Refining trip starting and ending locations when estimating travel-demand at large urban scale

Demand estimation is an important step for multiple applications in urban studies. However, the level of accuracy required depends on objective study. In dynamic traffic microsimulation, demand needs to be accurate, as it intended describe individuals' trips a very small-scale. this case, poor trip initiation, path, and endings could result wrong city-block scale's state. Estimating at large-scale with high-resolution not only challenging because requires large volume data from sources, but underlying mathematical problem considerable thus hard solve. paper, we address issue starts ends when modeling perimeters. We propose enhance location initiation termination by merging heterogeneous public datasets. To do so, develop series algorithms that identify fine-mesh areas where reliably start or end share estimated within these sub-areas, following distribution purposes (Home, Work, Shop, etc.). The method deployed Lyon city, France, validated extraction it. Micro-simulation results show demand, once accurately distributed, changes overall network's performance, confirming significant influence dynamics.