Methodology for Locating Link Count Sensors that Accounts for Reliability of Prior Estimates from Origin–Destination Matrices

Fulvio Simonelli; Andrea Papola; Vittorio Marzano; Iolanda Vitiello

doi:10.3141/2263-20

Methodology for Locating Link Count Sensors that Accounts for Reliability of Prior Estimates from Origin–Destination Matrices

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2263-20 ◽

2011 ◽

Vol 2263 (1) ◽

pp. 182-190 ◽

Cited By ~ 1

Author(s):

Fulvio Simonelli ◽

Andrea Papola ◽

Vittorio Marzano ◽

Iolanda Vitiello

Keyword(s):

Link Count

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Minimum Shared-Link-Count Forwarding for Alleviating Congestion in Wireless Sensor Networks

Engineering Vibration, Communication and Information Processing - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-13-1642-5_38 ◽

2018 ◽

pp. 423-440 ◽

Cited By ~ 1

Author(s):

Sanu Thomas ◽

Thomaskutty Mathew

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Wireless Sensor ◽

Link Count

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Better estimation of origin–destination matrix using automated intersection movement count data

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2014-0555 ◽

2015 ◽

Vol 42 (7) ◽

pp. 490-502 ◽

Cited By ~ 1

Author(s):

Hediye Tuydes-Yaman ◽

Oruc Altintasi ◽

Nuri Sendil

Keyword(s):

Measurement Errors ◽

Programming Model ◽

A Priori ◽

Mathematical Formulation ◽

A Priori Information ◽

Matrix Estimation ◽

Flow Estimation ◽

Proposed Model ◽

Priori Information ◽

Link Count

Intersection movements carry more disaggregate information about origin–destination (O–D) flows than link counts in a traffic network. In this paper, a mathematical formulation is presented for O–D matrix estimation using intersection counts, which is based on an existing linear programming model employing link counts. The proposed model estimates static O–D flows for uncongested networks assuming no a priori information on the O–D matrix. Both models were tested in two hypothetical networks previously used in O–D matrix studies to monitor their performances assuming various numbers of count location and measurement errors. Two new measures were proposed to evaluate the model characteristics of O–D flow estimation using traffic counts. While both link count based and intersection count based models performed with the same success under complete data collection assumption, intersection count based formulation estimated the O–D flows more successfully under decreasing number of observation locations. Also, the results of the 30 measurement error scenarios revealed that it performs more robustly than the link count based one; thus, it better estimates the O–D flows.

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Origin–Destination Flow Estimation from Link Count Data Only

Sensors ◽

10.3390/s20185226 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5226

Author(s):

Subhrasankha Dey ◽

Stephan Winter ◽

Martin Tomko

Keyword(s):

Real World ◽

Count Data ◽

Road Traffic ◽

Physical Space ◽

Simulated Data ◽

Accuracy Score ◽

Travel Times ◽

Estimation Model ◽

Flow Estimation ◽

Link Count

All established models in transportation engineering that estimate the numbers of trips between origins and destinations from vehicle counts use some form of a priori knowledge of the traffic. This paper, in contrast, presents a new origin–destination flow estimation model that uses only vehicle counts observed by traffic count sensors; it requires neither historical origin–destination trip data for the estimation nor any assumed distribution of flow. This approach utilises a method of statistical origin–destination flow estimation in computer networks, and transfers the principles to the domain of road traffic by applying transport-geographic constraints in order to keep traffic embedded in physical space. Being purely stochastic, our model overcomes the conceptual weaknesses of the existing models, and additionally estimates travel times of individual vehicles. The model has been implemented in a real-world road network in the city of Melbourne, Australia. The model was validated with simulated data and real-world observations from two different data sources. The validation results show that all the origin–destination flows were estimated with a good accuracy score using link count data only. Additionally, the estimated travel times by the model were close approximations to the observed travel times in the real world.

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