Next Article in Journal
Lightweight Long Short-Term Memory Variational Auto-Encoder for Multivariate Time Series Anomaly Detection in Industrial Control Systems
Previous Article in Journal
Skew-Circulant-Matrix-Based Harmonic-Canceling Synthesizer for BIST Applications
 
 
Article

Using Connected Vehicle Trajectory Data to Evaluate the Impact of Automated Work Zone Speed Enforcement

1
Department of Civil Engineering, Purdue University, 207 S Martin Jischke Dr, West Lafayette, IN 47907, USA
2
Project Engineer, RK&K, 651 East Park Drive, Harrisburg, PA 17111, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Enrico Meli and Mariusz Kostrzewski
Sensors 2022, 22(8), 2885; https://doi.org/10.3390/s22082885
Received: 17 February 2022 / Revised: 1 April 2022 / Accepted: 6 April 2022 / Published: 9 April 2022
(This article belongs to the Topic Intelligent Transportation Systems)
Work zone safety is a high priority for transportation agencies across the United States. Enforcing speed compliance in work zones is an important factor for reducing the frequency and severity of crashes. This paper uses connected vehicle trajectory data to evaluate the impact of automated work zone speed enforcement on three work zones in Pennsylvania and two work zones in Indiana. Analysis was conducted on more than 300 million datapoints from over 71 billion records between April and August 2021. Speed distribution and speed compliance studies with and without automated enforcement were conducted along every tenth of a mile, and the results found that overall speed compliance inside the work zones increased with the presence of enforcement. In the three Pennsylvania work zones analyzed, the proportions of vehicles travelling within the allowable 11 mph tolerance were 63%, 75% and 84%. In contrast, in Indiana, a state with no automated enforcement, the proportions of vehicles travelling within the same 11 mph tolerance were found to be 25% and 50%. Shorter work zones (less than 3 miles) were associated with better compliance than longer work zones. Spatial analysis also found that speeds rebounded within 1–2 miles after leaving the enforcement location. View Full-Text
Keywords: connected vehicle; trajectory; speeds; automated enforcement connected vehicle; trajectory; speeds; automated enforcement
Show Figures

Figure 1

MDPI and ACS Style

Mathew, J.K.; Li, H.; Landvater, H.; Bullock, D.M. Using Connected Vehicle Trajectory Data to Evaluate the Impact of Automated Work Zone Speed Enforcement. Sensors 2022, 22, 2885. https://doi.org/10.3390/s22082885

AMA Style

Mathew JK, Li H, Landvater H, Bullock DM. Using Connected Vehicle Trajectory Data to Evaluate the Impact of Automated Work Zone Speed Enforcement. Sensors. 2022; 22(8):2885. https://doi.org/10.3390/s22082885

Chicago/Turabian Style

Mathew, Jijo K., Howell Li, Hannah Landvater, and Darcy M. Bullock. 2022. "Using Connected Vehicle Trajectory Data to Evaluate the Impact of Automated Work Zone Speed Enforcement" Sensors 22, no. 8: 2885. https://doi.org/10.3390/s22082885

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop