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A platform for research: civil engineering, architecture and urbanism
Impact of Vehicle Dynamic Systems on a Connected Vehicle-Enabled Pavement Roughness Estimation
Given the advancements of wireless communications and sensor technologies, numerous studies were conducted to investigate the feasibility of using probe vehicle data (i.e., vehicle floor acceleration) for pavement condition assessment. Challenges of this approach are that there are a huge variety of vehicle operating speeds and vehicle dynamic systems, which can directly impact the measurements. To address this issue, the University of Virginia Center for Transportation team previously developed an improved acceleration-based metric that incorporates vehicle speeds. As a follow-up, the study presented in this paper proposes a methodology that can incorporate the impact of vehicle dynamic systems for better pavement roughness measurements. Profile and probe data were collected on a 17.38-km (10.8-mi) segment using three different vehicles. The analysis found that different vehicle dynamic parameters can result in significantly different vibration responses, which necessitates a calibration process in terms of vehicle dynamic parameters. Furthermore, the vibration responses are found to be approximately linearly correlated between different vehicle systems and thus a linear regression model is suitable for a calibration model. Finally, assuming a network screening system using agency-owned vehicles, a calibration procedure in terms of vehicle dynamic systems was developed. Case studies using real probe data collected from different vehicles are also presented to demonstrate that the proposed calibration procedure can improve pavement roughness estimates.
Impact of Vehicle Dynamic Systems on a Connected Vehicle-Enabled Pavement Roughness Estimation
Given the advancements of wireless communications and sensor technologies, numerous studies were conducted to investigate the feasibility of using probe vehicle data (i.e., vehicle floor acceleration) for pavement condition assessment. Challenges of this approach are that there are a huge variety of vehicle operating speeds and vehicle dynamic systems, which can directly impact the measurements. To address this issue, the University of Virginia Center for Transportation team previously developed an improved acceleration-based metric that incorporates vehicle speeds. As a follow-up, the study presented in this paper proposes a methodology that can incorporate the impact of vehicle dynamic systems for better pavement roughness measurements. Profile and probe data were collected on a 17.38-km (10.8-mi) segment using three different vehicles. The analysis found that different vehicle dynamic parameters can result in significantly different vibration responses, which necessitates a calibration process in terms of vehicle dynamic parameters. Furthermore, the vibration responses are found to be approximately linearly correlated between different vehicle systems and thus a linear regression model is suitable for a calibration model. Finally, assuming a network screening system using agency-owned vehicles, a calibration procedure in terms of vehicle dynamic systems was developed. Case studies using real probe data collected from different vehicles are also presented to demonstrate that the proposed calibration procedure can improve pavement roughness estimates.
Impact of Vehicle Dynamic Systems on a Connected Vehicle-Enabled Pavement Roughness Estimation
Zeng, Huanghui (author) / Park, Hyungjun (author) / Smith, Brian L. (author)
2018-12-31
Article (Journal)
Electronic Resource
Unknown
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