Forecasting travel behavior using Markov Chains-based approaches

Recent advances in agent-based micro-simulation modeling have further highlighted the importance of a thorough full synthetic population procedure for guaranteeing the correct characterization of real-world populations and underlying travel demands. In this regard, we propose an integrated approach including Markov Chain Monte Carlo (MCMC) simulation and profiling-based methods to capture the behavioral complexity and the great heterogeneity of agents of the true population through representative micro-samples. The population synthesis method is capable of building the joint distribution of a given population with its corresponding marginal distributions using either full or partial conditional probabilities or both of them simultaneously. In particular, the estimation of socio-demographic or transport-related variables and the characterization of daily activity-travel patterns are included within the framework. The fully probabilistic structure based on Markov Chains characterizing this framework makes it innovative compared to standard activity-based models. Moreover, data stemming from the 2010 Belgian Household Daily Travel Survey (BELDAM) are used to calibrate the modeling framework. We illustrate that this framework effectively captures the behavioral heterogeneity of travelers. Furthermore, we demonstrate that the proposed framework is adequately adapted to meeting the demand for large-scale micro-simulation scenarios of transportation and urban systems.     

Shifting towards environment-friendly modes: profiling travelers using Q-methodology

Due to a variety of reasons, the previous century is characterized by an extraordinary growth in car use that has continued into the current century. This has resulted in serious environmental repercussions. Despite technological advancements, the externalities remain an ecological threat that can not be discarded by policy makers. Therefore, it is essential that policy makers focus on reducing car use and on stimulating the shift towards more environment-friendly transport modes. In this study, Q-methodology is adopted as the technique to segment people, and to ascertain which approaches and determinants matter to medium distance travel. Segmentation is important, as policy measures will be more efficient and effective if they are fine-tuned on specific target groups. The analysis revealed that four discourses preponderate the paradigm of environmentally sustainable transport: travelers who use public transport as a dominant alternative, car-dependent travelers, travelers with a positive perception of using public transport, and travelers with a preference for car use. Concerning rational, economic motives, individuals evaluate travel time reliability as most important. To increase the reliability policy makers should consider the use of separate bus lanes and traffic light manipulation. In addition, public transport can be made even more attractive, when costs of cars are made more variable by road or congestion charging. When the s motives are discussed, the differences between the different groups of travelers were more pronounced. Next to increasing the benefits of using public transport, policy makers should also pay attention to removing psycho-social barriers.

Recovery of North-East Atlantic temperature fields from profiling floats: Determination of the optimal float number from sampling and instrumental error analysis

Argo is an international project that is deploying an array of temperature and salinity profiling floats over the global ocean. Here we use the error formulation derived from Optimal Statistical Interpolation to estimate statistical errors associated with the recovery of the temperature field in the North-East Atlantic ocean. Results indicate that with the present distribution of floats (119 in the considered domain), scales of wavelength larger than 500 km can be recovered with a relative uncertainty (rms error relative to the standard deviation of the field) of about 7% at 50 m, 8% at 200 m and 10% at 1000 m. This corresponds to mean absolute errors of 0.111 °C at 50 m, 0.104 °C at 200 m and 0.073 °C at 1000 m.The splitting of total errors into instrumental and sampling contributions reveals that, in the present scenario, errors are more due to the small number of floats than to instrumental errors, especially at upper levels. For scales larger than 500 km this will hold true until 200–250 floats are deployed (less than 200 for deep levels). In such a simulated scenario, the number of observations and the technology become approximately equally limiting factors for the accuracy of the temperature field mapping, with total relative errors of less than 2% at upper levels and about 3% at 1000 m.