title: Revisiting Street Intersections Using Slot-Based Systems.
abstract: Since their appearance at the end of the 19th century, traffic lights have been the primary mode of granting access to road intersections. Today, this centuries-old technology is challenged by advances in intelligent transportation, which are opening the way to new solutions built upon slot-based systems similar to those commonly used in aerial traffic: what we call Slot-based Intersections (SIs). Despite simulation-based evidence of the potential benefits of SIs, a comprehensive, analytical framework to compare their relative performance with traffic lights is still lacking. Here, we develop such a framework. We approach the problem in a novel way, by generalizing classical queuing theory. Having defined safety conditions, we characterize capacity and delay of SIs. In the 2-road crossing configuration, we provide a capacity-optimal SI management system. For arbitrary intersection configurations, near-optimal solutions are developed. Results theoretically show that transitioning from a traffic light system to SI has the potential of doubling capacity and significantly reducing delays. This suggests a reduction of non-linear dynamics induced by intersection bottlenecks, with positive impact on the road network. Such findings can provide transportation engineers and planners with crucial insights as they prepare to manage the transition towards a more intelligent transportation infrastructure in cities.
authors.affiliation: Senseable City Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, United States of America.
authors.forename: Remi
authors.initials: R
authors.lastname: Tachet
authors.affiliation: Senseable City Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, United States of America.|Istituto di Informatica e Telematica del CNR, Pisa, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Santi
authors.affiliation: Senseable City Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, United States of America.
authors.forename: Stanislav
authors.initials: S
authors.lastname: Sobolevsky
authors.affiliation: Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, United States of America.
authors.forename: Luis Ignacio
authors.initials: LI
authors.lastname: Reyes-Castro
authors.affiliation: Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, United States of America.
authors.forename: Emilio
authors.initials: E
authors.lastname: Frazzoli
authors.affiliation: ETH Zurich, Computational Social Science, Clausiusstrasse 50, CH-8092, Zurich, SWITZERLAND.
authors.forename: Dirk
authors.initials: D
authors.lastname: Helbing
authors.affiliation: Senseable City Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, United States of America.
authors.forename: Carlo
authors.initials: C
authors.lastname: Ratti
mesh_terms: D008377:Maps as Topic; D014186:Transportation