Real-time smoothing of car-following data through sensor-fusion techniques

AbstractObservation of vehicles kinematics is an important task for many applications in ITS (Intelligent Transportation Systems). It is at the base of both theoretical analyses and application developments, especially in case of positioning and tracing/tracking of vehicles, car-following analyses and models, navigation and other ATIS (Advanced Traveller Information Systems), ACC (Adaptive Cruise Control) systems, CAS and CWS (Collision Avoidance Systems and Collision Warning Systems) and other ADAS (Advanced Driving Assistance Systems). Modern technologies supply low-cost devices able to collect time series of kinematic and positioning data with medium to very high frequency. Even more data can be (almost continually) collected if vehicle-to-vehicle (V2V) communications come true. However, some of the ITS applications (as well as car-following models, on which many ADAS and ACC are based) require highly accurate measures or, at least, smooth profiles of collected data. Unfortunately, even relatively high-cost devices can collect biased data because of many technical reasons and often this bias could lead to unrealistic kinematics, incorrect absolute positioning and/or inconsistencies between vehicles (e.g. negative spacing). As a consequence, data need filtering in most of the ITS applications. To this aim proper algorithms are required and several sensors and sources of data possibly integrated in order to obtain the maximum quality at the minimal cost. This work addresses the previous issues by developing a specific Kalman smoothing approach. The approach is developed in order to deal with car-following conditions but is conceived to take into account also navigation issues. The performances are analysed with respect to real-world car-following data, voluntarily biased for evaluation purposes. Assessment is carried out with reference to different mixtures of sensors and different sensors accuracies

I modelli di offerta

I modelli di offerta in ambito dinamico intraperiodale e, segnatamente, il modello di congestione e di propagazione del flusso (e modelli di calcolo dei costi di percorso) possono essere formalizzati secondo due tipi di approcci principali: modelli analitici; modelli di simulazione discreta. Per entrambi i tipi di modelli questa pubblicazione intende mostrare i principali approcci disponibili e le loro possibilità di impiego in contesti finalizzati ad applicazioni di tipo ATIS (Advanced Traveller Information Systems

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