TY  - JOUR
PY  - 2023//
TI  - Traffic conflict prediction using connected vehicle data
JO  - Analytic methods in accident research
A1  - Islam, Zubayer
A1  - Abdel-Aty, Mohamed
SP  - e100275
EP  - e100275
VL  - 39
IS  - 
N2  - Transportation safety studies have been mostly focused on using crash data that are rare events. Alternatively, conflict estimation can be used to assess safety. This has been proven as a proactive design methodology that does not rely on crashes and requires shorter observation. Traditionally, the safety studies involving both these reactive and proactive methods were based on aggregated data that does not take individual vehicle dynamics into consideration. This paper addresses this research gap by proposing a novel real-time conflict prediction methodology that uses previous instance trajectory data of individual vehicles to understand whether there can be potential conflict in the near future. A long-short term memory (LSTM) model is developed that can apprehend a conflict situation 9 s in the future. Data from connected vehicles have been used. The proposed model returned a recall of 81% with a false alarm rate of 28%. The predictive model has the potential to be implemented on vehicle dashboards to warn drivers of a conflict. The authors have also used SHAP (SHapley Additive exPlanation) to interpret the results from the LSTM model. It was deduced that acceleration above 0.3 m/s2, deceleration within −1.5 m/s2 to −0.25 m/s2, and speed of more than 40kph were responsible for inducing a conflict.<p />  <p>Language: en</p>
LA  - en
SN  - 2213-6657
UR  - http://dx.doi.org/10.1016/j.amar.2023.100275
ID  - ref1
ER  -