Prediction of Bicycle Trajectory Considering Stressful Avoidance Behaviors
When the space in non-motorized lanes is restricted,cyclists in an overtaken scenario will generate stressful avoidance behaviors to ensure their own safety.In order to clarify their stress reaction when being overtaken,and to design the non-motorized lane according to their behavioral characteristics,a bicycle trajectory prediction model for stress behavior classification is proposed.The model decomposes the dynamic characteristics of bicycles from the frequency domain perspective,classifies the stressful avoidance behaviors into uniform speed,acceleration,and deceleration behaviors based on the cadence range,and uses the whale algorithm to improve the long and short-term memory neural network model to predict the classified cycling trajectories.The proposed method was tested using the 2415 overtaken events obtained from Xi'an City.The results indicate that the proportions of the three avoidance behaviors are 11.3%,38.3%,and 50.4%,respectively.The predicted trajectory of uniform speed avoidance has a small fluctuation throughout the whole process,with an average lateral displacement of 0.15 m.The predicted trajectory of accelerated avoidance shows larger lateral displacement,with an average of 0.83 m.The predicted trajectory of decelerated behavior has a lateral displacement of 0.47 m.The root mean square errors of these three behaviors are 0.0619,0.0513,and 0.0587,with their goodness of fit as 0.9589,0.9774,and 0.9687,respectively.Compared to the results of the model without the consideration of stress-based behaviors,the proposed model improves the prediction accuracy by 11.07%,13.22%,and 12.21%,respectively.
NETL
NSTL
万方数据
维普
