Modeling Human-Like Interaction Between Cyclists and Vehicles

Introduction

The study, using real-world data, aims to model the interaction between crossing bicycles and right-turning vehicles, attempting to alter the simulator’s guidance at the tactical level. In ten days of video recordings from an urban intersection, 517 valid cases were collected.

Interaction overview plot — Figure 1. Trajectories of crossing bicycles and right-turning vehicles.

Modeling

A logistic regression model of crossing order was employed with features of bicycles (0) and vehicles (1):

speed (v)
speed difference (dv)
distance to crossing point (d2x)
predicted PET (ppet).

The dataset was split into training (80%) and testing (20%) subsets. Models with different feature combinations were cross-validated.

$$ \log\left(\frac{p}{1-p}\right) = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \ldots + \beta_k x_k $$

Simulation

The selected model was implemented in SUMO: At 20 m before the crossing point, the crossing order was estimated, and the lagging object was forced to slow down. A comparison was performed between real data, SUMO’s default model, and this new model.

In reality, 74% of bicycles crossed before vehicles.
In the default model, vehicles were more conservative - bicycles crossed first in about 90% of cases.
The new model brings this ratio closer to 50%, though still not identical to real data.
In the default model, PET distributions vary strongly by crossing order, whereas in the real-world data and new model, PET distributions are similar (yet differ by ~1 s).

Conclusion

The study of interactions between bicycles and vehicles in real traffic contributes to improving simulator realism.
Although the new model did not perfectly reproduce the crossing-order distribution, it significantly improved the PET distribution, bringing it closer to reality.