32 research outputs found
A new methodological framework for within-day dynamic estimation of pollutant emissions in a large congested urban network
This paper presents a new methodological framework to address the problem of estimating pollutant emissions for large congested urban networks in a within-day dynamic context. It consists of three main modules: 1) a module to compute pollutant emissions for general links; 2) a module to compute pollutant emissions for all links approaching a signalized intersection; 3) a module to compute pollutant emissions for all links approaching an unsignalized intersection. A dynamic mesoscopic assignment model is performed to derive the main dynamic input of each one of the modules. All the modules have been tested in a real case study (the district of Eur in the city of Rome, Italy), so confirming the reliability of the developed models and their applicability for the estimation of pollutant emissions
Comparing pre- and post-pandemic greenhouse gas and noise emissions from road traffic in Rome (Italy): a multi-step approach
This study presents the results of a traffic simulation analysis and emissions (greenhouse gas and noise) assessment comparing pre-pandemic (2019) and post-pandemic (2022) periods. The estimation of road traffic demand is based on conventional data sources and floating car data; next, the traffic simulation procedure was performed providing road network traffic volumes, which are the input for the emission models. The diffusion of teleworking, e-commerce, as well as the digitization of many processes, services and activities, lead to a significant change in urban mobility. Results show a significant though still not complete resumption of commuters travel activity (−10% compared to pre-pandemic period) in the morning peak-hour. This translates into an 11% reduction of greenhouse gas emissions and a 0.1% increase in noise emissions
Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory
This paper describes a general approach to the specification the stable regime speed-flow function, for motorways, as a part of the stable regime Stochastic Fundamental Diagram consistent with main assumptions of Transportation Systems Theory. Main original elements are:
• Specification of speed-flow functions consistent with travel time function, such as BPR-like functions;
• Calibration from disaggregate data, say data from single vehicle trajectories;
• Specification of the speed r. v. distribution consistent with those used in RUT for route choice behavior modelling, such as Gamma, Inv-Gamma
On Transport Monitoring and Forecasting during COVID-19 Pandemic in Rome
This paper presents the results of a study on the Rome mobility system aiming at estimating the impacts of the progressive lockdown, imposed by the government, due to the Covid-19 pandemic as well as to support decision makers in planning the transport system for the restart towards a post-Covid "new normal". The analysis of data obtained by the transport monitoring system has been fundamental for both investigating effects of the lockdown and feeding transport models to predict the impacts on future actions. At first, the paper focuses on the so-called transport analytics, by describing mobility trends for the multimodal transportation system of Rome. Then, the results of the simulated scenarios to design public transport services, able to ensure passengers social distancing required in the first post-Covid months, are presented and discussed
Stima delle condizioni di deflusso del traffico stradale
The aim of this study is the estimation of traffic flow conditions either in
urban or in freeway contests; in order to reach the objective of the
research, the micro and macro simulations have been adopted as model
tools, verified through specific experiments, carried out detecting
measurements with operational tools, as vehicles equipped with
differential GPS devices, and fixed traffic detectors, as radar technologies.
These tools either, technological or methodological, are different but
completing and give the opportunities to be integrated each other. The
macroscopic and microscopic models have been studied and analyzed; on
the basis of the results obtained during the calibration and validation of
some of the existing car-following models four new microscopic models
have been formulated; one of them considers the interaction between the
follower vehicle and 2 leader vehicles.
Then, the attention has been focalized on the fusion of data detected by
two different sensor types in order to improve the traffic flow estimation.
Starting from the application of the procedure reported in Wang,
Papageorgiou (2005) based on the correction through the Extended
Kalman Filter of the second order traffic model, also a different type of
measurement has been taken into account, such as probe vehicles, which
has been added to the conventional fixed ones, in order to improve the
estimation process.
Different data fusion techniques have been analyzed, such as the fusion of
measurements and the fusion of estimations. Moreover, an application
with freeway real data has been carried out in order to validate the
procedure
Stima delle condizioni di deflusso del traffico stradale
The aim of this study is the estimation of traffic flow conditions either in
urban or in freeway contests; in order to reach the objective of the
research, the micro and macro simulations have been adopted as model
tools, verified through specific experiments, carried out detecting
measurements with operational tools, as vehicles equipped with
differential GPS devices, and fixed traffic detectors, as radar technologies.
These tools either, technological or methodological, are different but
completing and give the opportunities to be integrated each other. The
macroscopic and microscopic models have been studied and analyzed; on
the basis of the results obtained during the calibration and validation of
some of the existing car-following models four new microscopic models
have been formulated; one of them considers the interaction between the
follower vehicle and 2 leader vehicles.
Then, the attention has been focalized on the fusion of data detected by
two different sensor types in order to improve the traffic flow estimation.
Starting from the application of the procedure reported in Wang,
Papageorgiou (2005) based on the correction through the Extended
Kalman Filter of the second order traffic model, also a different type of
measurement has been taken into account, such as probe vehicles, which
has been added to the conventional fixed ones, in order to improve the
estimation process.
Different data fusion techniques have been analyzed, such as the fusion of
measurements and the fusion of estimations. Moreover, an application
with freeway real data has been carried out in order to validate the
procedure
Traffic state estimation based on data fusion techniques
"The capability to detect and\/or forecast traffic. conditions is of utmost importance in road management. applications. Recent advances in technology have made. available numerous new monitoring systems exploiting larger fleet of probe vehicles. Together with traditional volume and. time mean speed measurements relative to a local section. monitored continuously in time, probe vehicles provide. additional type of data, such as space mean speed and travel. time, relative to road segments monitored in specific time intervals. Therefore, the aim of this paper is to study how to exploit available information detected by new monitoring devices in. the estimation of traffic flow conditions. Different types of data fusion techniques have been analyzed,. namely measurement data fusion and state vector fusion, in. several simulations carried out on a simple test network,. traveled by probe vehicles and composed of 9 cells with an on. ramp and an off ramp and with two fixed traffic sensors. located in two different cells. Test results are promising and indicate higher accuracy of estimates obtained with new methods, particularly in the case of measurement data fusion.
On the Short-term Prediction of Traffic State: An Application on Urban Freeways in ROME
Abstract This paper explores the traffic state estimation on freeways in urban areas
combining point-based and route-based data in order to properly feed a second order traffic
flow model, recursively corrected by an Extended Kalman Filter. In order to overcome the
possible lack of real-time information, authors propose to use simulation-based data in order
to improve the accuracy of the traffic state estimation. This model was tested on a urban
freeway stretch in Rome, for which a set of real-time data during the morning of a typica