With the increasing popularity of Digital Twin, there is an opportunity to employ deep
learning models in symbiotic simulation system. Symbiotic simulation can replicate multiple …

In traditional traffic simulation studies, vehicle behavior has typically been modeled using
complex analytical frameworks, which often struggle to encompass the full range of …

Symbiotic simulation systems that incorporate data-driven methods (such as machine/deep
learning) are effective and efficient tools for just-in-time (JIT) operational decision making …

In the field of traffic simulation, the shift towards large-scale, time-driven models marks a
significant departure from traditional event-driven mechanisms, necessitating robust traffic …

We present a dynamic model approximation strategy that allows to significantly increase
computational efficiency of the simulation while maintaining proper validity. This can be …

Typically, there is more than one possible answer to the question of how to model and
simulate a problem. Combinations of different simulation paradigms and techniques are …

Many single model methods have been applied to real-time short-term traffic flow prediction.
However, since traffic flow data is mixed with a variety of ingredients, the performance of …

With the growing popularity of digital twin and autonomous driving in transportation, the
demand for simulation systems capable of generating high-fidelity and reliable scenarios is …

The emergence of smart cities is set to transform transportation systems by leveraging real‐
time traffic data streams to monitor urban dynamics. This complements traditional …

This paper presents a step-by-step guide to generating and simulating a traffic scenario
using the open-source simulation tool SUMO. It introduces the common pipeline used to …