TY  - JOUR
PY  - 2022//
TI  - A data-driven method for congestion identification and classification
JO  - Journal of transportation engineering, Part A: Systems
A1  - Zarindast, Atousa
A1  - Poddar, Subhadipto
A1  - Sharma, Anuj
SP  - e04022012
EP  - e04022012
VL  - 148
IS  - 4
N2  - Congestion detection is one of the key steps in reducing delays and associated costs in traffic management. With the increasing use of global positioning system (GPS)-based navigation, promising speed data are now available. This study used extensive historical probe data (year 2016) in Des Moines, Iowa. We used Bayesian change point detection to segment the speed signal and detect temporal congestion. The detected congestion events were then classified as recurrent congestion (RC) or nonrecurrent congestion (NRC). This paper thus presents a robust statistical, big-data-driven expert system and a big-data-mining methodology for identifying both recurrent and nonrecurrent congestion.<p />  <p>Language: en</p>
LA  - en
SN  - 2473-2907
UR  - http://dx.doi.org/10.1061/JTEPBS.0000654
ID  - ref1
ER  -