Abstract:Data about the movements of diverse objects, including human beings, animals, and commodities, are collected in growing amounts as location-aware technologies become pervasive. Clustering has become an increasingly important analytical tool for revealing travel patterns from large-scale movement datasets. Most existing methods for origin-destination (OD) flow clustering focus on the geographic properties of an OD flow but ignore the temporal information preserved in the OD flow, which reflects the dynamic chan… Show more
“…Travel time is usually used as an indicator for ease of access. Without integration into Google Maps, the GIS analysis would need large amounts of fundamental geographical data to create origin-destination (OD) matrices (Bahoken and Olteanu-Raimond, 2013;Cao et al, 2019;Ma et al, 2013;Xiang and Wu, 2019;Xu et al, 2019). Obtaining a travel time matrix of sometimes more than one thousand routes is a significant challenge for researchers.…”
Park and Ride (P&R) systems play a potentially important role in transportation planning to decrease the undesirable effects of private cars in the Central Business District (CBD). In order to achieve this objective, an essential component to be investigated is the catchment areas of these P&R facilities. However, a limited number of studies have applied the Geographic Information System (GIS) to study the spatial boundary accessibility of the catchment areas of P&R. This study aims to analyze the spatial boundary accessibility of the catchment areas of P&R facilities using three GIS methods. The first method uses geometric shapes to analyze the catchment areas of P&R facilities according to regular shapes, such as parabolas or circles. The market area is the second method used to analyze travel time via the tool ArcGIS Network Analyst to determine the catchment area of P&Rs. Finally, the dynamic accessibility method determines how accessible a facility can be through a study of the spatial boundary accessibility of P&Rs based on the travel time and distance between zones and P&R. The result shows that the static methods identify the spatial boundary accessibility through the calculation of the size of the shape of each P&R separately, while the dynamic method identifies the level of accessibility in detail for all P&R and also the accessibility of each zone to reach a facility. In conclusion, the dynamic accessibility method presents better accuracy than static methods in order to estimate the spatial boundary accessibility of the catchment area of P&Rs.
“…Travel time is usually used as an indicator for ease of access. Without integration into Google Maps, the GIS analysis would need large amounts of fundamental geographical data to create origin-destination (OD) matrices (Bahoken and Olteanu-Raimond, 2013;Cao et al, 2019;Ma et al, 2013;Xiang and Wu, 2019;Xu et al, 2019). Obtaining a travel time matrix of sometimes more than one thousand routes is a significant challenge for researchers.…”
Park and Ride (P&R) systems play a potentially important role in transportation planning to decrease the undesirable effects of private cars in the Central Business District (CBD). In order to achieve this objective, an essential component to be investigated is the catchment areas of these P&R facilities. However, a limited number of studies have applied the Geographic Information System (GIS) to study the spatial boundary accessibility of the catchment areas of P&R. This study aims to analyze the spatial boundary accessibility of the catchment areas of P&R facilities using three GIS methods. The first method uses geometric shapes to analyze the catchment areas of P&R facilities according to regular shapes, such as parabolas or circles. The market area is the second method used to analyze travel time via the tool ArcGIS Network Analyst to determine the catchment area of P&Rs. Finally, the dynamic accessibility method determines how accessible a facility can be through a study of the spatial boundary accessibility of P&Rs based on the travel time and distance between zones and P&R. The result shows that the static methods identify the spatial boundary accessibility through the calculation of the size of the shape of each P&R separately, while the dynamic method identifies the level of accessibility in detail for all P&R and also the accessibility of each zone to reach a facility. In conclusion, the dynamic accessibility method presents better accuracy than static methods in order to estimate the spatial boundary accessibility of the catchment area of P&Rs.
“…However, the location information of the flow is lost during this process. Xiang et al [31] and He et al [32] measured the similarity between flows based on the neighborhood radius of OD point, while the meaning of the formula for calculating the similarity between flows is vague.…”
Section: B Spatial Similarity Measurementmentioning
With the development of mobile positioning technology, a large number of Origin-Destination (OD) flow data with spatial and temporal details have been produced. These OD flow could give us a great opportunity to research geographical phenomena such as spatial interaction and mobility patterns. The OD flow clustering approach is an effective way to explore the main mobility patterns of the objects. At the same time, similarity measurement plays a key role in OD flow clustering. However, most of the previous OD flow similarity measurement methods failed to make full use of the spatial information of the flow including spatial proximity and geometric similarity. In this paper, we considered both position information and geometric properties of OD flow and propose a new method to measure the spatial similarity between OD flows. Specifically, the proposed method sets the neighbor threshold with the length of OD flows and the parameter dynamically. Based on the constraint of the OD points' location, the directions of the flows are implicitly restricted. The sole-parameter has a practical value as it determines the maximum length difference and the maximum directional difference that can be tolerated between similar flows. The proposed method passed a simulation experiment with synthetic flows and a case study with 283,008 taxi trips in Beijing in one day. The results show that the proposed method can discover the dominant mobility pattern from a large number of flow data effectively. In the case study, the dominant flow clusters reveal the taxi mobility patterns of residents at different distances in Beijing. INDEX TERMS Mobility pattern, origin-destination flow, spatial similarity, spatial clustering.
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