Automated assessment of safety-critical dynamics in multi-modal transportation systems
PI: O. Grembeck, UC Berkeley; Co-PI: David Ragland, UC Berkeley
Abstract: Recent mobility trends reveal that travel is becoming increasing multi-modal in nature. Given the increase in the emphasis on multi-modal mobility there is a need to efficiently account for the multi-modal safety challenges it introduces. In light of this, it is of immense concern that the corresponding improvements made to traffic safety have not been commensurate across all modes. In this regard, one of the major challenges associated with efficiently designing and planning for a safe multi-modal environment is a limited understanding of multi-modal traffic behavior as explained by existing data streams that are available to researchers and agencies. This study proposes the development of a comprehensive report card of safety-critical multimodal dynamics of a signalized intersection. The data sources used as part of this study include a combination of in-pavement sensors and video cameras, which enable us to study such interactions over long periods of time. Conducting this rigorous, quantitative assessment of safety-critical multi-modal traffic dynamics would serve as a framework to harness sensing technologies used for mobility purposes to quantify safety-related performance of intersections. Moreover, the insights of this are less data-intensive.
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