Open AccessOptimising a vehicle's approach towards an adaptively controlled intersection
Author(s) -
Katwijk Ronald T.,
Gabriel Sabine
Publication year - 2015
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2014.0155
Subject(s) - intersection (aeronautics) , controller (irrigation) , reduction (mathematics) , computer science , control (management) , model predictive control , transport engineering , real time computing , engineering , automotive engineering , mathematics , artificial intelligence , agronomy , geometry , biology
Traffic emissions at controlled intersections can be reduced when the possibilities of infrastructure‐to‐vehicle communication are put to good use. In this study the authors present two applications: one infrastructure‐based and one vehicle‐based that in concert are able to significantly reduce the overall emissions at a controlled intersection. The infrastructure‐based application employs a model‐predictive control approach, an advanced form of traffic‐adaptive control. The vehicle‐based application uses information gained from the infrastructure‐based application (i.e. the estimated time that the vehicle approaching the intersection will be allowed to enter the intersection and cross the stop line) to improve a vehicle's approach towards an intersection. Both applications have the same aim: to avoid unnecessary accelerations, decelerations and delay. For both peak and off‐peak hours, the results show that the application of a model‐predictive controller as opposed to the more traditional traffic‐actuated controller is beneficial both in terms of travel time reduction (∼15.5% in both cases) and carbon‐di‐oxide (CO 2 ) reduction (2.9 and 9.3%, respectively). Together with an approach advice the amount of CO 2 emitted in both cases can be further reduced with an additional 7%, assuming a 100% equipment ratio.