Data Fusion for Non-Motorized Safety Analysis
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abstract
Data-driven approaches play a critical role in developing safety improvement investment decisions. However, for non-motorized travel, exposure to risk has often been the missing piece of the puzzle. Safety analysts have been struggling with the lack of availability of exposure data, making it difficult to discern a trend in crash rates and identify high-risk locations for pedestrians and bicyclists. While short-term counts cannot be considered policy relevant (until they are scaled to a long-term representative value), continuous monitoring of non-motorized traffic using automatic sensors are often not cost effective. Moreover, every sensor has some limitations in terms of coverage, accuracy, and reliability. In the era of big data, global positioning system (GPS) data, cell phone tracking apps, fitness tracking devices or bike sharing systems hold great potential to observe travel activity but they include a range of biases related to representation. Recognizing these limitations and benefiting from the advancements in technologies, this project aims to develop effective methodologies to fuse together different data sources to develop accurate and reliable exposure estimates for safety analysis. The proposed framework will bring together traditional and emerging data sources, and will be developed in such a way that it can be up- or down-scaled based on the available data sources of a study area. The exposure estimation output will then be used for crash assessment tailored to the needs of the study area. The proposed approach will increase the quality and representativeness of data and help safety analysts to effectively derive benefits from potential sources in their decision making.
