Back to Search Start Over

Traffic demand estimation using path information from Bluetooth data.

Authors :
Cipriani, E.
Gemma, A.
Mannini, L.
Carrese, S.
Crisalli, U.
Source :
Transportation Research Part C: Emerging Technologies. Dec2021, Vol. 133, pN.PAG-N.PAG. 1p.
Publication Year :
2021

Abstract

• Estimate the traffic demand integrating information provided by traditional and new technologies. Explore the inherent properties of extended coverage information. • Deal with splitting ratios of demand flows among paths identified by coupled Bluetooth sections. • Derivate the analytical solving procedure. • Increase of the intercepted path flows or spatial coverage leads to more accurate estimates. • Assess the effectiveness of the proposed method on a study network in Rome, Italy. Recent advances in technology have made available numerous new monitoring systems that collect updated traffic measurements both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities for Origin-Destination (OD) traffic demand estimation and forecast. Therefore, the aim of this paper is to study how to exploit available information detected by new monitoring devices in the estimation of traffic demand. Starting from the formulation proposed by Spiess (1987, 1990), in this paper a new method to estimate the traffic demand by means of Bluetooth data is proposed. It explores inherent properties of this information in an off-line (and static) context, where mathematical formulation of the estimation problem can be derived. The effectiveness of the proposed method has been investigated in an extensive plan of experiments carried out both on test networks and on a study network consisting in a part of the city of Rome, Italy, obtaining promising results in both applications. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0968090X
Volume :
133
Database :
Academic Search Index
Journal :
Transportation Research Part C: Emerging Technologies
Publication Type :
Academic Journal
Accession number :
153903405
Full Text :
https://doi.org/10.1016/j.trc.2021.103443