Identifying human mobility patterns using smart card data

Figures & data

Table 1. Summary of studies clustering individuals’ temporal travel patterns.

Study	Aim	Analysis	Clustering technique	Additional analysis and/or interpretation information	Application			Key findings w.r.t patterns identified
					City/Region	Modes	Tap- in (I) / Tap out (O)
Kieu et al., 2015a	Identify users that travel at regular times	Based on the intersection of % of regular OD trips and % of habitual time trips	A-priori segmentation of users (after DBSCAN of stations)	Trip attributes (travel time, mode, transfers), product type	South East Queensland, Australia	BCF	IO	Four segments defined a-priori: “Transit commuters”, “Regular OD passenger”, “Habitual time passenger” and “Irregular passenger”.
Goulet-Langlois et al., 2016	Identify users with similar activity sequence structure	Principal component analysis of multi-week activity sequences	Hierarchical	Socio-demographic information	London, United Kingdom	BMC	I – B; IO- MC	11 segments distinguished based on temporal profiles. The profiles of 40% of frequent users are not characterised by a conventional working pattern; for 9 out of the 11 segments, travel segments are strongly associated with socio-demographic characteristics
Long et al., 2016	Identifying “extreme” users	Percentage and frequency of trips in certain time periods or of certain length	NA	Household survey, inferred home and job locations	Beijing, China	BM	IO (for most trips)	2% of all travellers are labelled “extreme” falling into one of the following cases (in order of prevalence): “Recurring itinerants”, “Night owls”, “Early birds” ad “Tireless itinerants”
Briand et al., 2017	Year-on-year changes	Day-of-the-week and start hour per trip	Two-step Gaussian mixture model	Fare products, spatial entropy of travel destinations	Gatineau, Canada	B	I	Stable shares of (10) temporal profiles over the years
El Mahrsi et al., 2017	Identify groups with similar boarding times	Number of trips per hour-day	Multinomial mixture model	Fare products	Rennes, France	MB	I	13 clusters, morning travel behaviour is more regular and consistent than the evening one
Ghaemi et al., 2017	Reduce high-dimensionality data by transforming the temporal travel representation	Three-dimensional clock-like space projection of hour-day travel	Hierarchical		Gatineau, Canada	B	I	18 clusters distinguished based on travel frequency and typical temporal profile
He et al., 2020	Assessing impacts of different distance metrics	Time-series	Hierarchical		Gatineau, Canada	B	I	A different number of clusters resulting from cross-correlation distance (11 clusters) and dynamic time warping (6) with the former found more suitable
Kieu et al., 2018	Scalability of spatial-behavioural segmentation	Frequency of use, frequency of train use, frequency of transfers, number of unique tap-in and tap-out pairs	Spatial affinity propagation, k-means, Hierarchical	Inferred home location, areas	New South Wales, Australia	BTC+	IO	The proposed Spatial affinity propagation algorithm obtains nearly as good clustering results as the benchmark methods within half the computation time
Manley et al., 2018	Identifying regularities in users’ travel patterns	Time series with binary indications for travel mode and specific services	DBSCAN	Stations	London, United Kingdom	BMC	I – B; IO- M	High travel regularity observed for trips originating from suburbs and destined to central areas
Medina, 2018	Common weekly patterns	Start time and duration of activities	DBSCAN	Household survey	Singapore	BTM	IO	More than 100 clusters of 14-dimension vectors containing information on start time, duration and work or study activities over 7 days. Largest cluster corresponds to 5-weekday workers.
Yang et al., 2018	Predict travel frequency per user based on user segments	Entropy metric related to number of locations visited per time-of-day and weekday-weekend periods	k-means		Shenzhen, China	M	IO	“Regular”, “Variable” and “Irregular” users for which Markov models can predict with high accuracy at the individual level
Cui & Long, 2019	Analysing intra-user regularity of travel patterns for “extreme” and “non-extreme” users	Number of days travelled per week, Travel frequency per day of the week and temporal differences between daily trips	A variant of DBSCAN (denominated OPTICS)		Beijing, China	BM	IO (for most trips)	34 clusters, 4 of which are considered extreme users (“Early birds”, “Night owls”, “Tireless itinerants”, “Recurring itinerants”)
Deschaintres et al., 2019	Intra-user regularity of weekly patterns	Number of trips made on each day of the week	k-means for clustering usage-weeks, Hierarchical for clustering users	Fare products	Montreal, Canada	BM	I	12 user segments with monthly pass users exhibiting greater variability in their usage patterns than annual pass holders
Ji et al., 2019	Determine the socio-economic and job-house relation factors influencing metro commuting patterns	Commuting distance, boarding time to work and boarding time from work	Gaussian mixture model	Household survey	Nanjing, China	M	IO	Three clusters of “classic”, “off-peak” and “long-distance” commuters and nine types of job-housing relations
Viallard et al., 2019	Week-on-week evolution	Number of trips per day of the week	k-means		Gatineau, Canada	B	I	Six clusters with mostly stable patterns but some showing seasonality
Egu & Bonnel, 2020	Measuring inter-user variability over days	Number of days on which both users have travelled	Hierarchical	Fare products	Lyon, France	BTM	I	Intrapersonal variability on weekdays is almost as high as on weekends
Lei et al., 2020	Temporal motifs for travel patterns	Construct a temporal graph of trip sequence, statistical analysis	NA	Land-use	Nanjing, China	M, Bike-sharing	IO (no inter-mode transfers)	11 motifs describe 98% of the data, most are common to metro and bike-sharing trips. The most common motifs are round, chain, single-trip and loop.
Liu & Cheng, 2020	Extract travel patterns, enrich with demographics, illustrate for planning purposes (night tube)	Trip counts per day-of-the-week and start hour	k-means	Inferred home location, demographics	London, United Kingdom	MC	IO	11 clusters that fall into three categories of “Regular peak-time”, “Off-peak noon” and “Randomly evening/ weekend”
Moradi & Trepanier, 2020	Measuring intra-user pattern stability	A binary vector of hour-day travel	Hierarchical (using a semicircle projection)		Gatineau, Canada	B	I	50% of users are found to exercise “high stable” temporal patterns, 40% defined as “mid stable” and 10% “unstable”
Eltved et al., 2021	User groups response to long-term disruptions	Regularity and intensity of travel as well as a weekday/weekend indicator	k-means		Copenhagen, Denmark	MT	IO	8 clusters distinguished based on the frequency of travel on different days-of-the-week and activity type (e.g. “rare weekday”, “occasional leisure”). Commuters reduced most their travel after a disruption.
Liu et al., 2021	Role of age and environments on travel by older users	Entropy metric related to trip frequency per hour	k-means	Personal user information available from card registration	Shizuoka, Japan	BC	IO	Two clusters: low-frequency and high seasonal variability (87% of older adults) and high-frequency and low seasonal variability (13%). The latter tend to be the younger-old (65-74) and live in highly developed areas.
Cats & Ferranti, 2022a	Identify market segments in terms of habitual temporal travel patterns	Number of trips per day-of-the-week and hour-of-the-day combinations	Day of the week analysis: k-means followed by hierarchical; Hourly week pattern analysis: Gaussian Mixture Model	Inferred home location	Stockholm, Sweden	BTMC+	I	Five clusters of “Weekday commuters”, “Lower peaks”, “Late travellers”, “Early birds” and “Flat curve”

Modes: B- Bus; T – Tram; M- Metro; C – Commuter train; + – other modes (i.e. ferry, funiculars).

DBSCAN – Density-Based Scanning Algorithm with Noise; ISODATA – iterative self-organising data analysis technique.

Table 2. Summary of studies clustering individual’s spatial-temporal travel patterns.

Study	Aim	Analysis	Clustering technique	Additional analysis and/or interpretation information	Application			Key findings w.r.t patterns identified
					City/ Region	Modes	Tap- in (I) / Tap out (O)
Ma et al., 2013	Distinguish between users based on the regularity of spatiotemporal characteristics	Spatial and temporal features such as number of travel days, number of similar first boarding times and similar stations and route sequences	DBSCAN per user and k-means to cluster users		Beijing, China	BM	IO (for most trips)	Five clusters varying from very low to very high travel regularity
Goulet-Langlois et al., 2018	Measuring regularity of users’ travel patterns in terms of frequency and sequence	Sequence of activity locations and respective activity durations per day	NA (regular and irregular users are identified based on percentile values of an entropy metric)		London, United Kingdom	BMC	I – B; IO- M	Much of the regularity and predictability of travel patterns is captured by the order of travel events (predictions are three times more accurate)
Ma et al., 2017	Identifying commuter/ regular users	Number of days travelled, number of occurrences for most frequent start location, destination location and respective boarding times and routes	DBSCAN followed by ISODATA (an unsupervised learning method)	Online survey, areas between ring-roads	Beijing, China	BM	IO (for most trips)	“Absolute commuters”, “Average commuters” and “Noncommuters” are distinguished and can be retrieved with high accuracy based on a survey conducted
Wang, Zhang et al., 2020	Identify irregular (undirected) users	Weighted sum of travel frequency at visited stations and time periods	k-means for temporal patterns followed by DBSCAN for detecting irregular travellers	Stations	Beijing, China	M	IO	9.5% of all activity sequences are identified as “irregular” which are then clustered into 13 groups in relation to time spent travelling and unusual travel speed
Gutierrez et al., 2020	Latent profile analysis of tourists travel patterns	Variables related to number of active days, number of transactions, average group size, number of stations, municipalities and zones visited, main stations visited, number of routes used, and share of transactions at main stations and areas.	Gaussian mixture model	Stations	Tarragona, Spain	B	I	Five visitor classes characterised by party size and, the duration and intensity of their use
He et al., 2021	Integrate spatial- and temporal aspects of daily behaviour	Dynamic time warping distance of station-sequences in daily profile	Hierarchical clustering (with sampling)		Gatineau, Canada	B	I	Ten clusters of users in terms of daily travel trajectory that tend to have distinctive boarding areas
Kaewkluengklom et al., 2021	Year-on-year changes in cluster membership	Distribution of trips over four daily time periods, top three most used ODs and most used stations	k-means	Card type, land-use type and density, population density	Shizuoka, Japan	BC	IO	Ten clusters with two main profiles – one for regular commuters and one for holiday and specific activities users. Regular commuters show stable patterns and clusters’ size remained stable
Pieroni et al., 2021	Identify the travel patterns of low-income workers	Nine variables pertaining to the four categories of temporal patterns and variability, spatial patterns and variability, activity pattern and variability, and socio-economic.	k-means	Land-use types	Sao Paulo, Brazil	BMC	I	Eight user clusters, two of which show commuting patterns which are characteristic of low-paid employment (early first transaction, longer elapsed time between transactions, high usage frequency), also confirmed by relating those to origin/destination land-use and socio-demographic characteristics
Zhang, Yang et al., 2021	Examine variations in activity spaces of vulnerable users’ groups	Indicators related to travel frequency, travel distance, radius and shape index of activity space, and place and time entropy to reflect diversity of activities per month	k-means	Population groups based on card type (low-income, disabled and elderly)	Wuhu, China	B	I	Low-income users, disabled users and users residing in suburbs travel more frequently, longer distances and have a larger activity range than the population average. The elderly users travel more frequently, similar distances to population average and have a smaller activity range.
Faroqui et al., 2022	Identify targeted groups of users based on their activities and trips for advertising purposes	Similarity in terms of location, time and type of activity	Agglomerative hierarchical technique (Ward method)	Activity location type (station, vehicle, billboard) and purpose of ads (work, education, shopping)	South East Queensland, Australia	BCF	IO	91 activity-trip groups defined as users with similar type, time and location of activity and spatial-temporal features of their trip
Cats & Ferranti, 2022b	Identify the extent to which users visit different and distinctive parts of the network	Share of user-trips destined to each zone, and the probability that a user visits a zone in relation to the overall visiting volume of this zone	K-means (for visiting frequency) and Gaussian Mixture model (for spatial extent)	Inferred home zone, and respective zonal income, social index and share of foreign background	Stockholm, Sweden	BTMC+	I	Three zonal visiting frequency profiles of “Local”, “Commuter” and “Explorer” with varying home-zones and socio-demographics. 15 visiting spatial extent profiles with diverse geographical orientations.

Table 3. Summary of studies clustering network elements based on related travel patterns.

Study	Aim	Analysis	Clustering technique	Additional analysis and/or interpretation information	Application			Key findings w.r.t patterns identified
					City/Region	Modes	Tap- in (I) / Tap out (O)
Roth et al., 2011	Identify the polycentric structure of activity centers	Descending order to inflows and bundling stations in proximity	Hierarchical (own variant)	Travel distance	London, United Kingdom	M	IO	Ten clusters corresponding to different geographical parts of the city with few centers attracting most flows and revealing a polycentric structure
Kim et al., 2014	Identify zones and related flows	Average frequency of flows	Hierarchical (own variant)		Seoul, South Korea	M	IO	1745 movement patterns between zones related to both origin and destination areas that obtained best coverage and accuracy metrics
Cats et al., 2015	Identifying and classifying activity centers	Temporal profile of metrics related to boarding and alighting flows	Two-stage hierarchical for identification and classification of geographical clusters		Stockholm, Sweden	BTMC	I	Identifying 17 activity centers which are then partitioned into 6-8 classes for different boarding and alighting flow relations. Limited evidence for the realisation of Stockholm’s policy to become more polycentric.
Kieu et al., 2015b	Computational efficiency of travel pattern clustering	Based on OD pairs	Modified DBSCAN		South East Queensland, Australia	BC+	IO	Proposed algorithm obtains the same clustering results as the classical DBSCAN in a less than 1% of the computation time
Zhong et al., 2015	Variability of relations between stations over time	Group stations with high inter-flows	Community detection		Singapore	BM	IO	The number of clusters varies between 7 and 9 for different days of the week and some stations exhibit different patterns (i.e. belong to different clusters) on different days of the week
El Mahrsi et al., 2017	Identifying stations’ roles based on temporal profile	Boarding flows per hour and day per station	Poisson mixture model		Rennes, France	MB	I	14 clusters that belong to two main categories: mostly balanced with some peaks during rush hours or unbalanced between morning and afternoon
Kim et al., 2017	Relation between travel patterns and station area	Boarding and alighting flows per hour and day of the week	k-means	Land-use and socio-demographic variables	Seoul, South Korea	M	IO	Five clusters described as commercial (business), commercial (entertainment), mixed (commercial), mix (residential) and residential.
Luo et al., 2017	Identify clusters of stations with similar travel patterns for data-driven OD generation	Ratios between intra-cluster flows and inter-cluster flows and distances	k-means	Time of day and weekdays/ weekends	The Hague, the Netherlands	BT	IO	12 geographically contiguous clusters with general patterns (number and composition of clusters) remaining stable over different time periods
Wang et al., 2017	Identify functions of stations using static geographical information	Origin and destination stations	Affinity propagation	Points of Interest (POI) characteristics; time of day and weekdays/ weekends	Shanghai, China	M	IO	Ten clusters such as “High-tech development regions” and “Emerging residential regions” when combining static POI characteristics and dynamic mobility data
Zhang et al., 2018	Estimate the impact of zonal variables on the spatial distribution of demand and differences between public transport and taxi	Average daily trips	Community detection	Population density and average income per zone	Singapore	BM, Taxi	IO	Communities identified based on smart card data differ for weekdays and weekends whereas for taxi data no such difference is observed and the communities are spatially more coherent and aggregate
Yap et al., 2019	Identify hubs and determining line bundles to be prioritised for transfer synchronization	Constructing a graph representing transfer flows between neighbouring stations	DBSCAN for hub identification followed by modularity-based community detection for line bundles determination	Time periods	The Hague, the Netherlands	BT	IO	Identifying 23 clusters for 696 transfer locations, more than 70% of the transfers take place at 6 of which (“hubs”). For those hubs “line bundles” are identified for transfer synchronization.
Zhao et al., 2019	Correlation analysis between clustering results and surrounding land uses	Normalized hourly ridership per station	k-medoids	Land uses	Nanjing, China	M	IO	Five clusters focused on city centre and sub-centers, border between core and suburbs, main urban area and university towns, industrial parks, and major long-distance railway hubs
Gan et al., 2020	Explaining boarding and alighting flows profile with local environment variables	Normalized boarding and alighting flows per hour	k-means	Land-uses	Nanjing, China	M	IO	7 clusters distinguished based on land-use and main trip generators and attractors in proximity
Kim, 2020	Enhance urban structure analysis with spatial mobility patterns (in addition to temporal)	Similarity between the distribution of boarding flows and alighting flow probabilities per time period	Spectral clustering followed by hierarchical clustering	Land-use, residential and employment density	Seoul, South Korea	MB	IO	Dividing the territory into 25 clusters with high internal homogeneity in relation to land-use mix, number of residents and jobs
Li et al., 2020	Identify important features for classifying stations	PCA of eight indicators that relate to shares of users in different periods and statistics of the time series of users counts	Own 2-step heuristic		Beijing, China	M	IO	Six clusters ranging from “Residential and office mixed type but partial living type” (double-peak distributing on working days) to “Transportation hub and tourism commercial type” (no obvious working day peak)
Wang, Luo et al., 2020	Identify the hierarchy amongst lines	Representing users flows using a dual graph (C-space)	Community detection	Time periods	The Hague, the Netherlands	BT	IO	Four communities of lines in terms of network hierarchy. Tram lines tend to rank higher than bus lines, especially those traversing connecting rail stations.
Zhuang et al., 2020	Relate stations to each other and location type	Boarding flow per time of day period and day	Affinity propagation	Points of Interest (POI) characteristics	Singapore	BTM	IO	Nine clusters of POI location semantics with stations groups labels such as “Under-development areas”, “Old residential areas” and “Emerging commercial areas”
Wang et al., 2021	Identifying functional areas and their changes over years	Frequency of visits per station per time period	Gaussian mixture model		Beijing, China	M	IO	Five clusters denoted as “Multimodal interchange hubs and leisure”, “ Residential”, “Employment”, “Mixed but mainly residential” and “Mixed employment and residential”, evolution happens in increments through mixed phases
Zhang, Marshall et al., 2021	Explaining changes in movements over years using station area variables	Weekday peak time flows	Community detection	Socio-economic data at neighbourhood-level (population and job density, median house price, planning policy)	London, United Kingdom	M	IO	London became more polycentric and compact between 2013 and 2017. Five communities (geographically contiguous) are identified in both periods
Cats & Ferranti, 2022c	Identify and classify urban areas based on how attractive they are as activity centers	Share of visits by non-residents (based on home-zone inference) from all visits for stations included in a clustered area per day-of-the-week and hour-of-the-day combination	k-means	Time periods	Stockholm, Sweden	BTMC+	I	Four key clusters based on their zonal visiting profile labelled “Primary attraction”, “Secondary attraction”, “Low intensity destinations” and “Residential destinations” with a clear centre-periphery order
Park et al., 2022	Forecast daily pattern of boarding flows	Boarding flows per time window	Discriminative functional mixture model (fun FEM)		Seoul, South Korea	M	IO	Six clusters based on the temporal profile of alighting flows, resonates with typical land-use and activities in proximity
Yong et al., 2021	Identifying employment, residential and balanced station types	Job-housing ratio and commute properties	k-means	Residential and employment density	Chongqing, China	M	IO	“Residential”, “Employment” and “Balanced” clusters based on boarding and alighting flow ratios throughout the day
Zhou et al., 2022	Topological and boarding and alighting flow characteristics	Boarding and alighting flows as well as degree and betweenness centrality	k-means		Beijing, China	M	IO	Six clusters in relation to absolute flow levels throughout the day
Chan Chun et al., 2023	Classifying stations in relation to their urban function	Numbers of boarding and alighting flows per hour	k-means and a principal component analysis (PCA)	Land use, population, user travel times	Seoul, South Korea	M	IO	Four groups: “Main commercial”, “Common commercial”, “Residential” and “The others”. The former consists of four business areas of varying spheres of influence (strength).

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