Activity-travel Patterns Research Articles

Week-long activity-based modelling: a review of the existing models and datasets and a comprehensive conceptual framework

ABSTRACT Activity-based travel demand models emerged mainly to fix the conceptual, statistical, and operational deficiencies of conventional trip-based models. This is done by microstimulating the activity scheduling behaviour of individuals/households instead of modelling the number of trips between the zones of an urban area. In the “Next Generation” of activity-based models (ABMs), researchers are making an effort to improve their capacity to replicate the travel-activity patterns of urban populations more realistically. Expanding the modelling time frame from a single day to an entire week is one of the essential aspects of the “Next Generation” of ABMs. Although there is still a long way to go before a comprehensive and operational week-long ABM can be developed, the literature on its different aspects, the theoretical and conceptual frameworks, and the efforts to collect multi-day travel-activity diaries are now at a stage that is worth a comprehensive and systematic review. Therefore, the current study is devoted to exploring the existing literature on multi-day activity-based modelling, categorising its elements in a systematic manner, searching for the research gaps in the existing models and proposing a comprehensive framework to fill those gaps.

Activity-travel pattern inference based on multi-source big data

ABSTRACT We provide a comprehensive review of the literature on inferring activity-travel patterns (ATP) using multi-source big data; the increasing number of publications over time on this subject, demonstrates the importance of big data in this task. Our aims are to identify the advantages and research gaps in ATP inference and to promote further developments in this field. We clarify the fundamental concepts (i.e. ATP and its components), commonly used data sources, and inference processes employed in ATP inference studies. Emphasis is placed on two prominent big data sources: mobile phone data and smart card data. We outline the various approaches involved in the inference process, and we highlight existing shortcomings in data sources, ATP inference methodologies, and result validation. Based on the review, it is evident that future research should address several limitations in ATP inference. Firstly, it is necessary to improve the comprehensive understanding of ATP and understand the interrelationships among its different components. Secondly, it is necessary to integrate different data sources and leverage their respective strengths to gain deeper insights into activity-travel behaviour. Lastly, further investigation into emerging technologies such as artificial intelligence in ATP inference is warranted to improve inference accuracy. The findings of this study could provide valuable insights for policy makers, enabling them to gain a deeper understanding of activity-travel choice behaviour and develop more effective policies related to transportation system.

Heterogeneity in activity and travel mode patterns of older Indonesians

Older adults aged 50 years and over may experience varied participation levels in out-of-home activities and travel modes, with their ability to walk being a significant factor. However, the heterogeneity of the activity–travel patterns of people at this age, especially in the Indonesian context, is not well understood. Using Yogyakarta as a case study and applying a latent class cluster analysis, this study is the first to categorize older adults’ participation in activities and use of travel modes and to understand the distinct characteristics of older adults. The model results revealed four discrete groups based on older adults’ activity participation and another four discrete groups based on their travel mode use. In the activity participation group, active older adults are the largest group, followed by inactive, working, and very active older adults. Meanwhile, those reliant on their motorized vehicles make up the largest travel mode group, followed by low-cost-vehicle users, captive riders of personal motorcycles, and car users. The model results also reveal that captive riders of personal motorcycles tend to be male and the youngest-old, while smartphone users are more likely to belong to the car users group. Walking ability constraints also significantly affect heterogeneity in older adults’ activity and travel mode, where those with no constraints on their ability to walk tend to be categorized as active older adult car users. The findings of this study may help policymakers identify older people with particular activity patterns and travel modes and develop policies to accommodate their mobility needs.

Jointly modelling activity start time, travel mode, companionship, and destination location choices

Activity-travel decisions, including time use (i.e. activity start time), travel choices (i.e. mode and companionship), and destination location choices are interdependent and complex in nature. They are often controlled by observed factors such as sociodemographic, vehicle ownership, built environment, land use, and unobserved factors like attitude, preference, or habits which typically are not captured in the survey data. To accommodate these complex interactions and capture trade-offs among the time use-travel-land use choices, this study proposes a joint discrete choice model by introducing unobserved factors that are common to them. The model results confirm the presence of complex interdependencies among these choice dimensions. In addition, this study highlights the contribution of observed and unobserved factors introduced in the model by calculating the total variance of utility differences. Lastly, this study provides behavioural insights on the activity-travel patterns, which can be further used to develop robust travel demand management policies.

Social demographics imputation based on similarity in multi-dimensional activity-travel pattern: A two-step approach

In response to the absence of demographics in increasingly emerging big data sets, we propose a novel method for inferring the missing demographic information based on similarity in people’s daily multi-dimensional activity-travel patterns as well as the characteristics of the area they move about. Instead of using isolated activity-travel attributes to infer social demographic features, our proposed method first calculates the similarity of people’s multidimensional daily activities and travels as well as characteristics of their visiting locations, between those for whom the social demographics are to be imputed (target) and those with known demographics (base) using a polynomial function. The weights of the function are determined using the permutation feature importance method, and then dynamic time warping is used to align the multidimensional activity sequences of the base and target sample and measure their similarities. For each person in the target database, a matched list is created consisting of those with the most similar activity-travel sequences in the base sample. A support vector machine is then trained using the base sample as input to impute the demographics of the target sample. The proposed model is trained using a national travel survey and validated by applying it to a GPS dataset. The results show that the proposed method outperforms existing methods in predicting four selected demographics: gender, age, education level, and work status, with an accuracy range between 91% and 94% for the national dataset and 88% to 91% for the GPS data. This study highlights the importance of considering the multidimensional and sequential nature of peoples’ daily activity-travel patterns in the imputation of demographic features.

Exploring Trip Chain Pattern Choices at Urban Relaxation Facilities: A Case Study of Lego-Lego in Central Point of Indonesia

Lego-Lego at Central Point of Indonesia (CPI) is one of the famous relaxation facility areas currently hosting numerous visitors, specifically on weekends. People tend to spend their time at the first multi-purpose facilities in the morning and afternoon, causing traffic congestion and long queues at the entrance and exit. Therefore, this study explored the trip chain activity pattern and behavior of visitors using the facilities provided in Lego-Lego. This study was considered necessary because the government and stakeholders need to understand the trip chain pattern choices preferred by the people to give infrastructure, manage traffic problems, and formulate relevant regulations. The data were collected through interview-based questionnaires randomly distributed to visitors and analyzed using a multinomial logistic regression model to determine individual and trip characteristics. The results showed the variables with significant impacts on the trip chain and the trip chain models for the morning and afternoon were similar. It was also discovered that the probability of selecting trip chain pattern 1 reduced as the time activity increased while the probability of selecting patterns 2, 3, and 4 increased. This indicated that time activity influenced the trip chain activity pattern, but the cost was not. Another important observation was that the greater the diversity of facilities influencing the activity-travel patterns, the more time was required to engage in the activities.

Data-Driven Framework for Generating Travelers with Demographic-Activity-Travel Information

Transportation demand models and traffic simulations, particularly in the realm of innovative activity-based models, necessitate comprehensive demographic-activity-travel data. Although cell phone data holds the potential to provide detailed insights into activity-travel patterns, transportation planners often resort to aggregate data, constrained by limited socio-demographic attributes because of stringent privacy protection policies. This creates a significant challenge: how to reconstruct detailed traveler profiles from such aggregate data. In this paper, we propose a novel data-driven integration framework, DATG, based on improved Markov and XGBoost classification models, with which we are able to reconstruct travelers with diverse socio-demographic attributes as well as their one-day activity-travel information. We test our model on a real cell phone data set and validate the proposed model by relying on travel survey data. Our framework is able to provide fundamental data support for agent-based models, activity-based models, and transportation planning.

Co-evolution of Smart Small Vehicles and Human Spatial Experiences: Case Study on Battery-Sharing Electric Two-Wheelers Experiment

Small-format mobility services have been introduced in many cities to promote sustainable urban development. In some cities, these services are primarily seen as entertainment rather than significant transport modes. Research has studied the roles of experiential/hedonic and functional/instrumental motivations in users’ adoption intent for such services. However, there is still a limited understanding of how actual spatial experiences of mobility travels shape travel behaviors. This study explores the role of spatial experience in mobility travels. Specifically, the research question revolves around whether better spatial knowledge leads to better spatial experiences, thereby satisfying users’ functional/instrumental and experiential/hedonic values for mobility trips. Additionally, we examine how spatial knowledge affects travel behaviors regarding trip chaining and vehicle charging. To assess road users’ spatial knowledge, we use sketch maps to examine changes after three months of using battery-sharing two-wheelers. A mixed-methods approach and multiple data sources are employed to provide deeper insights, including sketch maps, questionnaire surveys on attitudes, and a panel data analysis on activity-travel patterns. The results indicate that spatial experience significantly influences perceived values and, consequently, travel behaviors. Improved knowledge leads to greater satisfaction with mobility travel. Furthermore, an interaction effect is found between cognitive distance and cognitive direction concerning users’ satisfaction with the driving range and charging issues of electric vehicles.

Freight activity-travel pattern generation (FAPG) as an enhancement of freight (trip) generation modelling: Methodology and case study

Trip-based models and activity-based models represent two extreme ends of the spectrum of travel demand models in data granularity requirement and ability to reflect the underlying motivation to travel. Modelling of representative freight activity-travel patterns (RFAPs) has the potential to serve as the bridge between these approaches. RFAP clusters represent homogeneous groups of establishments, where utility maximization models predict the probability that an establishment belongs to a particular cluster. However, it is still an open question how to define, interpret and model activity-travel patterns in the context of freight system. To answer this question, this study conducted a large-scale establishment-based freight survey (EBFS) in seven cities of India and resulted in a sample of 432 establishments and their 1613 shipment records. In the first part, this paper proposes a novel approach for identifying RFAPs based on the notion that “activities” that inspire trip-making for passenger is equivalent to “freight orders” in the case of establishments. The cluster analyses revealed the presence of three well separated main clusters and nine less separated nested clusters. Through interpretation and labelling of these RFAPs, freight travel market is categorized into useful segments. The results suggested that a priori industrial classification systems used in trip-based models are overly simplified representations of the complex structure of the travel patterns. In the last part, freight activity-travel pattern generation (FAPG) models are developed which predicts the probability that an establishment exhibits a particular RFAP. The FAPG models developed using these RFAPs could replace the traditional freight generation (FG) and freight trip generation (FTG) models due to its ability to convert the assigned activity-patterns to trips or tonnage. For example, FAPG model suggest that at an employment level of 120, there is a 56% probability that establishments will exhibit MDV-HFMH (medium duty vehicles - high frequency medium haul) pattern which, in turn, implies that FP = 1630 tons/year; shipment frequency, i.e., FTP = 8 trips/week; length of haul = 240.6 km and commercial vehicle type choice = MDV. Thus, FAPG models can present an enhanced representation of freight flows since both FG and FTG are jointly modeled in this approach. That is, the best features of both commodity-based modelling (i.e., ability to capture the fundamental mechanism that drives freight demand) and vehicle-based modelling (i.e., ability to capture freight traffic implications) are included in FAPG models. The study findings are expected to assist in identifying the variations in establishments’ preferences so that it is possible to identify the type of transport supply improvements that the establishments will respond to accurately, and thus prioritize the infrastructure investments. Moreover, the discussions on these findings are expected to improve the behavioral and spatial foundations of traditional freight models.

On the role of subjective well-being in mediating the relationship between spatiotemporal and health variables

IntroductionThe effects of health on various activity-travel patterns have been investigated in various studies. However, it is hard to disregard that activity-travel patterns can also shape the health of individuals. The literature shows that activity-travel patterns insignificantly correlate with health parameters. It is hypothesised that there are some variables such as subjective well-being that intermediate the effects of activity-travel patterns on health. This paper aimed to investigate the role of two subjective well-being variables (daily experience and global subjective well-being) to mediate the effects of spatiotemporal variables on health. The health variables covered physical, mental and social health which refer to people's limitations to engage in various activities including social activities caused by physical, mental and emotional, and emotional problems, respectively. MethodModified structural equation modelling is used to explore whether daily experience and global subjective well-being are intermediate variables between spatiotemporal and health variables. ResultsThe results confirmed that endogenous global subjective well-being mediated the effects of spatiotemporal variables on physical and social health, but not on mental health. Endogenous daily experience was not found to significantly correlate with any health parameters. ConclusionDaily experience is a short-term (cognitive) well-being shaped by people's time and space variables, whereas global subjective well-being is a long-term well-being variable shaped by short-term variables (e.g., daily experience and daily time and space variables) and life circumstances. The long-term well-being variable was found to significantly correlate with health, but not the short-term well-being variable. Opportunities offered by spatial conditions show a higher magnitude in improving people's daily experience and global subjective well-being than the activity-travel participation variables.

Integration of ridesharing and activity travel pattern generation

This paper proposes a mobility system serving as an innovative platform that synchronises a ridesharing model and an Activity Travel Pattern (ATP) generator, enabling a synergistic enhancement of participants’ mobility experiences. Through system-level integration, it can significantly improve the performance of ridesharing systems while also exploring the intricate relationship between ridesharing and participants’ ATPs. Numerical examples demonstrate the advantages of ridesharing systems when armed with flexible/variable announcements from the ATPs-based participants. The results show that incorporating ATP-based announcements in the system increases the chances of finding a match, and the attractiveness of the ridesharing mode for participants is improved. Despite their lower number, ATP-based participants contribute significantly to finding matches and obtaining distance savings. The study also highlights the key role of tour gyration in the performance of the ridesharing system. It incentivises participants with distant activity locations to join the ridesharing system as ATP-based participants.

Model-Based Vehicle-Miles Traveled and Emission Evaluation of On-Demand Food Delivery Considering the Impact of COVID-19 Pandemic

The market for on-demand food delivery (ODFD) has increased considerably, especially during the COVID-19 pandemic. It is crucial for transportation and environmental agencies to understand how ODFD has reshaped the travel patterns of people, affecting vehicle-miles traveled (VMT) as well as pollutant emissions in the transportation system. However, the lack of public data from food delivery companies makes it challenging to quantify the impact of on-demand delivery on the real-world transportation network. In this research, we propose a comprehensive framework to quantify the VMT and emissions incurred by ODFD with three main components: (i) a daily activity generation tool, Comprehensive Econometric Micro-simulator for Daily Activity-travel Patterns, to create a simulation scenario of ODFD behaviors based on a real-world roadway network and population demographics in the City of Riverside, California; (ii) an efficient order dispatching and routing algorithm, adaptive large neighborhood search, to obtain a high quality order dispatching and routing plan; (iii) an emission evaluation model, emission factor (EMFAC), to evaluate pollutant emissions from all dining-related trips. Both short-term and long-term impacts of the COVID-19 pandemic are evaluated. Experimental results show that ODFD has great potential to reduce the dining-related VMT and emissions. The total dining-related VMT in the during-pandemic case decreased by 38% and in the after-pandemic case reduced by 6% to 9%, and the corresponding environmental impacts were reduced accordingly. Meanwhile, emissions reduced significantly with more electric vehicles involved in food delivery. With 100% electric delivery fleet, the ODFD service can save 14% to 22% of emissions after the COVID-19 pandemic.

Evolution of Mode Use During the COVID-19 Pandemic in the United States: Implications for the Future of Transit

The COVID-19 pandemic has brought about transformative changes in human activity-travel patterns. These lifestyle changes were naturally accompanied by and associated with changes in transportation mode use and work modalities. In the United States, most transit agencies are still grappling with lower ridership levels, thus signifying the onset of a new normal for the future of transit. This paper addresses this challenge using a novel panel survey data set collected from a representative sample of individuals across the United States. The study involved the estimation of a panel multinomial probit model of mode choice to capture both socio-economic effects and period (pre-, during-, and post-COVID) effects that contribute to changes in mode choice. This paper provides rich insights into the evolution of commute mode use as a result of the pandemic, with a particular focus on public transit. Through a rigorous modeling approach, this paper provides a deep understanding of how transit use has evolved, how it is likely to evolve into the future, and the socio-economic and demographic characteristics that affect the evolution (and expected future use) of public transit. Results suggest that transit patronage is likely to remain depressed by about 30% for the foreseeable future, in the absence of substantial changes in service configurations. This study also shows that minority groups and those living in higher density regions are more likely to exhibit a return to transit use in the post-pandemic period.

Data Domotopia: introduction to the quantitative survey

This paper describes the Data Domotopia a 2300 + respondent self-administered web-based survey. It includes 100 + multi-purpose items about home-making and stillness in a moving world. We suppose that home-making can reveal coping strategies and resilience practices to make everyday life work – as home is a central location in people’s activity-travel patterns. To describe this phenomenon, the concept of Domotopia is introduced, defining how people arrange, use, and experience their homes to cope with the pathologies of accelerated and liquid modernity (Bauman 2005). While the Data Domotopia is based on a mixed-method combining qualitative and quantitative material, this paper focuses mainly on the description of the questionnaire – which is organized into three interrelated layers: the dwelling, the dwellers, and the neighborhood. Each of these layers unfolds in functional, social, emotional and sensory components. The survey covers most of the contemporary issues related to home-making. This includes the domestic space and gender issues; the socio-spatial resources (mobility, action space, core, and wider social network); lifestyles, ideals, and residential aspiration; time pressures, time use, organization and stress; equipment, rules and arrangements; interpersonal relations, cohabitation and negotiation, dominance and power. Intakes on the Data Domotopia is given by two concrete cases about the time-space coverage of the habitual action space, and about inter-personal task allocation. These examples show the potential of the data to study domocentric stillness and resilience to urban pathologies. The data – aggregated to the infra-communal level – is available for research purposes.

Analysis of Changes in Time Use and Activity Participation in Response to the COVID-19 Pandemic in the United States: Implications for Well-Being

This research aims to investigate the well-being implications of changes in activity-travel and time-use patterns brought about by the COVID-19 pandemic. The study uses American Time Use Survey (ATUS) data from 2019 and 2020 to assess changes in activity-travel and time-use patterns. It applies two methods—a well-being scoring method and a time-poverty analysis method—to evaluate the impacts of these changes on society. The results show that individuals experienced diminished well-being during the pandemic even when their time-poverty statistics showed an improvement; this is because the pandemic did not allow individuals to pursue activities in a way that would enhance well-being. In general, well-being is positively associated with the pursuit of discretionary activities in the company of others in favored out-of-home locations. This explains why people have rapidly embraced traveling again in a post-pandemic era. At the same time, people desire more discretionary time (less time poverty); because the elimination of the commute contributes to this, workers are reluctant to return fully to the workplace. Planning processes need to account for a new normal in which activity-travel patterns will be increasingly shaped by the human desire to accumulate positive life experiences.

Effects of Periodic Location Update Polling Interval on the Reconstructed Origin–Destination Matrix: A Dutch Case Study Using a Data-Driven Method

Global System for Mobile Communications (GSM) data provides valuable insights into travel demand patterns by capturing people's consecutive locations. A major challenge, however, is how the polling interval (PI; the time between consecutive location updates) affects the accuracy in reconstructing the spatio-temporal travel patterns. Longer PIs will lead to lower accuracy and may even miss shorter activities or trips when not properly accounted for. In this paper, we analyze the effects of the PI on the ability to reconstruct an origin–destination (OD) matrix. We also propose and validate a new data-driven method that improves accuracy in case of longer PIs. The new method first learns temporal patterns in activities and trips, based on travel diaries, that are then used to infer activity-travel patterns from the (sparse) GSM traces. Both steps are data-driven thus avoiding any a priori (behavioral, temporal) assumptions. To validate the method we use synthetic data generated from a calibrated agent-based transport model. This gives us ground-truth OD patterns and full experimental control. The analysis results show that with our method it is possible to reliably reconstruct OD matrices even from very small data samples (i.e., travel diaries from a small segment of the population) that contain as little as 1% of the population’s movements. This is promising for real-life applications where the amount of empirical data is also limited.

Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents.

This study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment. Focusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities. Results of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city. Integrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.

Formulation and solution for calibrating boundedly rational activity-travel assignment: An exploratory study

Parameter calibration of the traffic assignment models is vital to travel demand analysis and management. As an extension of the conventional traffic assignment, boundedly rational activity-travel assignment (BR-ATA) combines activity-based modeling and traffic assignment endogenously and can capture the interdependencies between high dimensional choice facets along the activity-travel patterns. The inclusion of multiple episodes of activity participation and bounded rationality behavior enlarges the choice space and poses a challenge for calibrating the BR-ATA models. In virtue of the multi-state supernetwork, this exploratory study formulates the BR-ATA calibration as an optimization problem and analyzes the influence of the two additional components on the calibration problem. Considering the temporal dimension, we also propose a dynamic formulation of the BR-ATA calibration problem. The simultaneous perturbation stochastic approximation algorithm is adopted to solve the proposed calibration problems. Numerical examples are presented to calibrate the activity-based travel demand for illustrations. The results demonstrate the feasibility of the solution method and show that the parameter characterizing the bounded rationality behavior has a significant effect on the convergence of the calibration solutions.

An Online Interactive Dashboard to Explore Personal Exposure to Air Pollution

Studies increasingly examine individual exposure to air pollution while accounting for person-specific activity-travel patterns. Supporting policymakers and local communities using the resulting data requires transparent and ethical communication of exposure levels to affected individuals and other stakeholders. This paper asks how an interactive online dashboard might represent individual-level air pollution exposure profiles to different audiences while respecting individuals’ geoprivacy. Using data from 37 Oxford (UK) residents, it shows that heterogeneous individual-level exposure profiles can be shared ethically through different combinations of visualisation method, spatial and temporal resolution of data representation and Geomasking techniques for different dashboard user groups.

Delineating Urban Community Life Circles for Large Chinese Cities Based on Mobile Phone Data and POI Data—The Case of Wuhan

In the recent decade, a new concept, urban community life circle (CLC), has been introduced and widely applied to Chinese community planning and public service facilities configuration alongside people-oriented urbanization. How to delineate the CLC has become a core task of urban CLC planning. The traditional way to determine the CLC using administrative boundaries does not fully consider the needs of residents. Recent research on urban CLC delineation is usually based on residential behavior survey using sample surveys or GPS data. However, it is difficult to generalize the sample surveys or GPS surveys for one specific community to that for others, because of the extremely high cost. Due to the ubiquity of the location-based service (LBS) data, i.e., the mobile phone data and points of interest (POI) data, they can serve as a fine-grained and continuous proxy for conducting human daily activity research with easy accessibility and low cost. Mobile phone data can represent the daily travel activities of residents, and POI data can comprehensively describe the physical conditions. In this paper, we propose a method from both the social and physical perspectives to delineate the CLC based on mobile phone and POI data, named DMP for short. The proposed DMP method is applied to Wuhan. We decipher the CLC’s boundary and residents’ travel activity patterns and demonstrate that (1) the CLC is not a regular circle but a non-homogeneous corridor space extending along streets; and (2) adjacent CLCs are found to share some daily facilities. Based on these findings, we propose that CLC planning should be data-based and people-oriented in general. In addition, sufficient space in the overlapping region of the CLCs should be preserved for future planning of public service facilities configuration, given that adjacent CLCs share some daily facilities.