Capturing correlation with a mixed recursive logit model for activity-travel scheduling

Representing activity-travel scheduling decisions as path choices in a time–space network is an emerging approach in the literature. In this paper, we model choices of activity, location, timing and transport mode using such an approach and seek to estimate utility parameters of recursive logit models. Relaxing the independence from irrelevant alternatives (IIA) property of the logit model in this setting raises a number of challenges. First, overlap in the network may not fully characterize perceptual correlation between paths, due to their interpretation as activity schedules. Second, the large number of states that are needed to represent all possible locations, times and activity combinations imposes major computational challenges to estimate the model. We combine recent methodological developments to build on previous work by Blom Västberg et al. (2016) and allow to model complex and realistic correlation patterns in this type of network. We use sampled choices sets in order to estimate a mixed recursive logit model in reasonable time for large-scale, dense time-space networks. Importantly, the model retains the advantage of fast predictions without sampling choice sets. In addition to estimation results, we present an extensive empirical analysis which highlights the different substitution patterns when the IIA property is relaxed, and a cross-validation study which confirms improved out-of-sample fit.     

A latent class accelerated hazard model of social activity duration

Over the past decade, activity scheduling processes have gained increasing attention in the field of transportation research. However, still little is known about the scheduling of social activities even though these activities account for a large and growing portion of trips. This paper contributes to this knowledge. We analyze how the duration of social activities is influenced by social activity characteristics and characteristics of the relationship between the respondent and the contacted person(s). To that end, a latent class accelerated hazard model is estimated, based on social interaction diary data that was collected in the Netherlands in 2008. Chi-square tests and analyses of variance are used to test for significant relations between the latent classes and personal and household characteristics. Findings suggest that the social activity characteristics and the characteristics of the relationship between the socializing persons are highly significant in explaining social activity duration. This shows that social activities should not be considered as a homogenous set of activities and it underlines the importance of including the social context in travel-behavior models. Moreover, the results indicate that there is a substantial amount of latent heterogeneity across the population. Four latent classes are identified, showing different social activity durations, and different effects for both categories of explanatory variables. Latent class membership can be explained by household composition, socio-economic status (education, income and work hours), car ownership and the number of interactions in 2 days.     

Should we abandon activity type analysis? Redefining activities by their salient attributes

This paper poses a challenge and begins a search. The challenge is to reconsider the usefulness of traditional activity types (“work”, “shopping”, etc.) in the understanding and modelling of travel behaviour. The search is for the more salient attributes of activities that may serve to better explain complex travel behaviours—such as activity scheduling and tour formation. In particular, this paper focuses on explicit measures of the spatial, temporal and interpersonal flexibility of activities, along with several traditional attributes (frequency, duration, involved persons, travel time, and location). Data from a recent in-depth week-long activity scheduling survey was used to define and compare these attributes. Results show that considerable variability in the attributes between and within traditional activity groups is evident. This casts considerable uncertainty on assumptions that statically assign levels of spatial, temporal, and interpersonal flexibility to any given activity type. A Principal Components Analysis further revealed eight new distinct clusters of activities that share like attributes. The relative role of each attribute in each component is examined, and subjective interpretations emerged (e.g., “Long and frequent”, “Space and time flexible” “Social networking”). The implications of these results for future model development and research are discussed. Future research should continue to expand the search for salient attributes and link them more directly to decision processes.     

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.     

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.     

Daily activity pattern recognition by using support vector machines with multiple classes

The focus of this paper is to learn the daily activity engagement patterns of travelers using Support Vector Machines (SVMs), a modeling approach that is widely used in Artificial intelligence and Machine Learning. It is postulated that an individual’s choice of activities depends not only on socio-demographic characteristics but also on previous activities of individual on the same day. In the paper, Markov Chain models are used to study the sequential choice of activities. The dependencies among activity type, activity sequence and socio-demographic data are captured by employing hidden Markov models. In order to learn model parameters, we use sequential multinomial logit models (MNL) and multiclass Support Vector Machines (K-SVM) with two different dependency structures. In the first dependency structure, it is assumed that type of activity at time ‘t’ depends on the last previous activity and socio-demographic data, whereas in the second structure we assume that activity selection at time ‘t’ depends on all of the individual’s previous activity types on the same day and socio-demographic characteristics. The models are applied to data drawn from a set of California households and a comparison of the accuracy of estimation of activity types and their sequence in the agenda, indicates the superiority of K-SVM models over MNL. Additionally, we show that accuracy in estimating activity patterns increases using different sets of explanatory variables or tuning parameters of the kernel function in K-SVM.     

A week in the life of baby boomers: how do they see the spatial–temporal organization of their activities and travel?

In this paper, we take an initial look at the spatial and temporal flexibility in the activity patterns of the so-called “baby-boomer” cohort (born 1947–1966) in comparison with younger and older adults. Using a unique longitudinal survey carried in Quebec City from 2002 to 2005, we explore activity patterns and trip rates over a seven-day observation period during the first wave, and take a first look at some aspects of their evolution over two subsequent waves at about one-year intervals. We model the propensity to undertake activities within selected conventional non-work classifications such as “shopping” and “leisure”, and also according to respondents’ own perceptions of the spatial and temporal flexibility of each out-of-home activity that they had executed. While we cannot strictly separate cohort effects from age-related effects, after controlling for gender and household structure, we infer that age and related lifestyle effects dominate in explaining these propensities. However, the boomers were the only age stratum to increase their total out-of-home activity participation over the course of the panel, an intriguing starting point for the future study of this cohort.

Activity patterns in space and time: calculating representative Hagerstrand trajectories

Daily activity diaries can be recorded as sequences of characters representing events and their contexts as they unfold during the day. Dynamic programming algorithms as used in bioinformatics have been used by a number of researchers to measure the similarities and differences between travel patterns on the basis of temporal sequencing of events, activity transition, and total activity time. The resultant similarity matrices have been shown to be more effective in classifying sequential patterns than classifications based on alternative similarity indices. The basic algorithms can be amended to include the geographic coordinates of events by a suitable amendment to the definition of distance. This permits quantitative classification of Hagerstrand-type activity trajectories on the basis of both activity and spatial similarity. Such a classification can be used to group similar trajectories and to identify representative trajectories that are analogous to measures of central tendency in univariate statistics, giving more concrete meaning to the concept of the activity pattern than any other method now available. The paper illustrates the effect of considering both events and locations in the classification of daily activity patterns using activity diary data gathered in the town of Reading. The algorithm has been implemented in the Clustal_TXY alignment software package.

Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS)

Studies of urban travel behaviour typically focus on weekday activities and commuting. This is surprising given the rising contribution of discretionary activities to daily travel that has occurred during the last few decades. Moreover, current understanding of the relationship between travel behaviour and land use remains incomplete, with little research carried out to explore spatial properties of activity-travel behaviour during the off-peak and weekend time periods. Weekend behaviours, for example, influenced by the availability of time and the spatiotemporal distribution of “weekend” destinations, likely produce spatially and temporally distinct activity-travel patterns. Using data from the first wave of the Toronto Travel-Activity Panel Survey (TTAPS), this paper examines an area of research that has received little attention; namely, the presence of spatial variety in activity-travel behaviour. The paper begins by looking at the extent to which individuals engage in spatially repetitive location choices during the course of a single week. Area-based measures of geographical extent and activity dispersion are then used to expose differences in weekday-to-weekend and day-to-day activity-travel patterns. Examination of unclassified activities carried out over a 1 week period reveals a level of spatial repetition that does not materialise across activities classified by type, travel mode, and planning strategy. Despite the inherent spatial flexibility offered by the personal automobile, spatial repetition is also found to be surprisingly similar across travel modes. The results also indicate weekday-to-weekend, and day-to-day fluctuations in spatial properties of individual activity-travel behaviour. These findings challenge the utility of the short-run survey as an instrument for capturing archetypal patterns of spatial behaviour. In addition, the presence of a weekday-to-weekend differential in spatial behaviour suggests that policies targeting weekday travel reduction could have little impact on travel associated with weekend activities.

Use of an activity-based diary to collect household travel data

The paper describes the results of a pilot survey that used a one-day activity diary to collect origin-destination data, as opposed to a travel-based diary. The design of the diary is discussed, together with a comparison to a more conventional travel diary. The paper examines the extent to which the activity diary appears to have been capable of collecting good travel data that is at least comparable to travel diary efforts. In addition, a substantial portion of the paper is concerned with a comparison of the retrieval methods for the diaries. Two alternative methods were pilot-tested, one being the use of telephone retrieval and the other being mailback retrieval. Although the pilot test used small samples, the evidence appears to be strong that mailback is preferable to telephone retrieval, while telephone retrieval did not seem capable of providing some of the benefits often ascribed to it.