Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes
The paper deals with the problem of transit system design for a mixed fleet of electric and internal combustion buses and introduces a model for the vehicle type choice that involves computation of lifetime internal and external cost. Unlike previous works focused on transit network design problem, this model assumes the set of routes as fixed. It introduces instead different fast charging alternatives and constraints related to battery autonomy, energy consumption and power transfer from the electricity grid. Results of a real-size numerical application carried out on a transport corridor in Rome are illustrated
The paper deals with the application of Artificial Neural Networks to model the car following driver's behaviour. The study is based on experimental data collected by several GPS equipped vehicles that follow each other on urban roads. A 'swarm' stochastic evolutionary algorithm has been applied in training phase to improve convergence of a usual error-back propagation algorithm. Validation tests highlight that ANNs provide a quite good approximation of driving patterns and can be suitably implemented in micro-simulation models. More advanced applications to ITS may concern Advanced Driver Assistance Systems and are addressed to conform braking actions to drivers' expectations.
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