonstrated first on a toy network and then on a large real-world network.Results show that the model improves the prediction performance with respect to a conventional Kalman Filtering approach. We also show that, on the basis of the estimation of the morning commute, the model can be used to predict the evening commute without need of running additional simulations.

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