Activity-travel Patterns Research Articles

Exploring Individual Activity-Travel Patterns Based on Geolocation Data from Mobile Phones

Data mining techniques can extract useful activity and travel information from large-scale data sources such as mobile phone geolocation data. This paper aims to explore individual activity-travel patterns from samples of mobile phone users using a two-week geolocation data set from the Paris region in France. After filtering the data set, we propose techniques to identify individual stays and activity places. Typical activity places such as the primary anchor place and the secondary place are detected. The daily timeline (i.e., activity-travel program) is reconstructed with the detected activity places and the trips in-between. Based on user-day timelines, a three-stage clustering method is proposed for mobility pattern analysis. In the method framework, activity types are first identified by clustering analysis. In the second stage, daily mobility patterns are obtained after clustering the daily mobility features. Activity-travel topologies are statistically investigated to support the interpretation of daily mobility patterns. In the last stage, we analyze statistically the individual mobility patterns for all samples over 14 days, measured by the number of days for all kinds of daily mobility patterns. All individual samples are divided into several groups where people have similar travel behaviors. A kmeans++ algorithm is applied to obtain the appropriate number of patterns in each stage. Finally, we interpret the individual mobility patterns with statistical descriptions and reveal home-based differences in spatial distribution for the grouped individuals.

Potential of telecommuting for different employees in the Indian context beyond COVID-19 lockdown

During the last decade, telecommuting has become quite popular whereas, in developing countries like India, this phenomenon is limited to a select group of employees due to various factors. The COVID-19 pandemic, the nationwide lockdown and the subsequent new normal scenario, has made the adoption of telecommuting by different type of employees indispensable thus giving us the opportunity to conduct empirical research towards understanding the factors influencing telecommuting adoption, productivity and activity-travel behaviour during telecommuting in India. The data used in this survey has been collected in two phases to capture the evolving situation of the pandemic. The analysis has been done based on revealed preference data considering three time periods i.e., before COVID-19, during COVID-19 nationwide lockdown, after the termination of the lockdown (after first wave) and for the post-COVID 19 scenario where stated preferences are considered. In the first step, telecommuting behaviour, activity-travel pattern during telecommuting and productivity is estimated based on aggregate sample data for various socio-economic groups for the period before and during COVID-19 (1. during lockdown 2. during new normal). Next, factors influencing telecommuting adoption and productivity are determined which has been used in our stated preference survey conducted in the second phase. A logistic regression model has been developed to test the association of existing telecommuting behaviour, activity-travel pattern, factors influencing productivity, socio-economic characteristics and occupation categories with future telecommuting adoption. The findings of this research can not only provide insights to urban planners and policymakers to design sustainable travel demand management strategies but can also help employers to design appropriate telecommuting strategies at the organization level which will help to attain the desired productivity levels. Our empirical analysis reveal two major findings, i.e., a large percentage of employees can achieve their desired productivity by working from home and the ‘rebound’ effect as identified in literature seems to have little impact in the Indian context. The novelty of this research lies in the comprehension of the adoption process, and the behavioural analysis including adoption, productivity, activity, and travel of telecommuters in the context of a developing country for the first time.

Revealing correlation patterns of individual location activity motifs between workdays and day-offs using massive mobile phone data

Recently, people are increasingly interested in understanding broader and longer-term individual activity-travel behaviors across days or even weeks. However, the relationship of individual daily activity-travel behaviors over multi-days only remains partially revealed, especially between workdays and day-offs. Thus, we develop an effective framework to extract individual daily activity-travel patterns from massive mobile phone network data on basis of location activity motifs (LAMs), which are beneficial to combining the locations, activities, and trips in daily activity-travel behaviors. We then discover that the modified number of LAMs over time conforms well to the classic exploration and preferential return (EPR) model after excluding the influence of the number of trips, which reproduces the human activity-travel behavior characteristics on daily scale and indicates that the complex relationship of LAMs exists between workdays and day-offs. Furthermore, we emphasize three categories of correlation patterns while the relationship of LAMs between workdays and day-offs is instantiated using association rules mining algorithm. Ultimately, the regular individual differences and obvious spatial heterogeneity reveal the formation mechanism of correlation patterns. These empirical results contribute to develop different but related transportation strategies between workdays and day-offs by understanding individual daily activity-travel behaviors.

Activity-travel scheduling in stochastic multi-state supernetworks with spatial and temporal correlations

Multi-state supernetworks are capable of representing activity-travel patterns at a high level of detail and thus are a powerful tool for activity-travel scheduling (ATS) of multidimensional choice facets. To alleviate the limitations of the common deterministic network representation, travel time and activity duration uncertainty has been incorporated in multi-state supernetworks. However, the extension unrealistically assumed that all uncertain components are independent. This study suggests an approach of ATS considering spatially and temporally correlated travel times and activity durations in stochastic time-dependent (STD) multi-state supernetworks. Support points are used as a representation of the stochasticity of the STD multi-state supernetwork. ATS is formulated as a pathfinding problem subject to space–time constraints based on recursive formulations. A series of numerical experiments is implemented to demonstrate the applicability of the suggested approach.

Modelling of Activity-Travel Pattern with Support Vector Machine

Activity based travel demand modelling involves lot of uncertainty due to the complex and varying decision making behaviour of each individual. This study contributes to the literature by assessing the suitability of Support Vector Machine (SVM) in modelling the activity pattern and travel behaviour of workers. Activity and travel behaviour of workers consists of decision outcomes, which can be modelled as classification and regression problems. SVM is a good classifier and regressor with good testing and learning capability, hence the present study used SVM for modelling. It was found that support vector machine models are well performing to predict the activity pattern and travel behaviour of workers. The SVM models developed in the study predicts the temporal variation of mode wise work activity generation. Prediction of temporal mode share of commuters is advantageous to policy makers to experiment the implementation of temporary Travel Demand Management (TDM) actions effectively.

Inference of activity patterns from urban sensing data using conditional random fields

The study of human activity–travel patterns for urban planning has evolved a long way in theories, methodologies, and applications. However, the scarcity of data has become a major barrier for the advancement of research in the field. Recently, the proliferation of urban sensing and location-based devices generates voluminous streams of spatio-temporal registered information. In this study, we propose an approach using the linear-chain Conditional Random Fields (CRFs) model to learn the spatio-temporal correspondences of different types of activities and the inter-dependencies among sequential activities from training dataset such as the household travel or time use surveys, and to infer the hidden activity types associated with urban sensing data. The performance of the CRFs model is compared against the Random Forest (RF) model, which has been used in a number of existing studies. The results show that the linear-chain CRFs models generally outperform the RF counterparts with respect to classification accuracy of activity types, in particular for those travelers having more outdoor daily activities. The proposed methodology is demonstrated by reconstructing the activity landscape of the surrounding area of a major Mass Rail Transit station in Singapore using the transit smart card transaction data. The inferred activities from the transit smart card data are expected to complement the ground surveys and improve our understanding of the interactions of different components of activities/travels as well as the relationship between urban space and human activities.

Exploratory analysis on worker's independent and joint travel patterns during weekdays and weekends

Understanding the intra-household interaction elements such as independent and joint activity participation and travel, over multiple days are very less explored, especially in the context of developing countries like India. This paper discusses the results of a study on the independent and joint travel of working people, during weekdays and weekends. Four types of daily activity-travel patterns are examined, such as Simple-Independent, Simple-Joint, Complex-Independent and Complex-Joint. Multinomial logit modelling is adopted to model the type of activity-travel pattern as a function of socio-demographic attributes and day of week variables. Models were developed for whole-week, weekdays and weekends separately. The data used for this study is collected using questionnaire drop off and pickup method, from Calicut city, Kerala State, India. Analyses presented here are based on a sample of 936 working people. The results indicate that males are more likely to perform Complex-Independent type of travel pattern, compared to females. The influence of age, gender, marital status, education level and exclusive vehicle availability are similar during weekdays and weekends. The presence of elderly persons, young children and students at home are observed to be significant in influencing the choice of activity-pattern types during weekdays, whereas during weekends, these variables did not emerge as significant ones. On Sunday, the possibility for joint travel is significantly higher than other days of the week. Studies in this line are expected to assist transportation planners in formulating day of week specific policy strategies.

Identifying business activity-travel patterns based on GPS data

As employers, suppliers, and transport providers, organisations generate a large portion of traffic flows on transport networks. However, despite the significance of business travel to overall mobility, the underlying activity compositions of the movement and decision-making processes within organisations are not well understood. In this study, a new method is developed based on GPS data to identify typical business activity-travel patterns and characterise the travel behaviour of a specific vehicle and corresponding drivers.Using GPS data collected from the real operation of 6,500 commercial vehicles over a period of three months, the proposed method was tested. In total, five profiles were constructed, accommodating activity-travel patterns associated with vans, cars, trucks-35 t (light trucks), trucks-3ax (medium trucks), and buses. Similarities and differences in these profiles across vehicle types were revealed, and specific locations corresponding to the activities of the patterns were further examined. Moreover, using these profiles as a reference, the travel practice of a specific vehicle was evaluated. The experimental results demonstrate the potential and effectiveness of the approach in depicting business travel patterns, providing a deep understanding of business travel behaviour, and assisting the design and evaluation of policies for more sustainable business transport.

Analysing the impact of the COVID-19 outbreak on everyday travel behaviour in Germany and potential implications for future travel patterns

IntroductionThe global Coronavirus (COVID-19) pandemic is having a great impact on all areas of the everyday life, including travel behaviour. Various measures that focus on restricting social contacts have been implemented in order to reduce the spread of the virus. Understanding how daily activities and travel behaviour change during such global crisis and the reasons behind is crucial for developing suitable strategies for similar future events and analysing potential mid- and long-term impacts.MethodsIn order to provide empirical insights into changes in travel behaviour during the first Coronavirus-related lockdown in 2020 for Germany, an online survey with a relative representative sample for the German population was conducted a week after the start of the nationwide contact ban. The data was analysed performing descriptive and inferential statistical analyses.Results and DiscussionThe results suggest in general an increase in car use and decrease in public transport use as well as more negative perception of public transport as a transport alternative during the pandemic. Regarding activity-related travel patterns, the findings show firstly, that the majority of people go less frequent shopping; simultaneously, an increase in online shopping can be seen and characteristics of this group were analysed. Secondly, half of the adult population still left their home for leisure or to run errands; young adults were more active than all other age groups. Thirdly, the majority of the working population still went to work; one out of four people worked in home-office. Lastly, potential implications for travel behaviour and activity patterns as well as policy measures are discussed.

Measuring accessibility of urban scales: A trip-based interaction potential model

In the interaction process of city space and citizens’ activity pattens, the regular travel behavior under the cumulative impacts of urban land use is required to be assessed. The aim of this study is to formulate and estimate attraction choice models that provide measurements of accessibility on various scales reflecting the choice of people to travel to facilities or activity places and characterize the interaction between land use patterns and transportation facilities. Based on a dataset inclusive of big data from varied sources, measurement methodologies are proposed encompassing the multidimensional aspect of the accessibility estimation issues. We sketch the characteristics of service facilities and travel impedance in the calibration processes. Logit models and gravity models are applied to simulate the impacts of different scales of trip length on the accessibility scores. Accessibility is aggregated over spatial elements of different scales and trips and herein the spatial accessibility of study zones is estimated as well as the potential of citizens’ travel choices and activity patterns. Such spatial interaction models have potential implication for enhancing our understanding of the cumulative environmental influences on citizen’s travel behavior and vice versa. It can be a substantial part of a more composed proposal of life convenience of residential citizens reflecting the happiness of living in an urban community.

Visualizing, clustering, and characterizing activity-trip sequences via weighted sequence alignment and functional data analysis

Smartphone-based activity-travel surveys have enabled the collection of continuous, multi-day data on individuals’ trips and activities with high spatial and temporal resolution. However, the multi-dimensional nature of these data makes it challenging to compare activity-travel patterns and identify clusters of individuals with similar patterns and use them to study behaviors and forecast travel demands. To address this challenge, we adopt a discrete, step-based view on time and transform the episodic-based diary into a sequence of states observed at a sample interval. The resulting sequences can visually characterize variations in activity-travel patterns across days of a week and among different individuals. Motivated by techniques in genomics and data science, we apply sequence alignment methods to measure the pairwise similarity between these activity-trip sequences. To address its practical implementation in transportation studies, we define and compare four weighting schemas: (1) the unit-cost distance, which assigns equal weights to all substitution operations between states; (2) the fixed-flexible weighted distance based on the time geography framework, where costs differ for substitutions involving fixed and flexible activities; (3) the trip-activity weighted distance considering travel as a derived demand, where costs differ for substitutions between trips and activities; and (4) transition-based weighted distance, where costs are based on the global or time-varying activity and trip transition rates estimated from the data. Then, we calculate the pairwise distances between individuals’ sequences and use them as inputs to a hierarchical clustering algorithm to group individuals with similar sequences. We visualize the state distributions of the identified clusters to infer and compare behavior patterns, and use functional data regression methods to estimate the time-dependent probabilities of engaging in various activities and trips. To demonstrate our methods, we analyze a smartphone-based survey dataset collected in the Minneapolis-St. Paul metropolitan area. We also conduct sensitivity analysis on the selection of cost metrics and sample intervals to ensure the robustness of our methods and discuss their implications in practice. By identifying population subgroups with distinct daily activity-travel patterns and explaining how these patterns vary over one day and depend on user profiles, our weighted sequence alignment approach provides an intuitive and flexible method for extracting and characterizing individuals’ activity-travel behaviors for use in transportation planning.

Interpersonal and Intrapersonal Variabilities in Daily Activity-Travel Patterns: A Networked Spatiotemporal Analysis

Interpersonal and intrapersonal variabilities are two important perspectives to understand daily travel behaviors, while only a small number of studies incorporate them for understanding human dynamics. This paper employed a network analysis approach to detecting daily activity-travel patterns of 680 Beijing’s residents within a week and then used a multilevel multinomial logit model to analyze the intrapersonal variability in patterns and the socioeconomic linkages behind them. Results suggest that most activity-travel patterns have significant day-to-day intrapersonal and interpersonal variabilities. This suggests that the application of a typical day of activity-travel behaviors to measure and represent a week’s or even longer-term behaviors may be biased, due to the existence of day-to-day intrapersonal variability. This study also provides a hint for the selection of days of a week to conduct a diary survey for activity pattern mining or travel demand modeling.

The trade-off behaviours between virtual and physical activities during the first wave of the COVID-19 pandemic period

IntroductionThe first wave of COVID-19 pandemic period has drastically changed people’s lives all over the world. To cope with the disruption, digital solutions have become more popular. However, the ability to adopt digitalised alternatives is different across socio-economic and socio-demographic groups.ObjectiveThis study investigates how individuals have changed their activity-travel patterns and internet usage during the first wave of the COVID-19 pandemic period, and which of these changes may be kept.MethodsAn empirical data collection was deployed through online forms. 781 responses from different countries (Italy, Sweden, India and others) have been collected, and a series of multivariate analyses was carried out. Two linear regression models are presented, related to the change of travel activities and internet usage, before and during the pandemic period. Furthermore, a binary regression model is used to examine the likelihood of the respondents to adopt and keep their behaviours beyond the pandemic period.ResultsThe results show that the possibility to change the behaviour matter. External restrictions and personal characteristics are the driving factors of the reduction in ones' daily trips. However, the estimation results do not show a strong correlation between the countries' restriction policy and the respondents' likelihood to adopt the new and online-based behaviours for any of the activities after the restriction period.ConclusionThe acceptance and long-term adoption of the online alternatives for activities are correlated with the respondents' personality and socio-demographic group, highlighting the importance of promoting alternatives as a part of longer-term behavioural and lifestyle changes.

Daily activity-travel and fragmentation patterns in the weekly cycle: evidence of the role of ICT, time use, and personal networks

ABSTRACT This paper presents an analysis of people’s time use patterns, incorporating the mediating role of ICT and personal networks on their daily activity-travel patterns, explicitly addressing the issue of activity fragmentation for social-based episodes. Implementing a seven-day time use survey, which includes temporal, spatial, and social characteristics for each activity, and using a theoretical framework based on current theory, a structural equation model was estimated to study these relationships. The results suggest a positive effect of ICT episodes on social activity interaction, activity fragmentation, and out-of-home social allocated time. We also found that ICTs encourage social trip generation only indirectly. Finally, those who had a social network physically close showed higher levels of fragmentation in their social episodes and less out-of-home social activity time. These results add evidence to this research stream, enhancing, and further clarifying ICT’s role in people’s daily activity behavior.

Spatial Distribution of Travel Activities and its Relationship with Points of Interest

This study explores the spatial distribution characteristics of travel activities and their relationship with land use, using data from the resident travel survey in 2015 of Xiaoshan District of Hangzhou City, China. A new classification method is proposed to classify the travel activity patterns into three groups: single-activity travel, multi-activity intermittent travel, and multi-activity continuous travel. The main findings are: (a) the length of activity chain and the proportion of multi-activity travels increase with the distance between residence and activity centre; (b) the non-home destinations of single-activity travel, multi-activity intermittent travel and multi-activity continuous travel agglomerate towards the activity centre, and the degree of agglomeration increases in this order; (c) the distribution density of Point Of Interest (POI) and activity destinations have strong positive correlations in space; (d) some attributes of POIs and demographics have significant influence on multi-activity continuous travels. These findings are useful in inducing the activities through reasonable combinations and spatial interconnections of POIs in urban planning.

Revisiting the impacts of virtual mobility on travel behavior: An exploration of daily travel time expenditures

Abstract There has been a growing interest in the association between online activities and daily activity-travel patterns. An analysis of this relationship is even more crucial considering the major disruptions to out-of-home activity participation and travel due to the COVID-19 pandemic. This study contributes to the literature by exploring the relationships between Information and Communication Technology (ICT) use (focusing on telework and online shopping) and time spent traveling using different transportation modes. Using Tobit regression models, we investigate the impacts of ICT use on three travel alternatives: (1) automobile, (2) public transit, and (3) active travel. The results show that the effects of ICT use vary across these three travel modes. For example, all else being equal, respondents with higher durations of telework tend to spend less time on auto and transit. Respondents with higher durations of online shopping spend more time walking and bicycling. This study also explores whether the effects of ICT use on travel durations vary across groups with different socio-demographics and residential location characteristics. For instance, the study finds the greater the level of land-use mixture, the stronger the association between online shopping and time spent bicycling and walking. The research findings can inform planners and decision-makers on the relationships between ICT use and overall travel behavior in order to assess travel demand under different levels of ICT use.

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

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.

Activity participation, episode duration and stop-making behavior of pilgrims in a religious event: An exploratory analysis

Activity travel pattern of pilgrims in a religious setting is a complex process. Extant literature on religious tourism has taken minimal efforts in addressing such complexity, which has led to a paucity of information on preferred activity participation destinations and trip chain sequences of pilgrims. So, the present research objective is two-fold. First, to examine the causal effects of socio-demographics and daily local temperature on activity participation, trip chain type, and time allocation of individuals using structural equation modeling (SEM) that can help identify the dominant activity patterns. Second is to explore the impact of socio-demographic variables and activity patterns on the propensity of stop-making behavior using an ordered logit (OL) framework to better plan and manage the influx of flows. The primary data was collected using an activity-travel diary by taking the case study of the Kumbh Mela event, which is considered as the world's largest mass religious gathering, held at Ujjain, India, in 2016. From the results, it is observed that Males have a lower tendency to take multiple stops for primary religious activities and have simple trip chains. An increase in the members of a family visiting Kumbh decreases their overall time spent across various activities. As the mercury (temperature) rises, it reduces tourist's participation in recreational and discretionary activities. Individuals who participate in primary and secondary religious activities tend to spend more time at Kumbh as compared to individuals who primarily visit for recreational purposes. These empirical findings provide meaningful insights for managing large religious events.

Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data: An activity space-based approach

Understanding the travel behaviors and activity patterns of vulnerable people is important for addressing social equity in urban and transportation planning. With the increasing availability of large-scale individual tracking data, new opportunities have emerged for studying people's travel behaviors and activity patterns. However, the data has not been fully exploited to examine the travel characteristics of vulnerable people and their implications for understanding transport-related disadvantage. This study proposes a methodological framework based on the concept of activity space that enables a comprehensive examination of vulnerable people's spatiotemporal travel characteristics and an investigation of the geographies of transport disadvantage. Using the proposed framework, a case study that investigates the bus activities of the vulnerable population using four-month smart card data is carried out in the city of Wuhu, China. The case study suggests that vulnerable people possess distinct travel behaviors that differ considerably from the mainstream population and that the implications of transport disadvantage, as revealed by the participation in bus activities, vary across different demographic groups and across different spatial contexts. Some of the empirical insights obtained from this study also differ from conclusions drawn from previous studies and will enrich our understandings of vulnerable people's activities. Overall, the paper makes two major contributions. Methodologically, the proposed framework can overcome some of the deficiencies of activity space-based approaches for understanding transport disadvantage and contribute broadly to the studies of travel behaviors and activities patterns using individual-level tracking data. Empirically, the study identifies varying spatial and temporal implications of transport disadvantage associated with different vulnerable groups, which could further shed light on public transit planning and service design.

Understanding Chinese tourist mobility and consumption-related behaviours in London using Sina Weibo check-ins

In this paper, we detail an individual-level analysis of under-exploited location-based social network (LBSN) data extracted from Sina Weibo, a comprehensive source for data-driven research focused on Chinese populations. The richness of the Sina Weibo data, coupled with high-quality venue and attraction information from Foursquare, enables us to track Chinese tourists visiting London and understand behaviours and mobility patterns revealed by their activities and venue-based ‘check-ins’. We use these check-ins to derive a series of indicators of mobility which reveal aggregate and individual-level behaviours associated with Chinese tourists in London, and which act as a tool to segment tourists based on those behaviours. Our data-driven tourist segmentation indicates that different groups of Chinese tourists have distinctive activity preferences and travel patterns. Our primary interest is in tourists’ consumption behaviours, and we reveal that tourists with similar activity preferences still exhibit individualised behaviours with regards to the nature and location of key consumption activities such as shopping and dining out. We aim to understand more about Chinese tourist shopping behaviours as a secondary activity associated with multi-purpose trips, demonstrating that these data could permit insights into tourist behaviours and mobility patterns which are not well captured by official tourism statistics, especially at a localised level. This analysis could be up-scaled to incorporate additional LBSN data sources and broader population subgroups in order to support data-driven urban analytics related to tourist mobilities and consumption behaviours.