.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="178f44913370c1a86d7a6355205fde40"] { grid-template-columns: minmax(0, 0.995fr) minmax(0, 0.0049999999999997fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="178f44913370c1a86d7a6355205fde40"] > .tb-grid-column:nth-of-type(2n + 1) { grid-column: 1 } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="178f44913370c1a86d7a6355205fde40"] > .tb-grid-column:nth-of-type(2n + 2) { grid-column: 2 } .tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end}.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="fab8bb265f1c5e8360979431c8e7f268"] { grid-template-columns: minmax(0, 0.995fr) minmax(0, 0.0049999999999997fr);grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="fab8bb265f1c5e8360979431c8e7f268"] > .tb-grid-column:nth-of-type(2n + 1) { grid-column: 1 } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="fab8bb265f1c5e8360979431c8e7f268"] > .tb-grid-column:nth-of-type(2n + 2) { grid-column: 2 } .tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="811bf40f85c5a91d7af184a88efeae6c"] { grid-template-columns: minmax(0, 0.175fr) minmax(0, 0.825fr);grid-row-gap: 0px;grid-auto-flow: row } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="811bf40f85c5a91d7af184a88efeae6c"] > .tb-grid-column:nth-of-type(2n + 1) { grid-column: 1 } .wp-block-toolset-blocks-grid.tb-grid[data-toolset-blocks-grid="811bf40f85c5a91d7af184a88efeae6c"] > .tb-grid-column:nth-of-type(2n + 2) { grid-column: 2 } .wp-block-toolset-blocks-grid-column.tb-grid-column[data-toolset-blocks-grid-column="3034fbe886c11054e95b46b09d3e4112"] { display: flex; 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This project investigates how roadside LiDAR sensing and generative scene prediction models can support more informative and uncertainty-aware traffic safety assessment at urban intersections. Conventional surrogate safety measures, such as time-to-collision and post-encroachment time, are widely used to identify potential traffic conflicts. However, these measures are primarily based on observed kinematic relationships and may not fully capture road-user behavior, interaction context, or uncertainty caused by sensing noise, occlusion, and tracking errors. The project will develop and evaluate a pilot infrastructure-centric modeling framework that uses roadside LiDAR data to forecast short-term movements of vehicles, pedestrians, cyclists, and other road users from a fixed roadside perspective. Rather than treating observed trajectories as deterministic, the framework will explore probabilistic scene prediction methods that represent multiple plausible future interactions and provide uncertainty estimates. Building on these forecasts, the project will develop prototype intention-aware and uncertainty-aware surrogate safety measures. These measures may include probabilistic conflict indicators, uncertainty-calibrated risk scores, and other metrics that supplement conventional kinematic safety measures. The proposed metrics will be evaluated using roadside LiDAR data and manually reviewed conflict events, with attention to prediction accuracy, uncertainty calibration, interpretability, and practical relevance for proactive safety analysis. The project will also produce implementation-oriented resources, including a prototype Roadside Safety Forecasting Toolkit, example workflows, evaluation scripts, documentation, and a practitioner-facing brief. These products are intended to support future research, agency evaluation, and potential application of infrastructure-based safety monitoring and connected vehicle support systems.
