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Bicycle/Pedelec

The Bicycle/Pedelec Routing is used for all analyses in GOAT that contain cycling trips.

1. Objectives​

Bicycle/Pedelec routing is used for many indicators in GOAT, such as Catchment Areas, Heatmaps and PT Nearby Stations. As GOAT also allows the creation of Scenarios on the Street Network, a custom routing algorithm is needed that also reflects the changes of the scenario in the accessibility analyses. For the mode of bicycle/pedelec, we thereby only consider paths that are suitable for cycling. Furthermore, the surface and slope have an impact on the cycling speed and are therefore considered in the routing. The average cycling speed can be adjusted by the user whenever an accessibility analysis is performed. Depending on the slope and surface of a path segment, the speed is adjusted accordingly.

2. Data​

Routing Network​

Data from the Overture Maps Foundation is used as a routing network in GOAT. It includes the transportation infrastructure with edges (for any continuous path not bisected by another) and nodes (for any point where two distinct paths intersect), representing real-world networks.

Topography and Elevation​

Elevation data is sourced from Copernicus as Digital Elevation Model (DEM) tiles.

3. Technical Details​

Data Pre-processing​

The following steps are performed on the data to enable quick and accurate routing for bicycle/pedelec:

  1. Attribute Parsing: Categorizing attributes of edges (street class and surface).
  2. Geospatial Indexing: Utilizing Uber's H3 grid-based indexing for efficient routing.
  3. Surface Impedance Computation: Calculating impedance considering surface properties.
  4. Slope Impedance Computation: Overlaying DEM on edges to compute slope profiles.

Routing Process Steps​

Sub-network Extraction​

  1. Buffer Region: Based on user-origin, travel time, and speed.
  2. Edge Filtering: Include only relevant edges for cycling.

For bicycle/pedelec routing, the edges of the following street classes are considered:

secondary, tertiary, residential, living_street, trunk, unclassified, parking_aisle, driveway, alley, pedestrian, crosswalk, track, cycleway, bridleway and unknown.

You can find further information on this classification in the Overture Wiki.

Artificial Edge Creation​

User-provided origin points are typically located a short distance away from the street network. To account for the additional time (or cost) of cycling from the origin to its nearest street, artificial (or simulated) edges are created.

Edge Cost Computation​

For all edges in the sub-network, a cost value (represented as time) is calculated based on path length and cycling speed.

Cost function for bicycle: cost = (length * (1 + slope impedance + surface impedance)) / speed

Cost function for pedelec: cost = (length * (1 + surface impedance)) / speed

If an edge is of class pedestrian or crosswalk, we assume the rider would dismount and walk their bicycle/pedelec. The cost for this type of segment is: cost = length / speed

Network Propagation​

To compute the shortest path from the origin point to various destinations, a custom implementation of the well-known Dijkstra Algorithm is used.

The implementation has a time complexity of O(ElogV), is written in Python, and uses the just-in-time compiler Numba.

4. Further Readings​

  • E. Pajares, B. Büttner, U. Jehle, A. Nichols, and G. Wulfhorst, ‘Accessibility by proximity: Addressing the lack of interactive accessibility instruments for active mobility’, Journal of Transport Geography, Vol. 93, p. 103080, May 2021, doi: 10.1016/j.jtrangeo.2021.103080.