Trend analysis of activity generation attributes over time

Activity generation models are relatively poorly developed in activity-based travel demand modelling frameworks. This research investigates whether observed distributions of activity attributes (activity frequency, start time and duration) used as inputs in the activity generation component of an activity-based travel demand model have changed over time. This research empirically examines changes in the distributions of activity generation attributes over time in the Greater Montreal area (GMA), Quebec, Canada. It also focuses on how these attributes vary with peoples’ socio-demographic characteristics. This research relies on the 1998, 2003 and 2008 origin–destination (O–D) household travel surveys of the GMA. The comparative analysis at three time points in a 10-year period clearly reveals that distributions of activity attributes are significantly changing over time. Work and school activities show similar trends; frequency “1” has increased and frequency “2+” has decreased over time. The occurrence of shopping activity on weekdays is decreasing over time. Start time and duration distributions for each activity have also changed significantly over time. The research allows preparing activity attributes for the application of an activity-based model, TASHA, such that they reflect temporal changes in travel behaviour of the GMA.     

Analysis of long-distance vacation travel demand in the United States: a multiple discrete–continuous choice framework

This study analyzes the annual vacation destination choices and related time allocation patterns of American households. More specifically, an annual vacation destination choice and time allocation model is formulated to simultaneously predict the different vacation destinations that a household visits in a year, and the time (no. of days) it allocates to each of the visited destinations. The model takes the form of a multiple discrete–continuous extreme value (MDCEV) structure. Further, a variant of the MDCEV model is proposed to reduce the prediction of unrealistically small amounts of vacation time allocation to the chosen destinations. To do so, the continuously non-linear utility functional form in the MDCEV framework is replaced with a combination of a linear and non-linear form. The empirical analysis was performed using the 1995 American Travel Survey data, with the United States divided into 210 alternative destinations. The model estimation results provide several insights into the determinants of households’ vacation destination choice and time allocation patterns. Results suggest that travel times and travel costs to the destinations, and lodging costs, leisure activity opportunities (measured by employment in the leisure industry), length of coastline, and weather conditions at the destinations influence households’ destination choices for vacations. The annual vacation destination choice model developed in this study can be incorporated into a larger national travel modeling framework for predicting the national-level, origin–destination flows for vacation travel.     

How derived is the demand for travel? Some conceptual and measurement considerations

This paper contests the conventional wisdom that travel is a derived demand, at least as an absolute. Rather, we suggest that under some circumstances, travel is desired for its own sake. We discuss the phenomenon of undirected travel – cases in which travel is not a byproduct of the activity but itself constitutes the activity. The same reasons why people enjoy undirected travel (a sense of speed, motion, control, enjoyment of beauty) may motivate them to undertake excess travel even in the context of mandatory or maintenance trips. One characteristic of undirected travel is that the destination is ancillary to the travel rather than the converse which is usually assumed. We argue that the destination may be to some degree ancillary more often than is realized. Measuring a positive affinity for travel is complex: in self-reports of attitudes toward travel, respondents are likely to confound their utility for the activities conducted at the destination, and for activities conducted while traveling, with their utility for traveling itself. Despite this measurement challenge, preliminary empirical results from a study of more than 1900 residents of the San Francisco Bay Area provide suggestive evidence for a positive utility for travel, and for a desired travel time budget (TTB). The issues raised here have clear policy implications: the way people will react to policies intended to reduce vehicle travel will depend in part on the relative weights they assign to the three components of a utility for travel. Improving our forecasts of travel behavior may require viewing travel literally as a “good” as well as a “bad” (disutility).     

Understanding travellers’ preferences for different types of trip destination based on mobile internet usage data

New mobility data sources like mobile phone traces have been shown to reveal individuals’ movements in space and time. However, socioeconomic attributes of travellers are missing in those data. Consequently, it is not possible to partition the population and have an in-depth understanding of the socio-demographic factors influencing travel behaviour. Aiming at filling this gap, we use mobile internet usage behaviour, including one’s preferred type of website and application (app) visited through mobile internet as well as the level of usage frequency, as a distinguishing element between different population segments. We compare the travel behaviour of each segment in terms of the preference for types of trip destinations. The point of interest (POI) data are used to cluster grid cells of a city according to the main function of a grid cell, serving as a reference to determine the type of trip destination. The method is tested for the city of Shanghai, China, by using a special mobile phone dataset that includes not only the spatial-temporal traces but also the mobile internet usage behaviour of the same users. We identify statistically significant relationships between a traveller’s favourite category of mobile internet content and more frequent types of trip destinations that he/she visits. For example, compared to others, people whose favourite type of app/website is in the “tourism” category significantly preferred to visit touristy areas. Moreover, users with different levels of internet usage intensity show different preferences for types of destinations as well. We found that people who used mobile internet more intensively were more likely to visit more commercial areas, and people who used it less preferred to have activities in predominantly residential areas.     

Incorporating trip chaining into analysis of destination choice

A model of destination choice is developed in this study employing “prospective utility” of a destination zone as its attraction measure. The prospective utility accounts for future dependency of destination choice and makes possible relevant treatment of interdependent choices in a trip chain. A parameter is included in the model to represent the magnitude of the future dependency. Empirical estimation results show that destination choice is in fact future dependent and coefficients of travel time and zonal attribute variables may be biased if this depedency is not accounted for.     

Hierarchical ordering of nests in a joint mode and destination choice model

This paper seeks to explore the relationship between mode and destination choice in an integrated nested choice model. A fundamental argument can be made that in certain circumstances, the ordering of choices should be reversed from the usual sequence of destination choice preceding mode choice. This results in a travel demand model where travelers are more likely to change destinations than to change transportation modes. For small and medium size urban areas, particularly in the United States, with less well developed public transit systems that draw few choice riders, this assumption makes much more sense than the traditional modeling assumptions. The models used in the new travel modeling system developed for Knoxville, Tennessee utilize this reversed ordering, with generally good results, which required no external tinkering in the logsum parameters.     

The changing allocation of activities in space and time by the use of ICT—“Fragmentation” as a new concept and empirical results

Theoretical and empirical research about the impact of information and communication technologies (ICT) on transport relies on the hypothesis that ICT use leads to a reorganization of activities in time and space thus having as a consequence impacts on travel behavior. The breaking up of activities into discrete pieces by the use of ICT is the starting point of the fragmentation concept that underlies the present article. The concept argues that transport demand increases by the fragmentation of activities and explores the relevant mechanisms for this process. In all, however, the concept is still rather vague. Therefore, the authors discuss some elements of the concept on a theoretical level, in particular the question why individuals “fragment” their activities. In the empirical section they use a data set about activities, ICT use and travel behavior in Germany to find out how far an activity like work, which is particularly apt for fragmentation, shows signs of temporal and spatial disintegration. With the help of a cluster analysis they identify groups with different “fragmentation behavior” and investigate if a statistically significant relation exists between fragmentation behavior and ICT use. Accordingly, the focus of the article lies on the impact of ICT use on the performance of activities by different behavioral groups. The link to travel behavior is made by examining mode choices for different purposes and travel related attitudes.     

Social networks and joint/solo activity–travel behavior

The social dimension of activity–travel behavior has recently received much research attention. This paper aims to make a contribution to this growing literature by investigating individuals’ engagements in joint activities and activity companion choices. Using activity–travel diary data collected in Hong Kong in 2010, this study examines the impact of social network attributes on the decisions between solo and joint activities, and for joint activities, the choices of companions. Chi-square difference tests are used to assess the importance of social network variables in explaining joint activity behavior. We find that the inclusion of social network attributes significantly improves the goodness-of-fit of the model with only socioeconomic variables. Specifically, individuals receiving emotional support and social companionship from family members/relatives are found to more likely undertake joint activities with their family members/relatives; the size of personal social networks is found to be a significant determinant of companion choices for joint activities; and activity companions are found to be significant determinants of travel companions. The findings of this study improve the understanding about activity–travel, especially joint activity–travel decisions.     

Travel-based multitasking: review of the role of digital activities and connectivity

ABSTRACT

Travel-based multitasking, also referred to as travel time use, is now a well-established concept, whose existence is supported by the technological trajectories, with mobile information and communication technologies (ICT) and vehicle automation working together to allow travel time to be more productive and enjoyable. Despite existence of reviews of travel-time multitasking studies, the systematic overview of the role digital activities, i.e. those that necessarily require modern ICT equipment to participate, has been limited, often wrapped under the umbrella term “use of ICT”, potentially obscuring their complexity and sophistication. Similarly, the role of connectivity and its attributes, e.g. speed (bandwidth), reliability, price, ease of use, data allowance or security, deserves a more systematic overview given its key role in enabling digital online activities and hence the travel-based multitasking options. This paper provides a review of 77 empirical travel-based multitasking-studies that explored the role of digital activities or connectivity. In particular, the review discusses the existing typologies of digital activities, dividing them into hardware-centric, function-centric or a combination of both (mixed). Subsequently, key contributions are discussed with respect to the treatment of digital activities and connectivity and its attributes. Based on the review, it is possible to observe that the existing studies have looked only at a handful of rather restricted online activities that do not sufficiently capture the sophistication with which individuals interact with the virtual world nowadays. Furthermore, the role of connectivity, although deeply embedded in the “C” of the “ICT” concept, has not been looked at or modelled in any detail in studies related to travel time use or its quality. This existing shortcoming might have resulted in an insufficient understanding of the mechanisms driving travel time use, the associated experience indicators of customer satisfaction, productivity or the consequences for relative attractiveness of transport modes. All of these considerations remain, however, crucial for designing, appraising and operating transport infrastructure and services that are able to take the advantage of lifestyle digitisation to meet the increasing customer needs while also delivering broader economic, social benefits and possibly also environmental benefits.     

A model for integrated analysis of household location and travel choices

We develop a model for integrated analysis of household location and travel choices and investigate it from a theoretical point of view.Each household makes a joint choice of location (zone and house type) and a travel pattern that maximizes utility subject to budget and time constraints. Prices for housing are calculated so that demand equals supply in each submarket. The travel pattern consists of a set of expected trip frequencies to different destinations with different modes. The joint time and budget constraints ensure that time and cost sensitivities are consistent throughout the model. Choosing the entire travel pattern at once, as opposed to treating travel decisions as a series of isolated choices, allows the marginal utilities of trips to depend on which other trips are made.When choosing trip frequencies to destinations, households are assumed to prefer variation to an extent varying with the purpose of the trip. The travel pattern will tend to be more evenly distributed across trip ends the less similar destinations and individual preferences are. These heterogeneities of destinations and individual preferences, respectively, are expressed in terms of a set of parameters to be estimated.     

An analysis of destination choice for opaque airline products using multidimensional binary logit models

We investigate how customers respond to an opaque airline product offered by a European carrier. In this opaque product design, customers are randomly assigned to travel to one of approximately ten destinations; however, for a fee they may exclude one or more destinations from the choice set (or a particular package design) prior to learning which destination they will travel to. We use a multidimensional binary logit model to predict the probability that one or more alternatives will be chosen by a customer. Results show that customers are more likely to pay to exclude destinations located close to the origin airport and destinations that speak the same language as the origin airport. Length of stay, cost of living at the destination, and measures of destination attractiveness are also found to be significant predictors for some package designs. Based on these findings, we offer general recommendations for how to design opaque packages for airline customers.     

The relationship between land use and intrazonal trip making behaviors: Evidence and implications

This paper addresses the relationship between land use, destination selection, and travel mode choice. Specifically, it focuses on intrazonal trips, a sub-category of trip making where both trip origin and trip destination are contained in the same geographic unit of analysis, using data from the 1994 Household Activity and Travel Diary Survey conducted by Portland Metro. Using multinomial logit and binary logistic models to measure travel mode choice and decision to internalize trips, the evidence supports the conclusions that (1) intrazonal trips characteristics suggest mode choice for these trips might be influenced by urban form, which in turn affects regional trip distribution; (2) there is a threshold effect in the ability of economic diversity/mixed use to alter travel behavior; and (3) greater emphasis to destinations within the area where an individual’s home is located needs to be given in trip distribution models.     

Should we all just stay home? Travel,out-of-home activities,and life satisfaction

How and why travel contributes to our life satisfaction is of considerable import for transportation policy and planning. This paper empirically examines this relationship using data from the American Time Use Survey. It finds that, controlling for relevant demographic, geographic, and temporal covariates, travel time per day is significantly and positively associated with life satisfaction. This relationship is attenuated, but still significant, when the amount of time spent participating in out-of-home activities is controlled for. Time spent bicycling is strongly associated with higher life satisfaction, though it attains significance only in some models; time spent walking is also quite positive, though it is not significant. However, both walking and bicycling are positively and significantly associated with life satisfaction when time spent on purely recreational walking and bicycling is included. Life satisfaction is positively and significantly associated with time spent traveling for the purposes of eating and drinking, religious activities, volunteering, and playing and watching sports. Travel time exhibits a strong positive relationship with life satisfaction in smaller towns and cities, but in large cities the association weakens, and for very large cities travel time may actually not be associated with life satisfaction at all. This may be due to the costs of traffic congestion, which disproportionately exists in large cities. In all, while the associations between travel and life satisfaction are clear, the causal story is complex, with the positive relationships potentially being explained by (1) travel allowing us to access destinations that make us happy, (2) the act of travel itself being fulfilling, and/or (3) intrinsically happier people being more likely to travel. In all likelihood, all three factors are at play.     

The impacts of built environment on home-based work and non-work trips: An empirical study from Iran

This paper aims to explore the impact of built environment attributes in the scale of one quarter-mile buffers on individuals’ travel behaviors in the metropolitan of Shiraz, Iran. In order to develop this topic, the present research is developed through the analysis of a dataset collected from residents of 22 neighborhoods with variety of land use features. Using household survey on daily activities, this study investigates home-based work and non-work (HBW and HBN) trips. Structural equation models are utilized to examine the relationships between land use attributes and travel behavior while taking into account socio-economic characteristics as the residential self-selection. Results from models indicate that individuals residing in areas with high residential and job density, and shorter distance to sub-centers are more interested in using transit and non-motorized modes. Moreover, residents of neighborhoods with mixed land uses tend to travel less by car and more by transit and non-motorized modes to non-work destinations. Nevertheless, the influences of design measurements such as street density and internal connectivity are mixed in our models. Although higher internal connectivity leads to more transit and non-motorized trips in HBW model, the impacts of design measurements on individuals travel behavior in HBN model are significantly in contrast with research hypothesis. Our study also shows the importance of individuals’ self-selection impacts on travel behaviors; individuals with special socio-demographic attributes live in the neighborhoods with regard to their transportation patterns. The findings of this paper reveal that the effects of built environment attributes on travel behavior in origins of trips do not exactly correspond with the expected predictions, when it comes in practice in a various study context. This study displays the necessity of regarding local conditions of urban areas and the inherent differences between travel destinations in integrating land use and transportation planning.     

Exploring the links between personal networks, time use, and the spatial distribution of social contacts

Although the study of the role of the social context in travel behavior and activity patterns has recently gained attention, the empirical evidence supporting the relationship between social networks and the temporal and spatial characteristics of social activities is still limited. With this motivation, this paper studies the link between “longer term” (social networks) and “shorter term” (social activities) social decisions, by exploring the intertwined relationship between the individuals’ personal networks attributes, and the spatiotemporal characteristics of their daily social activities. The paper contributes to the literature by adding two key aspects to the study of the role of social networks on travel behavior: the social networks’ structure, and the spatiality of all individuals participating on the social activities. Based on data which link people’s personal networks and time use, and using a structural equation modeling approach, the paper studies the influence of individual and interactional attributes on the duration, distance, and number of people involved in social daily activities. The results show that aspects such as tie social closeness, gender and age similarity, and network density, help to understand social activity duration and distance, complementing traditional socio-demographic aspects such as income, occupation, and accessibility to services. In this way, socio-demographic attributes are not enough to explain the spatiotemporal dimension of daily activities which makes necessary to include variables related to the social context to explain with a higher level of accuracy both the duration and distance traveled to the activity.     

Comparison on travel scheduling between driving and walking trips by habitual car users

Research on walking behavior has become increasingly more important in the field of transportation in the past decades. However, the study of the factors influencing the scheduling decisions related to walking trips and the exploration of the differences between travel modes has not been conducted yet. This paper presents a comparison of the scheduling and rescheduling decisions associated with car driving trips and walking trips by habitual car users using a data set collected in Valencia (Spain) in 2010. Bivariate probit models with sample selection are used to accommodate the influence of pre-planning on the decision to execute a travel as pre-planned or not. The explicative variables considered are: socio-economic characteristics of respondents, travel characteristics, and facets of the activity executed at origin and at destination including the scheduling decisions associated with them. The results demonstrate that a significant correlation exists between the choices of pre-planning and rescheduling for both types of trips. Whether for car driving or walking trips, the scheduling decisions associated with the activity at origin and at destination are the most important explicative factors of the trip scheduling and rescheduling decisions. However, the rescheduling of trips is mainly influenced by modifications in the activity at destination. Some interesting differences arise regarding the rescheduling decision processes between travel modes: if pre-planned, walking trips are less likely to be modified than car driving trips, showing a more rigid rescheduling behavior.     

Synthesis of first practices and operational research approaches in activity-based travel demand modeling

Regional travel models in the United States are clearly evolving from conventional models towards a new generation of more behaviorally realistic activity-based models. The new generation of regional travel demand models is characterized by three features: (1) an activity-based platform, that implies that modeled travel be derived within a general framework of the daily activities undertaken by households and persons, (2) a tour-based structure of travel where the tour is used as the basic unit of modeling travel instead of the elemental trip, and (3) micro-simulation modeling techniques that are applied at the fully-disaggregate level of persons and households, which convert activity and travel related choices from fractional-probability model outcomes into a series of discrete or “crisp” decisions.While the new generation of model has obvious conceptual advantages over the conventional four-step models, there are still numerous technical issues that have to be addressed as well as a better understanding of practical benefits should be achieved before the new generation of models can fully replace conventional models. The paper summarizes the recent successful experience in the development and application of activity-based demand models for Metropolitan Planning Organizations in the US. Moving activity-based approaches into practice is analyzed in a broad context of travel demand modeling market tendencies and policy implications.     

Redefining activity types: Who participates in which leisure activity?

This paper analyzes the activity choices of individuals and the links between socio-demographics, daily schedules and activity attributes using a new activity choice framework. Activities are first clustered into groups based on their salient attributes, such as duration, frequency, flexibility, planning times, and number of involved persons, rather than their functional types (work, leisure and household obligations), using a K-means cluster technique. This led to the creation of several new activity groups such as “long, temporally fixed, personally flexible activities”, “short and flexible activities”. These activity groups form the choice set for the mixed logit activity choice modeling structure developed for the leisure activities in the second part of the paper. The model results reveal the significant relationships between socio-demographics, temporal characteristics, and characteristics of the schedules on leisure activity choice. The results demonstrate how changing demographics and other activities in individuals’ schedules may affect the nature of the leisure activities and present the substitution and complimentary effects that these new activity groups have on one another.     

Transit competitiveness in polycentric metropolitan regions

This paper analyzes the potential to, and impacts of, increasing transit modal split in a polycentric metropolitan area – the Philadelphia, Pennsylvania region. Potential transit riders are preselected as those travelers whose trips begin and end in areas with transit-supportive land uses, defined as “activity centers,” areas of high-density employment and trip attraction. A multimodal traffic assignment model is developed and solved to quantify the generalized cost of travel by transit services and private automobile under (user) equilibrium conditions. The model predicts transit modal split by identifying the origin–destination pairs for which transit offers lower generalized cost. For those origin–destination pairs for which transit does not offer the lowest generalized cost, I compute a transit competitiveness measure, the ratio of transit generalized cost to auto generalized cost. The model is first formulated and solved for existing transit service and regional pricing schemes. Next, various transit incentives (travel time or fare reductions, increased service) and auto disincentives (higher out of pocket expenses) are proposed and their impacts on individual travel choices and system performance are quantified. The results suggest that a coordinated policy of improved transit service and some auto disincentives is necessary to achieve greater modal split and improved system efficiency in the region. Further, the research finds that two levels of coordinated transit service, between and within activity centers, are necessary to realize the greatest improvements in system performance.     

Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.