TraSculptor: Visual Analytics for Enhanced Decision-Making in Road Traffic Planning

2025-07-13

AI Summary

To address inflexible road network interaction and difficulties in multi-alternative comparison within traffic planning platforms, this paper proposes an interactive traffic planning decision support system. The method introduces a historical state tree and a road state matrix to enable direct map-based editing, visualizable modification tracking, and parallel comparative analysis of planning alternatives. Integrating interactive map editing, dynamic traffic simulation, versioned state management, and matrix-based comparative visualization, the system enhances both interpretability and decision-making efficiency. Empirical validation is conducted using the Braess paradox case and the Sioux Falls network; expert evaluation confirms that the system effectively supports collaborative analysis and evidence-based decision-making in complex transportation planning scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

The design of urban road networks significantly influences traffic conditions, underscoring the importance of informed traffic planning. Traffic planning experts rely on specialized platforms to simulate traffic systems, assessing the efficacy of the road network across various states of modifications. Nevertheless, a prevailing issue persists: many existing traffic planning platforms exhibit inefficiencies in flexibly interacting with the road network's structure and attributes and intuitively comparing multiple states during the iterative planning process. This paper introduces TraSculptor, an interactive planning decision-making system. To develop TraSculptor, we identify and address two challenges: interactive modification of road networks and intuitive comparison of multiple network states. For the first challenge, we establish flexible interactions to enable experts to easily and directly modify the road network on the map. For the second challenge, we design a comparison view with a history tree of multiple states and a road-state matrix to facilitate intuitive comparison of road network states. To evaluate TraSculptor, we provided a usage scenario where the Braess's paradox was showcased, invited experts to perform a case study on the Sioux Falls network, and collected expert feedback through interviews.

Problem

• Inefficient flexible interaction with road network structure and attributes
• Lack of intuitive comparison of multiple road network states
• Challenges in interactive modification and state comparison for traffic planning

Innovation

• Interactive road network modification on map
• History tree for multiple state comparison
• Road-state matrix for intuitive analysis