Encapsulating and Visualizing Disaggregated Origin–Destination Desire Lines to Identify Demand Corridors

Origin–destination (O-D) data contain relevant information on the spatial structure of transportation demand; the data reveal the raw travel demand, notwithstanding transportation supply or networks. Visualizing an O-D matrix to identify demand corridors for small sets of data can be simple. In contrast, with the increasing complexity of travel paths, the amount of data collected is large and contains more information that needs to be analyzed. This process is challenging with significant conceptual and imaginary barriers. Spatial aggregation methods are usually used to reduce the complexity of O-D data. Such treatment, especially in an urban context, reduces the richness of data substantially. An approach is proposed to visualize an O-D matrix and to identify major corridors; this approach aims to cluster desire lines—the shortest and most direct trajectory between an origin and destination—so that the main concentrations of flow can be identified. The clustering of desire lines instead of O-D points allows the identification of what can be labeled raw demand corridors. Demand corridors may be used to identify collective trajectories, to evaluate transportation networks, and to propose strategies more adapted to transportation demand. The notion of corridors was clarified, and various applications of observed O-D sets from a survey in Montreal, Canada, were examined to identify major demand corridors. Results showed that demand collective corridors could be used as planning or decision-making tools. These corridors allow planners to encapsulate travel patterns, an approach that can assess the performance of current networks, identify potential development axes, or allow evaluation of corridor projects by comparing them with a reference unit.