Daily Activity-travel Patterns Research Articles

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.

Effect of travel satisfaction and heterogeneity of activity-travel patterns of other persons in the household on social and mental health: The case of Bandung Metropolitan area

Travel satisfaction (TS) is used in this study as a proxy for subjective well-being in the travel domain, which is hypothesised to mediate the relationship between the activity–travel pattern and health variables. Moreover, multilevel modelling with instrumental variables is used, so that the heterogeneity of other household members’ activity–travel patterns can be included. Considering the heterogeneity effects of other household members, this study shows that TS has a positive correlation with both social health (SH) and mental health (MH). The model shows that those who stay at home and work or study longer tend to have a high TS, and in turn high SH and MH. It can be understood that people who have longer time commitments to obligatory activities appreciate their trips more because they are observed to have the lowest MH conditions. Longer times spent on studying and working and in-home activities are indicated to limit people’s participation with other household members, which in turn correlates with low MH. Any efforts to undertake more travel for those who have more commitments at home and in the workplace can result in better TS and in turn SH and MH. This study confirms that more regular daily activity–travel patterns with regular breaks from obligatory activities are observed to correspond to better MH. More regular daily activity–travel patterns of other household members can make individuals organise their own more regular activity–travel patterns, which correlates with better SH and MH.

Heterogeneity in job application decisions in two-adult households under social influence: A latent class mixed logit model

Work schedules with their start and end times substantially affect traffic flows, particularly during peak hours. Therefore, the study of work schedules is important to understand daily activity travel patterns. Particularly adults in households with children need to trade-off the number of working hours versus their time expenditure on child care, household tasks and leisure. Work schedule preferences are reflected in job application decisions in the sense that people are less likely to apply if the job profile is incongruent with their time use preferences. Job application decisions may also be influenced by attitudes of members of a household’s social network and peer groups. The aim of this study is to analyze unobserved heterogeneity and the effects of job properties and social influence on the decision to apply for a job in two-adult households with children. To that end, a stated choice experiment, in which respondents are asked whether they would apply for a job of a particular profile and a certain set of attitudes of members of their social network, is constructed and implemented. A latent class mixed logit model with two latent classes is applied to estimate the effects of work schedule attributes, social influence and socio-demographic characteristics on the probability of applying for a particular job. Results show that in both classes the effects of job attributes are more significant than social influence, and that the effects of the number of working hours and salary differ show considerable unobserved heterogeneity.

Measuring joint space-time accessibility in transit network under travel time uncertainty

In densely populated areas, joint activity participation such as shopping and eating outside represents a substantial portion of individuals' daily activity-travel patterns. Individuals have several considerations in making joint activity choice behaviour such as space-time coordination, activity start time, activity location and joint activity duration. In a congested transit network, the travel time uncertainty significantly affects individuals' activity and travel choice behaviour. To conduct some important joint activities, individuals normally have high expectations of on-time arrival. Thus, the measurement of space-time accessibility in transportation networks should be extended to consider joint activity choices and travel time uncertainty. In this paper, a new method for measuring space-time accessibility is proposed for individuals' joint activities in transit network with consideration of travel time uncertainty. A reliable alighting location model is proposed to identify all feasible alighting locations in transit network to perform joint activities by explicitly considering constraints of on-time arrival probabilities. Individuals’ joint space-time accessibility (JSTA) is measured based on reliable alighting locations, travel time budget, and points of interests. Massive smart card data from metro network in Nanjing, China are used to show the merits of the proposed JSTA measure. The results show that there is a significant difference between the space-time accessibility to independent activity and that to joint activity under various on-time arrival probabilities. The effects of spatial (de)concentration of anchor locations and minimum joint activity duration on JSTA, and the role of additional participants are also presented in various scenarios. The proposed JSTA measure can be used to help policy makers to evaluate transit network development and land use planning in urban areas.

Heterogeneity in Activity-travel Patterns of Public Transit Users: An Application of Latent Class Analysis

Public transit is considered a sustainable mode of transport that can address automobile dependency and provide environmental, economic, and societal benefits. However, with typical temporal and spatial constraints such as fixed routes and schedules, transfer requirements, waiting times, and access/egress issues, public transit offers lower accessibility and mobility services than private vehicles and thus it is considered a less attractive mode to many prospective users. To improve the performance of transit and in turn to increase its usage, a broader understanding of the daily activity-travel patterns of transit users is fundamental. In this context, this study analyzed transit-based activity-travel patterns by classifying users via Latent Class Analysis (LCA). Using data from the 2017 National Household Travel Survey, the LCA model suggested that transit users could be split into five distinct classes where each class has a representative activity-travel pattern. Class 1 constituted employed white males who made transit-dominant simple work tours. Class 2 was composed of employed white females who made complex work tours. Employed white millennials comprised Class 3 and made multimodal complex tours. Transit Class 4 were non-white younger or older adult groups who made transit-dominant simple non-work tours. Last, Class 5 members made complex non-work tours with recurrent transit use and comprised single older women. This study provided insights regarding the variations of activity-travel patterns and the associated market segments of transit users in the United States. The results can assist transit agencies in identifying transit user groups with particular activity patterns and to consider market strategies that can address their travel needs.

COVid-19 influenced households’ Interrupted Travel Schedules (COVHITS) survey: Lessons from the fall 2020 cycle

The spread of the novel coronavirus disease-2019 (COVID-19) since early in 2020 has affected every aspect of daily life, including urban passenger travel patterns. Lockdowns to control the spread of COVID-19 created unprecedented travel demand contexts that have never been seen in modern history. So, it is essential to benchmark trends of travel behaviour, especially people's daily travel patterns that are necessary to develop a comprehensive understanding of the impacts of COVID-19. A multi-cycle benchmarking household travel study: the COVid-19 influenced Households' Interrupted Travel Schedules (COVHITS) Survey was implemented in the Greater Toronto Area with a random sample of over 4000 households. The results indicated a stark alteration in people's daily activity-travel patterns due to COVID-19. The pandemic caused a substantial decline in mobility in the study area. The average weekday household trip rate dropped from 5.2 to 2.0 trips. Transit modal shares suffered severely during the paramedic, while private car dependency was enhanced. Overall, transit modal share dropped from 17.3% to 8.1% in the study area, while the modal share of private cars increased from 70.8% to 74.1%. Factors such as having to work from home, ownership of private cars, and household incomes influenced mobility levels of the people in the study area during the pandemic. While overlooked, travel demand analysis can reveal effective strategies to curb the spread of such contagious diseases. An econometric model and analysis of sample data reveal several potential strategies. These include: (1) working/learning from home should be implemented until the end of the pandemic; (2) transit agencies should provide as much transit frequency as possible (particularly for bus routes) during peak hours to avoid crowding inside transit vehicles and project a positive image of public transit; and (3) strict restrictions should be implemented in regions with lower confirmed COVID-19 cases, as they became attractive destinations during the pandemic.

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.

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.

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.

Exploration of Statewide Fragmentation of Activity and Travel and a Taxonomy of Daily Time Use Patterns using Sequence Analysis in California

Sequence analysis is used in this paper to measure fragmentation in activity participation and travel. Fragmentation here is defined as the sequencing of many short and long activities and trips that happen in a personal daily schedule. Studying sequences of daily episodes (each activity at a place and each trip) is preferable over other techniques of studying activity–travel behavior because sequences include the entire trajectory of a person’s activity during a day while jointly considering the number of activities and trips, their ordering, and their durations. We first identify places visited and duration at each place on a minute-by-minute basis, then we derive representative daily behavior patterns using hierarchical clustering. Our study shows there are at least nine distinct daily patterns with different sequencing of activities and travel as well as travel time ratios and modal split. These patterns include typical commute to work or school, staying at home all day, or traveling extensively. As expected, day of the week plays a major role in the type of daily activity–travel patterns. Travel time ratios are also examined for each daily pattern and we find differences in the role played within each pattern between central city, suburban, exurban, and rural dwellers. In a comparison of couples, we find systematically higher fragmentation in households that have children and their parents are employed, with women showing higher fragmentation in the activity–travel patterns.

Mode choice behaviour of students, integrating residential location characteristics: a study from Kochi City, India

Mode choice decision of individuals plays a vital role in transportation planning. Individual travel behaviour models can be improved by extending the set of influencing variables used for modelling. In a developing country like India, students contribute a major share of total travel demand especially during morning and evening peak hours of traffic; whose individual travel characteristics are very less studied by transportation professionals. This paper presents exploratory and statistical analysis of mode choice behaviour of students in Kochi City, India. The socio-demographic characteristics, activity-travel behaviour as well as residential location characteristics of students during a usual working day is collected using activity-travel survey data, in Kochi Municipal Corporation. Preliminary analysis gives details on daily activity-travel pattern, mode choice preferences and other particulars of commuters in the study area. Statistical models were developed for understanding the factors affecting mode choice decision and separate mode choice models are also developed for different categories of students. Simulation of choice probabilities over different attributes is also done to identify the potential policy variables that can promote the use of sustainable modes.

Exploring the correlations between spatiotemporal daily activity-travel patterns and stated interest and perception of risk with self-driving cars

Abstract. The key to Autonomous Vehicles (AVs) successful penetration of markets lies in identifying specific needs that AVs satisfy for daily activity-travel participation of individuals. In this paper we explore whether and to what extent people’s exhibited spatiotemporal activity-travel patterns correlate with their stated perceptions about self-driving cars. We investigate the travel diaries of 3,411 survey respondents who live in the Puget Sound region of the U.S. in 2017 using sequence analysis. In parallel, we apply hierarchical clustering to identify people’s attitudes based on their stated interest and perception of risks about AVs. A multinomial regression model is built to examine the correlations between AV attitude clusters and daily activity-travel patterns. Statistically significant correlations are then identified. The model results suggest that people exhibiting different activity-travel behavior patterns also express distinct attitudes towards the uses of AVs. The model shows that people who travel to work during the day are more likely to be positive to AVs. In particular, the group traveling to work later than the regular 8-to-5 schedule shows stronger interest and less concerns to AVs, which can be partially explained by the diverse activities they do throughout the day, the variety of travel modes they use and presumably more schedule flexibility they need than the public transportation system offers.

A Dynamic Discrete Choice Activity-Based Travel Demand Model

This paper presents a dynamic discrete choice model (DDCM) for daily activity–travel planning. A daily activity–travel pattern is constructed from a sequence of decisions of when, where, why, and how to travel. Individuals’ preferences for activity–travel patterns are described by the sum of the utility of all travel and activity episodes in that pattern, but components of the utility functions, such as travel times, may be stochastic. In each decision stage, individuals act as if they maximized the expected utility of the remainder of the day. The DDCM-model presented allows for a detailed treatment of timing decision consistent with other choice dimensions, respects time–space constraints, and enables the inclusion of explicitly modeled uncertainties in, for example, travel time. In a case study, a model for daily planning of activity and travel on workdays is estimated whereby individuals can perform any number of trips that each is a combination of one of 1,240 locations, four modes, and six activities. Simulation results indicate that the model within sample accurately replicates timing decisions, trip lengths, and the distributions of the number of trips, tours, and trips per tour.

Coordinated Daily Activity Pattern Type in an Applied Activity-Based Travel Model System: Specifics of Modeling Large Households and Modality Preferences

This paper describes a modified approach to modeling an individual daily activity-travel pattern (DAP) coordinated at the household level. The model was primarily introduced to handle large households that are typical for the city of Jerusalem. However, the developed method proved useful in adding more behavioral aspects to the model. The study introduced daily modality and added the emphasis on modeling it for all household members simultaneously. It is of special practical value for Jerusalem since such population sectors as ultra-Orthodox Jewish and Arabs are characterized by a large share of persons who have an entire day of travel implemented in a non-motorized fashion. In addition to daily modality, this paper presents a simple but useful approach to understand at-home time-use. A binary choice model was formulated for the main reason of being inactive (working-at-home or other). The paper also discusses possible extensions of the current approach as well as alternative approaches to frame DAP type choice in the context of a complete activity-based travel model (ABM). In particular, this model can be easily extended in order to better address the joint nature of the choices using the Gibbs sampler. The paper discusses how DAP type choice can be framed in many ways and with different levels of detail subject to the ultimate model system design.

Using Hazard-Based Duration Models to investigate the Impacts of RUC on Libyan Drivers Travel Patterns

This paper studies the perceived impacts of road user charging (RUC) on drivers daily activity travel patterns in urban areas by using one of the large Libyan cities (Benghazi) as the case study. The analysis uses full parametric hazard models and data from a field-based RUC experiment that was conducted in Benghazi. The database consists of activity-based travel durations. This paper focuses on the analysis of the durations of drivers travel-to-work trips and addresses the changes on the patterns of driver’s trips over the study period to investigate the differences attributable to origin (home-based), time, gender and age as a result of the RUC impacts. The results indicate that RUC can have a positive impact in reducing traffic congestion with reduction of driver’s trips crossing restricted areas during peak period by around 35.5%. The paper concludes that RUC could serve as a good instrument in reducing traffic congestion and in improving the environment in city centre of Benghazi. The results could be of benefit to transport policy makers.

Daily activity-travel scheduling behaviour of non-workers in the National Capital Region (NCR) of Canada

This paper uses household travel survey data (of the National Capital Region of Canada) and a comprehensive random utility maximizing travel options modelling approach to investigate non-workers’ activity-travel scheduling behaviour. The empirical model reveals that the presence of children shapes the daily activity-travel patterns of non-workers by reducing the flexibility of out-of-home activity-type choices. Availability of private cars increases flexibility in travelling and increases the spread of spatial locations of out-of-home activities of non-workers. Income plays a significant role in non-workers’ activity-travel behaviour and it seems that non-workers from lower to middle-income households are less active (return home early) than those living in higher income households. In general, it is found that male non-workers are less active than the female non-workers and it is also evident that non-workers living in single detached houses are less active (return home early) than those living in condos/apartments. These findings have an implication to health issues as the average age of non-workers is over 50years and the majority of detached houses are far from the central business district.

Forecasting travel behavior using Markov Chains-based approaches

Recent advances in agent-based micro-simulation modeling have further highlighted the importance of a thorough full synthetic population procedure for guaranteeing the correct characterization of real-world populations and underlying travel demands. In this regard, we propose an integrated approach including Markov Chain Monte Carlo (MCMC) simulation and profiling-based methods to capture the behavioral complexity and the great heterogeneity of agents of the true population through representative micro-samples. The population synthesis method is capable of building the joint distribution of a given population with its corresponding marginal distributions using either full or partial conditional probabilities or both of them simultaneously. In particular, the estimation of socio-demographic or transport-related variables and the characterization of daily activity-travel patterns are included within the framework. The fully probabilistic structure based on Markov Chains characterizing this framework makes it innovative compared to standard activity-based models. Moreover, data stemming from the 2010 Belgian Household Daily Travel Survey (BELDAM) are used to calibrate the modeling framework. We illustrate that this framework effectively captures the behavioral heterogeneity of travelers. Furthermore, we demonstrate that the proposed framework is adequately adapted to meeting the demand for large-scale micro-simulation scenarios of transportation and urban systems.

A user equilibrium model for combined activity–travel choice under prospect theoretical mechanisms of decision-making under uncertainty

ABSTRACTRather than considering single trips as the unit of analysis, the activity-based modeling paradigm of travel demand analysis has led to reconceptualizations and innovations in traffic flow models by focusing on complete daily activity–travel patterns. The vast majority of these travel demand and traffic flow models have either implicitly or explicitly assumed that travelers choose between alternatives by maximizing their utility under a deterministic representation of the choice alternatives. While this behavioral assumption leads to tractable, easy-to-apply models, the validity of the assumption largely went untested. This paper investigates the user equilibrium of activity–travel patterns under uncertainty from the perspective of prospect theory. A formulation of the static activity-based user equilibrium model is proposed. In particular, we adopt the concept of a multi-state supernetwork to represent the choice space of activity–travel patterns. A numerical example using hypothetical scenarios is presented to illustrate the proposed model and solution algorithm.

Examination of the Impacts of Telecommuting on the Time Use of Nonmandatory Activities

How and to what extent telecommuting engagement affects time allocation among nonmandatory activities are examined to help understand the impacts of telecommuting on daily activity–travel patterns. Five categories of nonmandatory activities are considered: shopping, maintenance, discretionary, escort, and in-home shopping. The hypothesis is that telecommuting relaxes the temporal and spatial constraints related to work activities at the regular workplace, and telecommuters may allocate some of the time budget to other nonmandatory activities, which may or may not lead to additional travel. The structural equations model approach is applied to capture the impacts of telecommuting as well as the interactions among the nonmandatory activities. The activity durations by type along with the number of total daily trips are considered as endogenous (dependent) variables. By incorporating work hours at the regular workplace and daily telecommuting hours as exogenous variables, the models can reveal how people may reallocate their time among different nonmandatory activities given different levels of telecommuting engagement (either part-day or full-day). All types of telecommuting arrangements increased nonmandatory activity durations (compared with those of nontelecommuters). Full-day telecommuters have higher durations of discretionary activities, while part-day telecommuters have higher durations of maintenance and out-of-home shopping errands. Telecommuting also increased total daily trip rates for both telecommuters and their household members. This study used data obtained from the 2010–2011 Regional Household Travel Survey in the New York metropolitan region.

Investigating the impacts of weather variability on individual’s daily activity–travel patterns: A comparison between commuters and non-commuters in Sweden

Understanding travel behaviour change under various weather conditions can help analysts and policy makers incorporate the uniqueness of local weather and climate within their policy design, especially given the fact that future climate and weather will become more unpredictable and adverse. Using datasets from the Swedish National Travel Survey and the Swedish Meteorological and Hydrological Institute that spans a period of thirteen years, this study explores the impacts of weather variability on individual activity–travel patterns. In doing so, this study uses an alternative representation of weather from that of directly applying observed weather parameters. Furthermore, this study employs a holistic model structure. The model structure is able to analyse the simultaneous effects of weather on a wide range of interrelated travel behavioural aspects, which has not been investigated in previous weather studies. Structural equation models (SEM) are applied for this purpose. The models for commuters and non-commuters are constructed separately. The analysis results show that the effects of weather can be even more extreme when considering indirect effects from other travel behaviour indicators involved in the decision-making processes. Commuters are shown to be much less sensitive to weather changes than non-commuters. Variation of monthly average temperature is shown to play a more important role in influencing individual travel behaviour than variation of daily temperature relative to its monthly mean, whilst in the short term, individual activity–travel choices are shown to be more sensitive to the daily variation of the relative humidity and wind speed relative to the month mean. Poor visibility and heavy rain are shown to strongly discourage the intention to travel, leading to a reduction in non-work activity duration, travel time and the number of trips on the given day. These findings depict a more comprehensive picture of weather impact compared to previous studies and highlight the importance of considering interdependencies of activity travel indicators when evaluating weather impacts.