Modeling household activity–travel interactions as parallel constrained choices

The daily activity-travel patterns of individuals often include interactions with other household members, which we observe in the form of joint activity participation and shared rides. Explicit representation of joint activity patterns is a widespread deficiency in extant travel forecasting models and remains a relatively under-developed area of travel behavior research. In this paper, we identify several spatially defined tour patterns found in weekday household survey data that describe this form of interpersonal decision-making. Using pairs of household decision makers as our subjects, we develop a structural discrete choice model that predicts the separate, parallel choices of full-day tour patterns by both persons, subject to the higher level constraint imposed by their joint selection of one of several spatial interaction patterns, one of which may be no interaction. We apply this model to the household survey data, drawing inferences from the household and person attributes that prove to be significant predictors of pattern choices, such as commitment to work schedules, auto availability, commuting distance and the presence of children in the household. Parameterization of an importance function in the models shows that in making joint activity-travel decisions significantly greater emphasis is placed on the individual utilities of workers relative to non-workers and on the utilities of women in households with very young children. The model and methods are prototypes for tour-based travel forecasting systems that seek to represent the complex interaction between household members in an integrated model structure.     

A GA-based household scheduler

One way of making activity-based travel analysis operational for transport planning is multi-agent micro-simulation. Modelling activity and trip generation based on individual and social characteristics are central steps in this method. The model presented here generates complete daily activity schedules based on the structure of a household and its members’ activity calendars. The model assumes that the household is another basic decision-making unit for travel demand aside from individual mobility needs. Results of the model are schedules containing complete information about activity type and sequence, locations, and means of transportation, as well as activity start times and durations. The generated schedules are the outcome of a probabilistic optimisation using genetic algorithms. This iterative method improves solutions found in a random search according to the specification of a fitness criterion, which equals utility here. It contains behavioural assumptions about individuals as well as the household level. Individual utility is derived from the number of activities and their respective durations. It is reduced by costs of travelling and penalties for late, respectively early arrival. The household level is represented directly by the utility of joint activities, and indirectly by allocation of activities and means of transportation to household members. The paper presents initial tests with a three-person household, detailing resulting schedules, and discussing run-time experiences. A sensitivity analysis of the joint utility parameter impact is also included.     

Peak-spreading: How much?

The Nationwide Personal Transportation Study (NPTS) data for 1977 and 1983 show very little evidence of peak-travel-period elongation, so that spreading is a poor explanation of the absence of worsening congestion. The peak-spreading that occurred was limited to the smaller metropolitan areas, where the scope for location adjustments by households and firms to relieve congestion was much less than in the larger policentric metropolitan areas. Blue-collar and sales workers had more off-peak commutes than other occupations (e.g., professionals), suggesting that institutionalized (i.e., compulsory) alternative work schedules are more effective than voluntary spontaneous actions. This view is reinforced by confirmation of the well-known household and family restrictions on flexible working hours.     

Effects of household structure and selected travel characteristics on trip chaining

This paper analyzes trip chaining, focusing on how households organize non-work travel. A trip chaining typology is developed using household survey data from Portland, Oregon. Households are organized according to demographic structure, allowing analysis of trip chaining differences among household types. A logit model of the propensity to link non-work trips to the work commute is estimated. A more general model of household allocation of non-work travel among three alternative chain types — work commutes, multi-stop non-work journeys, and unlinked trips — is also developed and estimated. Empirical results indicate that the likelihood of linking work and non-work travel, and the more general organization of non-work travel, varies with respect to household structure and other factors which previous studies have found to be important. The effects of two congestion indicators on trip chaining were mixed: workers who commuted in peak periods were found to have lower propensity to form work/non-work chains, while a more general congestion indicator had no effect on the allocation of non-work trips among alternative chains.     

Activity-based market equilibrium for capacitated multimodal transport systems

Empirical studies have shown that demand for multimodal transport systems is highly correlated with activity schedules of individuals. Nonetheless, existing analytical equilibrium models of multimodal systems have only considered trip-based demand. We propose a new market equilibrium model that is sensitive to traveler activity schedules and system capacities. The model is based on a constrained mixed logit model of activity schedule choice, where each schedule in the choice set is generated with a multimodal extension of the household activity pattern problem. The extension explicitly accounts for both passenger choices of activity participation and multimodal choices like public transit, walking, and vehicle parking. The market equilibrium is achieved with Lagrangian relaxation to determine the optimal dual price of the capacity constraint, and a method of successive averages with column generation finds an efficient choice set of activity schedules to assign flows over the dynamic network load capacities. An example illustrates the model and algorithm, effects similar to Vickrey’s morning commute model can be observed as a special case. A case study of the Oakville Go Transit station access “last mile” problem in the Greater Toronto Area is conducted with 166 survey samples reflecting 3680 individuals. Results suggest that a $10 fixed parking fee at Oakville station would lead to a reduction of access auto share from 54.8% to 49.5%, an increase in access transit share from 20.7% to 25.9%, and a disutility increase of 11% for the of single-activity residents of Oakville.     

A behavioral housing search model: Two-stage hazard-based and multinomial logit approach to choice-set formation and location selection

Residential location search has become an important topic to both practitioners and researchers as more detailed and disaggregate land-use and transportation demand models are developed which require information on individual household location decisions. The housing search process starts with an alternative formation and screening stage. At this level households evaluate all potential alternatives based on their lifestyle, preferences, and utilities to form a manageable choice set with a limited number of plausible alternatives. Then the final residential location is selected among these alternatives. This two-stage decision making process can be used for both aggregate zone-level selection as well as searching disaggregate parcel or building-based housing markets for potential dwellings. In this paper a zonal level household housing search model is developed. Initially, a household specific choice set is drawn from the entire possible alternatives in the area based on the average household work distance to each alternative. Following the choice set formation step, a discrete choice model is utilized for modeling the final residential zone selection of the household. A hazard-based model is used for the choice set formation module while the final choice selection is modeled using a multinomial logit formulation with a deterministic sample correction factor. The approach presented in the paper provides a remedy for the large choice set problem typically faced in housing search models.     

A household-level activity pattern generation model with an application for Southern California

This paper develops and estimates a multiple discrete continuous extreme value model of household activity generation that jointly predicts the activity participation decisions of all individuals in a household by activity purpose and the precise combination of individuals participating. The model is estimated on a sample obtained from the post census regional household travel survey conducted by the South California Association of Governments in the year 2000. A host of household, individual, and residential neighborhood accessibility measures are used as explanatory variables. The results reveal that, in addition to household and individual demographics, the built environment of the home zone also impacts the activity participation levels and durations of households. A validation exercise is undertaken to evaluate the ability of the proposed model to predict participation levels and durations. In addition to providing richness in behavioral detail, the model can be easily embedded in an activity-based microsimulation framework and is computationally efficient as it obviates the need for several hierarchical sub-models typically used in extant activity-based systems to generate activity patterns.     

Examining the effects of out-of-home and in-home constraints on leisure activity participation in different seasons of the year

Using multi-day, multi-period travel diaries data of 56 days (four waves of two-week diaries) for 67 individuals in Stockholm, this study aims to examine the effects of out-of-home and in-home constraints (e.g. teleworking, studying at home, doing the laundry, cleaning and taking care of other household member[s]) on individuals’ day-to-day leisure activity participation decisions in four different seasons. This study also aims to explore the effects of various types of working schedules (fixed, shift, partial- and full-flexible) on individuals’ decisions to participate in day-to-day leisure activities. A pooled model (56 days) and wave-specific models (14 days in each wave) are estimated by using dynamic ordered Probit models. The effects of various types of working schedules are estimated by using 28 days of two waves’ data. The results show that an individual’s leisure activity participation decision is significantly influenced by out-of-home work durations but not influenced by in-home constraints, regardless of any seasons. Individuals with shift working hours engage less in day-to-day leisure activities than other workers’ types in both spring and summer seasons. The thermal indicator significantly affects individuals’ leisure activity participation decisions during the autumn season. Individuals exhibit routine behaviour characterized by repeated decisions in participating in day-to-day leisure activities that can last up to 14 days, regardless of any seasons.     

A model of household time allocation taking into consideration of hiring domestic helpers

Household maintenance such as childcare not only induces activities and travel but also impose time constraints on individuals’ participation in other activities and travel. Instead of sharing household responsibilities, households may hire domestic helpers for household maintenance. Alternatively, they may get helps from members of the extended family such as parents of household heads. This paper develops a model to analyze households’ trade-offs between hiring domestic helpers for household maintenance and taking these responsibilities by household members. We will apply household economic theories to develop a time allocation model incorporating interactions among household members. We assume that households trade off the money they are willing to spend for hiring helpers with the time they may need to spend for household maintenance activities to maximize utilities, subject to time constraints. The model may be used to analyze the impacts of domestic helpers on household members’ time allocation to subsistence, maintenance and recreation activities. It may also be applied to analyze the impacts of government policies regarding the minimum salary of domestic helpers and the change of household members’ wage rates on households’ decision to hire helpers. The paper extends the current literature on intra-household activity–travel interactions by considering external helps from domestic helpers, which may contribute to the understanding of activity–travel patterns of household members.     

Factors influencing commuting choices in suburban labor markets: A case analysis of Pleasanton,California

Worsening suburban congestion in recent years has sparked considerable interest in the travel behavior of suburban workers. This paper examines workplace characteristics have influenced the modal and temporal travel choices of suburban employees, using Pleasanton, California, a fast-growing suburb of the San Francisco Bay Area, as a case setting. The incidence of ridesharing was found to be highest for large companies of single-tenant sites with predominantly white-collar workers. Employers who offer workers flex-time privileges tend to be smaller firms with professional staffs situated in multitenant complexes. Many opt for flex-time because they do not have a critical mass of workers to successfully launch and sustain ridesharing programs. Flex-time programs were found to hinder the formation of carpools and vanpools in suburban settings like Pleasanton. The preferred traffic management program, it is argued, would encourage the staggering of work schedules across, not within, companies in order to promote more ridesharing.     

A simulation-based vessel-truck coordination strategy for lighterage terminals

Lighterage contributes significantly to the maritime industry since thousand tons of necessary supplies have to be delivered to mother vessels anchored near sea every day. A single lighter waits to receive cargoes from multiple suppliers at the berth, while the trucks wait for their turns to enter the terminal. Due to the limited space, the terminal operation becomes vulnerable to the lack of coordination of their arrivals. In this study, despite a traditional industry, we are interested in applying the emerging technologies to improve the operation efficiency. We develop a simulation-based coordination strategy to construct coordinated schedules for both the lighters and suppliers to reduce congestions in the lighterage terminal The discrete event simulation model is developed based on understanding the real-world terminal in Singapore, and the controlled arrival method, determining coordinated arrival schedules of trucks, is introduced and embedded to the simulation model. The simulation model is tuned up with a benchmark setting of 6-month historical data. To find the optimal strategy, an advanced bi-objective simulation optimization algorithm is employed. According to our findings, the proposed strategy could significantly improve the efficiency of both lighters and trucks in various indicators. At the end, a mobile application prototype is proposed to deliver the coordinated schedules to different parties, and improve the communication between parties.     

An integrated model for discrete and continuous decisions with application to vehicle ownership,type and usage choices

This paper proposes an integrated econometric framework for discrete and continuous choice dimensions. The model system is applied to the problem of household vehicle ownership, type and usage. A multinomial probit is used to estimate household vehicle ownership, a multinomial logit is used to estimate the vehicle type (class and vintage) choices, and a regression is used to estimate the vehicle usage decisions. Correlation between the discrete (number of vehicles) and the continuous (total annual miles traveled) parts is captured with a full variance–covariance matrix of the unobserved factors. The model system is estimated using Simulated Log-Likelihood methods on data extracted from the 2009 US National Household Travel Survey and a secondary dataset on vehicle characteristics. Model estimates are applied to evaluate changes in vehicle holding and miles driven, in response to the evolution of social societies, living environment and transportation policies.     

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.     

Impact of family in-home quality time on person travel demand

This paper introduces the concept of Primary Family Priority Time (PFPT), which represents a high priority household decision to spend time together for in-home activities. PFPT is incorporated into a fully specified and operational activity based discrete choice model system for Copenhagen, called COMPAS, using the DaySim software platform. Structural tests and estimation results identify two important findings. First, PFPT has a place high in the model hierarchy, and second, strong interactions exist between PFPT and the other day level activity components of the model system. Forecasts are generated for a road pricing and congestion scenario by COMPAS and a comparison version of the model system that excludes PFPT. COMPAS with PFPT exhibits less mode changing and time-of-day shifting in response to pricing and congestion than the comparison version.     

An econometric model of vehicle use in the household sector

Vehicle-use modelling at the household level has taken on new importance with the pressures on governments to encourage more efficient utilisation of increasingly scarce nonreplenishible liquid fuels. The fundamental energy equation recognizes two direct influences on consumption—the fuel efficiency of the vehicle and the amount of use. Until recently, the interrelationship between vehicle choice and vehicle utilisation at the household level was acknowledged but ignored. The availability of reliable vehicle-use data at the household level now enables a more serious effort at amending the imbalance of research effort where the reliance has been predominantly on vehicle choice modelling and gross (exogenous) assumptions on utilisation as a basis for predicting fuel consumption. This paper proposes an econometric method for identifying the influences on household vehicle use. It differs from previous empirical work in that vehicle kilometers, fuel cost per kilometer and vehicle fuel efficiency are endogenous, with utilisation of each vehicle endogeneously dependent on the utilisation of each and every household vehicle. The data are drawn from wave 1 of a four-wave panel of 1436 households in the Sydney metropolitan area. The empirical findings expose a set of influences on use hitherto not considered. The model specification provides an appropriate module for integration with household-based discrete choice models of vehicle choice.     

Network-oriented household activity pattern problem for system optimization

The recently emerging trend of self-driving vehicles and information sharing technologies, made available by private technology vendors, starts creating a revolutionary paradigm shift in the coming years for traveler mobility applications. By considering a deterministic traveler decision making framework at the household level in congested transportation networks, this paper aims to address the challenges of how to optimally schedule individuals’ daily travel patterns under the complex activity constraints and interactions. We reformulate two special cases of household activity pattern problem (HAPP) through a high-dimensional network construct, and offer a systematic comparison with the classical mathematical programming models proposed by Recker (1995). Furthermore, we consider the tight road capacity constraint as another special case of HAPP to model complex interactions between multiple household activity scheduling decisions, and this attempt offers another household-based framework for linking activity-based model (ABM) and dynamic traffic assignment (DTA) tools. Through embedding temporal and spatial relations among household members, vehicles and mandatory/optional activities in an integrated space-time-state network, we develop two 0–1 integer linear programming models that can seamlessly incorporate constraints for a number of key decisions related to vehicle selection, activity performing and ridesharing patterns under congested networks. The well-structured network models can be directly solved by standard optimization solvers, and further converted to a set of time-dependent state-dependent least cost path-finding problems through Lagrangian relaxation, which permit the use of computationally efficient algorithms on large-scale high-fidelity transportation networks.     

A comprehensive analysis of household transportation expenditures relative to other goods and services: an application to United States consumer expenditure data

This paper proposes a multiple discrete continuous nested extreme value (MDCNEV) model to analyze household expenditures for transportation-related items in relation to a host of other consumption categories. The model system presented in this paper is capable of providing a comprehensive assessment of how household consumption patterns (including savings) would be impacted by increases in fuel prices or any other household expense. The MDCNEV model presented in this paper is estimated on disaggregate consumption data from the 2002 Consumer Expenditure Survey data of the United States. Model estimation results show that a host of household and personal socio-economic, demographic, and location variables affect the proportion of monetary resources that households allocate to various consumption categories. Sensitivity analysis conducted using the model demonstrates the applicability of the model for quantifying consumption adjustment patterns in response to rising fuel prices. It is found that households adjust their food consumption, vehicular purchases, and savings rates in the short run. In the long term, adjustments are also made to housing choices (expenses), calling for the need to ensure that fuel price effects are adequately reflected in integrated microsimulation models of land use and travel.     

Exploratory analysis of Zipf’s universal power law in activity schedules

People’s behavior is governed by extremely complex, multidimensional processes. This fact is well-established in the transportation research community, which has been working on travel behavior (travel demand) models for many years. The number of degrees of freedom in a person’s activity schedule is enormous. However, the frequency of occurrence of day-long activity schedules obeys a remarkably simple, scale-free distribution. This particular distribution has been observed in many natural and social processes and is commonly referred to as Zipf’s law, a power law distribution. This research provides evidence that activity schedules from various study areas exhibit a universal power law distribution. To this end, an elaborate analysis using 13 household travel surveys from diverse study areas discusses the effect of proportional outlier removal on the power law’s exponent value. Statistical evidence is provided for the hypothesis that activity schedules in all these datasets exhibit a power law distribution with a common exponent value. The study proposes that a Zipf power law could be used as an additional dimension within a travel demand model’s validation process. Contrary to other validation methods, no new data is required. The observation of a Zipf power law distribution in the generated schedules appears to be a necessary condition. Additionally, the universal activity schedule distribution might enable the full integration of activity schedules in models based on universal mobility patterns.

     

How flexible is flexible? Accounting for the effect of rescheduling possibilities in choice of departure time for work trips

In departure time studies it is crucial to ascertain whether or not individuals are flexible in their choices. Previous studies have found that individuals with flexible work times have a lower value of time for late arrivals. Flexibility is usually measured in terms of flexible work start time or in terms of constraints in arrival time at work. Although used for the same purpose, these two questions can convey different types of information. Moreover, constraints in departure time are often related not only to the main work activity, but to all daily activities. The objective of this paper is to investigate the effect of constraints in work and in other daily trips/activities on the willingness to shift departure time and the willingness to pay for reducing travel time and travel delay. We set up a survey to collect detailed data on the full 24-hour out-of-home activities and on the constraints for each of these activities. We then built a stated preference experiment to infer preferences on departure time choice, and estimated a mixed logit model, based on the scheduling model, to account for the effects of daily activity schedules and their constraints. Our results show that measuring flexibility in terms of work start time or constraints at work does not provide exactly the same information. Since one-third of the workers with flexible working hours in the survey indicated that they have restrictions on late work-arrival times, their willingness to pay will be overestimated (almost doubled) if flexibility information is asked only in terms of fixed/flexible working hours. This clearly leads to different conclusion in terms of demand sensitivity to reschedule to a later departure time. We also found that having other activities and constraints during the day increases the individuals’ willingness to pay to avoid being late at work, where the presence of constraints on daily activities other than work is particularly relevant for individuals with no constraints at work. The important impact of these findings is that if we neglect the presence of constraints, as is common practise in transport models, it will generally lead to biased value-of-time estimates. Results clearly show that the shift in the departure time, especially towards a late departure time, is strongly overestimated (the predicted shift is more than double) when the effect of non-work activities and their constraints is not accounted for.