Activity-travel Patterns Research Articles

Trip chaining propensity and tour mode choice of out-of-home workers: evidence from a mid-sized Canadian city

Suburban development patterns, flexible work hours, and increasing participation in out-of-home activities are making the travel patterns of individuals more complex, and complex trip chaining could be a major barrier to the shift from drive-alone to public transport. This study introduces a cohort-based approach to analyse trip tour behaviors, in order to better understand and model their relationships to socio-demographics, trip attributes, and land use patterns. Specifically, it employs worker population cohorts with homogenous activity patterns to explore differences and similarities in tour frequency, trip chaining, and tour mode choices, all of which are required for travel demand modeling. The paper shows how modeling of these important tour variables may be improved, for integration into an activity-based modeling framework. Using data from the Space–Time Activity Research (STAR) survey for Halifax, Canada, five clusters of workers were identified from their activity travel patterns. These were labeled as extended workers, 8 to 4 workers, shorter work-day workers, 7 to 3 workers, and 9 to 5 workers. The number of home-based tours per day for all clusters were modeled using a Poisson regression model. Trip chaining was then modeled using an Ordered Probit model, and tour mode choice was modeled using a Multinomial logit (MNL) model. Statistical analysis showed that socio-demographic characteristics and tour attributes are significant predictors of travel behavior, consistent with existing literature. Urban form characteristics also have a significant influence on non-workers’ travel behavior and tour complexity. The findings of this study will assist in the future evaluation of transportation projects, and in land-use policymaking.

How would you change your travel patterns if you used an electric vehicle? A stated adaptation approach

The real environmental benefits of a transition towards EVs highly depend on the future EV-users’ activity-travel patterns adapted to their new vehicle’s capacity. Despite its importance, the impact of this adaptation is largely unknown. In this study, a stated adaptation experiment has been conducted to investigate changes of travel patterns as a result of range limitations or the opposite, abundant range. The basis for this experiment is a one-day travel diary among active drivers in Greater Stockholm. The main findings of this study are the following: (1) Drivers facing range limitations are more likely to make use of alternative means of transport (mainly public transport) if the travel time difference between car and public transport is low and if not many transfers are needed for the public transport trip. (2) In case of (perceived) range limitations, shopping trips and trips visiting friends or relatives are more likely to be cancelled than working trips. (3) The main trip purpose of additional trips in case of sufficient EV range is shopping. (4) A non-negligible number of public transport trips are likely to be replaced by EV. Shortly, the effects of the transition towards electric vehicle use on personal mobility seem to depend on the availability of accessible substitutes. Besides that, a rebound effect has been observed in this study.

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.

Activity-travel patterns of workers and students: a study from Calicut city, India

Travel behaviour studies in activity-based perspective treat travel as a result of individual’s desire to participate in different activities. This approach is more significant in the context of developing countries, as the transportation problems are more severe here. Since, commuters contribute to a major share in the travel, understanding their travel behaviour is essential. This paper aims to explore the travel behaviour of commuters in Calicut city, Kerala State, India and thereby model their activity-travel patterns. Household, personal and activity-travel information from 12920 working people and 9684 students formed the database for this study. The data collection was performed by means of home-interview survey by face-to-face interview technique. From preliminary analysis, several simple and complex tours were identified for the study area. Working people’s work participation and students’ education activity participation decision are modelled as mandatory activity participation choice in a binary logit modelling framework. Results of this mandatory activity participation model revealed that male workers are more likely to engage in work compared to females. Presence of elderly persons is found to negatively influence the work participation decisions of workers. This may be due to the fact that, work activity may be partially or completely replaced with the medical requirements of the elderly. The chances for work activity participation increase with increase in number of two-wheelers at home. In the case of students, as the education level increases, they are found to be less likely to participate in education activities. Students are observed to follow simple activity-travel pattern. Complex tours are found to be performed by males, compared to females. Activity-travel pattern of the study group are predicted using the developed models. The percentages correctly predicted indicate reasonably good predictability for the models. These kind of studies are expected to help the town planners to better understand city’s travel behaviour and thus to formulate well-organised travel demand management policies.

EHV-1 Infection of the Equine Male Genital Tract

Using a panel regression model and a multidimensional three-week household time-use and activity diary, this study analyses the complexity of the day-to-day variability in individuals' activity-travel patterns by applying a multidimensional sequence alignment model. It is found that the variability between weekend and weekday pairs is much greater than between weekday-weekday pairs or weekend-weekend pairs. The variability of other household members' activity-travel patterns is found to significantly influence an individual's activity-travel patterns. The results also show that the variability in the activity-travel patterns of workers and students is greater when conducting a particular discretionary activity on weekdays. Due to performing discretionary activities more often and for longer, non-workers tend to have more predictable activity-travel patterns. Undertaking multitasking activities within different activities on weekdays significantly impacted the different degrees of variability in an individual's activity-travel patterns. Having different health and built environment characteristics also corresponds with different degrees of predictability of the activity-travel patterns, particularly in the worker/student case.

Analysis of telecommuting behavior and impacts on travel demand and the environment

The discussion of whether, and to what extent, telecommuting can curb congestion in urban areas has spanned more than three decades. This study develops an integrated framework to provide the empirical evidence of the potential impacts of home-based telecommuting on travel behavior, network congestion, and air quality. In the first step, we estimate a telecommuting adoption model using a zero-inflated hierarchical ordered probit model to determine the factors associated with workers’ propensity to adopt telecommuting. Second, we implement the estimated model in the POLARIS activity-based framework to simulate the potential changes in workers’ activity-travel patterns and network congestion. Third, the MOVES mobile source emission simulator and Autonomie vehicle energy simulator are used to estimate the potential changes in vehicular emissions and fuel use in the network as a result of this policy. Different policy adoption scenarios are then tested in the proposed integrated platform. We found that compared to the current baseline situation where almost 12% of workers in Chicago region have flexible working time schedule, in the case when 50% of workers have flexible working time, telecommuting can reduce total daily vehicle miles traveled (VMT) and vehicle hours traveled (VHT) up to 0.69% and 2.09%, respectively. Considering the same comparison settings, this policy has the potential to reduce greenhouse gas and particulate matter emissions by up to 0.71% and 1.14%, respectively. In summary, our results endorse the fact that telecommuting policy has the potential to reduce network congestion and vehicular emissions specifically during rush hours.

The changes of activity-travel participation across gender, life-cycle, and generations in Sweden over 30\xa0years

This study utilised the Swedish national travel survey covering a period of over 30 years. We investigated the long-term trends in activity-travel patterns of individuals in different life-cycle stages and generations using cohort analysis and a path model. The main findings are summarised as follows. The women, including mothers, in younger generations have become more active in out-of-home non-work activities and their trip chaining has become more complex, compared to their male counterparts. While men are still driving more than women, the gap is decreasing in the younger generations. The gender difference among teenagers in terms of out-of-home time use diminishes in younger generations. Teenagers of younger generations spend more of their leisure time inside their homes, possibly due to the rise of online activities and gaming and more time-consuming school trips, the latter attributed to changes in school choice policy. Older adults travel more, possibly due to better paratransit transport service, supported by better health services.

Multi-day activity-travel pattern sampling based on single-day data

Although it is important to consider multi-day activities in transportation planning, multi-day activity-travel data are expensive to acquire and therefore rarely available. In this study, we propose to generate multi-day activity-travel data through sampling from readily available single-day household travel survey data. A key observation we make is that the distribution of interpersonal variability in single-day travel activity datasets is similar to the distribution of intrapersonal variability in multi-day. Thus, interpersonal variability observed in cross-sectional single-day data of a group of people can be used to generate the day-to-day intrapersonal variability. The proposed sampling method is based on activity-travel pattern type clustering, travel distance and variability distribution to extract such information from single-day data. Validation and stability tests of the proposed sampling methods are presented.

PROTOCOL: The effects of road infrastructure, and transport and logistics services interventions on women's participation in informal and formal labour markets in low- and middle-income countries: a systematic review.

PROTOCOL: The effects of road infrastructure, and transport and logistics services interventions on women's participation in informal and formal labour markets in low- and middle-income countries: a systematic review.

Incorporating free-floating car-sharing into an activity-based dynamic user equilibrium model: A demand-side model

Free-floating car-sharing (FFC) has recently received increasing attention due to the flexibility in mobility services. Existing studies related to FFC mainly focus on the analysis of operational management and user preferences. Efforts to model the dynamic choices of free-floating shared cars (SCs) in individuals’ daily multi-modal multi-activity trip chains have still been rare. This study proposes a tolerance-based dynamic user equilibrium model of activity-travel scheduling that formulates free-floating SC as an alternative transport mode for conducting daily activities. The model embeds the choice of SC into daily trip chains by extending the state-of-the-art multi-state supernetwork representation. The dynamic traffic flows and supply-demand interactions of SCs are captured endogenously. Moreover, traveler heterogeneity and different pricing schemes are taken into account. A path-flow swapping method is suggested to solve the tolerance-based dynamic user equilibrium model. Numerical examples of various scenarios demonstrate that fleet size, distribution, and rental-parking price of FFC significantly influence the choice of SC and activity-travel pattern.

Interactive online machine learning approach for activity-travel survey

This article proposes a framework for an interactive activity-travel survey method, implementable on mobile devices such as smartphones. The proposed method was developed to reduce the burden (i.e., frequency of questions) on respondents in long-term behavioral surveys, without relying on external data sources. The method employs an online travel context estimation model and an online machine learning method as interactive processes. The estimation model is used for automatically estimating travel contexts during surveys, while the online machine learning method is used for dynamically updating the estimation model, based on answers from respondents. The proposed method was examined by simulations using data obtained from a past probe person survey. The results suggest that the frequency of inputs by respondents in surveys can be significantly reduced, while maintaining high accuracy of the obtained data. For example, the method successfully estimated certain types of trips (e.g., commuting) and the behaviors of certain respondents (e.g., those whose activity-travel pattern is recurrent) because of the learning process and reduced survey burden on them. Meanwhile, although the method could not always precisely estimate some other types of trips, it eventually obtained accurate results because of the interaction process. Therefore, the proposed method could be useful to reduce the burden on respondents in long-term surveys, while maintaining high data quality and capturing traveler heterogeneity.

Watching the clock on the way to work? Analysing trends in commuting activities, modes and gender differences in commute times, using hazard-based duration modelling methods

This study uses Hazard-based duration modelling methods to investigate commute patterns of males and females, with the aim of exploring the variability of commute times and modes over a period of nine years beginning in 2003. Securing two major datasets of the UK National Travel Survey (NTS) and Tyne and Wear household travel survey, as well as the level of detail that the chosen datasets offer, made it possible to ascertain the complexity of commuter travel at its fundamental level. Duration models have been very popular when analysing duration related activities. However, duration modelling research in the context of transport so far has been restricted to cross sectional one-off datasets. This is the first study that investigates commuting durations at a disaggregate level over a sustained period of nine years using duration modelling methods to acquire a fundamental understanding of the changes in commute durations. Gender aspects as well as the transport modes, including non-motorised transport (NMT), car and public transport (PT), were also addressed in the analysis. Probability density functions (PDFs), survival functions (S(f)) and hazard functions (H(f)) were employed when carrying out in-depth investigations into the patterns of commuting activities generated by males and females for the whole period of study over nine years, followed by year on year analysis. The descriptive analysis shows that commuting times are becoming longer as time goes by. Year on year analysis reveals that commute trips by car made by males are more likely to prevail in the system compared to PT, especially towards the end of the study period in 2010–11. The opposite is true for female commuting trips. NMT was an attractive mode for both males and females in 2009–10 even for longer commute trip durations of over 50min. As the complexity of the activity travel patterns of males and females has not been given adequate attention in previous research, this study made a step forward in investigating the gender aspects, with specific attention being given to the differences in commute times. Transport authorities' and policy makers' timely interventions, such as quality bus partnerships, cycle city guides, and cycle to work schemes, as well as Metro system reinvigorations, including smart ticketing, have been taken into account when rationalizing the results and the changes to commute times over the study period.

MobRISK: a model for assessing the exposure of road users to flash flood events

Abstract. Recent flash flood impact studies highlight that road networks are often disrupted due to adverse weather and flash flood events. Road users are thus particularly exposed to road flooding during their daily mobility. Previous exposure studies, however, do not take into consideration population mobility. Recent advances in transportation research provide an appropriate framework for simulating individual travel-activity patterns using an activity-based approach. These activity-based mobility models enable the prediction of the sequence of activities performed by individuals and locating them with a high spatial–temporal resolution. This paper describes the development of the MobRISK microsimulation system: a model for assessing the exposure of road users to extreme hydrometeorological events. MobRISK aims at providing an accurate spatiotemporal exposure assessment by integrating travel-activity behaviors and mobility adaptation with respect to weather disruptions. The model is applied in a flash-flood-prone area in southern France to assess motorists' exposure to the September 2002 flash flood event. The results show that risk of flooding mainly occurs in principal road links with considerable traffic load. However, a lag time between the timing of the road submersion and persons crossing these roads contributes to reducing the potential vehicle-related fatal accidents. It is also found that sociodemographic variables have a significant effect on individual exposure. Thus, the proposed model demonstrates the benefits of considering spatiotemporal dynamics of population exposure to flash floods and presents an important improvement in exposure assessment methods. Such improved characterization of road user exposures can present valuable information for flood risk management services.

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.

Detecting and visualizing cohesive activity-travel patterns: A network analysis approach

This article presents a network analytical framework to detect individual-based activity-travel patterns (ATPs) in space and time. Compared to many existing classification methods (e.g., hot-spot detection, sequential alignment method), the network method substantiates the social meanings underlying the interconnectedness and similarities of people's activity trajectories and better integrates spatial interaction (colocation or distance-decay) and temporal connections (concurrence or sequence) of daily lives in the measure of similarity. This approach enables us to detect variant community structures, with individuals in the same community interacting relatively more than individuals belonging to different communities, by decomposing the complex trajectories into different meaningful events (e.g., activities, trips, tours, and subsequences). We also demonstrate the practicality and scientific merit of the network analysis approach in a case study of household travel behavior in Charlotte, North Carolina. Results derived from disaggregated survey data establish the effectiveness and flexibility of the network methods to detect cohesive communities of individuals and ATPs by different narratives of everyday-life events. This study also suggests that the network analysis approach has great potential to classify large datasets of other space-time trajectories and to discover policy-sensitive activity, trip, and tour patterns that help us develop policy and planning alternatives for sustainable communities and mobility.

Assessing the impacts of collection-delivery points to individual’s activity-travel patterns: A greener last mile alternative?

The transport impacts of collection-delivery points (CDPs), as an alternative to home delivery, are rarely studied. As e-shopping becomes increasingly popular, trips to collect deliveries at CDP, especially by car travel, may generate a considerable amount of external effects, such as emissions. Therefore, this paper analysed the “picking up/leaving goods” trips selected from the Swedish National Travel Survey and jointly modelled the individuals’ mode choice and trip chaining decisions using a panel cross-nested logit model. The roles of trip chain characteristics, individual socio-demographics and land use characteristics on each trip chain and mode choice combination are investigated. The results indicate observed and unobserved heterogeneities of trip chaining and mode choice decisions among populations. Young adults living with partners/spouses, single adults with children and partnered adults with children have the preference of using cars in collection-delivery trips compared to other life-cycle groups. A sensitivity analysis is carried out to estimate the effect of distance to CDPs on vehicle kilometres travelled. The calibrated model is used to estimate the VKT of collection-delivery trips in the greater Stockholm area. The results indicate a 22.5% reduction of VKT from collection-delivery trips by relocating 5% CDPs from urban areas to suburban and rural areas.

Structural equation models to analyze activity participation, trip generation, and mode choice of low-income commuters

Low-income commuters have distinct activity-travel characteristics from non-low-income commuters. This study examines low-income commuters’ activity-travel pattern for a better understanding the mechanism of activity participation and travel behaviour based on the travel survey data collected in Nanjing, China. Structural equations modelling (SEM) methodology was adopted to estimate the complex relationships among socio-demographics, accessibility, activity participation, trip generation and mode choice. Results show that strong relationships do exist among socio-demographics, activity engagement and travel behavior. Specifically, we can understand travel behaviour better by including activity participation endogenously in the model. Furthermore, it allows us to better forecast how increasing any one type of activity will affect demand for other activities, as well as trip generation and mode choice. Lastly, the results reveal the effects of accessibility variables on activity participation and travel behaviour in which population density measure has more ubiquitous effects. Findings in this study might provide insightful policy implications for improving the travel environment of the low-income commuters.

Analysing the complexity of day-to-day individual activity-travel patterns using a multidimensional sequence alignment model: A case study in the Bandung Metropolitan Area, Indonesia

Using a panel regression model and a multidimensional three-week household time-use and activity diary, this study analyses the complexity of the day-to-day variability in individuals' activity-travel patterns by applying a multidimensional sequence alignment model. It is found that the variability between weekend and weekday pairs is much greater than between weekday-weekday pairs or weekend-weekend pairs. The variability of other household members' activity-travel patterns is found to significantly influence an individual's activity-travel patterns. The results also show that the variability in the activity-travel patterns of workers and students is greater when conducting a particular discretionary activity on weekdays. Due to performing discretionary activities more often and for longer, non-workers tend to have more predictable activity-travel patterns. Undertaking multitasking activities within different activities on weekdays significantly impacted the different degrees of variability in an individual's activity-travel patterns. Having different health and built environment characteristics also corresponds with different degrees of predictability of the activity-travel patterns, particularly in the worker/student case.

Influencing Mechanism Analysis of Holiday Activity–Travel Patterns on Transportation Energy Consumption and Emissions in China

Energy shortage and atmospheric pollution problems are getting more serious in China, and transportation is the main source of energy consumption, pollutants, and carbon emissions. This study combined the activity-based analysis method with emission models, and investigated the influence mechanism of people’s activity travel scheduling on transportation energy consumption and emissions on holidays. Based on the holiday travel behavior survey data, the multinomial logistic regression model was first applied to explore the decision mechanisms of individual travel-mode choices in holidays. Next, the emission model was integrated with an activity-based travel demand model to calculate and compare transportation energy consumption and emissions under different policy scenarios. The results showed that socio-demographic characteristics had significant effects on holiday activity–travel patterns, and combined mode chains had a larger number of activity points than single mode chains. With an increase in the trip time of cars, and decrease of travel distance and the number of activity points, transportation energy consumption and emissions could be reduced greatly with an adjustment of holiday activity–travel patterns. The reduced portion is mainly attracted by slow traffic and public transport. However, the effects of a single policy strategy are very limited, thus portfolio policies need to be considered by policy makers.

A game-theoretic model of car ownership and household time allocation

The explosive growth of private cars in China and other developing countries has attracted a great deal of renewed research interest in car ownership. This paper investigates households’ car ownership decision-making process from the perspective of household time allocation. Applying the game-theoretic approach to capturing household members’ interactive decision-making mechanism, we propose a two-stage model that links household members’ short-term time allocation decisions to long-term car ownership decisions. The first stage models the bargaining of household members (e.g., husband and wife) over the car ownership decision, taking into consideration of government policies for regulating car ownership; and the second stage is a generalized Nash equilibrium model for activity-travel pattern analysis incorporating individuals’ interactions concerning activity participation. The existence and uniqueness of the generalized Nash equilibrium solution is examined, and a heuristic procedure that combines backwards induction and method of exhaustion is adopted to solve the two-stage game. The proposed model is applied to an empirical case study in Beijing, which demonstrates the applicability of the model in predicting car ownership and examining interactions between car ownership and household time allocation. The empirical model is applied to assess the impacts of plate-number-based vehicle usage rationing policies on car ownership and time allocation to travel and daily activities. Results show that the model can be applied to evaluate the car ownership impacts of car usage rationing policies.