  This paper demonstrates accurate traffic modeling and forecast using
stochastic cell-automata (CA) and distributed fiber-optic sensing (DFOS).
Traffic congestion is a dominant issue in highways. To reduce congestion,
real-time traffic control by short-term forecast is necessary. For achieving
this, data assimilation using a stochastic CA model and DFOS is promising. Data
assimilation with a CA enables us to model real-time traffic flow with simple
processes even when rare or sudden events occur, which is challenging for usual
machine learning-based methods. DFOS overcomes issues of conventional point
sensors that have dead zones of observation. By estimating optimal model
parameters that reproduce observed traffic flow in the simulation, future
traffic flow is forecasted from the simulation. We propose an optimal model
parameter estimation method using mean velocity as an extracted feature and the
particle filter. In addition, an estimation methodology for the microscopic
traffic situation is developed to set the initial condition of simulation for
forecast in accordance with observation. The proposed methods are verified by
simulation-based traffic flow. The simulation adopts the stochastic
Nishinari-Fukui-Schadschneider model. The optimal model parameters are
successfully derived from posterior probability distributions (PPDs) estimated
from DFOS data. In contrast, those estimated from point sensors fail. The PPDs
of model parameters also indicate that each parameter has different
sensitivities to traffic flow. A traffic forecast up to 60 minutes later is
carried out. Using optimal model parameters estimated from DFOS, the forecast
error of mean velocity is approximately $\pm$10 km/h (percentage error is 18%).
The error attains half of it when conventional point sensors are used. We
conclude that DFOS is a powerful technique for traffic modeling and short-term
forecast.
