Characterize Older Driver Behavior for Traffic Simulation and Vehicle Emission Model
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Characterize Older Driver Behavior for Traffic Simulation and Vehicle Emission Model

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    Final Report
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    The use of traffic simulation models is becoming more widespread as a means of assessing traffic, safety and environmental impacts as a result of infrastructure, control and operational changes at disaggregate levels. It is imperative that these simulation models are well calibrated so that they accurately reflect real world conditions and scenarios, produce meaningful results, and take full advantage of the capabilities of these advanced modeling approaches. Especially for vehicle tailpipe emissions modeling, it is becoming increasingly evident that individual driving style and second-by-second vehicle operations (i.e. velocity, speed and engine load) are determinants of the level and composition of tailpipe emissions – yet, data collection has been limited. This project was focused on the lead-vehicle behavior of older drivers relative to younger drivers on clear roads in daylight conditions. A forward facing video camera was used to isolate the instances where the driver was not constrained by Tech Report vehicles or traffic control devices. The study included 10 sections of a 7.2 mile route in Shelburne, Vermont that was driven by each of the 35 volunteers three times and includes both horizontal curves, vertical curves and a mix of traffic control types. The primary objective was to assess whether there is a difference in second-by-second driving style of older versus younger drivers through examination of operating speed and acceleration noise. Moreover, these data were analyzed to determine under which conditions these differences are statistically significant – likely to be indicators of variation in driving style. The segments were chosen to reflect areas where the drivers were most likely to be unconstrained. A baseline simulation experiment was also developed using a microscopic traffic simulation modeling program, namely AIMSUN. Based on the field data collection above, various scenarios were designed to study how lead vehicle speed varies including along basic uniform roadway segments and with grade in the microscopic traffic operations. The development of API was a key step in this project. The API was developed and applied in this project to study lead vehicle behavior. Its main functions include during every simulation step (0.5 second) to detect a lead vehicle based on user-defined criteria, trace it, capture its attributes, force the change of vehicle behavior (such as speed reduction, etc.) and finally output necessary performance measures, i.e., speed and acceleration/deceleration and position.
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