Someone Trying to Read the City's Traffic

My interest in transportation began with a morning rush-hour observation—vehicles converging and dispersing at an intersection like a river with a rhythm. Since then I have been curious whether beneath this apparent chaos lies a pattern that can be measured, predicted, and optimized.

During my undergraduate studies I focused on traffic simulation and flow theory. Using SUMO and VISSIM I reconstructed several intersections and mid-scale networks to study signal timing, capacity, and flow-model calibration; meanwhile I processed floating-car trajectory data in Python to identify spatio-temporal congestion patterns.

Going forward I hope to pursue intelligent transportation and big data in graduate study—using simulation as a proving ground, data as the driver, and algorithms as the bridge—to explore reinforcement learning for signal control and digital-twin cities.