RCMVis: A Visual Analytics System for Route Choice Modeling

• Published in: IEEE transactions on visualization and computer graphics Vol. 29; no. 3; pp. 1799 - 1817
• Main Authors: Shin, DongHwa, Jo, Jaemin, Kim, Bohyoung, Song, Hyunjoo, Cho, Shin-Hyung, Seo, Jinwook
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Clustering; Computational modeling; Data models; Data visualization; Domains; Foci; Interactive systems; Logit models; Mathematical analysis; Modelling; origin-destination; Reasoning; Roads; Route choice; Route choice modeling; Route selection; Subject specialists; Traffic signals; Trajectory; trajectory data; urban planning; Visual analytics
• ISSN: 1077-2626; 1941-0506; 1941-0506
• DOI: 10.1109/TVCG.2021.3131824

We present RCMVis, a visual analytics system to support interactive Route Choice Modeling analysis. It aims to model which characteristics of routes, such as distance and the number of traffic lights, affect travelers' route choice behaviors and how much they affect the choice during their trips. Through close collaboration with domain experts, we designed a visual analytics framework for Route Choice Modeling. The framework supports three interactive analysis stages: exploration, modeling, and reasoning. In the exploration stage, we help analysts interactively explore trip data from multiple origin-destination (OD) pairs and choose a subset of data they want to focus on. To this end, we provide coordinated multiple OD views with different foci that allow analysts to inspect, rank, and compare OD pairs in terms of their multidimensional attributes. In the modeling stage, we integrate a <inline-formula><tex-math notation="LaTeX">k</tex-math> <mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="seo-ieq1-3131824.gif"/> </inline-formula>-medoids clustering method and a path-size logit model into our system to enable analysts to model route choice behaviors from trips with support for feature selection, hyperparameter tuning, and model comparison. Finally, in the reasoning stage, we help analysts rationalize and refine the model by selectively inspecting the trips that strongly support the modeling result. For evaluation, we conducted a case study and interviews with domain experts. The domain experts discovered unexpected insights from numerous modeling results, allowing them to explore the hyperparameter space more effectively to gain better results. In addition, they gained OD- and road-level insights into which data mainly supported the modeling result, enabling further discussion of the model.