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

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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.

Introduction The paper deals with the adjustment of timedependent Origin-destination (O-D) demand matrix, which is the fundamental input of ITS application for traffic predictions. The usual problem is to search for temporal O-D matrices that are "near" an a priori estimate (seed matrices) and that best fit traffic counts. However information on link flows is not fully effective in describing the state of the network; recent technologies for tracking vehicles provide a new kind of information on route travel times that can integrate usual information on traffic flows at count sections. Objective The object of the paper is to analyse the effectiveness of different types of information in the off-line simultaneous adjustment of dynamic O-D demand, starting from seed matrices with different degrees of reliability.

Road traffic monitoring is one of the key applications in the Intelligent Transport System field. New technologies are now provided in this field and among the most relevant ones there is the DSRC (Dedicated Short Range Communication) set of protocols and standards where vehicles wirelessly communicate. In this paper, we deal with the application of Vehicular Ad-Hoc Networks to road traffic monitoring and we present the design of two distributed protocols based on the DSRC. A realistic simulation of a main expressway in Rome, Italy, is implemented and the performances of the two proposed monitoring methodologies are evaluated in case of regular traffic conditions and in case of a car accident. In both cases the protocols are able to capture in a very quick time (few seconds) the current traffic conditions even on a quite long road of about 70 km. A discussion about the impact of the market penetration rate of the on-board DSRC devices on the protocols performance is also provided. © 2014 IEEE

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