Choi, SeokHwanSeo, SeongbumYoo, SangbongJang, YunChaine, RaphaëlleDeng, ZhigangKim, Min H.2023-10-092023-10-092023978-3-03868-234-9https://doi.org/10.2312/pg.20231288https://diglib.eg.org:443/handle/10.2312/pg20231288Traffic congestion, which increases every year, has a negative impact on environmental pollution and productivity. Congestion pricing policy has been shown to be effective in Singapore, London, and Stockholm as one of the ways to solve traffic congestion. Pricing policy has different effects depending on a target area, pricing scheme, and toll. In general, congestion pricing policy researchers conduct statistical analysis of simulation model predictions within a fixed region and time range. However, existing research techniques make analyzing all traffic data characteristics with spatiotemporal dependency difficult. In this paper, we propose a visualization system for analyzing the influence of congestion pricing policy using SUMO and TCI. Our system provides a district-level analysis process to explore the influence of pricing policy over time and area.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing -> Visual analytics; Information visualizationHuman centered computingVisual analyticsInformation visualization10.2312/pg.20231288125-1262 pages