Browsing by Subject "Road transportation"

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    Implementation and Performance Evaluation of In-vehicle Highway Back-of-Queue Alerting System Using the Driving Simulator
    (IEEE Xplore, 2021-09) Zhang, Zhengming; Shen, Dan; Tian, Renran; Li, Lingxi; Chen, Yaobin; Sturdevant, Jim; Cox, Ed; Electrical and Computer Engineering, School of Engineering and Technology
    This paper proposes a prototype in-vehicle highway back-of-queue alerting system that is based on an Android-based smartphone app, which is capable of delivering warning information to on-road drivers approaching traffic queues. To evaluate the effectiveness of this alerting system, subjects were recruited to participate in the designed test scenarios on a driving simulator. The test scenarios include three warning types (no alerts, roadside alerts, and in-vehicle auditory alerts), three driver states (normal, distracted, and drowsy), and two weather conditions (sunny and foggy). Driver responses related to vehicle dynamics data were collected and analyzed. The results indicate that on average, the drowsy state decreases the minimum time-to-collision by 1.6 seconds compared to the normal state. In-vehicle auditory alerts can effectively increase the driving safety across different combinations of situations (driver states and weather conditions), while roadside alerts perform better than no alerts.
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    A Quasi-Experimental Evaluation of High Emitter Non-Compliance and its Impact on Vehicular Tailpipe Emissions in Atlanta, 1997-2001
    (2006-01) Zia, Asim; Norton, Bryan G.; Noonan, Douglas S.; Rodgers, Michael O.; DeHart-Davis, Leisha
    A quasi-experimental evaluation is employed to assess the compliance behavior of high emitters in response to Atlanta’s Inspection and Maintenance program between 1997 and 2001 and to predict the impact of compliance behavior on vehicular tailpipe emissions of ozone precursors, such as carbon monoxide, hydrocarbons and nitrogen oxide. Remote sensing data of a sample of approximately 0.8 million observations of on-road vehicles are matched with IM program data and vehicle registration data to identify the compliant and non-compliant high emitters. A mixed-pool time-series regression analysis is carried out to predict changes in the vehicular tailpipe emissions due to the compliance and non-compliance of the high emitters in the Atlanta airshed.