Exploiting floating car data for time-dependent Origin–Destination matrices estimation. Issue 2 (4th March 2018)

• Record Type: Journal Article
• Authors: Nigro, Marialisa; Cipriani, Ernesto; del Giudice, Andrea
• Is Part Of: Journal of intelligent transportation systems. Volume 22:Issue 2(2018)
• Page Start: 159
• Page End: 174
• Publication Date: 2018-03-04
• Subjects: dynamic demand estimation problem -- floating car data -- origin-destination (OD) estimation -- traffic modelling -- SPSA; Intelligent transportation systems -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.312
• DOI: 10.1080/15472450.2017.1421462
• Languages: English
• ISSNs: 1547-2450

Abstract

ABSTRACT: The study evaluates the added value generated by estimating dynamic demand matrices by information gathered from Floating Car Data (FCD). Firstly, adopting a large dataset of FCD collected in Rome, Italy, during May 2010, all the monitored trips on a specific district of the city (Eur district) have been collected and analysed in terms of (i) spatial and temporal distribution; (ii) actual route choices and travel times. The data analysis showed that demand data from FCD are usually not suitable to retrieve directly demand matrices, due to a strong dependence of this information from the penetration rate of the monitoring device. Instead, origin–destination travel times and route choice probabilities from FCD are a much more reliable and powerful information with respect to FCD origin–destination flows, since they represent the traffic conditions and behaviors that vehicles experiment along the path. Thus, several synthetic experiments have been conducted adopting both travel times and route choice probabilities as additional information, with respect to standard link measurements, in the dynamic demand estimation problem. Results demonstrated the strength and robustness associated to these network based data, while link measurements alone are not able to define the real traffic pattern. Adopting both the information of origin–destination travel times and route choice probabilities during the demand estimation process, the spatial and temporal reliability of the estimated demand matrices consistently increases.