Dynamic Route Flow Estimation in Road Networks Using Data from Automatic Number of Plate Recognition Sensors

The traffic flow on road networks is dynamic in nature. Hence, a model for dynamic traffic flow estimation should be a very useful tool for administrations to make decisions aimed at better management of traffic. In fact, these decisions may in turn improve people’s quality of life and help to imple...

Full description

Saved in:
Bibliographic Details
Published in:Sustainability 2021-04, Vol.13 (8), p.4430
Main Authors: Sánchez-Cambronero, Santos, Álvarez-Bazo, Fernando, Rivas, Ana, Gallego, Inmaculada
Format: Article
Language:English
Subjects:
Conservation laws
Decisions
Kinematics
Literature reviews
Management decisions
Partial differential equations
Propagation
Quality of life
Recognition
Roads
Route choice
Sensors
Sustainability
Traffic assignment
Traffic congestion
Traffic flow
Traffic management
Traffic models
Transportation networks
Travel
Travel time
Vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The traffic flow on road networks is dynamic in nature. Hence, a model for dynamic traffic flow estimation should be a very useful tool for administrations to make decisions aimed at better management of traffic. In fact, these decisions may in turn improve people’s quality of life and help to implement good sustainable policies to reduce the external transportation costs (congestion, accidents, travel time, etc.). Therefore, this paper deals with the problem of estimating dynamic traffic flows in road networks by proposing a model which is continuous in the time variable and that assumes the first-in-first-out (FIFO) hypothesis. In addition, the data used as model inputs come from Automatic Number of Plate Recognition (ANPR) sensors. This powerful data permits not only to directly reconstruct the route followed by each registered vehicle but also to evaluate its travel time, which in turn is also used for the flow estimation. In addition, the fundamental variable of the model is the route flow, which is a great advantage since the rest of the flows can be obtained using the conservation laws. A synthetic network is used to illustrate the proposed method, and then it is applied to the well-known Nguyen-Dupuis and Eastern Massachusetts networks to prove its usefulness and feasibility. The results on all the tested networks are very positive and the estimated flows reproduce the simulated real flows fairly well.
ISSN:2071-1050
2071-1050
DOI:10.3390/su13084430