New methods for modeling and integrating bicycle activity and injury risk in an urban road network

To date little is known about cyclist speeds and travel times along segments and delays through intersections at the disaggregate level for the entire network. In order to address these limitations, the general objective of this thesis is to propose new methods to model and estimate bicycle activity and injury risk at different spatial levels combining different sources of data. The proposed methods are then used to identify risk factors as well as to map risk indicators based on accidents and hard braking along corridors and at intersections in the entire network. Among the contributions, this thesis proposed a modeling framework to simultaneously study cyclist injury occurrence and bicycle activity to overcome the issues of endogeneity. A similar methodology was then applied from a multimodal perspective, to study the activity and safety outcomes for cyclists, pedestrians and motor-vehicle occupants. This thesis expanded on the previous safety work by developing and applying a methodology to combine short and long-term count data with a new source of bicycle GPS data, from a Smartphone application, to compute and map average annual daily bicycle flows and cyclist risk of injury throughout the entire network of intersections and road segments. Using the smartphone GPS data, hard deceleration data was extracted and proposed as a surrogate safety measure and its correlation with accidents was evaluated. Finally, a methodology to compute speeds and delays was proposed. The results from this thesis were then combined into a routing concept capable of identifying the safety, shortest and fastest routes based on each cyclist's preferences.


  • English

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  • Pagination: 1 file

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Filing Info

  • Accession Number: 01604779
  • Record Type: Publication
  • Source Agency: ARRB
  • Files: ATRI
  • Created Date: Jul 18 2016 4:45PM