Human Travel Behavior Research Articles

Nonrepresentativeness of Human Mobility Data and its Impact on Modeling Dynamics of the COVID-19 Pandemic: Systematic Evaluation.

In recent years, a range of novel smartphone-derived data streams about human mobility have become available on a near-real-time basis. These data have been used, for example, to perform traffic forecasting and epidemic modeling. During the COVID-19 pandemic in particular, human travel behavior has been considered a key component of epidemiological modeling to provide more reliable estimates about the volumes of the pandemic's importation and transmission routes, or to identify hot spots. However, nearly universally in the literature, the representativeness of these data, how they relate to the underlying real-world human mobility, has been overlooked. This disconnect between data and reality is especially relevant in the case of socially disadvantaged minorities. The objective of this study is to illustrate the nonrepresentativeness of data on human mobility and the impact of this nonrepresentativeness on modeling dynamics of the epidemic. This study systematically evaluates how real-world travel flows differ from census-based estimations, especially in the case of socially disadvantaged minorities, such as older adults and women, and further measures biases introduced by this difference in epidemiological studies. To understand the demographic composition of population movements, a nationwide mobility data set from 318 million mobile phone users in China from January 1 to February 29, 2020, was curated. Specifically, we quantified the disparity in the population composition between actual migrations and resident composition according to census data, and shows how this nonrepresentativeness impacts epidemiological modeling by constructing an age-structured SEIR (Susceptible-Exposed-Infected- Recovered) model of COVID-19 transmission. We found a significant difference in the demographic composition between those who travel and the overall population. In the population flows, 59% (n=20,067,526) of travelers are young and 36% (n=12,210,565) of them are middle-aged (P<.001), which is completely different from the overall adult population composition of China (where 36% of individuals are young and 40% of them are middle-aged). This difference would introduce a striking bias in epidemiological studies: the estimation of maximum daily infections differs nearly 3 times, and the peak time has a large gap of 46 days. The difference between actual migrations and resident composition strongly impacts outcomes of epidemiological forecasts, which typically assume that flows represent underlying demographics. Our findings imply that it is necessary to measure and quantify the inherent biases related to nonrepresentativeness for accurate epidemiological surveillance and forecasting.

Evaluating the Interplay between Trajectory Segmentation and Mode Inference Error

Travel behavior changes are essential to transportation decarbonization. Travel diaries, consisting of sequences of trips between places, are typically used to instrument human travel behavior. However, these diaries are only as accurate as the underlying methods used to construct them. Travel diary algorithms have been a popular research topic since the advent of Global Positioning System tracking surveys. These algorithms have typically been validated using prompted recall of presegmented trips, thus disregarding the continuity of mode inference. Phone operating systems have adopted battery-conserving techniques, but the resulting data collection errors have not been studied extensively. We introduce a framework to evaluate the accuracy of trip length computations and mode inference by analyzing continuous mode-segmented trajectories for groups of trips. We then use the framework to identify the input data quality and the impact of postprocessing. Our primary inputs to this evaluation are MobilityNet, a public dataset containing information from three artificial timelines covering 15 different travel modes, and sample open-source travel diary creation algorithms from the OpenPATH project. Our framework concretely shows that the variance of the distance error drops from [Formula: see text] to [Formula: see text] (Android, iOS) after postprocessing. Similarly, the weighted F-scores for mode inference increase from [Formula: see text] to [Formula: see text] (iOS, Android) between random forest and geographic information system-based models. We hope that this standardized method will be adapted to evaluate other, potentially proprietary, travel diary algorithms. The results can be used to understand and improve the state of the art in the travel diary creation field.

Influence of residential built environment on human mobility in Xining: A mobile phone data perspective

The emergence of large-scale spatiotemporal trajectory data offers an excellent opportunity to characterize collective human mobility patterns and their relationship with the urban built environment. Such an approach can generate a complementary understanding of traditional individual travel behavior based on travel survey data. Using mobile phone data, this study aims to investigate how residential built environments affect residents’ mobility at an aggregated level. Specifically, three indicators (movement distance, activity space, and the number of stops) were derived from raw mobile phone data to characterize human mobility. The decay coefficients and average values were then employed to reveal the aggregated characteristics of movement distance, activity space, and the number of stops. Furthermore, linear regression was applied to examine the relationship between human mobility indicators and residential built environments. The results indicate that the living-built environment could better explain the activity space than the movement distance and number of stops. In addition, some differences in the relationship between human mobility and residential built environments are identified between weekdays and weekends. These findings could provide new insights into human mobility and its interaction with the built environment, thus advancing our understanding of human travel behavior from both individual and collective perspectives.

Minimum taxi fleet algorithm considering human spatiotemporal behaviors.

With the development of information technology, more and more travel data have provided great convenience for scholars to study the travel behavior of users. Planning user travel has increasingly attracted researchers' attention due to its great theoretical significance and practical value. In this study, we not only consider the minimum fleet size required to meet the urban travel needs but also consider the travel time and distance of the fleet. Based on the above reasons, we propose a travel scheduling solution that comprehensively considers time and space costs, namely, the Spatial-Temporal Hopcroft-Karp (STHK) algorithm. The analysis results show that the STHK algorithm not only significantly reduces the off-load time and off-load distance of the fleet travel by as much as 81% and 58% and retains the heterogeneous characteristics of human travel behavior. Our study indicates that the new planning algorithm provides the size of the fleet to meet the needs of urban travel and reduces the extra travel time and distance, thereby reducing energy consumption and reducing carbon dioxide emissions. Concurrently, the travel planning results also conform to the basic characteristics of human travel and have important theoretical significance and practical application value.

Revealing human mobility trends during the SARS-CoV-2 pandemic in Nigeria via a data-driven approach

We employed emerging smartphone-based location data and produced daily human mobility measurements using Nigeria as an application site. A data-driven analytical framework was developed for rigorously producing such measures using proven location intelligence and data-mining algorithms. Our study demonstrates the framework at the beginning of the SARS-CoV-2 pandemic and successfully quantifies human mobility patterns and trends in response to the unprecedented public health event. Another highlight of the paper is the assessment of the effectiveness of mobility-restricting policies as key lessons learned from the pandemic. We found that travel bans and federal lockdown policies failed to restrict trip-making behaviour, but had a significant impact on distance travelled. This paper contributes a first attempt to quantify daily human travel behaviour, such as trip-making behaviour and travelling distances, and how mobility-restricting policies took effect in sub-Saharan Africa during the pandemic. This study has the potential to enable a wide spectrum of quantitative studies on human mobility and health in sub-Saharan Africa using well-controlled, publicly available large data sets. Significance: The mobility measurements in this study are new and have filled a major data gap in understanding the change in travel behaviour during the SARS-CoV-2 pandemic in Nigeria. These measurements are derived from high-quality data samples by state-of-the-art data-driven methodologies and could be further adopted by other quantitative research related to human mobility. Additionally, this study evaluates the impact of mobility-restricting policies and the heterogeneous effects of socio-economic and socio-demographic factors by a time-dependent random effect model on human mobility. The quantitative model provides a decision-making basis for the Nigerian government to provide travel-related guidance and make decisions in future public health events. Open data set: https://github.com/villanova-transportation/Nigeria-mobility-COVID19-SAJS

Changes on local travel behaviors under travel reduction-related interventions during COVID-19 pandemic: a case study in Hong Kong

The emerging Omicron variant poses a serious threat to human health. Public transports play a critical role in infection spread. Based on the data of nearly 4 billion smartcard uses, between January 1, 2019 and January 31, 2021 from the Mass Transit Railway Corporation of Hong Kong, we analyzed the subway travel behavior of different population groups (adults, children, students and senior citizens) due to the COVID-19 pandemic and human travel behavior under different interventions (e.g. work suspension, school closure). Due to the pandemic, the number of MTR passengers (the daily number of passengers in close proximity in subway carriages) decreased by 37.4% (40.8%) for adults, 80.3% (78.5%) for children, 71.6% (71.6%) for students, and 33.5% (36.1%) for senior citizens. Due to work from home (school suspension), the number of contacted adults (students/children) in the same carriage during the rush hours decreased by 39.6% (38.6%/43.2%). If all workers, students, and children were encouraged to commute avoiding rush hours, the possible repeated contacts during rush hour of adults, children and students decreased by 73.3%, 77.9% and 79.5%, respectively. Since adults accounted for 87.3% of the total number of subway passengers during the pandemic, work from home and staggered shift pattern of workers can reduce the infection risk effectively. Our objective is to find the changes of local travel behavior due to the pandemic. From the perspective of public transports, the results provide a scientific support for COVID-19 prevention and control in cities.

Graph Supported Mode Detection within Mobile Phone Data Trajectories

Mobile phone data (MPD) has been used in various studies to analyze human travel behavior in time and space. While the pure number of available trips is promising, data quality and the information density of each individual trajectory is relatively low. Travel mode detection of MPD-trajectories is a challenging task, since geographic location data is noisy and the events within a trajectory are irregular (event-based instead of time discrete). In this paper, we present a method to identify travel modes typically observed in urban areas, such as walking, bicycle, tram, bus, and car. The method requires concise network graphs for each mode. Annotated GPS trajectories were collected from volunteers as ground truth to train and validate various machine learning algorithms. The cleaned trajectories of the MPD are segmented into individual trips, which are mapped on the network graphs using a map-matching algorithm. Various features, such as trip distance and travel speed, were analyzed to identify the most suitable features for classifying the available modes with a random forest (RF) and a support-vector machine algorithm. With the RF algorithm, about 80% of all trips were associated to the correct mode. Since the total dataset was only comprised of about 600 trips, which then needed to be split into an evaluation dataset and a training dataset, we suspect the accuracy of the method will increase with more data. Based on the results, this work presents a proof of concept for determining the travel mode of MPD-trajectories.

Local travel behaviour under continuing COVID-19 waves– A proxy for pandemic fatigue?

COVID-19 continues to threaten the world. Relaxing local travel behaviours on preventing the spread of COVID-19, may increase the infection risk in subsequent waves of SARS-CoV-2 transmission. In this study, we analysed changes in the travel behaviour of different population groups (adult, child, student, elderly) during four pandemic waves in Hong Kong before January 2021, by 4-billion second-by-second smartcard records of subway. A significant continuous relaxation in human travel behaviour was observed during the four waves of SARS-CoV-2 transmission. Residents sharply reduced their local travel by 51.9%, 50.1%, 27.6%, and 20.5% from the first to fourth pandemic waves, respectively. The population flow in residential areas, workplaces, schools, shopping areas, amusement areas and border areas, decreased on average by 30.3%, 33.5%, 41.9%, 58.1%, 85.4% and 99.6%, respectively, during the pandemic weeks. We also found that many other cities around the world experienced a similar relaxation trend in local travel behaviour, by comparing traffic congestion data during the pandemic with data from the same period in 2019. The quantitative pandemic fatigue in local travel behaviour could help governments partially predicting personal protective behaviours, and thus to suggest more accurate interventions during subsequent waves, especially for highly infectious virus variants such as Omicron.

Exploring the Impact of Localized COVID-19 Events on Intercity Mobility during the Normalized Prevention and Control Period in China.

Uncontrolled, large-scale human mobility can amplify a localized disease into a pandemic. Tracking changes in human travel behavior, exploring the relationship between epidemic events and intercity travel generation and attraction under policies will contribute to epidemic prevention efforts, as well as deepen understanding of the essential changes of intercity interactions in the post-epidemic era. To explore the dynamic impact of small-scale localized epidemic events and related policies on intercity travel, a spatial lag model and improved gravity models are developed by using intercity travel data. Taking the localized COVID-19 epidemic in Xi'an, China as an example, the study constructs the travel interaction characterization before or after the pandemic as well as under constraints of regular epidemic prevention policies, whereby significant impacts of epidemic events are explored. Moreover, indexes of the quantified policies are refined to the city level in China to analyze their effects on travel volumes. We highlight the non-negligible impacts of city events and related policies on intercity interaction, which can serve as a reference for travel management in case of such severe events.

Modeling the Impact of COVID-19 on Traffic Operation, Emission and Queue Length: a Case Study

The global COVID-19 outbreak has caused a drastic change in human travel behavior. Most of the measures that have been taken by governments and local authorities involve behavioral changes from citizens to reduce their social contact to a minimum in order to control the spread of the virus. Thus, these actions affect individual activity patterns and transport systems differently. This paper quantifies the impact of COVID-19 on intersection operation performance using data from the control management room at Greater Amman Municipality (GAM) in Jordan. In this paper, the operational performance of one of the heaviest signalized intersections in Amman has been modeled under COVID-19 prevention measures. The intersection's performance has been evaluated under three scenarios, namely, before CVOID-19 hits, using an odd/even license plate strategy with activities closure, and using a 70% capacity restriction. In the Analytical Hierarchy Process (AHP), multi-criteria have been used. Three criteria groups (efficiency, capacity, and environmental) have been developed, which have contained eight sub-criteria. The weights of the criteria groups have been varied based on practitioners' and academic perspectives. The application of the developed methodology has enabled the selection of the most suitable travel restriction decision that has helped improve the transportation system's operational status. The evaluation sub-criteria in the study are vehicle delay, queue length, stopped delay, stops, travel time, vehicle safety, CO emission, and fuel consumption. Results have indicated that, by comparing the intersection operation performance before and after COVID-19, there is a significant improvement in intersection performance due to the travel restrictions during the pandemic effect. Moreover, AHP has indicated that the odd/even system is the best strategy to improve intersection performance. The results have indicated that the odd/even system has the best operational performance compared to the other two travel restrictions.

Exploring Human Mobility Patterns and Travel Behavior: A Focus on Private Cars

The ever-increasing number of automobiles, especially private cars, has irrevocably altered social life, revolutionized travel experience, and reshaped the face of urban transportation systems. Existing research mainly focuses on trajectory data from floating cars, while human mobility and travel behavior by private cars are still little understood. To bridge this gap, we employ a private car trajectory data set collected from real-world urban environments. This article provides a new perspective on human mobility, with a focus on private cars, by investigating mobility patterns and capturing the spatiotemporal evolution of urban hot zones from people’s arrive–stay–leave (ASL) behavior. Understanding these hot zones and travel behavior patterns is necessary when considering changes or improvements in urban networks. Decisions impacting the safety and emissions impacts of urban travel, including, for example, design improvements at curves or to reduce left-turn conflicts, improvements to pedestrian facilities, and the placement of alternative energy refueling stations, will rely on similar analyses across many different urban areas. Our article provides a proof of concept, and we then outline new research opportunities and challenges involving private car trajectory data.Along with the increased process of urbanization and industrialization, recent years have witnessed an ever-increasing number of automobiles, especially private cars <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , also known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passenger cars</i> , those with less than seven seats and usually registered to individuals for personal use. Compared to floating cars <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> , private cars constitute the vast majority of automobiles. For instance, the nationwide ownership of automobiles in China exceeded 263 million by the end of 2019 <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> , and more than 88% of these vehicles (225 million) were registered to individuals. As shown in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1(a)</xref> , ownership of private cars exhibited a growing trend from 2013 to 2019. As seen in Sivak’s technical report <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> , per capita car ownership in the United States was 0.756 in 2015 and increased to 0.766 by 2016. According to <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</xref> , in the last two decades, private cars accounted for more than 75% of passenger transport activity in the European Union. As shown in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1(b)</xref> , even though a slightly decreasing trend is expected by 2030, it is still expected to be larger than 72%.

Identifying hidden visits from sparse call detail record data

Despite many studies on trip inference using call detail record (CDR) data, a fundamental understanding of their limitations is lacking. In particular, because of the sparse nature of CDR data, users may travel to a location without being revealed in the data, which we refer to as a hidden visit. The existence of hidden visits hinders our ability to extract reliable information about human mobility and travel behavior from CDR data. In this study, we propose a data fusion approach to obtain labeled data for statistical inference of hidden visits. In the absence of complementary data, this can be accomplished by extracting labeled observations from more granular cellular data access records, and extracting features from voice call and text messaging records. The proposed approach is demonstrated using a real-world CDR dataset of 3 million users from a large Chinese city. Different machine learning models are used to infer whether a hidden visit exists during an observed displacement. The test results show significant improvement over the naive no-hidden-visit rule, which is an implicit assumption adopted by most existing studies. Based on the proposed model, we estimate that over 10% of the displacements extracted from CDR data involve hidden visits.

Modeling and analyzing the traffic flow during evacuation in Hurricane Irma (2017)

Hurricane evacuation modeling is challenging due to a scarcity of evacuation data and the complexity of human decision-making and travel behavior. We build a system for rapidly predicting the hurricane evacuation traffic flow based on hurricane forecasting, evacuation orders, the road network, and population information. The system integrates an evacuation demand model, an origin–destination model, and a route choice model into a link flow-based mean-field traffic model. We evaluate and calibrate the model with traffic observations from Hurricane Irma (2017), which induced a massive evacuation and traffic congestions throughout Florida State. The model skillfully captures the spatial and temporal evacuation features, including peak traffic flows and daily traffic fluctuations. The model can be applied to support evacuation management. Our analysis shows that a minor adjustment to the evacuation order could considerably alleviate the traffic congestion during Hurricane Irma.

Understanding Urban Area Attractiveness Based on Private Car Trajectory Data Using a Deep Learning Approach

With the fast development of urbanization and motorization, an increasing number of people choose to buy private cars to fulfill their daily travel needs. In particular, many people from various positions of the city drive their cars to specified areas, and then they will stop and stay for a certain period of time, leading to a spatiotemporal evolution of urban area attractiveness (AA). In this paper, we aim at understanding urban AA based on analyses of private car trajectory datasets. Specifically, by extracting point-of-stop (PoS) data from the private car trajectories, we design the variational Bayesian Gaussian mixture models (VBGMM) to deduce the probability density distribution of PoSs and connect it to the variation of AA. We establish a deep learning model based on long short-term memory (LSTM) to capture the evolution of the AA. Furthermore, we integrate dropout in the LSTM method to address challenging issues such as overfitting and time-consuming training of complex neural networks in the AA prediction. We conduct experiments by using real-world private car trajectory data to evaluate the performance of the proposed method. The results validate that our proposed method outperforms existing ones in terms of various metrics. To the authors’ knowledge, our work is the first one to utilize private car trajectory data to study urban area attractiveness, thereby facilitating a new perspective regarding an understanding of human travel behavior and the evolution of urban mobility.

Discover Trip Purposes From Cellular Network Data With Topic Modeling

The widespread use of mobile phones has generated a large amount of individual trajectory data. Such data can greatly help analyze and understand human daily travel behavior. In this paper, we use the topic modeling technique to infer trip purposes based on pseudonymized users’ trajectory data from cellular network and points of interest (POIs) from online map services. The adapted latent Dirichlet allocation method is used to model the trip generation process and then infer trip purposes behind the data. The experiments are performed on a data set of 27,732 trip records in Beijing on weekdays. Ten topics are discovered. This method can easily infer different trip purposes based on three trip attributes, i.e., trip departure time, stay duration, and POI categories for destinations, and most of the topics/trip purposes are explainable.

Travellers’ Perception About Vehicular Emissions’ and its Impact on Pedestrian Travel Behaviour

Abstract Vehicular emissions have many impacts on human health and travel behaviour. A lot of evidence on the health effects of vehicular emissions is available but very few studies have looked at the impacts on travel behaviour. The current study attempts to fill this research gap by analysing the factors that influence pedestrian travel behaviour concerning vehicle emissions based on travellers’ perceptions in the Indian context. For this, a stated preference questionnaire survey was conducted and a factor-based regression followed by a mediation analysis was used to analyse the responses. Results showed that a person’s perceived impacts about vehicular emissions had a greater impact on their travel behaviour. Public concern and environmental attitude lead to direct changes in travel behaviour whereas vehicle technology and negligent attitude had significant indirect effects. The present study findings are useful to the urban planning policymakers in reducing the impact of vehicular emissions on pedestrians by implementing strategies that lessen human exposure to transport emissions.

Human mobility forecasting with region-based flows and geotagged Twitter data

One of the main lines of research in the discipline of mobility mining is the development of predictors able to anticipate human travel behaviour in great detail. However, access to the high-resolution spatio-temporal data on which most existing solutions are based is rather limited due to multiple factors, e.g. costly access to third-party data. These restrictions give rise to a problem of developing predictors of human mobility in most setting, since the amount of data available to train these prediction models is insufficient. This paper explores the feasibility of using a public data source such as Twitter to predict the number of trips at the nationwide level. The proposed approach combines a large set of geotagged Twitter posts with an open data source published by the Spanish government on traveller mobility based on mobile phone location. Both datasets are used as input to Machine Learning models to validate the use of Twitter data for improving the prediction of these models. The results show that Twitter data have considerable value as a predictor of large-scale human mobility, especially for Long Short-Term Memory (LSTM) models. As a result, the relevance of this work resides in demonstrating that the use of Twitter could be considered as an alternative to substantially enhance the prediction of mobility within a country when it is combined with other open data sources.

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

Massive taxi trajectory data can be easily obtained in the era of big data, which is helpful to reveal the spatiotemporal information of human travel behavior but neglects activity semantics. The activity semantics reflect people’s daily activities and trip purposes, and lead to a deeper understanding of human travel patterns. Most existing literature analyses of activity semantics mainly focus on the characteristics of the destination. However, the movement from the origin to the destination can be represented as the flow. The flow can completely represent the activity semantic and describe the spatial interaction between the origin and the destination. Therefore, in this paper, we proposed a two-layer framework to infer the activity semantics of each taxi trip and generalized the similar activity semantic flow to reveal human travel patterns. We introduced the activity inference in the first layer by a combination of the improved Word2vec model and Bayesian rules-based visiting probability ranking. Then, a flow clustering method is used to uncover human travel behaviors based on the similarity of activity semantics and spatial distribution. A case study within the Fifth Ring Road in Beijing is adopted and the results show that our method is effective for taxi trip activity inference. Six activity semantics and four activity semantics are identified in origins and destinations, respectively. We also found that differences exist in the activity transitions from origins to destinations at distinct periods. The research results can inform the taxi travel demand and provide a scientific decision-making basis for taxi operation and transportation management.

The architecture of complexity in the relationships between information and communication technologies and travel: A review of empirical studies

The use of information and communication technologies (ICT) has become an integral part of people’s lives, having complex interactions with different facets of human activity participation and travel behaviour. Studies over decades have tried to understand the relationships between ICT and travel. However, the fast evolution of ICT systems and the complex nature of these relationships have resulted in a limited understanding of the overall architecture, the sub-components, and their interactions. This study aims to achieve two objectives. First, we update the current state of knowledge on ICT-travel relationships by conducting an extensive literature review. Second, based on the findings of the review, we propose a conceptual model that identifies sub-components and their interrelationships that need to be better understood to accurately grasp the impacts of ICT on transportation. We summarized 186 empirical studies from the perspectives of survey methods, ICT devices, services, applications, direct relationships with travel behaviour related variables and higher-order or indirect impacts on other parameters such as emissions and congestion. It was observed that the nature of empirical results would depend on assumptions, objectives and type of data used. The findings of the review were then used to classify architecture in the relationship between ICT and travel into four distinct types. The merits, demerits, and challenges associated with the analysis across types were then illustrated. We then develop a conceptual model that highlighted and discussed the possible interactions between 1) ICT systems, 2) travel-related factors and 3) higher-order variables.

Revealing temporal stay patterns in human mobility using large‐scale mobile phone location data

AbstractThe emergence of large‐scale human spatiotemporal tracing data inspired researchers to investigate human mobility patterns from different perspectives with potential implications for urban planning, traffic management, and emergency response. Previous studies investigated the mechanism of human mobility by constructing spatial mobility motifs based on mobile phone datasets, but ignored their temporal characteristics. To compensate for this, this study extracted temporal stay patterns based on spatial mobility motifs. Using mobile phone location data from Shenzhen, China, we identified stop location sequences for users and constructed home‐based mobility motifs according to predefined rules, and observed that approximately 97% of users can be characterized by the 10 identified home‐based mobility motifs. We designed a method to quantify the similarity of temporal stay characteristics, and the primary temporal stay patterns were extracted by classifying users with similar stay rhythms. Our results enrich the knowledge of human travel behavior from both spatial and temporal perspectives.