Modeling Micro Traffic Flow Phenomena Based on Vehicle Types and Driver Characteristics Using Cellular Automata and Monte Carlo

The modeling of micro traffic flow on a highway has been extensively observed and studied in various aspects, such as driver characteristics in car-following and lane-changing behaviors. Regarding car-following and lane-changing, an interesting aspect is how to model the movement conditions of vehicles on a highway that exhibit unique characteristics regarding the speed of four-wheeled or more vehicles passing through it. This condition occurs on the Porong Highway in Sidoarjo, East Java, Indonesia. Based on these conditions, this study develops a microscopic traffic flow model incorporating driver characteristics categorized into three types: careful drivers, ordinary drivers, and skilled drivers, each with distinct vehicle speed traits. These driver characteristics are integrated into the Nagel-Schreckenberg Stochastic Traffic Cellular Automata (NaSch STCA) model, which we refer to as the Modified NaSch STCA. The Monte Carlo simulation is employed to generate events through random numbers for the Occupied Initial State, Slowdown Probability, and Probability of Lane Changing. These three components are integral parts of the Modified NaSch STCA model. Experiments (simulations) were conducted on the constructed vehicle movement model, and one of the outcomes is that the travel time obtained from the NaSch STCA model is significantly faster than that obtained from the Modified NaSch STCA model. This condition is attributed to the unique vehicle speed characteristics on the Porong Highway, where the average speed vr = 38 km/h is relatively lower than the average speed typically observed on a highway.

@article{Harsono2024,
  title     = {Modeling Micro Traffic Flow Phenomena Based on Vehicle Types and Driver Characteristics Using Cellular Automata and Monte Carlo},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Tri Harsono and Kohei Arai},
  doi       = {10.14569/IJACSA.2024.0150896},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150896}
}