Fulvio Simonelli

Human-Like Adaptive Cruise Control Systems through a Learning Machine Approach

In this work an Adaptive Cruise Control (ACC) model, with human-like driving capabilities,based on a learning machine approach, is proposed. The system is based on a neural network approach and is intended to assist the drivers in safe car-following conditions. The proposed approach allows for an extreme flexibility of the ACC that can be continuously trained by drivers in order to accommodate their actual driving preferences as these changes among drivers and over time. The model has been calibrated against accurate experimental data consisting in trajectories of vehicle platoons gathered on urban roads. Its performances have been compared with those of a conventional car-following model.

The Role of the Uncertainty in ATIS Applications

In recent years the interest toward ATIS (Advanced Traveller Information Systems) is constantly increasing, probably because of the intent of solving over-congestion with minimal expenditure. Great efforts have been devoted to both technological and modeling aspects; however, a unified theory does not yet exist, as well as a general agreement on the objectives that can be achieved. It is not widely recognised what can be expected from ATIS, how the information should be designed and how to assess/forecast the network level effects. Here the authors will present a theoretical framework and some practical analyses, aimed to deal with the additional uncertainties sometimes introduced in the route choice problem by the presence of ATIS.

Design of Signal Setting and Advanced Traveler Information Systems

This article analyzes the role of advanced traveler information systems (ATIS) in conjunction with signal setting (SS) design. ATIS and SS are considered as planning options for network optimization under recurrent traffic conditions. The traffic network is considered in within-day static, as is usual for planning-oriented models. Both day-to-day static (equilibrium) and dynamic states are investigated. Notably, equilibrium and stability can influence the feasibility of SS solutions. In addition, the role of ATIS is assessed with regard to its suitability in equilibrating SS optima and/or stabilizing SS equilibria. Travelers’ compliance with information, which plays a crucial role in both optimization and stabilization, is explicitly modeled. A formal modeling framework is introduced, allowing SS and ATIS to be represented, and several benefits and drawbacks of SS and ATIS options are explored. The model is then used for simulations on a hypothetical network. The results suggest the potential of the modeling framework and lead to nontrivial findings about the role of ATIS.

Endogenous Driver Compliance and Network Performances under ATIS

In recent years a great effort has been made in studying many aspects related to modeling and design of ATIS, but a unified theory does not yet exist and there is no consensus on the objectives that can be achieved. This paper aims to show that the explicit modeling of the compliance as an endogenous variable depending on the information accuracy leads to the conclusion that the only objective worth pursuing (at least under recurrent conditions) is the provision of correct information, while the formulation of ATIS in the perspective of a system optimum is suitable only in cases where there is a not negligible incidence of non familiar travelers.

An artificial neural network approach for spatially extending road traffic monitoring measures

This paper focuses on road traffic monitoring and proposes a method based on artificial neural networks for extending data collected on some road links to others. The method may be used to reduce the costs of monitoring equipment since it can estimate the data to be monitored on road segments where there is no equipment installed. The approach is tested on a small network, assuming different neural network frameworks. The numerical results show that the approach is promising, being able in most cases to estimate traffic flows with acceptable errors.

Estimating the effects of energy-efficient driving profiles on railway consumption

In this paper we study the optimisation of railway driving profiles to minimise energy consumption. An optimisation model is proposed for designing the optimal driving profile and a solution procedure. The model and method are tested on some railway sections in order to estimate the potential effects of energy-efficient driving on consumption.

Kinematic formulation of energy-efficient train speed profiles

This paper proposes a kinematic model for optimising train speed profiles along homogeneous sections with a view to minimising energy consumption. By using the proposed model a very parsimonious, computationally light energy-saving optimisation problem suitable for real-time applications can be formulated. The model is tested on several appropriate case studies.