Urban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images

ABSTRACT

Auditing and mapping traffic infrastructure is a crucial task in urban management. For example, signalized intersections play an essential role in transportation management; however, effectively identifying these intersections remains unsolved. Traditionally, signalized intersection data are manually collected through field audits or checking street view images (SVIs), which is time-consuming and labor-intensive. This study proposes an effective protocol to identify signalized intersections using road networks and SVIs. First, we propose a six-step geoprocessing model to generate an intersection feature layer from road networks. Second, we utilize up to three nearest SVIs to capture streetscapes at each intersection. Then, a deep learning-based image segmentation model is adopted to recognize traffic light-related pixels from each SVI. Last, we design a post-processing step to generate new features characterizing SVIs’ segmentation results at each intersection and build a decision tree model to determine the traffic control type. Results demonstrate that the proposed protocol can effectively identify signalized intersections with an overall accuracy of 97.05%. It also proves the effectiveness of SVIs for auditing urban infrastructures. This study can directly benefit transportation agencies by providing a ready-to-use smart audit and mapping solution for large-scale identification and mapping of signalized intersections.

KEYWORDS:

• Infrastructure audit
• signalized intersection detection
• street view images
• deep learning
• image segmentation

Acknowledgments

We thank the editor and the anonymous reviewers, whose comments and suggestions helped improve the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Raw data were collected from TxDOT Roadway Inventory website (https://www.txdot.gov/inside-txdot/division/transportation-planning/roadway-inventory.html) and the Mapillary data platform (https://www.mapillary.com/dataset/places). Derived data supporting the findings of this study cannot be made publicly available due to the corresponding author [X.L.] on request.