Fig 1.
The conceptual model of the ABM.
The street network (nodes and links), regions and barriers are loaded in the simulation. A set of OD pairs is defined per each run and assigned to the scenarios where agents complete trips on the basis of different route choice models. The AC scenario is based on cumulative angular change minimisation; the RB scenario incorporates a region-based route choice model; the BB scenario, a barrier-based route choice model; in the RBB scenario, agents make use of a combination of the three approaches, combining the effect of regionalisation processes, barriers and least cumulative angular change.
Fig 2.
The gateways selection and formulation of a coarse plan (for visualisation purposes, not all the viable exit nodes are shown): a), b) The agent cognitively moves from a region to another, till c) the destination’s region is reached, on the basis of the gateways’ locations. Data source (street network): OpenStreetMap data [94].
Fig 3.
Fine plan: Intra-region route with barriers identification.
a) At the region’s entry, the agent recalls the congruent sub-regional map and b) identifies possible barriers that could further help the navigation across the region; c) the agents moves towards the corresponding barrier sub-goal and reassess the exit nodes, on the basis of the new position and orientation towards the destination; d) the agent reaches the chosen exit-node and activates the new sub-regional map. Data source (street network): OpenStreetMap data [94].
Fig 4.
The case study areas: London (UK), left, and Paris (FR), right, bounding box 8000 x 8000 m; walkable street network and barriers.
The map is oriented north. Data source (street network, railways, parks and water bodies): OpenStreetMap data [94].
Table 1.
Statistics of the ABM scenarios’ routes.
Fig 5.
Distribution of the routes’ deviations ratio from the road distance shortest-path (SP), on the basis of the number of regions traversed along the routes.
Fig 6.
Length of the ABM scenarios’ routes vs deviation ratio from the road distance Shortest Path (SP).
Saturation and value in the markers’ colours indicate the portion of the routes walked along natural barriers (see legend). The lines are regression lines between the two variables.
Fig 7.
Movement flows of agents across the street network resulting from the four ABM scenarios (nr. of agents per street segment, median across runs): London.
AC: Least cumulative angular change scenario; RB: Region-based scenario; BB: Barrier-based scenario; RBB: Region-barrier based scenario. Data source (street network): OpenStreetMap data [94].
Fig 8.
Movement flows of agents across the street network resulting from the four ABM scenarios (nr. of agents per street segment, median across runs): Paris.
AC: Least cumulative angular change scenario; RB: Region-based scenario; BB: Barrier-based scenario; RBB: Region-barrier based scenario. Data source (street network): OpenStreetMap data [94].
Table 2.
Statistics of the ABM scenarios.
Fig 9.
Portion of the routes of the ABM scenarios walked along pedestrian roads, major roads and natural barriers.
Boxes that share the same letter indicate not statistically significant differences (Games-Howell post-hoc test). AC: Least cumulative angular change scenario; RB: Region-based scenario; BB: Barrier-based scenario; RBB: Region-barrier based scenario.