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Using home locations as the ‘anchor points’ to visualize the activity-travel pattern of individual citizens makes the urban mobility pattern between individual citizens more comparable. Different from previous methods, here we recognize the importance of the place of residence (home location) by considering it as the center of a person’s daily activities. After merging, there are 1494 SBVCs that have more than one working adult who participated in TCS 2011. Fig. 2 shows a sample individual HMM for one SBVC located relatively far away from the Central Business District (CBD) of Hong Kong, China. The idea of home location being an anchor point of activities is clearly illustrated by Fig. 2a. Moreover, as is shown in Fig. 2b, the toolbox identified three regions of interest (ROIs) for the subject, which are depicted as ellipses colored in red, green, and blue. The ROIs are automatically ranked in a way that they represent the most likely travel path of individuals. Fig. 2c shows the stop location counts in each of the three ROIs, with the red ROI being the most frequently visited region, while the green ROI is the second frequently visited one and so on.
Figure 2. A sample of an individual Hidden Markov Model (HMM) for one Street Block & Village Cluster (SBVC) far away from Central Business District (CBD) in Hong Kong, China. ROI stands for Region of Interest
Fig. 3 shows the home and stop locations, as well as the activity space, in the form of one standard deviation ellipse for the street block far away from the CBD. In comparison, it is clear that the three ROIs in Fig. 2 provide a much better coverage of the stop locations than the activity space (In Fig. 3, a large number of stop locations are not covered by the standard deviation ellipse). Moreover, the HMM method provides more information about the most likely travel path of the subjects in the form of a transition matrix. The transition matrix in Fig. 2d shows the probability of a subject moving from one ROI to another. In this example, if the subject is currently in the red ROI, it has a 58% chance of traveling to the green ROI and 42% of chance traveling to the blue ROI. Fig. 2e shows the probability of the location of the subject at the start of the survey day. It can be observed that our method can provide more information (such as the most likely travel path) about the activity-travel pattern than the activity space.
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Next, we use the EMHMM toolbox to identify common activity-travel patterns. After many trials, four clusters, as shown in Fig. 4, having the most distinctive patterns and highest value of interpretation are found. Groups 1 and 4 are visually different from Groups 2 and 3 in terms of the relative spatial distribution of the green and blue ROIs. The major difference between Group 1 and Group 4 is that the latter has a smaller blue ROI. On the other hand, the major difference between Group 2 and Group 3 is that the latter has a smaller green ROI and a larger blue ROI than the former. From Fig. 4, there are also clear differences in the mostly likely travel paths (the values in the row named ‘prior’ shows the likelihood of starting at a certain ROI, and the value in matrix shows the probability of traveling from one ROI to another ROI). After starting their day at the red ROI, subjects in Group 1 and Group 4 have more than 90% of chance to travel to the green ROI, with almost 0% of chance of traveling to the blue ROI. On the contrary, after starting their day at the red ROI, Group 2 and Group 3 are having about 64% of chance to travel to the green ROI, and about 26% of chance to travel to blue ROI. Moreover, Group 4 has about 52% of chance to start their day at the red ROI, and 45% at the blue ROI, while the other three groups have more than 90% of chance to start their day at the red ROI.
Figure 4. Clustering results of all individual Hidden Markov Models (HMMs) in Hong Kong, China. ROI stands for Region of Interest. The first rows of the transition matrixes refer to the probabilities of the individual having his/her first stop location in each ROI
Till now, no trip purpose information from the TCS 2011 dataset was incorporated. To get a better understanding of the new insights obtained, Table 1 shows the percentage of the types of places in each ROI of each group, using the trip purpose information available in TCS 2011 dataset. A few technical points are worth mentioning. First, the three ROIs have been generated independently for each group. Second, the three ROIs are generated solely based on the HMM model of stop-travel-stop pattern, that is, without defining the nature of the stop (e.g. home, work or others) a priori. Despite the above, there are noticeable similarities, especially in relation to the red ROIs. Specifically, more than 99% of stop locations in the red ROIs are labeled as ‘home’ for all four groups. This means that the red ROI typically refers to the home location. While about 99% of the red ROIs represent home locations across the four groups, the nature of the green and blue ROIs is much more variable. For the green ROIs, 80.8% (Group 3) to 84.8% (Group 4) (Table 1) were work locations. In other words, the green ROIs generally represent work locations but there are variations across groups. These shares can be considered as probabilities of the ROIs being associated with certain activities (such as work or leisure). For the blue ROIs, the situation is even more contrasting across groups with 14.2% (Group 2) to 75.6% (Group 4) being locations other than home and work place. For Groups 2 and 3, blue ROIs actually also refer predominantly to work-related locations (85.8% and 82.1% respectively). In other words, people in Groups 2 and 3 have their daily mobility primarily determined by various work activities. Inspired by the well-known term work-life balance, which differentiates work activities and other non-work activities such as leisure or family activities (Gregory and Milner, 2009; Lin et al., 2009; Jones et al., 2012), Group 2 and Group 3 are therefore labelled as having a ‘work-oriented lifestyle’ with both the green and blue ROIs primarily work-related. In contrast, working adults in Group 4 have a much more balanced daily routine represented by the home, work and other locations. This applies also but to a smaller extent to Group 1. Hence, these two groups are labelled as having a ‘balanced lifestyle’. Spatially, the results suggest that, with home at the center, people with a balanced lifestyle (Groups 1 and 4) were having a relatively compact zone of other (non-work) activities around their homes but a relatively long commuting distance (as shown by the large extent of green ROIs around the center). This is particularly clear in the case of Group 4 with a very compact blue ROI. For the cluster of work-oriented lifestyle, these people were having the second highest frequent work stops near their homes, as shown by the more compact green ROIs around 10 km of their homes. Yet, they made work-related stops far away from home, as shown by the blue ROI being scattered and covering a bigger spatial extent of up to 40 km in different directions. Most stops in their daily life (including both the green and blue ROIs) were related to work.
Group ROI Nature of Place Home Work Others 1 Red 99.7 0.1 0.2 Green 0 82.9 17.1 Blue 0 44.6 55.4 2 Red 99.5 0.1 0.4 Green 0 84.7 15.3 Blue 0 85.8 14.2 3 Red 99.5 0.1 0.4 Green 0 80.8 19.2 Blue 0 82.1 17.9 4 Red 99.6 0.1 0.3 Green 0 84.8 15.2 Blue 0 24.4 75.6 Notes: ROI stands for Region of Interest. Percentages in table represent the shares of certain types of stop locations in the corresponding Region of Interests (ROIs) Table 1. The nature of stops within each Region of Interest (ROI) / %
Table 2 summarizes the number of SBVCs and the number of unweighted and weighted sample size in each group. When the weighted sample size is considered, Groups 2 and 3 together represented about 61.6% of the surveyed working adults. In other words, more than half of Hong Kong’s working adults tend to have a work-oriented lifestyle, with their daily mobility pattern largely determined by work and work-related activities. In contrast, about 38.4% of the surveyed working adults belonged to Groups 1 and 4, suggesting that they are having a relatively more balanced lifestyle with a more or less non-work related ROI near their home locations.
Type of data statistics Group 1 Group 2 Group 3 Group 4 Total Number of SBVCs 606 375 357 156 1494 Unweighted sample size 10402 (26.4%) 20712 (52.6%) 3676 (9.3%) 4571 (11.6%) 39361 (100%) Weighted sample size 707977 (26.7%) 1377322 (52.0%) 254654 (9.6%) 308740 (11.7%) 2648693 (100%) Notes: SBVCs stand for Street Blocks and Village Clusters; values in parenthesis are the proportion of the group in total samples Table 2. Relative importance of the four activity-travel groups across Hong Kong, China
Fig. 5 maps the geographical locations of the SBVCs belonging to each group. The dispersed patterns suggest that the methodology is powerful in differentiating different neighborhoods beyond the simple district divisions in Hong Kong. Spatially, the general pattern is for the street blocks closer to the urban core, that is, Hong Kong Island and Kowloon peninsula, to be in the work-oriented lifestyle of Groups 2 and 3. Some clear exceptions are found in the Stanley, the Mid-levels, and Chai Wan on Hong Kong Island, and Kowloon Tong, Hung Hom and Tai Kok Tsui on the Kowloon peninsula. Quite on the contrary, people living further away from the urban core tend to be in the balanced lifestyle cluster of Groups 1 and 4. Some clear exceptions are found around Kwun Tong, Shatin and Tsuen Wan.
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To answer RQ3 regarding the difference in the duration of time spent at home between working adults of different lifestyles who live near or far away from CBD, we incorporate the time information (time spent at each stop location) in the analysis. To get a broader understanding of the time usage difference, Table 3 lists the means and standard deviations (s.d.) of the duration spent at each ROI by district. The top three values for each ROI are marked with asterisks. Focusing on the red ROI (notably homes), all top values were found in urban core areas, with individual workers spending an average of 6.89 h at home in Wan Chai. The figure was the lowest (6.13 h) for working adults living on Islands. Furthermore, Fig. 6 presents a scatterplot of average time spent at the red ROI by district. There is a negative relationship between the distance of home location to the urban core and time spent at home. The correlation coefficient (−0.85) is negative, strong and statistically significant at 0.05 level. In other words, people living near urban core tend to spend more time at the home location and its immediate neighborhood than those who live in peripheral areas. Time spent with family members at home has proven to be important for the quality of life for married couples (Greenhaus et al., 2003; Offer and Schneider, 2011). With more time to spend locally, people can also have a higher chance to interact with their neighbors, which can in turn cultivate social capital and contribute to the wellbeing of people (Loo et al., 2017), as well as the long-term success of communities (Putnam, 1993).
Type of districts Districts Red ROI Green ROI Blue ROI Urban Core Districts Territory-wide Mean 6.49 8.96 6.70 s.d. 0.56 1.57 4.56 Central & Western Mean 6.74* 8.99 5.21 s.d. 0.72 2.03 5.13 Wan Chai Mean 6.89* 8.36 5.31 s.d. 1.00 2.59 4.96 Yau Tsim Mong Mean 6.69* 8.85 5.49 s.d. 0.79 2.12 4.91 Kowloon City Mean 6.62 8.59 5.09 s.d. 0.74 1.88 4.73 Sham Shui Po Mean 6.69* 8.68 6.31 s.d. 0.55 1.64 4.88 Eastern Mean 6.63 8.64 7.08 s.d. 0.53 1.57 4.61 Southern Mean 6.42 9.11 7.48 s.d. 0.45 1.20 4.29 Wong Tai Sin Mean 6.58 8.73 5.93 s.d. 0.42 1.22 4.37 Kwun Tong Mean 6.55 8.91 6.24 s.d. 0.37 1.05 3.78 Kwai Tsing Mean 6.47 9.06 6.78 s.d. 0.49 1.72 4.45 Other Districts Sha Tin Mean 6.49 8.80 7.31 s.d. 0.42 1.29 4.52 Tsuen Wan Mean 6.45 9.37* 7.92* s.d. 0.45 1.92 4.34 Sai Kung Mean 6.37 9.38* 7.75* s.d. 0.43 0.98 3.97 Islands Mean 6.13 9.10 8.07* s.d. 0.69 1.13 3.57 Tai Po Mean 6.43 8.68 6.29 s.d. 0.57 1.48 5.08 Tuen Mun Mean 6.29 9.26* 7.13 s.d. 0.43 1.19 3.71 Yuen Long Mean 6.25 9.44* 7.19 s.d. 0.56 1.86 4.73 North Mean 6.30 8.90 5.97 s.d. 0.51 1.24 5.21 Notes: * Top three ranks (There are ties at the third rank. Hence, four values are marked); s.d., standard deviations Table 3. Time spent at each Region of Interest (ROI) in different districts of Hong Kong, China /h
Figure 6. Relationship of the average distance from home to urban core and the average time spent in the red Region of Interest (ROI) in 18 districts of Hong Kong, China
Fig. 7 illustrates the gender difference in time spent at the red ROI (RQ4). The districts are ordered by distance from the CBD of the city. Generally, employed women do spend more time at home compared to employed men regardless of where they live. The time-spent-at-home gap between different genders are bigger in districts located closer to the urban core, while the difference is smaller in districts located relatively far from the CBD, such as Tuen Mun, Yuen Long, and the North. To check if there is any statistically significant difference between the time spent at the red ROI by males and females at the SBVC level in Hong Kong, a t-test was conducted. The resulting P-value < 0.01 suggests that the difference is statistically significant. The gender gap of time spent at home identified can be explained by the gender difference in household responsibilities. Women are still generally expected to take more domestic responsibilities than men even when both of them are working (Hanson and Hanson, 1980; Coltrane, 2000; Offer and Schneider, 2011). The difference further widens when they have children (Loo and Lam, 2013; Jolly et al., 2014). Table 4 shows the weighted average time spent of married couples at the red ROI by lifestyle cluster. Moreover, the gender difference within the same lifestyle group is also statistically significant (P < 0.01, df = 390 840 for the balanced lifestyle; P < 0.01, df = 574 892 for the work-oriented lifestyle). In other words, married women tend to spend more time at home than their counterparts regardless of their lifestyle.
Figure 7. Average time spent in the Red Region of Interest (ROI) for subjects across 18 districts in Hong Kong, China /min
Districts Work-oriented lifestyle Balanced lifestyle Female Male Female Male Territory-wide 6.58 6.39 6.63 6.40 Central & Western# 6.77 6.60 6.66 6.73 Wan Chai# 7.37 6.72 7.06 6.54 Yau Tsim Mong# 7.12 6.63 6.90 6.41 Kowloon City# 6.63 6.52 7.00 6.47 Sham Shui Po# 6.80 6.59 7.01 6.55 Eastern# 6.75 6.53 6.86 6.52 Southern# 6.57 6.30 6.39 6.39 Wong Tai Sin# 6.63 6.39 6.77 6.56 Kwun Tong# 6.65 6.49 6.69 6.47 Kwai Tsing# 6.58 6.45 6.69 6.17 Sha Tin 6.53 6.35 6.62 6.49 Tsuen Wan 6.51 6.24 6.68 6.44 Sai Kung 6.44 6.33 6.42 6.22 Islands 6.57 6.21 4.92 5.21 Tai Po 6.72 6.32 6.46 6.38 Tuen Mun 6.36 6.28 6.34 6.19 Yuen Long 6.28 6.19 6.23 6.31 North 6.22 6.29 6.31 6.28 Notes: # urban core districts Table 4. Weighted average time spent at home by married couples with different lifestyles /h
Applying the Hidden Markov Model to Analyze Urban Mobility Patterns: An Interdisciplinary Approach
doi: 10.1007/s11769-021-1173-0
- Received Date: 2020-05-29
- Available Online: 2020-08-27
- Publish Date: 2021-01-05
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Key words:
- activity-travel pattern /
- urban mobility /
- activity sequences /
- cluster analysis /
- Hidden Markov Model
Abstract: 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 activity-travel 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.
Citation: | LOO Becky P Y, ZHANG Feiyang, HSIAO Janet H, CHAN Antoni B, LAN Hui, 2021. Applying the Hidden Markov Model to Analyze Urban Mobility Patterns: An Interdisciplinary Approach. Chinese Geographical Science, 31(1): 1−13 doi: 10.1007/s11769-021-1173-0 |