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.     

Patterns of car usage and restraint modelling

Transport demand forecasting procedures have traditionally employed household based modal split models implicitly assuming a selection of mode for each trip based on relative generalised cost. A detailed examination of the trip patterns of a sample of household in West Yorkshire shows that in fact there is little discretionary choice of public transport; public transport trips in car owning households generally being explained in terms of the specific unavailability of the car for such trips. Two versions of a category analysis model for modal split are based on this observation and applied to household data for Glamorgan and Monmouthshire to show that such a procedure is workable and produces results comparing favourably with traditional approaches. The likely implications of three types of restraint policy are examined and it is concluded that the existing interdependence in trip patterns and modal choice within the household is of great significance in determining their effects. In particular it appears that positive attempts to increase vehicle occupancy at the peak are likely to be more favourable to public transport finances than the more negative policies to restrain use of the car for journey to work, or second car ownership.     

When strike comes to town… anticipated and actual behavioural reactions to a one-day,pre-announced,complete rail strike in the Netherlands

We conducted secondary analysis on data collected among rail users, days before and after a national rail strike in the Netherlands. Our aim was to compare anticipated and actual behavioural reactions to the rail strike, investigate associations with traveller and trip characteristics, and perceived behavioural control and satisfaction with the chosen alternative. Forty-four percent of the people who had anticipated to travel by train on the day of the strike abandoned their trip, 24% switched to car as driver, 14% switched to another mode (as passenger), 18% stayed with the train and rescheduled the planned activity to another day. Almost half of people who had anticipated travelling by car expected to change behaviour as well. Multinomial logistic regression showed low preference for car among rail users. Considerable marginal effects were found for several variables, e.g.: young people and females were less likely to switch to car; short and middle distance trips were less likely abandoned or switched to another day; commute and business trips were more likely done by car, and business trips less likely cancelled. Despite high levels of perceived behavioural control and satisfaction with the chosen alternative, permanent modal shift as result of this strike is not expected.     

Moving from trip-based to activity-based measures of accessibility

This paper studies the properties and performance of a new measure of accessibility, called the activity-based accessibility (ABA) measure, and compares it to traditional measures of accessibility, including isochrone, gravity and utility-based measures. The novel aspect of the ABA is that it measures accessibility to all activities in which an individual engages, incorporating constraints such as scheduling, and travel characteristics such as trip chaining. The ABA is generated from the day activity schedule (DAS) model system, an integrated system based on the concept of an activity pattern, which identifies the sequence and tour structure among all the activities and trips taken by an individual during a day. A byproduct is an individual’s expected maximum utility over the choices of all available activity patterns, and from this the ABA is derived. The ABA is related to the logsum accessibility measures frequently derived from destination and mode discrete choice models. The key difference is that it is generated not by examining a particular trip, but by examining all trips and activities throughout the day.A case study using data from Portland, Oregon, demonstrates the rich picture of accessibility made available by use of the ABA, and highlights differences between the ABA and more traditional measures of accessibility. The ABA is successful in (a) capturing taste heterogeneity across individuals (not possible with aggregate accessibility measures), (b) combining different types of trips into a unified measure of accessibility (not possible with trip-based measures), (c) reflecting the impact of scheduling and trip chaining on accessibility (not possible with trip-based measures), and (d) quantifying differing accessibility impacts on important segments of the population such as unemployed and zero auto households (not possible with aggregate measures, and limited with trip-based measures).     

Trip chaining as a barrier to the propensity to use public transport

Trip chaining is a growing phenomenon in travel and activity behaviour. Individuals increasingly seek out opportunities to minimise the amount of travel required as part of activity fulfilment, given the competing demands on time budgets and their valuation of travel time savings. This search for ways of fulfilling (more) activities with less travel input has produced a number of responses, one of which is trip chaining. A particularly important policy implication of trip chaining is the potential barrier it creates in attracting car users to switch to public transport. This paper seeks to improve our understanding of trip chaining as a barrier to public transport use. A series of discrete choice models are estimated to identify the role that socio-economic and demographic characteristics of households have on the propensity to undertake trip chains of varying degrees of simplicity/complexity that involve use of the car or public transport with an embedded commuting or non-commuting primary purpose. Multinomial logit, nested logit and random parameter logit models are developed and contrasted to establish the gains in relaxing the strict conditions of the multinomial logit model.     

Can we reduce car use and, if so, how? A review of available evidence

Transport accounts for nearly a quarter of current energy-related carbon dioxide emissions with car travel constituting more than three quarters of all vehicle kilometres travelled. Interventions to change transport behaviour, and especially to reduce car use, could reduce CO₂ emissions from road transport more quickly than technological measures. It is unclear, however, which interventions are effective in reducing car use and what the likely impact of these interventions would be on CO₂ emissions. A two-stage systematic search was conducted focusing on reviews published since 2000 and primary intervention evaluations referenced therein. Sixty-nine reviews were considered and 47 primary evaluations found. These reported 77 intervention evaluations, including measures of car-use reduction. Evaluations of interventions varied widely in the methods they employed and the outcomes measures they reported. It was not possible to synthesise the findings using meta-analysis. Overall, the evidence base was found to be weak. Only 12 of the 77 evaluations were judged to be methodologically strong, and only half of these found that the intervention being evaluated reduced car use. A number of intervention approaches were identified as potentially effective but, given the small number of methodologically strong studies, it is difficult to draw robust conclusions from current evidence. More methodologically sound research is needed in this area.     

The effect of non-recreational transport cycling on use of other transport modes: A cross-sectional on-line survey

Accurate modelling of the health and environmental benefits of non-recreational transport cycling requires information about its effects on the use of other transport modes. Relevant research has not focussed on cycling for transport in a general context (as opposed to bikeshare), nor allowed for multi-modal trips. The influence of trip- and personal-characteristics on whether cycling replaces car-driving have yet to be considered. The present study aimed to address these research gaps. An on-line cross-sectional survey was completed by 1525 Australians who cycle for transport at least once per week. For the most recent trip completed (at least partly) by bicycle participants provided trip distance, and percentage travelled by car, public transport (PT), and walking. They also provided the percentage travelled by each mode for the same trip before taking up transport cycling; and a hypothetical future trip when riding is not possible. Compared to the same trip before, fifty percent of recent trips reduced car use, and around 1/3 eliminated a 100%-car trip. Reduced car use was significantly less likely for trips under 7.5 km, commuting, females, respondents under 55, and regular cyclists. Reduced car use was less likely for respondents who started riding because it is flexible, and more likely for those who started riding to avoid parking. Car-use was reduced by an average of 6.2 km per trip, and each bicycle-km cycled replaced 0.5 car-km. Participants report that since taking up cycling, even when they cannot use their bike, they use cars less and use PT more compared to before they took up cycling. Results suggest that previous studies underestimated the extent to which transport cycling replaces car travel, and highlights trip types and population groups to target with cycling promotion strategies. Information about the per-trip and per-bicycle-km replacement of car, PT and walking may be used for more accurate estimation of the benefits of transport cycling than has hitherto been possible.     

Rural mobility and mode choice: Evidence from the 2001 National Household Travel Survey

This article uses data from the 2001 National Household Travel Survey (NHTS) to compare travel behavior in rural and urban areas of the U.S. As expected, the car is the overwhelmingly dominant mode of travel. Over 97% of rural households own at least one car vs. 92% of urban households; 91% of trips are made by car in rural areas vs. 86% in urban areas. Regardless of age, income, and race, almost everyone in rural areas relies on the private car for most travel needs. Mobility levels in rural areas are generally higher than in urban areas. That results from the more dispersed residences and activity sites in rural areas, which increase trip distances and force reliance on the car. Somewhat surprisingly, the rural elderly and poor are considerably more mobile than their urban counterparts, and their mobility deficit compared to the rural population average is strikingly less than for the urban elderly and poor compared to the urban average. Data limitations prevented a measurement of accessibility, however, and it seems likely that rural areas, by their very nature, are less accessible than urban areas, especially for the small percentage of car-less poor and elderly households.     

Women’s complex daily lives: a gendered look at trip chaining and activity pattern entropy in Germany

It has long been argued in feminist studies that women’s daily lives are more complex than men’s. This is largely due to the gendered division of work, according to which women juggle more varied obligations, including employment, household work and caregiving. Complex activity patterns in turn encourage women to organise their trips in a more efficient manner in trip chains. This paper studies the complexity of activity patterns (measured by Shannon entropy) and trip chaining patterns from a gender specific perspective. The data used is the German Mobility Panel 1994–2012 which records respondents’ trips over the period of a week. The outcome variables are regressed on sociodemographics, residential and workplace spatial context attributes, cohort and period effects. Gender differences in the effects of variables are tested using interaction terms. The results suggest that women’s patterns are more complex than men’s. Some effects differed distinctly between men and women, suggesting that men and women are differently affected by circumstances impacting the complexity of their lives, most notably by having children and by having a partner.     

The intra-household choices regarding commuting and housing

This paper is an empirical analysis of the relationships between commuting decisions of spouses in dual-income households, where the role of housing and housing tenure is taken into account. The study is based on a large survey of US commuters and actual commuting and housing choices. Household commuting decisions are analyzed together with housing choices using the data from the 2001 American Housing Survey. A sample of dual-career spouses who commute to work by car only is used to explore the inter-relationship between male and female commuting decisions in such households and the effect of housing choices on their commuting. The inter-relationships between spousal commuting decisions are examined separately for the two forms of housing tenure: ownership and renting. In general, men commute further than women and owners commute further than renters.It is shown that for both renters and homeowners, journeys to work by men and women in such dual-earner households appear to be “complements” and not “substitutes”. This means that commute trips are jointly chosen to be longer (or shorter) for both spouses, as part of household selection of preferred housing and neighborhood characteristics. The residuals of equations explaining commute times and commute distances for men are strongly positively correlated with those for women. Many of the explanatory factors affect the men and women in similar ways. In particular, housing value is positively correlated with commute times and distances for both genders. The commute distance for women owners appears more sensitive to housing value than that of men, meaning women adjust their trips to work even more so than men do, as part of obtaining better housing.For both homeowners and renters, longer commute distances were correlated with higher salaries for both spouses. Somewhat surprisingly, it was found that commuting distances for women are similarly or even more sensitive to income increments than are those of men. Household size appears to affect women who own housing in ways opposite from those who rent. Other housing and neighborhood factors also affect the joint commuting decisions of these men and women.     

The impact of transit station areas on the travel behaviors of workers in Denver,Colorado

Transit development is one planning strategy that seeks to partially overcome limitations of low-density single use car oriented development styles. While many studies focus on how residential proximity to transit influences the travel behaviors of individuals, the effect of workplace proximity to transit is less understood. This paper asks, does working near a light rail transit station influence the travel behaviors of workers differently than workers living near a station? We begin by examining workers’ commute mode based on their residential and workplace proximity to transit station areas. Next, we analyze the ways in which personal travel behaviors differ between those who drive to work and those who do not. The data came from a 2009 travel behavior survey in the Denver, Colorado metropolitan area, which contains 8000 households, 16,000 individuals, and nearly 80,000 trips. We measure sustainable travel behaviors as reduced mileage, reduced number of trips, and increased use of non-car transportation. The results of this study indicate that living near a transit station area by itself does not increase the likelihood of using non-car modes for work commutes. But if the destination (work) is near a transit station area, persons are less likely to drive a car to work. People who both live and work in a transit station area are less likely to use a car and more likely to take non-car modes for both work and non-work (personal) trips. Especially for persons who work near a transit station area, the measures of personal trips and distances show a higher level of mobility for non-car commuters than car commuters – that is, more trips and more distant trips. The use of non-car modes for personal trips is most likely to occur by non-car commuters, regardless of their transit station area relationship.     

Intrapersonal variability in daily urban travel behavior: Some additional evidence

Researchers have used multiday travel data sets recently to examine day-to-day variability in travel behavior. This work has shown that there is considerable day-to-day variation in individuals' urban travel behavior in terms of such indicators of behavior as trip frequency, trip chaining, departure time from home, and route choice. These previous studies have also shown that there are a number of important implications of the observed day-to-day variability in travel behavior. For example, it has been shown that it may be possible to improve model parameter estimation precision, without increasing the cost of data collection, by drawing a multiday sample (rather than a single day sample) of traveler behavior, if there is considerable day-to-day variability in the phenomenon being modeled. This paper examines day-to-day variability in urban travel using a three-day travel data set collected recently in Seattle, WA. This research replicates and extends previous work dealing with day-to-day variability in trip-making behavior that was conducted with data collected in Reading, England, in the early 1970s. The present research extends the earlier work by examining day-to-day variations in trip chaining and daily travel time in addition to the variation in trip generation rates. Further, the present paper examines day-to-day variations in travel across the members of two-person households. This paper finds considerable day-to-day variability in the trip frequency, trip chaining and daily travel time of the sample persons and concludes that, in terms of trip frequency, the level of day-to-day variability is very comparable to that observed previously with a data set collected almost 20 years earlier in Reading, England. The paper also finds that day-to-day variability in daily travel time is similar in magnitude to that in daily trip rates. The analysis shows that the level of day-to-day variability is about the same for home-based and non-homebased trips, thus indicating that day-to-day variability in total trip-making is attributable to variation in both home-based and non-home-based trips. Day-to-day variability in the travel behaviors of members of two-person households was also found to be substantial.     

Factors affecting children’s journeys to school: a joint escort-mode choice model

A child’s mode of travelling to school is influenced by, or dependent on, parental choices. Thus, an increasing proportion of car trips may reflect parental choices and constraints. Whether a parent can escort their children to school may depend on their scheduling and spatial constraints, e.g., work schedule and job location in relation to home and school locations. This research aims to understand the effect of household bundling constraints on a child’s escort-mode choice. In this study, school trip data are drawn from the 2001 SCAG (Southern California Association of Governments) Post Census Regional Household Travel Survey. The study area is the five-county Los Angeles region. Our findings show that the parents’, especially the mother’s, increased working hours and more distant job locations result in an increased likelihood of several alternative escort-mode choices. Mothers who work longer hours and further away from home are less likely to chauffeur their children. These trips have been substituted by alternative escort choices such as independent travel and being escorted by fathers, or alternative mode choices such as active commuting and busing. The effect of increased working hours may be offset by the option of flexible working hours, which allows parents to arrange more escort trips. This study elucidates an important aspect in explaining children’s changing mode choice in journeys to school and sheds light on current policy efforts in reducing children’s car dependency.     

A dynamic model system of household car ownership,trip generation,and modal split: model development and simulation experiment

A dynamic model of household car ownership and mode use is developed and applied to demand forecasting. The model system consists of three interrelated components: car ownership, mechanized trip generation, and modal split. The level of household car ownership is represented as a function of household attributes and mobility measures from the preceding observation time point using an ordered-response probit model. The trip generation model predicts the weekly number of trips made by household members using car or public transit, and the modal split model predicts the fraction of trips that are made by public transit. Household car ownership is a major determinant in the latter two model components. A simulation experiment is conducted using sample households from the Dutch National Mobility Panel data set and applying the model system to predict household car ownership and mode use under different scenarios on future household income, employment, and drivers’ license holding. Policy implications of the simulation results are discussed.     

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.     

The call of the road: factors predicting students’ car travelling intentions and behaviour

The most common daily trip for employed persons and students is the commute to and from work and/or place of study. Though there are clear environmental, health and safety benefits from using public transport instead of private vehicles for these trips, a high proportion of commuters still choose private vehicles to get to work or study. This study reports an investigation of psychological factors influencing students’ travel choices from the perspective of the Theory of Planned Behaviour (TPB). Students from three different university campuses (n = 186) completed a cross-sectional survey on their car commuting behaviour. Particular focus was given to whether car commuting habits could add to understanding of commuting behaviour over and above behavioural intentions. Results indicated that, as expected, behavioural intention to travel by car was the strongest TPB predictor of car commuting behaviour. Further, general car commuting habits explained additional variance over and above TPB constructs, though the contribution was modest. No relationship between habit and intentions was found. Overall results suggest that, although student car commuting behaviour is habitual in nature, it is predominantly guided by reasoned action. Implications of these findings are that in order to alter the use of private vehicles, the factors influencing commuters’ intentions to travel by car must be addressed. Specifically, interventions should target the perceived high levels of both the acceptability of commuting by car and the perceived control over travel undertaken by private vehicle.     

Beyond the journey to work

This paper analyses the Nationwide Personal Transportation Study (NPTS) surveys of 1977 and 1983–1984 that reveal a remarkable increase in nonwork travel. This growth occurred among all city size classes but was stronger for suburban residents. Also interesting is that the rate of increase in nonwork trips was higher in the peaks (especially in the morning peak) than in the off-peak period. Some, but not all, of these trips may be price-elastic and might be diverted by congestion pricing strategies. The nonwork trip growth is concentrated in the “family and personal” and the “social and recreational” categories. Although higher-income households make more trips than low-income households, the increase in nonwork travel is common to all income groups. The growth in nonwork travel does not appear to be closely associated with the growth in female employment or trips related to the children of working women. The most convincing explanation of the growth in nonwork travel is that the trip cost savings (less time and distance) experienced because of more efficient spatial settlement patterns have provided an incentive to undertake more trips. Another implication is that urban economic models and urban transport policies have overemphasized the journey to work, especially to the central business district (CBD).     

Auto travel fuel elasticity in a rapidly developing urban area

This paper presents results of a study conducted to quantify the effect of fuel cost increases on household auto travel in Riyadh, the rapidly developing capital of Saudi Arabia. Responses of a stratified random sample of 1648 individual households provided the data base for the analysis. The auto trip measures of shrinkage ratio, arc and log-arc elasticities were calculated for households categorized by income and family size. The elasticity measures suggested the existence of significant relationship among the factors of fuel cost, the number of daily auto trips, and family size. It was found that as fuel prices increased, the number of daily trips decreased, and that this decrease in daily trips was greater with larger family size. A step-wise multiple regression analysis with three independent variables of car ownership, family size, and daily fuel expenditures was developed. The model was fairly accurate in predicting variations in daily household travel. The regression parameter of the variable fuel cost was also used to derive demand elasticity to fuel expenditures. Elasticity measures ranged between -0.30 and -0.37.     

Effect of environmental awareness on electric bicycle users’ mode choices

Travellers’ environmental awareness can affect their mode choices. The primary objective of this study is to identify the effect of electric bicycle (e-bike) users’ environmental awareness on their mode choice when the use of e-bikes is prohibited in urban areas in China. The data were collected via a questionnaire survey administered at ten locations in Nanjing, China. Using mixed multinomial logit (MMNL) models, we examined the relationship between the e-bike users’ mode choice and their environmental awareness, combined with socioeconomic and demographic characteristics and trip attributes. The results show that the level of environmental awareness, gender, age, education, income, the ownership of car and conventional bike, and trip distance affect e-bike users’ choices significantly. Those with a high level of environmental awareness are more likely to choose zero-emission transport modes. A stratified analysis reveals that the effect of environmental awareness is associated with their original transport mode choice prior to their use of the e-bike. With a high level of environmental awareness, original car users tend to opt for moderate- or zero-emission modes; original bus and metro users incline to choose a zero-emission mode or their original mode; and few original cyclists and walkers favour moderate- or high-emission modes. The results of the current study provide transport authorities with insights to establish sustainable urban transportation management policies and strategies to increase the share of zero- and low-emission transport modes.     

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.