Delineating intra-urban spatial connectivity patterns by travel-activities: A case study of Beijing, China

Abstract

Abstract-Travel activities have been widely applied to quantify spatial interactions between places, regions and nations. In this paper, we model the spatial connectivities between 652 Traffic Analysis Zones (TAZs) in Beijing by a taxi OD dataset. First, we unveil the gravitational structure of intra-urban spatial connectivities of Beijing. On overall, the inter-TAZ interactions are well governed by the Gravity Model Gij = λpipj/dij, where pi, pj are degrees of TAZ i, j and dij the distance between them, with a goodness-of-fit around 0.8. Second, the network based analysis well reveals the polycentric form of Beijing. Last, we detect the semantics of inter-TAZ connectivities based on their spatiotemporal patterns. We further find that inter-TAZ connections deviating from the Gravity Model can be well explained by link semantics.

Investigating Human Travel Patterns from an Activity Semantic Flow Perspective: A Case Study within the Fifth Ring Road in Beijing Using Taxi Trajectory Data

“…Taxis play an important role in public transportation systems in metropolises. Moreover, taxi trajectory data is a rich informative data source used to reveal travel patterns [13][14][15][16], identify urban functions [17][18][19][20][21], and discover urban structure [22][23][24]. However, many existing studies have focused on the spatial and temporal attributes of taxi trajectory data while ignoring activity semantic characteristics.…”

XStar: a software system for handling taxi trajectory big data

“…To date, many efforts have been devoted to explore the merit of taxi trajectory data and try to apply to various aspects, such as developing trajectory data mining methods (Dodge et al, 2009;Izakian et al, 2016;Liu & Karimi, 2006;Pfoser & Theodoridis, 2003;Zhou et al, 2017), inferring residents travel characteristics and patterns (Hu et al, 2014;Kang et al, 2015;Liu et al, 2012;Torrens et al, 2012), discovering spatio-temporal features of traffic flow (Ge et al, 2010;Liu & Ban, 2012;Wang et al, 2017;Wei et al, 2012;Zheng et al, 2011), and predicting travel time (Chen & Rakha, 2014;Jiang & Li, 2013;Xu et al, 2018b). Understandably, there is always a tradeoff between data volume or coverage and the difficulty of data accessing.…”

TripCube: A Trip-oriented vehicle trajectory data indexing structure

“…With the dramatic development of location-based services, vast amount of vehicle trajectory data can be easily gathered with GPS receivers equipped on vehicles. The abundance of trajectory data presents a valuable opportunity for scholars to discover previously unknown but potentially valuable information about vehicle movements and traffic situations, such as developing trajectory data mining methods (Dodge, Weibel, and Forootan, 2009;Izakian, Mesgari, and Abraham, 2016;Liu and Karimi, 2006;Pfoser and Theodoridis, 2003;Zhou et al, 2015), inferring residents travel characteristics and patterns (Hu, Miller, and Li, 2014;Kang, Liu, and Wu, 2015;Liu, Wang, Xiao, and Gao, 2012;Torrens et al, 2012), discovering spatio-temporal features of traffic flow (Ge et al, 2010;Liu and Ban, 2012;Wang, Wang, Song, and Raghavan, 2017;Wei, Zheng, and Peng, 2012;Zheng, Liu, Yuan, and Xie, 2011), and predicting travel time (Chen and Rakha, 2014;Jiang and Li, 2013). In the meantime, such data analysis avenues bring novel challenges, a crucial one being the development of appropriate solutions for the most efficient management of vehicle trajectory data (Jiang and Li, 2013;Kwan, 2016).…”