Mobile-based Urban Bike Route Planner using Urban Regulation-constrained Delaunay Graph

In the domain of bike route planning for urban environments, the solutions provided by large corporations (e.g. Google Maps, Waze-Google) are not tailored for this particular vehicle or do not reflect path cost structures that human interactions and agglomerations produce. Bikepath expenses different from the usual Euclidean or City-Block distance functions but relevant in a city relate to safety (in terms of accidents or criminality), slopes, path roughness, time-dependent (i.e. rush hour) costs, etc. To partially overcome these disadvantages, this manuscript presents the implementation of a bike route planning algorithm in a urban environment, which efficiently solves the problem of presenting the biker with a low cost route. At the same time, our application allows flexibility in the degree of usage of dedicated bike routes built by the city. This flexibility obeys to city regulations, which may prescribe more or less priority in the usage of dedicated bikepaths. Our algorithm integrates bike dispensers, bike routes, variety of costs (additional to travel length) and finds the suggested routes in a constrained Delaunay graph. The execution of the algorithm is enhanced by using the fact that large part of the travel might be pre-computed if the biker must pick up and return the city-provided bikes in specific dispenser points. Future work is needed in (a) adding more flexible heuristics as the city may decide to prioritize diverse environmental, economic, or transportation goals, (b) transcending canonical metrics, e.g. by considering non-symmetrical costs (d(p, q) ≠ d(q, p)).