The impact of urban road network morphology on pedestrian wayfinding behavior

Pedestrians do not always choose the shortest available route during the process of wayfinding. Instead, their route choices are influenced by strategies, also known as wayfinding heuristics. These heuristics aim to minimize cognitive effort of the pedestrian and their application usually leads to satisfactory route choices. Our previous study evaluated and analyzed resultant routes from the application of four well-known pedestrian wayfinding heuristics across nine distinct network morphologies via simulation. It was observed that the variation in the cost (difference in route length between a heuristic route and the shortest route, expressed as a percentage of the shortest route length) across the four heuristics increased with an increase in the irregularity of the network. Based on these results, we claimed that, people may opt for more diverse heuristics while walking through relatively regular networks, as route cost across heuristics are more similar in magnitude and thus applying any one of them would not result in a substantial difference in the travelled distance. Likewise, they may prefer specific heuristics in the relatively irregular networks, as some heuristics are significantly costlier than others, thus creating greater variation in cost across heuristics and hence would result in significantly greater travelled distances. In this study, we investigated this claim by comparing simulated routes with observed pedestrian trajectories in Beijing and Melbourne, two cities at opposite ends of the regularity spectrum, as established in the literature. Using statistical tests, we claim with confidence that on an average, heuristic choice distribution is uniform in Melbourne, a city having a regular network morphology. On the contrary, heuristic choice distribution was skewed in Beijing, a city with more irregularities in its street network morphology. This novel finding could help urban planners and future researchers in producing more accurate patterns of aggregate pedestrian movement in outdoor urban spaces.