1,721,076 research outputs found
Urban-scale macroscopic fundamental diagram: An application to the real case study of Rome
No full textThe paper investigates a recent and attractive concept in traffic modeling: the urban-scale Macroscopic Fundamental Diagram (MFD), which is able to link space-mean flow, density and speed in an urban area. Specifically, it explores the possibility to derive the diagram for the complex city context of Rome, Italy, in order to give some guidelines for using MFD in traffic management and control applications. To this aim, the study uses real data, specifically loop detectors and floating car data related to May 2013. Results adopting real data confirm what was obtained in previous studies, such as low scatter in the data points generating the MFD and the hysteresis phenomenon linked with the heterogeneity of traffic patterns and congestion. Some criticalities have been also underlined when deriving the MFD from real data. They are mainly due to the spatial and temporal coverage of the information. Finally, a simulation approach based on the dynamic traffic assignment has been tested as mimicking the presence of loop detectors over the entire road network. The urban-scale MFD resulting from the simulation has been adopted as the leading model to forecast the evolution of traffic inside a monitored area and, consequently, define the traffic management actions to be taken. © 2017, Aracne Ed. All rights reserved
Individual mobility patterns in urban environment
No full textThe understanding and the characterization of individual mobility patterns in urban environments is important in order to improve liveability and planning of big cities. In relatively recent times, the availability of data regarding human movements have fostered the emergence of a new branch of social studies, with the aim to unveil and study those patterns thanks to data collected by means of geolocalization technologies. In this paper we analyze a large dataset of GPS tracks of cars collected in Rome (Italy). Dividing the drivers in classes according to the number of trips they perform in a day, we show that the sequence of the traveled space connecting two consecutive stops shows a precise behavior so that the shortest trips are performed at the middle of the sequence, when the longest occur at the beginning and at the end when drivers head back home. We show that this behavior is consistent with the idea of an optimization process in which the total travel time is minimized, under the effect of spatial constraints so that the starting points is on the border of the space in which the dynamics takes place. Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
The impact of battery electric buses in public transport
No full textIn this work we evaluate the changes in energy demand and resulting air pollutant emissions from the introduction of Battery Electric Buses into the public transport fleet operating in Rome. Our approach to evaluation is based on the adoption of geo-referenced open-data set, published in GTFS format by the Rome's public transport agency, that contains detailed information about transit stops, routes and service schedules of the bus lines. We use an electric bus simulation tool to obtain specific energy consumption models based on real driving cycles. The findings of this study indicate noticeable reductions in the primary energy consumption and the emissions of both GHG gases and toxic local air pollutants such as NOx and PM. © 2017 IEEE
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Methodology to Estimate Functional Vulnerability Using Floating Car Data
Full text linkIn this work, a new methodology to estimate the functional vulnerability of the road network of the city of Catania (Italy) is developed with the purpose to improve the resilience of urban transport during critical events. While the traditional approach for the estimation of vulnerability is based on topological data, the proposed methodology is based on spatial-temporal mobility profiles obtained with floating car data (FCD). The algorithm developed for the estimation of vulnerability combines topological properties of the road network with mobility patterns obtained from FCD to evaluate the consequences of failure events on trajectories and their associated travel times. The core operation of the algorithm is based on the computation of all possible travel paths within their assigned geographical zone every time a road link is disrupted. The procedure may prove useful to evaluate wide failure events and to facilitate emergency plans
Localization of phenolic compounds and hydrolitic enzymes in ripe seeds of Brassica macrocarpa
No full textMultimodal choice model for e-mobility scenarios
No full textThe paper focuses on the definition, calibration and testing of a simulation model that is able to represent multimodal choice behaviours for electric vehicles. Taking into account the interchange between public transport and electric private mobility, the model estimates the parking demand at the Park & Ride sites equipped with charging stations. The model is based on a data-driven approach, in which mainly Floating Car Data and open data of public transport have derived the explanatory variables. Specifically, a machine learning method (Random Forest) has been used to calibrate and test the model in the real case of the metropolitan area of Rome (Italy). We first perform a stability analysis, letting the parameters of the model vary. We then carry out a sensitivity analysis on the variables that can affect the user propensity to adopt the Park & Ride. Finally, we profile and test an incentive policy to boost the choice of Park & Ride. Results suggest that the model succeeds in simulating Park & Ride by electric vehicles and, therefore, it can be extremely valuable for planning financial support to the multimodal travel choice and forecasting vehicle-to-grid scenarios
A Simplified Map-Matching Algorithm for Floating Car Data
No full textWe present a simplified map-matching algorithm that could be considered a robust tool to identify the correct path between consecutive GPS traces over a large number of scenarios avoiding ambiguous route assignment consistent with trajectory samples. Our formulation relies on a hidden Markov model (HMM) framework including multiple features such as the travelled distances between consecutive GPS traces, the signal quality and the direction of travel. The accuracy of the algorithm was evaluated using Floating Car Data (FCD) from a large fleet of privately owned cars and commercial vehicles equipped with devices capable of acquiring GPS positions with a sampling period of about 30 s. Experimental results showed an average accuracy of the model of about 85%. Results suggest our model is suitable not only to identify trajectories for specific origins and destinations, but also to extract traffic and travel time patterns
- …
