During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles.Intersection Situation Data (ISD) data set communicates MAP and signal phase and timing (SPaT) information. MAP information communicates an intersection’s location (latitude and longitude), elevation, and geometric features such as approaches and lane configuration. SPaT data communicates the (current) state of the intersection’s signal indication(s). The data is composed of discrete Row Groups. A Row Group is a collection of (approximately 3-4) consecutive rows with common attribute. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. Traveler Situation Data (TSD) was obtained from the data warehouse, and not the data clearinghouse. Only 19 messages were obtained from our query as the current mode of operation of the Test Bed is that the warehouse only contains a few static messages, which are meant to serve as a proxy for future operation in which query submissions will only return message(s) relevant to the context in which the query was submitted. The messages that returned per a query submission communicates a pertinent advisor message which is in part contextualized by location and content. NOTE: All Extra Files are attached in 2014 ITS World Congress Connected Vehicle Test Bed Demonstration Vehicle Situation Data

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

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.

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.

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.

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.

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.

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.

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.

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 speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

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 speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

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 speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

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 speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

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 speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

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 speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.