  Car following models have been widely applied and made remarkable
achievements in traffic engineering. However, the traffic micro-simulation
accuracy of car following models in a platoon level, especially during traffic
oscillations, still needs to be enhanced. Rather than using traditional
individual car following models, we proposed a new trajectory generation
approach to generate platoon level trajectories given the first leading
vehicle's trajectory. In this paper, we discussed the temporal and spatial
error propagation issue for the traditional approach by a car following block
diagram representation. Based on the analysis, we pointed out that error comes
from the training method and the model structure. In order to fix that, we
adopt two improvements on the basis of the traditional LSTM based car following
model. We utilized a scheduled sampling technique during the training process
to solve the error propagation in the temporal dimension. Furthermore, we
developed a unidirectional interconnected LSTM model structure to extract
trajectories features from the perspective of the platoon. As indicated by the
systematic empirical experiments, the proposed novel structure could
efficiently reduce the temporal and spatial error propagation. Compared with
the traditional LSTM based car following model, the proposed model has almost
40% less error. The findings will benefit the design and analysis of
micro-simulation for platoon level car following models.
