Abstract—Unfortunately, bike-related accidents are very common. Some of these accidents can be deadly. Most of the times, these accidents happen due to the lack of a safer means of commute, a bike lane. Further, many citizens could potentially be cyclists if bike-lanes were installed. Driven by the idea of improving safety and convenience for cyclists, we contribute to a model that estimates bike-lane demand in the city. We argue that the demand for bike lanes increases, as the number of bike-related accident increases. Further, the demand increases as the number of popular businesses increases since some citizens commute to work and get around by bike. Our model estimates the demand for bike lane using accidents and ratings of businesses. Accidents are defined by features that represent the severity as well as the cause of the accident. Our model uses the Weight of Evidence algorithm to determine the significance of the accident features. Further, the model uses an algorithm that breaks down roads into equally sized sections based on the US addressing standards. The estimation of bike-lane demand is expressed via scores assigned to road sections.
Index Terms—Smart city, bike lane demand, bicyclist safety, weight of evidence.
The authors are with the University of Missouri, Kansas City, Kansas City, USA (e-mail: avhmf@mail.umkc.edu, kuhailm@umkc.edu, avhmf@mail.umkc.edu).
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Cite: Aadarsh Vadakattu, Mohammad Amin Kuhail, and Harish Tata, "A Model for Estimating Bike-Lane Demand," International Journal of Modeling and Optimization vol. 9, no. 5, pp. 238-245, 2019.
