AI and Spatial Analytics for Smarter, Safer, and Sustainable Transportation
- alejandroperezvill07
- Apr 17
- 1 min read
As transportation systems evolve to become safer, more efficient, and environmentally sustainable, data-driven approaches are playing an increasingly vital role. This seminar highlights recent advances at the intersection of artificial intelligence (AI), geostatistics, and optimization, with a particular focus on challenges in winter transportation.
The presentation will begin with a discussion on methodologies for estimating winter road surface conditions (RSC). These conditions are essential for maintaining safety and mobility during adverse weather. By integrating computer vision, machine learning, and geostatistical modelling, the approach enables real-time and automated assessments that support proactive winter maintenance operations.
The seminar will then explore the strategic placement of Road Weather Information System (RWIS) stations using large-scale optimization and spatial analytics. This work improves sensor coverage and cost-efficiency by guiding the location and allocation of infrastructure.
Additional topics include the safety impact and implementation strategies for Driver Feedback Signs (DFS), as well as methods for traffic volume estimation and sensor deployment planning. These efforts support citywide transportation management and policy-making by combining crash data analysis, advanced statistical modelling, geostatistics, and simulation to inform effective investment and planning decisions.
Collectively, these projects demonstrate how the integration of AI and spatial analytics can support data-informed decision-making in transportation planning and operations. The seminar will conclude with a discussion on future research directions, including interdisciplinary applications and the development of smart and sustainable mobility systems.
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