Modeling spatiotemporal heterogeneity in interval-censored traffic incident time to normal flow by leveraging crowdsourced data: A geographically and temporally weighted proportional hazard analysis

Gu, Yangsong; Zhang, Hairuilong; Han, Lee D.; Khattak, Asad

doi:10.1016/j.aap.2023.107406

Cited by 5 publications

(1 citation statement)

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“…Considering the radial network shape of study routes, the adaptive bandwidth is employed to ensure the minimum required observation for model inference. As for the kernel function, this study employed the bi-square kernel to compute the decaying weight for neighbors from the near end to the far end [42]. The weight matrix is a diagonal matrix, with diagonal elements being the weight.…”

Section: Geographically Weighted Random Forestmentioning

confidence: 99%

Estimating Pavement Condition by Leveraging Crowdsourced Data

Gu,

Khojastehpour,

Jia

et al. 2024

Remote Sensing

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Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay road maintenance. By contrast, crowdsourced data, in a manner of crowdsensing, can provide real-time and valuable roadway information for extensive coverage. This study exploited crowdsourced Waze pothole and weather reports for pavement condition evaluation. Two surrogate measures are proposed, namely, the Pothole Report Density (PRD) and the Weather Report Density (WRD). They are compared with the Pavement Quality Index (PQI), which is calculated using laser truck data from the Tennessee Department of Transportation (TDOT). A geographically weighted random forest (GWRF) model was developed to capture the complicated relationships between the proposed measures and PQI. The results show that the PRD is highly correlated with the PQI, and the correlation also varies across the routes. It is also found to be the second most important factor (i.e., followed by pavement age) affecting the PQI values. Although Waze weather reports contribute to PQI values, their impact is significantly smaller compared to that of pothole reports. This paper demonstrates that surrogate pavement condition measures aggregated by crowdsourced data could be integrated into the state decision-making process by establishing nuanced relationships between the surrogated performance measures and the state pavement condition indices. The endeavor of this study also has the potential to enhance the granularity of pavement condition evaluation.

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Section: Geographically Weighted Random Forestmentioning

confidence: 99%