Evaluation of Remote Sensing Technologies for Collecting Roadside Feature Data to Support Highway Safety Manual Implementation

Mohammad Jalayer, Jie Gong, Huaguo Zhou, Mark Grinter

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Roadside feature data are critical inputs to highway safety models as described in the Highway Safety Manual (HSM). Collecting safety-related roadside feature data is an important step for HSM implementation. Many states’ department of transportations (DOTs) routinely collect data on roadside objects using a variety of sensing methods, which often incur in significant costs. At present, it is unknown which of these data collection methods or any combination of them is capable of efficiently collecting safety-related roadside feature data while minimizing costs and safety concerns. This research is designed to identify required roadside feature data for various types of facilities in the HSM and to characterize the capabilities of existing remote sensing methods (e.g., Mobile LiDAR) to collect those required data. To accomplish this objective, tasks such as literature reviews, a nation-wide survey, and large-scale field trial are performed in this research. The findings of this research suggest that either the mobile LiDAR or the combination of the video/photo log method with the aerial imagery method is capable of collecting required HSM-related roadside information. However, due to the high data reduction effort, the current mobile LiDAR method needs significant improvement in the data processing and in the feature extraction stage.

Original languageEnglish (US)
Pages (from-to)345-357
Number of pages13
JournalJournal of Transportation Safety and Security
Issue number4
StatePublished - Oct 2 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Transportation
  • Safety Research


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