Intelligent Network Flow Optimization Prototype Traffic Management Entity-Based Queue Warning

datahub.transportation.gov | Last Updated 21 May 2024

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains queue warning messages that were recommended by the INFLO Q-WARN algorithm and sent by the traffic management center to vehicles to warn drivers upstream of the queue. The objective of queue warning is to provide a vehicle operator sufficient warning of impending queue backup in order to brake safely, change lanes, or modify route such that secondary collisions can be minimized or even eliminated.

Tags: intelligent transportation systems (its), its joint program office (jpo), field test, application message, seattle, washington, dedicated short range communication (dsrc), freeway, arterial, intelligent network flow optimization (inflo), traffic management entity-based queue warning (q-warn), washington state department of transportation (wsdot), i-5

This dataset has the following 7 columns:

Column NameAPI Column NameData TypeDescriptionSample Values
DateGenerateddategeneratedtextDate and time when the queue warning was generated
RoadwayIDroadwayidnumberId of the roadway where the queue was detected
BOQMMLocationboqmmlocationnumberMile marker location of the back-of-queue
FOQMMLocationfoqmmlocationnumberMile marker location of the front-of-queue
SpeedInQueuespeedinqueuenumberAverage speed of vehicles in queue
RateOfQueueGrowthrateofqueuegrowthnumberRate of queue growth from the last location of queue. In other words, rate of growth of the queue from one time interval to another. It can positive indicating a growing queue or negative indicating a dissipating queue.
ValidityDurationvaliditydurationnumberInterval in seconds the recommendation is valid from the date and time when it was generated