Abstract
Understanding how urban residents process road network information and conduct wayfinding is important for both individual travel and intelligent transportation. However, most existing research is limited to the heterogeneity of individuals’ expression and perception abilities, and the results based on small samples are weakly representative. This paper proposes a quantitative and population-based evaluation method of wayfinding performance on city-scale road networks based on massive trajectory data. It can accurately compute and visualize the magnitude and spatial distribution differences of drivers’ wayfinding performance levels, which is not achieved by conventional methods based on small samples. In addition, a systematic index set of road network features are constructed for correlation analysis. This is an improvement on the current research, which focuses on the influence of single factors. Finally, taking 20,000 taxi drivers in Beijing as a case study, experimental results show the following: (1) Taxi drivers’ wayfinding performances show a spatial pattern of a high level on arterial road networks and a low level on secondary networks, and they are spatially autocorrelated. (2) The correlation factors of taxi drivers’ wayfinding performances mainly include anchor point, road grade, road importance, road complexity, origin-destination length, and complexity, and each factor has a different influence. (3) The path complexity has a higher correlation with the wayfinding performance level than with the path distance. (4) There is a critical point in the taxi drivers’ wayfinding performances in terms of path distance. When the critical value is exceeded, it is difficult for a driver to find a good route based on personal cognition. This research can provide theoretical and technical support for intelligent driving and wayfinding research.
Highlights
Wayfinding was formally defined by Lynch as the consistent use and organization of sensory cues from the external environment [1,2,3]
There is more than one alternative path between a pair of origin-destination (OD) points in a road network
(3) Based on the local wayfinding performance level (WPL) of all taxi trajectory data, the spatial distribution characteristics of the wayfinding performance are analyzed by the spatial autocorrelation analysis method. (4) e correlation factors and effects of the WPL are analyzed by statistical analysis methods from four aspects: feature point, road attribute, regional features of the road network, and OD features
Summary
Wayfinding was formally defined by Lynch as the consistent use and organization of sensory cues from the external environment [1,2,3]. Wayfinding is not randomly navigating in the environment but rather a purposeful activity from an origin to a destination by comprehensively mobilizing cognitive knowledge of the surroundings. It can be influenced by both environmental factors and individual differences [3,4,5]. Investigating the differences in wayfinding performance of city residents on road networks and discovering wayfinding behavior patterns and their correlation factors are helpful for understanding the universal law of human spatial knowledge acquisition and spatial consciousness development and important for both individual travel optimization and intelligent transportation planning
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