TY  - JOUR
PY  - 2024//
TI  - Data-driven drone pre-positioning for traffic accident rapid assessment
JO  - Transportation research part E: logistics and transportation review
A1  - Meng, Zhu
A1  - Zhu, Ning
A1  - Zhang, Guowei
A1  - Yang, Yuance
A1  - Liu, Zhaocai
A1  - Ke, Ginger Y.
SP  - e103452
EP  - e103452
VL  - 183
IS  - 
N2  - A rise in traffic accidents has led to both traffic congestion and subsequent secondary accidents. Effectively addressing this issue requires rapid accident investigation and management. In this paper, we aim to improve the efficiency of traffic accident assessment and investigation with the aid of drone technologies. Our approach involves strategically pre-positioning drones, enabling traffic supervisory agencies to dispatch drones immediately upon receiving an accident report. <br><br>METHODology-wise, we present a data-driven robust stochastic optimization (RSO) model, which encapsulates the uncertainty of traffic accidents within a scenario-wise Wasserstein ambiguity set. To the best of our knowledge, this is the first study that incorporates covariates, i.e., weather conditions, into the Wasserstein ambiguity set with the CVaR metric. We demonstrate that the proposed RSO model can be reformulated into a mixed-integer programming model, allowing an efficient solution approach. Via a real-world dataset of London traffic accidents, we validate the practical applicability of the RSO model. Across various parameter settings, our RSO model exhibits superior out-of-sample performance compared with various benchmark models. The numerical results yield valuable insights for traffic supervisory agencies.<p />  <p>Language: en</p>
LA  - en
SN  - 1366-5545
UR  - http://dx.doi.org/10.1016/j.tre.2024.103452
ID  - ref1
ER  -