Advanced traffic management systems (ATMS) and advanced traveller information systems (ATIS) are key applications in the field of intelligent transport system (ITS). Such applications rely on road traffic monitoring systems which represent a basic function in the design and management of any control system involving drivers and vehicles. In recent years, monitoring technologies based on probe vehicles have been emerging both as self-working systems and in cooperation with infrastructure based systems. Floating car data (FCD) systems are based on a number of probe vehicles that are equipped with satellite positioning devices and a mobile wireless connection ­usually GPRS - to send periodically their position-speed data to a central processing unit. FCD systems do not require the installation of a fixed monitoring infrastructure along the road and base their appeal on the spontaneous choice of users, who may decide to use the on-board device for various travel services, such as navigation or fleet management functions. The reliability of data collection is evidently limited by the penetration rate, but this rate has been considerably increasing in recent years. Nowadays, the wide use of positioning and communication devices are making FCD systems a promising tool for monitoring traffic flows. The proposed paper is focused on the analysis of daily speed patterns for the construction of an efficient, reliable and accurate historical database. The method aims at classifying the various road segments on the base of their typical speed profiles. In order to obtain a reliable segment classification, a preliminary analysis of homogeneous subsets of data has been carried out by recognizing the vehicle types from their observed maximum velocity and dividing the dataset in homogeneous classes associated with different vehicle types. A hierarchical technique based on the Ward’s method has been applied for the clustering of road segments providing a clear identification in the dataset of homogeneous classes of speed trends. Experiments have been carried out on a floating truck data (FTD) system designed and managed by an Italian company named W.A.Y. (Torino, Italy). Data are collected by an operation centre which receives data from various vehicle fleets equipped with on-board devices. More than 13000 probe vehicles are involved in the system; data provided by a vehicle are mapped to a travelling road segment only if the vehicle has been localized within the Italian motorway network or an important highway, such as a ring road. First results, computed on a section of the highway A4 from Brescia to Milan, show that the proposed procedure is able to identify the typical daily trends of velocity even from a dataset of moderate dimensions (2 months). Further development will be the extension of the analysis to a larger dataset in order to obtain a more accurate description of the traffic behaviour over the segments and for different motorways.

Typical speed profiles on motorways from floating vehicle data

PASCALE, ALESSANDRA;NICOLI, MONICA BARBARA;
2011-01-01

Abstract

Advanced traffic management systems (ATMS) and advanced traveller information systems (ATIS) are key applications in the field of intelligent transport system (ITS). Such applications rely on road traffic monitoring systems which represent a basic function in the design and management of any control system involving drivers and vehicles. In recent years, monitoring technologies based on probe vehicles have been emerging both as self-working systems and in cooperation with infrastructure based systems. Floating car data (FCD) systems are based on a number of probe vehicles that are equipped with satellite positioning devices and a mobile wireless connection ­usually GPRS - to send periodically their position-speed data to a central processing unit. FCD systems do not require the installation of a fixed monitoring infrastructure along the road and base their appeal on the spontaneous choice of users, who may decide to use the on-board device for various travel services, such as navigation or fleet management functions. The reliability of data collection is evidently limited by the penetration rate, but this rate has been considerably increasing in recent years. Nowadays, the wide use of positioning and communication devices are making FCD systems a promising tool for monitoring traffic flows. The proposed paper is focused on the analysis of daily speed patterns for the construction of an efficient, reliable and accurate historical database. The method aims at classifying the various road segments on the base of their typical speed profiles. In order to obtain a reliable segment classification, a preliminary analysis of homogeneous subsets of data has been carried out by recognizing the vehicle types from their observed maximum velocity and dividing the dataset in homogeneous classes associated with different vehicle types. A hierarchical technique based on the Ward’s method has been applied for the clustering of road segments providing a clear identification in the dataset of homogeneous classes of speed trends. Experiments have been carried out on a floating truck data (FTD) system designed and managed by an Italian company named W.A.Y. (Torino, Italy). Data are collected by an operation centre which receives data from various vehicle fleets equipped with on-board devices. More than 13000 probe vehicles are involved in the system; data provided by a vehicle are mapped to a travelling road segment only if the vehicle has been localized within the Italian motorway network or an important highway, such as a ring road. First results, computed on a section of the highway A4 from Brescia to Milan, show that the proposed procedure is able to identify the typical daily trends of velocity even from a dataset of moderate dimensions (2 months). Further development will be the extension of the analysis to a larger dataset in order to obtain a more accurate description of the traffic behaviour over the segments and for different motorways.
2011
Scientific Seminar SIDT (Società Italiana Docenti di Trasporti): Energy, Environment and Innovation in Sustainable Transport Systems
File in questo prodotto:
File Dimensione Formato  
2012_SIDT.pdf

Accesso riservato

: Pre-Print (o Pre-Refereeing)
Dimensione 760.01 kB
Formato Adobe PDF
760.01 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/629604
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact