TY  - JOUR
PY  - 2022//
TI  - Work zone crash occurrence prediction based on planning stage work zone configurations using an artificial neural network
JO  - Transportation research record
A1  - Cheng, Yang
A1  - Wu, Keshu
A1  - Li, Hanchu
A1  - Parker, Steven
A1  - Ran, Bin
A1  - Noyce, David
SP  - 377
EP  - 384
VL  - 2676
IS  - 11
N2  - Work zones are essential to maintain and improve road infrastructure. However, work zones affect traffic safety, and crashes and fatalities associated with work zones in the U.S.A. have increased substantially. Most existing work zone crash studies are not able to support the improvement of work zone planning and configuration, despite providing insights about individual crash level attributes. This study proposes an artificial neural network-based approach to predict the crash occurrence in work zones using only work zone configurations and design parameters. The goal is to explore whether using simple work zone configuration features available at the planning stage as the input can achieve satisfactory work zone crash prediction. The performance of the proposed model is satisfactory and comparable with existing studies using more comprehensive features. The proposed approach, early in the work zone design and planning stage, can provide designers and decision-makers with quick work zone safety evaluation for design alternatives and suggest extra resources and attention needed.<p />  <p>Language: en</p>
LA  - en
SN  - 0361-1981
UR  - http://dx.doi.org/10.1177/03611981221092716
ID  - ref1
ER  -