NDLI: Experimental analyses and clustering of travel choice behaviours by floating car big data in a large urban area

Content Provider	IET Digital Library
Author	Fusco, Gaetano Bracci, Agnese Caligiuri, Tommaso Colombaroni, Chiara Isaenko, Natalia
Abstract	This study introduces a general methodology to process sparse floating car data, reconstruct the routes followed by the drivers, and cluster them to achieve suitable choice sets of significantly different routes for calibrating behavioural models. This methodology is applied to a large set of floating car data collected in Rome in 2010. Results underlined that routes assigned to different clusters are actually very different to each other. Nevertheless, as expected according to Wardrop's principle, the clusters belonging to the same origin–destination have rather similar average route travel times, even if there is a large range between their minimum and maximum values. A focus on drivers’ behaviour highlighted their propensity to follow the same route to their usual destination, though the 12% of the drivers switched to an alternative route. However, the analysis conducted over the 1 month of observations did not reveal the existence of any systematic correlation between neither the change of route nor the change of departure time and the travel time experienced the day before.
Starting Page	270
Ending Page	278
Page Count	9
ISSN	1751956X
Volume Number	12
e-ISSN	17519578
Issue Number	Issue 4, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/12/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.0015
Journal	IET Intelligent Transport Systems
Publisher Date	2018-02-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automobile Behavioural Model Behavioural Sciences Computing Big Data Data Handling Technique Driver Information System Experimental Analysis Floating Car Big Data Floating Car Data Set Collection Large Urban Area Pattern Clustering Route Reconstruction Social And Behavioural Sciences Computing Sparse Floating Car Data Processing Travel Choice Behaviour Clustering Wardrop's Principle
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Route and Path Choices of Freight Vehicles: A Case Study with Floating Car Data

Understanding drivers' route choice behaviours in the urban network with machine learning models

Travel Modeling 101

Estimating Path Choice Models through Floating Car Data

A novel method for the detection of road intersections and traffic rules using big floating car data

Enhancements to transportation analysis and simulation (transims) (2004).

Experimental Analysis of Effects of Travel Time Information on Dynamic Route Choice Behaviour

Identification of contributing factors on travel mode choice among different resident types with bike-sharing as an alternative

Benefits and challenges of multi-driver simulator studies

Experimental analyses and clustering of travel choice behaviours by floating car big data in a large urban area

Similar Documents

Route and Path Choices of Freight Vehicles: A Case Study with Floating Car Data

Understanding drivers' route choice behaviours in the urban network with machine learning models

Travel Modeling 101

Estimating Path Choice Models through Floating Car Data

A novel method for the detection of road intersections and traffic rules using big floating car data

Enhancements to transportation analysis and simulation (transims) (2004).

Experimental Analysis of Effects of Travel Time Information on Dynamic Route Choice Behaviour

Identification of contributing factors on travel mode choice among different resident types with bike-sharing as an alternative

Benefits and challenges of multi-driver simulator studies

Experimental analyses and clustering of travel choice behaviours by floating car big data in a large urban area