Feasibility of Using In-Vehicle Video Data to Explore How to Modify Driver Behavior That Causes Nonrecurring Congestion (2011) / Chapter Skim
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An accurate time and location stamp that enables a proper link between vehicle data and complementary data is necessary to the vehicle and complementary data sources. Figure 3.1 shows the conceptual relationship between the data sets to be analyzed and the potential travel time reliability improvement measures.
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16Figure 3.1. Relationship between project data sets and travel time reliability improvement.Consequently, the team would not have access to the data, and thus it is not considered further.
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Quality of External Data Using in-vehicle video data to help assess the role of driver behavior in nonrecurring congestion requires analyzing not only the vehicle and driver data but also the complementary data. For instance, it has been documented that weather affects driving behavior and performance and leads to longer following distances, thereby decreasing throughput at intersections and resulting in longer travel time.
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It is a modular system utilizing a central processor that receives input from multiple sensors. Operated and controlled by the FAA, state and local governments, and some private agencies, the AWOS reports weather information at 20-min intervals but does not report special observations for rapidly changing weather conditions.
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Traffic count stations in West Virginia.
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20Figure 3.5. Traffic count stations in Pittsburgh, Pa.some of the variables in the original database are listed here.
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. Within each feasibility category the various measures that constitute a category are assigned a score so that the sum of scores within a category is 10.
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22Table 3.3. Sample of Traffic Count Data in Virginia Link ID Direction Lane Start Date and Time Interval Class Quality Class 15 507002 1 1 3/15/2007 7:00 15 1 4 507002 1 1 3/15/2007 7:15 15 1 6 507002 1 1 3/15/2007 7:30 15 1 20 507002 1 1 3/15/2007 7:45 15 1 26 507002 1 1 3/15/2007 8:00 15 1 20 507002 1 1 3/15/2007 8:15 15 1 26 507002 1 1 3/15/2007 8:30 15 1 32 507002 1 1 3/15/2007 8:45 15 1 20 507002 1 1 3/15/2007 9:00 15 1 22 507002 1 1 3/15/2007 9:15 15 1 8 507002 1 1 3/15/2007 9:30 15 1 10 507002 1 1 3/15/2007 9:45 15 1 10 507002 1 1 3/15/2007 10:00 15 1 6 507002 1 1 3/15/2007 10:15 15 1 4 507002 1 1 3/15/2007 10:30 15 1 7 507002 1 1 3/15/2007 10:45 15 1 11 507002 1 1 3/15/2007 11:00 15 1 8 507002 1 1 3/15/2007 11:15 15 1 6 507002 1 1 3/15/2007 11:30 15 1 10 507002 1 1 3/15/2007 11:45 15 1 18 507002 1 1 3/15/2007 12:00 15 1 4 507002 1 1 3/15/2007 12:15 15 1 16 507002 1 1 3/15/2007 12:30 15 1 18 507002 1 1 3/15/2007 12:45 15 1 12 507002 1 1 3/15/2007 13:00 15 1 18 507002 1 1 3/15/2007 13:15 15 1 9Table 3.4.
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Specifically, data for this study are strictly limited to VTTI researchers, and data reduction can be conducted only in a separate laboratory in which data reductionists cannot be seen by other personnel. Special instructions should be given to reductionists regarding what to do if they find sensitive data or if they meet participants socially and other such conduct instructions.
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Comprehensiveness Driver population Types of roadways Types of trips Types of vehicles Video Data Quality Driver's hand and foot movements captured Driver's face captured Front view Side view Rear view Vehicle Data Lane location for each target Projected collision time Speed Headway Accelerometer Braking GPS Lane-changing behavior Lateral placement Linkages Ability to link to environmental data Ability to link to operational data Ability to link to special-event data Ability to link to incident data Ability to link to traffic control devices Ability to link to work zone data Data Format and Structure Sampling rate suitability Event or continuous Reduced or raw Do the consent forms used in the study allow for the data to be released to third parties? Does the data set contain a sample of heterogeneous drivers (e.g., teens, older adults, novices)
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1 1 1 1 1 1 Comprehensiveness 15 6.40 6.40 6.50 3.40 3.80 5.30 Driver population 4 7 7 6 4 5 3 Types of roadways 4 7 7 7 4 4 7 Types of trips 1 7 7 6 1 1 6 Types of vehicles 1 1 1 7 1 1 7 Video Data Quality 40 3.40 3.40 6.20 7.00 7.00 6.20 Driver's hand and foot movements captured 2 1 1 7 7 7 7 Driver's face captured 2 7 7 7 7 7 7 Front view 2 7 7 7 7 7 7 Side view 2 1 1 3 7 7 3 Rear view 2 1 1 7 7 7 7 Vehicle Data 20 7.00 5.33 6.11 6.00 7.00 6.33 Lane location for each target 1 7 4 5 7 7 5 Projected collision time 1 7 1 7 7 7 7 Speed 1 7 7 7 7 7 7 Headway 1 7 1 7 7 7 7 Accelerometer 1 7 7 7 7 7 7 Braking 1 7 7 7 7 7 7 GPS 1 7 7 5 4 7 7 Lane-changing behavior 1 7 7 5 7 7 5 Lateral placement 1 7 7 5 1 7 5 Linkages 20 5.5 5.5 2.67 3.17 5.50 5.83 Ability to link to environmental data 2 7 7 2 3 7 7 Ability to link to operational data 2 7 7 2 3 7 7 Ability to link to special-event data 2 4 4 2 4 4 4 Ability to link to incident data 2 7 7 2 3 7 7 Ability to link to traffic control devices 2 6 6 6 3 6 6 Ability to link to work zone data 2 2 2 2 3 2 4 Data Format and Structure 5 5.20 5.20 7.00 7.00 7.00 7.00 Sampling rate suitability 1 7 7 7 7 7 7 Event or continuous 6 7 7 7 7 7 7 Raw or reduced 3 1 1 7 7 7 7 Overall Score (1–7)
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