Applying the Hidden Markov Model to Analyze Urban Mobility Patterns: An Interdisciplinary Approach  

作　　者：LOO Becky P Y ZHANG Feiyang HSIAO Janet H CHAN Antoni B LAN Hui 

机构地区：[1]Department of Geography,the University of Hong Kong,Hong Kong 999077,China [2]Department of Psychology,the University of Hong Kong,Hong Kong 999077,China [3]Department of Computer Science,the City University of Hong Kong,Hong Kong 999077,China

出　　处：《Chinese Geographical Science》2021年第1期1-13,共13页中国地理科学（英文版）

摘　　要：With the emergence of the Internet of Things(IoT), there has been a proliferation of urban studies using big data. Yet, another type of urban research innovations that involve interdisciplinary thinking and methods remains underdeveloped. This paper represents an attempt to adopt a Hidden Markov Model(HMM) toolbox developed in Computer Science for the analysis of eye movement patterns in Psychology to answer urban mobility questions in Geography. The main idea is that both people’s eye movements and travel behavior follow the stop-travel-stop pattern, which can be summarized using HMM. Methodological challenges were addressed by adjusting the HMM to analyze territory-wide travel survey data in Hong Kong, China. By using the adjusted toolbox to identify the activitytravel patterns of working adults in Hong Kong, two distinctive groups of balanced(38.4%) and work-oriented(61.6%) lifestyles were identified. With some notable exceptions, working adults living in the urban core were having a more work-oriented lifestyle. Those with a balanced lifestyle were having a relatively compact zone of non-work activities around their homes but a relatively long commuting distance. Furthermore, working females tend to spend more time at home than their counterparts, regardless of their marital status and lifestyle. Overall, this interdisciplinary research demonstrates an attempt to integrate spatial, temporal, and sequential information for understanding people’s behavior in urban mobility research.

关 键 词：activity-travel pattern urban mobility activity sequences cluster analysis Hidden Markov Model 

分 类 号：P208[天文地球—地图制图学与地理信息工程] TU984[天文地球—测绘科学与技术]