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Trip-Chaining Trends in the United States: Understanding Travel Behavior for Policy Making
Abstract
This paper uses data from the 1995 Nationwide Personal Transportation Survey and the 2001 National Household Travel Survey to examine trip-chaining trends in the United States. The research focuses on trip chaining related to the work trip and contrasts travel characteristics of workers who trip chain with those who do not, including their distance from work, current levels of trip making, and the purposes of stops made within chains. Trends examined include changes in the purpose of stops and in trip-chaining behavior by gender and life cycle. A robust growth in trip chaining occurred between 1995 and 2001, nearly all in the direction of home to work. Men increased their trip chaining more than women, and a large part of the increase was to stop for coffee (the Starbucks effect). It was found that workers who trip chain live farther from their workplaces than workers who do not. It was also found that, in two-parent, two-worker households that drop off children at school, women are far more likely than men to incorporate that trip into their commute and that those trips are highly constrained between 8:00 a.m. and 9:00 a.m. An analysis was done of workers who stopped to shop and those who did not but made a separate shopping trip from home; a large potential to increase trip-chaining behavior in shopping trips was found. Results of these analyses have important policy implications as well as implications for travel demand forecast model development. Finally, this paper uses these analyses to develop conclusions about the utility of transportation policies and programs that use the promotion of trip chaining as a primary travel demand management strategy.
... McGuckin et al. [17] indicated that public transport commuters are almost twice as likely to perform direct home-work-home trips, and that long-distance commuters have a higher propensity to include a non-work-related activity before going back home. Lee and McNally [18] highlight the possibility for individuals to perform activities opportunistically, e.g., short activities included within their commuting. ...
... Lee and McNally [18] highlight the possibility for individuals to perform activities opportunistically, e.g., short activities included within their commuting. Adler and Ben-Akiva [3] and McGuckin et al. [17] highlighted the importance of considering socio-economic characteristics. The household structure affects the number of undertaken activities [19,20]. ...
... The household structure affects the number of undertaken activities [19,20]. Having dependent children could lead, for instance, to perform more complex activity patterns, especially by women [17,20]. ...
This paper studies the relationship between activity pattern complexity and car use using two multi-day surveys involving the same participants but collected just before and about one year after they relocated their workplace. Measurable characteristics related to two latent variables, namely activity pattern complexity, or trip chaining (e.g., number of activities done within and outside the home–work tour), and to car use (e.g., usage rate, distance travelled by car) were selected. The study shows that the methodology adopted, partial least square structural equation modelling, quantifies the relation between the two variables, and is robust towards changes in important contextual characteristics of the individuals, namely workplace location. The findings indicate that the number of activities chained to commuting travels strongly impact mode choice and, in particular, car use. The paper also shows that chaining non-work-related activities has a stronger impact on car use. The results of this study suggest that planning and management solutions aimed at reducing car use, but focusing only on the commuting trip while neglecting the impact of other daily activities, may be less effective than expected.
... More than 50 years later, Americans' journeys to and from work have become more complicated. An influx of women to the workforce and the growing dominance of the dual-worker family has increased the numbers of workers on the road; many working parents (especially mothers) incorporate dropping children off at school into their daily commute (1,2). More people work multiple jobs, or balance employment and higher education (3). ...
... More people work multiple jobs, or balance employment and higher education (3). Less seismic shifts, such as the so-called Starbucks effect, have added a morning coffee stop to many commutes (2). ...
... These complex journeys to work, sometimes referred to as chained or tour commutes, have become more common over the last few decades (2); we found that they now constitute about a quarter of all WJs in Georgia. Commute complexity has been analyzed in relation to mode choice, congestion, sustainability, and demographic differences such as gender (e.g., McGuckin et al. [2], Paleti et al. [4], Zhu et al. [5], Concas and Winters [6]). ...
We use travel diary data from the 2017 National Household Travel Survey (NHTS) Georgia subsample to address critical issues associated with analyzing complex work journeys. To define the work journey, we discuss the importance of defining commute anchors by both purpose and location. We then compare two alternate measures for determining what portion of each journey should be counted as commute distance: the last leg of the journey (the NHTS default), and a modeled counterfactual simple commute to estimate the distance that would have been traveled had no stops been made. The average complex commute distance obtained using the counterfactual method was 63% higher than the estimate based on using the last leg alone. Using the last-leg method may understate Georgia’s annual commute distance by 2.6 billion miles (10% of the total, including both simple and complex commutes). We argue that the last-leg method is not an accurate gauge of work travel, particularly among populations such as women, who are more likely to trip chain on their commutes.
... This study explores the trip patterns of Generation-Z related to daily trip-chains and mileage in urban areas. The results support some theoretical findings that men travel more frequently and longer distances than women (e.g., McGuckin et al., 2005;Goel, 2023;Patnala et al., 2023;Su & Bell, 2012). However, many studies also show that women travel more than men (e.g., Lee et al., 2007;Susilo et al., 2019;J. ...
... This finding is supported by several previous studies (e.g., Carver et al., 2019;Chang et al., 2020;Huang et al., 2021;Klöckner & Friedrichsmeier, 2011;Mandic et al., 2020;Zong et al., 2019) that explain how the environment, residential location, work location, and distance affect the number of trips and travel patterns. Those who live farther from their workplace produce more trip-chains (McGuckin et al., 2005). The city's structure can affect travel patterns and mobility in large cities. ...
... Trip chain can reveal how people organize their activities across space and time, as well as the topological relationships among different activity locations. Deeper understanding of trip chains will provide a wealth of information for transportation planners and policy makers, therefore benefit land-use planning and improve urban accessibility, and even forecast and control global spreading of epidemics (Anderson, Anderson, & May, 1992;Hufnagel, Brockmann, & Geisel, 2004;Lloyd & May, 2001;McGuckin & Murakami, 1999;McGuckin, Zmud, & Nakamoto, 2005). ...
... Principally, "trip chain" refers to complex relationships between a set of activities and the interdependence of temporal (e.g., timing, duration, length, and sequence of trips) and spatial (e.g., location) characteristics associated with human mobility. Thus, the trip chain model in tourism can be defined as a sequential pattern of trip activities (or places visited for travel activities) made by travelers on a day-to-day basis (Golob & Hensher, 2007;McGuckin, Zmud, & Nakamoto, 2005). ...
Tourists tend to visit multiple destinations out of their variety-seeking motivations in their trips. Thus, it is critical to discover travel patterns involving multi-destinations in tourism research. Existing relevant research most relied on survey data or focused on citizens due to the lack of large-scale, fine-grained tourism datasets. Several scholars have mentioned the notion of trip chains, but few works have been done towards quantitatively identifying the structures of trip chains. In this paper, we propose a model for quantitatively characterizing tourist daily trip chains. After applying this model to tourist mobile phone big data, underlying tourist travel patterns are discovered. Through the framework, we find that: (1) Most "hybrid" (inter-city and intra-city) and "intra-city" (only intra-city) patterns can be captured by only 13 key trip chains relatively; (2) For two continuous days, almost all kinds of original chains have a rather high probability to transfer to either the first two transferred chains, or other infrequent chains in our study areas; (3) The principle of least efforts (PLE) affects tourists' structures of trip chains. We can use average degree and average travel distance to interpret tourist travel behavior (achieving tasks in PLE). This study not only demonstrate the complex daily travel trip chains from tourism big data, but also fill the gap in tourism literature on multi-destination trips by discovering significant and underlying patterns based on mobile datasets.
... The increasing complexity of modern life can lead to increased time poverty, which in turn can increase the tendency of travelers exploring opportunities to chain non-work activity purposes within a work tour to reduce travel and time costs and to gain efficiency in activity participation (McGuckin et al., 2005;Hensher and Reyes, 2000;Levinson and Kumar, 1995;Bianco and Lawson, 1996). However, increasing the number of complex work tours can also increase the reliance on more flexible travel modes (Hensher and Reyes, 2000), such as private vehicles that can allow much flexibility and convenience to the commuters to schedule either planned or spur of the moment non-work activities within the work tour under spatial and temporal constraints (Lee and McNally, 2003). ...
... Previous studies showed that the majority of workers who use transit in their work tours are more likely to make home-based simple tours (McGuckin et al., 2005). We observe that an equal share of simple and complex work tours is made by transit commuters. ...
We analyze the complex travel behavior of workers who utilize public transit as part of their work tours (“transit commuters”). Here, complex travel behavior is defined in terms of tours, where a tour is defined as a sequence of trips and activities that begins and ends at the same location and a work tour contains at least one non-home, work activity. The objective of this study is to investigate how transit commuters link non-work activities as part of work tours under transit operational constraints. In particular, we identify dominant patterns of work tours made by transit commuters and analyze these tours using a set of activity-travel analytics and data from the 2017 National Household Travel Survey (NHTS). The primary insights are: (1) about 80 percent of work tours consist of 7 dominant patterns whereas the remaining 20 percent of tours demonstrate a total of 106 diverse and more complicated patterns; (2) half of the transit work tours are complex; (3) most simple tours are transit-only tours whereas most complex tours are multi-modal tours; and (4) transit use is more complex than the traditional home to work commute with a diverse set of choices at various stages of activity scheduling. While policies associated with public transit typically focus only on the journey to work, this study considers the complete set of trips starting and ending at home including intermediate non-work activity, which can provide insights for land use and transit-related policies to better accommodate the complex travel behavior of commuters who utilize transit.
... Trips are driven by the purpose of activities which reflect traveller's value and attitude towards personal and social goals. The logistical efficiency of travel is an increasingly important factor for people to consider their trip plan (Su et al. 2009), and thus the trip chain has become more common in everyone's daily travel (McGuckin et al. 2005). ...
... Their preferences on tours like the trip sequences, destination choices and time arrangement are normally limited by reduced mobility. Other factors like gender and life cycle (McGuckin et al. 2005), income and household composition (Noland and Thomas 2007) would also influence travellers' trip chain behaviour. In comparison, older travellers are shown to have shorter trips in a tour, but prefer more than one tour a day (Schmöcker et al. 2010). ...
A series of pilots and studies of Mobility-as-a-Service have launched out in recent years, but services are generally recognized as still at the early stage of development, whether in terms of its concept, the level of urban construction and the acceptance of travellers. According to the earlier explorative study of MaaS in a workshop in the UK, the result referred to the lack of consensus among stakeholders and the mismatch of value propositions between service providers and users. This leads to the early market not fully considering the requirements of the different group of travellers, especially for the older people. Therefore, this study reviewed previous literature and summarized the logic relation of the relevant factors, in order to provide the evidence for future sustainable development of MaaS on the aspect of increasing older travellers’ social participation.
... Some authors [5][6][7] have found in their longitudinal studies that activity sequences are becoming increasingly more and more complex over time. Activity sequences have increased in the past decades, in great part due to changes in the location of specific activities, which have moved from in-home to out-home (e.g., stopping for coffee or meals) and to escorting activities (mainly escorting children to school). ...
... Considering Table 8 given below, which identifies by diagonal sum that about 69.2% of typical residents in Vilnius city do not undertake complex tours, it can be concluded that low travel complexity is not the main reason for poor public transport usage. However, it should be noted that complexity has the potential to increase over time and the public transport system may face additional challenges in the future as activity sequences become more complex [5][6][7] and lead to an increase in car usage [2]. Such a strong reliance on the car mode in Vilnius (distance based 75.7%) results in frequent congestion and consequently environmental and social costs such as air and noise pollution, high energy consumption, road accidents, and delays. ...
The approach defines the process of conducting an empirical research of the travel behavior patterns of residents of Vilnius city. It defines survey methodology and important mobility parameters such as activity sequences and their probabilities of homogeneous urban population segments during the weekday. This empirical research is based on a travel diary survey that was planned and executed in cooperation with Vilnius Municipality during preparation of sustainable mobility plan. The following work describes the research object, the questionnaire design, sampling strategy and the analysis of results based on characteristics of respondents. An innovative activity sequence-focused travel behavior research approach designed to collect data for a tour-based travel demand model.
... Features of multiple trips a day, which was descripted as trip chain, and it could have an impact on the decision making for pattern selection. [9][10][11][12][13] The relationship between mode choice and trip chain pattern has attracted researchers' interest. [14][15][16][17] Previous studies thought that the pattern of trip chains affected the mode choice decisions, such as a recent study by Li et al. 17 shown that there was more bicycle use in commuting trips than non-commuting trips. ...
... The complex chains (''hwhwh,'' ''hohoh,'' and ''hw + oh'') present 40.8% of all chains in our dataset which is comparable to some previous studies. [12][13][14] Tripping mode is considered to be the mode of tripping chain if used in a single chain or the (most commonly used) tripping mode that dominates the complex chain. As shown in Table 1, bicycles are the most commonly used mode of transportation in Bengbu, accounting for 37.83% of the mode of transportation. ...
The main purpose of this article is to compare the mode choice decisions between commuting and non-commuting trip chains and evaluate the uncertainties in the mode choice process. According to household survey data from a medium-sized city in China, the whole day of travel is divided into several types of travel chains. We used the multinomial logit model to estimate the impacts of factors on the choices of trip modes, which included walk, bike, public transit, and car. The entropy theory was introduced to evaluate the uncertainty of each traveler’s mode choice decision. The results indicate that there are great differences in mode choice between commuting and non-commuting trip chains. It is found that the causes and effects of different chains are different. The results help to understand the decision-making process of mode.
... Given this gendered labour division, women are more limited in time and space than men (Nisic, 2017). This is reflected in women's shorter commutes, as it is more important for them to choose a job close to home to take care of other obligations on short notice, such as running errands or dropping off and picking up children, if needed (McGuckin et al., 2005). Moreover, the cost-benefit ratio of commuting is worse for women than for men, as commuting is typically associated with monetary costs due to travel expenses (van Ommeren and Fosgerau, 2009), whereas earnings are usually lower among secondary earners. ...
With growing concern about the climate impact of travel, a central question is the extent to which working from home (WFH) can reduce commuting. Recently, the question has received even more attention as WFH has increased sharply with the onset of the COVID-19 pandemic. However, the state of research is marked by mixed results and lacking longitudinal evidence. We investigate the link between WFH and total weekly commuting time by applying fixed effects regression to panel data from the Australian HILDA Survey, covering the period 2002-2019. We go beyond previous research by examining the moderating roles of the extent of WFH, the duration of the WFH episode, and gender. Overall, we find that doing any work from home is associated with a significant decrease in employees' weekly commuting time of about 14% on average. The reduction sets in immediately with the start of WFH and tends to further increase thereafter. However, only high shares of WFH are associated with substantial drops in commuting time, and reductions are larger for women than men. Taking into account Australian workers' reported WFH preferences, our results suggest maximum potential future commuting time savings of about 17-25% compared to 2019.
... Economic changes produced greater differentiation in the types of employment offered in increasingly knowledge-based cities. Women began to participate much more in the workforce, and commute, in greater numbers. Travel behaviour studies began to focus on women's travel from many perspectives, including: gender differences in distances to work; mode of travel; automobile occupancy; and, the propensity to combine multiple destinations in one trip [1][2][3][4]. Gender was soon recognised as one of the key socio-demographic influences on commuting behaviour. Women and men who worked in the same occupational category often had different commuting patterns [5]. ...
The segmentation of commuters into either blue or white-collar workers remains is still common in urban transport models. Internationally, models have started to use more elaborate segmentations, more reflective of changes in labour markets, such as increased female participation. Finding appropriate labour market segmentations for commute trip modelling remains a challenge. This paper harnesses a data-driven approach using unsupervised clustering–applied to 2017–20 South East Queensland Travel Survey (SEQTS) data. Commuter types are grouped by occupational, industry, and socio-demographic variables (i.e., gender, age, household size, household vehicle ownership and worker skill score). The results show that at a large number of clusters (i.e., k = 8) a highly distinct set of commuter types can be observed. But model run times tend to require a much smaller number of market segments. When only three clusters are formed (k = 3) a market segmentation emerges with one female-dominated type (‘pink collar’), one male-dominated type (‘blue collar’) and one with both genders almost equally involved (‘white collar’). There are nuances as to which workers are included in each segment, and differences in travel behaviours across the three types. ‘Pink collar’ workers are mostly comprised of female clerical and administrative workers, community and personal service workers and sales workers. They have the shortest median commutes for both private motorised and active transport modes. The approach and methods should assist transport planners to derive more accurate and robust market segmentations for use in large urban transport models, and, better predict the value of alternative transport projects and policies for all types of commuters.
... In this sense, single workers have been found to have longer commutes than married workers, and married workers whose spouses also work have shorter commutes than those whose spouses do not work (Johnston-Anumonwo, 1992;Lee & McDonald, 2003;Turner & Niemeier, 1997). Thus, a larger gender commuting gap is observed when controlling for marriage, as women tend to coordinate dual roles (Giménez-Nadal & Molina, 2016;Hanson, 2010;McGuckin et al., 2005), resulting in greater spatiotemporal constraints (Kwan, 1999;Rapino et al., 2011). In contrast, commuting differences between men and unmarried women are generally smaller or insignificant (Hersch & Stratton, 1994;Kwon & Akar, 2021;Turner & Niemeier, 1997). ...
This article examines the origins of the shorter commutes typically observed for women, a phenomenon that contributes to the poorer work outcomes they typically suffer. The analysis extends previous research on the gender gap in commuting by using econometric decomposition techniques that are novel in this field which, combined with a Spanish nationally representative survey that allows for an exhaustive control of the different elements identified in the literature as possible determinants of gender differences in commuting to work, allows quantifying the specific influence of a wide range of individual, family, territorial and work-related elements. The evidence obtained shows that the gender gap in commuting is not the result of the relative characteristics of women, but of the presence of a systematic pattern of lower mobility that emerges when women are compared with observationally similar men. Yet, this pattern of lower mobility is not observed for certain groups of women whose behavior in the labor market is generally more egalitarian, such as women with higher education, without family responsibilities or without a partner, which is consistent with the presence of cultural or social constraints that tend to limit women’s mobility.
... A trip chain is generally accepted to be a sequence of trip segments between any pair of anchor/foundational activities (e.g. home, work, or school) bounded by stops of 30 minutes or less (Mcguckin, 2005). Household types has been found to be the main determinant of activity-time allocation, nature and complexity of trip chains (Lee, 2007). ...
Societal changes are occurring over the last decades. Importantly, households evolved from a relatively standard 4-5 members to a set of diversified structures (e.g., single-parenting, single elderly, etc.) with strong implications in the interpersonal relationships and daily organization. Together with increasing complexity of daily activities, personal mobility is becoming multifaceted, where regular daily commuting is no longer standard that turned into varied mobility plans over weeks, seasons and years. Moreover, urban mobility systems have shifted dramatically from conventional public transport modes (bus, underground, train, and taxi) to an intricate set of alternatives (more walking, biking, vehicle-sharing, minibus, transport-on-demand, etc.) that increase the range of possibilities for the daily set of interconnected trips. Information on urban mobility alternatives has also become ubiquitous, principally with the IoT and its mobile forms (e.g., smartphones). For shorter-term mobility decisions (e.g., going to a restaurant), sources like route planners (e.g., Google Maps) are standard now. However, despite the myriad information sources, it isn't always straightforward to make the most adequate choices for longer-term mobility choices, including structural decisions such as house/work locations, private car acquisition, or holding monthly cards. Actually, longer-term decisions involve all the complex issues referred to above. In the face of this complexity, the final decision is too often buying a private car (or several). Inter alia, the dominating modal share of cars is responsible for much of the unsustainable urban development (e.g., air pollution, noise, space deprivation, accidents, run-overs). The mediation of households and the complex urban mobility system is lacking. Such mediation services already exist in the energy sector with the Energy Service Companies (ESCO). Just like ESCO's do for energy services, the aim of this research is to explain and illustrate how Mobility Service Companies (MOSCO) can mediate household mobility planning and decision-making that, ultimately, can reduce their annual mobility budget and, eventually, environmental footprints. For that, a set of households are used as test beds for the proof-of-concept of this new mobility support service. The approach used here is to identify household mobility profiles. For that, we collect information regarding the weekly mobility patterns of household members and determine regular mobility requirements and the network of subordinations. Data collection is made through detailed personal interviews. We then compare current mobility indicators (e.g., annual budget, travel times, CO2 emissions) with those potentially obtained after presenting alternative mobility plans. Results suggest that households can significantly reduce their annual mobility budgets and the 22 2 corresponding environmental footprints. Still, these are potential reductions, and further action is required to effectively implement the new mobility plans for all household members. Anyhow, tackling the complexity of household mobility planning is required with potential gains both individually and collectively.
... In addition, recent studies have focused on daily activity patterns, which have been transformed with the advent of urbanization and the spatial spread of destinations (Bhat, Srinivasan, & Axhausen, 2005). This is particularly meaningful for women: since they are usually responsible for child-rearing, the fragmentation of children's activities across different locations creates new constraints on the activity patterns of mothers (McGuckin, Zmud, & Nakamoto, 2005;Abouelela et al., 2020). Some have argued that the entrance of women into the labor-force causes them to act more like men (e.g. ...
Mobility is a multifaceted phenomenon intertwined with the allocation of material and social resources in modern societies. Studies conducted in Western contexts have linked limited mobility to other forms of social marginalization, often based on class and gender divisions. However, little is known about gender factors shaping mobility patterns in non-Western contexts, especially the Arab world. I investigate this issue in a Jordanian community (n=200) and compare the results to three Arab communities in Israel (n=300). These communities share a language and culture but have developed in different institutional contexts. The results show that men are more mobile than women in both countries, but the nature of gendered differences and their magnitude varies. Israeli-Arab women are more likely to shop than their male counterparts, but this pattern is reversed in Jordan. Jordanian women are more educated than Israeli women but are less likely to work. Therefore, demographic and socio-economic factors have a significant effect on the diversity of daily activity patterns; nonetheless, these variables are not sufficient to explain these gender disparities. Thus, effective policy interventions must be considered. Increasing job opportunities within the towns along with efficient PT may be considered more appropriate for women from a normative perspective but at the same time might allow women to maintain their traditional household chores.
... Compared with big data, another way to collect data is to do surveys. Although sometimes constrained by unrepresentative samples and limited time, survey analysis gives insights in 'why' of commuters' actions (Bamberg et al., 2003;McGuckin et al., 2005;Long and Thill 2015). Most of these academic studies address larger cities. ...
Data have played a role in urban mobility policymaking for decades. Especially since the emergence of big data, many researchers have shown how to advance data use to improve understanding transport policy effects, but there is hardly insight in how this is adopted in policy practice. This study aims to address this gap by answering two questions: (1) how is data currently embedded in urban mobility policy- and decision-making; and (2) what are the advantages and limitations of more data use in these processes?
We chose two Dutch cities -Maastricht and Groningen-that were both involved in a national programme (BeterBenutten) that trialed (and funded) a more evidence-based policymaking approach. We did ten semi-structured interviews with the people work in the mobility department and analyzed the twenty-one most relevant policy reports to understand how more data reinforces/impedes transport policymaking in practice. We found that data use differed in long-term and short-term policy cycles. In the long-term policy cycle, data was regarded as less important than political and societal trends and developments; in the short-term cycle, data played a major role in prompting traffic regulations and policy adjustments.
Policy insights can be derived from this research on how data can be better embedded in policymaking practices (1) The support from national/regional level (i.e. BeterBenutten program): could provide extra opportunities for local governments to do ex-post policy assessments, which has been regarded as valuable resources for evidence-based decision-making by policymakers. (2) Survey data can still play a significant role in urban mobility planning by typically providing more insights in the ‘why’ of traveler behaviors than big data. (3) Transport policymakers need to strength their abilities in selecting suitable data (out of a much larger set) and having more (competent) personnel capacity to interpret data. (4) Promoting sustainable mobility is a strong driving force for the local governments to enhance data use.
... Trips related with reproductive labour, or what has also been termed as 'care work', are different in nature than work trips and they have often gone unrecognized in the design of transportation systems (Sánchez de Madariaga, 2013). For instance, care-related trips tend to take place during off-peak hours when transit is less frequent (Farber et al., 2016;McGuckin et al., 2005). Core riders who depend on transit to conduct this type of work would, arguably, face greater barriers as they navigate through a system that has not been designed with their travel needs in mind. ...
In recent years, public transit policy has often focused on chasing ‘choice’ riders, or those who have mode alternatives, while taking for granted ‘captive’ riders, or also referred to as transit dependents. This paper argues for a need to re-centre attention towards ‘captive’ riders through equity and sustainability perspectives, and to question the use of the term ‘captive’, as it alludes to marginalization. We conduct this research by examining the transit experiences of a sample of young captive riders in Don Valley Village and Crescent Town, two high-rise suburban neighbourhoods in the City of Toronto. Semi-structured interviews are used to gain insight into participants’ travel patterns and the challenges associated with public transit use. Participants accrue different types of costs with their experiences (i.e., time, money, safety, and comfort), which do limit their ability to participate in public life. The study is situated in the broader context of transit equity, which point to the need for service quality improvements for ‘captive’ riders. This study also shows why assessments of young captive riders’ experiences is essential for planning. Contrary to how captive riders are perceived, service quality issues prompted some of the study participants to switch to driving, which further questions the categorization of ‘choice’ and ‘captive’. Transit agencies are urged to consider further how to improve transit quality for ‘captive’ riders to contribute to equity but also to maintain transit loyalty among younger transit riders as their circumstances change.
... Most studies that have examined this issue have relied on quantitative methods and have identified the following main factors that explain this phenomenon: (i) labour market positions, (ii) household roles and responsibilities, (iii) life stages, (iv) gender-based perceptions and valorization of safety and risk, (v) cultural norms, (vi) physical barriers such as urban spatial structures that segregate housing from other land uses, (vii) weather and topographical conditions, and (viii) the lack of public transportation systems (Garrard et al., 2008;Krizek et al., 2009;Kunieda & Gauthier, 2007;McGuckin et al., 2005;Song et al., 2019, p. 141). Although there are variations across different territories, it appears that regardless of the scale considered, the lower the bicycle modal share, the lower the proportion of women among the cyclists . ...
... Survey data are the dominant data type used in almost all steps of the policy cycle except implementation and it is one of only two data types that has been embedded in agenda setting processes. Large-scale commuter travel surveys had been used in this step to detect mobility problems and then to set up corresponding policy measures, for instance, McGuckin et al. (2005) did a national survey to investigate participants' daily travel information so as to define mobility problems for policy measure design. Survey data are also the main resources for ex post assessments of urban mobility policies-nearly 53% of all the cases in policy evaluation process employed survey data as the main database, which shows that big data has not replaced this traditional data type in urban mobility policy assessments. ...
Data have played a role in urban mobility policy planning for decades, especially in forecasting demand, but much less in policy evaluations and assessments. The surge in availability and openness of (big) data in the last decade seems to provide new opportunities to meet demand for evidence-based policymaking. This paper reviews how different types of data are employed in assessments published in academic journals by analyzing 74 cases. Our review finds that (a) academic literature has currently provided limited insight in new data developments in policy practice; (b) research shows that the new types of big data provide new opportunities for evidence-based policy-making; however, (c) they cannot replace traditional data usage (surveys and statistics). Instead, combining big data with survey and Geographic Information System data in ex-ante assessments, as well as in developing decision support tools, is found to be the most effective. This could help policymakers not only to get much more insight from policy assessments, but also to help avoid the limitations of one certain type of data. Finally, current research projects are rather data supply-driven. Future research should engage with policy practitioners to reveal best practices, constraints, and potential of more demand-driven data use in mobility policy assessments in practice.
... This is because of the difficulty commuters have in optimizing multipurpose trips. It is found that individuals with multileg trip chains on their way to or from work generally live farther from work and thus travel longer than those without making stops (McGuckin, Zmud, and Nakamoto 2005;Justen, Mart ınez, and Cort es 2013). Added travel to the total commute, this is also evident in the difference between nonstop trips (15.73 minutes) and two-leg trip chains (23.33 minutes). ...
Commuting, like other types of human travel, is complex in nature, such as trip-chaining behavior involving making stops of multiple purposes between two anchors. According to the 2001 National Household Travel Survey, about half of weekday U.S. workers made a stop during their commute. In excess commuting studies that examine a region’s overall commuting efficiency, commuting is, however, simplified as nonstop travel from homes to jobs. This research fills this gap by proposing a trip-chaining-based model to integrate trip-chaining behavior into excess commuting. Based on a case study of the Tampa Bay region of Florida, this research finds that traditional excess commuting studies underestimate both actual and optimal commute and overestimate excess commuting. For chained commuting trips alone, for example, the mean minimum commute time is increased by 70 percent from 5.48 minutes to 9.32 minutes after trip-chaining is accounted for. The gaps are found to vary across trip-chaining types by a disaggregate analysis by types of chain activities. Hence, policymakers and planners are cautioned with regards to omitting trip-chaining behavior in making urban transportation and land use policies. In addition, the proposed model can be adopted to study the efficiency of nonwork travel.
... Travel behavior patterns and their interaction have many applications in the analysis of transportation policies. Past studies and various survey results show that the travel patterns of people are turning very complex day by day because of individual's desire for activity fulfillment with least amount of travel possible (Hensher and Reyes, 2000;McGuckin et al., 2005). People's desire of minimizing travel time may lead to the tendency of linking single trip of various activities in a single journey rather than making a number of unlinked trips for each of the activities separately. ...
Mumbai is one of the major cities in India, where the traffic volume is constantly being increased because of urbanization trend and changes in the socioeconomic status of the society. These conditions not only influence commuter's travel behaviour but also making it too complex. Mode choice and trip-chain choice are the two critical factors influencing this complex travel behaviour. In practice, different activity-based models are using different relationship patterns due to lack of consensus and proper empirical evidence. A few studies focus on the directionality of the trip-chain and mode choice decisions. Hence, this study investigates the hierarchical relationship between these two choice decisions including the influences of socio-demographic characteristics on them. This study uses Structural Equation Models for capturing this multidirectional relationship. This study uses a 15-day activity-travel data and activity-travel information obtained from the survey for defining the choice set. Two separate models are estimated for weekdays and weekends. From the model results, it was observed that the mode choice precedes trip-chain choice during weekdays and mode choice and trip-chain choice decisions are simultaneous during weekends. A number of socioeconomic characteristics also play major roles in influencing the relationships. The model results presented in this study are based on the individual level observations. Hence, for future research, it is necessary to incorporate household interactions and to study the relationship between these choices at the household level.
... Based on a survey, Mack and Tong (2015) reported that about 42% of the farmers' market trips originated from non-home places. In general, non-home-based trips have been found to account for over 30% of all daily trips (Mcguckin et al., 2005). Recognizing that workplaces may serve as important sites where people originate their trips from, several studies expanded the demand representation to also include employment in their location modeling. ...
Location analysis and modeling have been widely applied to support locational decisions for service provision. The general idea of such analysis has been for sited facilities to serve the demand of interest in an efficient and/or effective way. In many applications, service demand involves either general people or certain population groups in a region. Currently, population-based demand has been assessed mainly based on where people live, primarily using census population count data. This can be problematic given that people do not always stay at home or originate their trips from home. As a result, relying upon residential information may lead to an inaccurate evaluation of service demand in location modeling. This study investigates the impacts of alternative population characterizations on the classic p-median problem. A new model incorporating time-varying population distributions is introduced. An empirical study was conducted in three regions in Shanghai, China, where time-varying population distributions were derived using cell phone data. Analysis results show that solutions generated based on where people live can be far from the optimal that considers the temporal variability of population distributions. Discussion is provided on ways to remedy the issue.
... 2). Shopping or taking children to school on the way to work are widely observable examples of trip-chaining (see also McGuckin et al., 2005 for the US and Scheiner and Holz-Rau, 2017 for Germany). Importantly, the effects of trip chaining on people's satisfaction with their daily commute cannot be ignored. ...
In an increasingly globalised economic system, company relocations are common and occur at different scales, ranging from international moves to relocations within a relatively small geographical area such as a city. Regarding changes in commuting following relocation, transport studies have already provided valuable insights into changing trip characteristics such as mode choice and duration of the journey. However, wider impacts of relocations on mobility practices such as shifts in trip chaining, changes in employees' social practices and networks, their satisfaction with the new commute as well as adaptation strategies (e.g. residential relocation and increased car ownership) remain under-researched, especially whenever these changes are mainly local in scale and impact everyday life. Building on and extending previous research on relocations, we explicitly adopt a mobility biographies perspective that reconceptualises workplace relocation as an incisive life event that reshapes employee's mobility practices in complex ways. We use quasi-longitudinal survey data based on retrospection to reveal major mobility-related consequences of a company's decision to move their production facilities within the German city of Munich.
This paper aptly demonstrates how even a short-distance, intra-city company relocation can disrupt employees' daily routines and reshape their own and other people's mobility. It provides novel insights into changes in satisfaction with the commute itself as well as with reduced opportunities for trip chaining. Regarding adaptation to workplace relocation, moving house or buying a (second) car emerged as important responses. Furthermore, it was possible to demonstrate the wider effects of relocation on employees' social environment such as weakened social ties among workers due to reduced opportunities for after-work activities and negative post-relocation impacts on neighbourhoods and small businesses in the old company location. Many respondents viewed these changes as undesirable reductions in quality of life. The concluding part of the paper outlines some opportunities for future social research in this area.
... Bhat (1997Bhat ( , 1999, Wegmann and Jang (1998), Bhat and Singh (2000), Kuppam and Pendyala (2001) and Chu (2003Chu ( , 2004 have all investigated worker's trip-chaining behavior. David and Kumar (1995), Hensher and Reyes (2000) and McGuckin, Zmud, and Nakamoto (2005) studied characteristics of travel patterns considering activities and time constraints. Analysis by Strathman and Dueker (1995) showed that complex trip chains are oriented more to auto rather than public transport. ...
This paper investigates empirical relationships between trip chain type and mode class choice for developing countries. To formulate these two sets of decisions, four empirical models are developed using structural equation modeling (SEM). Those models are calibrated using one month travel dairy data collected in Dhaka city. SEM correlates the observed variables and identifies their relationship with trip-chaining type utility and mode class choice utility. The fitted models are selected based on statistical results and similarity with the real life situation. Direct relationship between trip-chaining and mode choice utilities are found insignificant. However, several socio-demographic factors influence both simultaneously. Consequently, it is essential to consider mode class choice concurrently for modelling trip chain. This study also investigates the influencing factors for work based and non-work based trip chains separately and effects of road users’ heterogeneity. The research results can be utilized to perceive trip chain-mode choice pattern for developing countries.
... Existing literature reveals no commonly accepted definition of a trip chain, although the concept is widely recognized amongst transport planners. Specifically, McGuckin, Zmud, and Nakamoto, defined trip chains consisting of a connected set of trip segments from home to work, from home to home, from work to home, or from work to work (31). Holzapfel proposed an alternative definition and trip chain was defined as a sequence of changes of place which do not take the form of home-activity-home (32). ...
The debate on the effects of the built environment (BE) on travel behavior has been ongoing despite a large number of studies completed in the past three decades. This study aims to inform the debate by extending the BE-travel behavior investigation to the scope of trip-chaining. Specifically, the study conceptualized the contexture frame for the relationship of BE attributes and trip-chain travel behavior and estimated 2-level hierarchical linear models (HLM) of chained trip tours with travel survey data from the Puget Sound region. The results show that travelers who live in areas with better transit access, higher residential and non-residential density, and higher level of land use mixture generated low percentage of miles traveled by vehicle (PVMT) during their daily tours. Furthermore, considering the cross-level interactive effect, the study demonstrates that the impacts of the non-residential density at work location and the residential density at home location on PVMT are moderated by vehicle ownership.
... Women are generally found to have less access to private motorized vehicles, rely more heavily on public transportation and walking, and make more chained trips, comprising multiple destinations at shorter distances and non-standard times for varying purposes (Gossen & Purvis, 2005;Krizek et al., 2005;Kunieda & Gauthier, 2007;Emond et al., 2009). Common explanatory factors for lower rates of cycling among women include: (i) labour market positions, (ii) household roles and responsibilities, (iii) life stages, (iv) gender-based perceptions and valorization of safety and risk, (v) cultural norms, (vi) physical barriers such as urban spatial structures which segregate housing from other land uses, (vii) weather and topographical conditions and (viii) lacking public transportation systems (McGuckin & Nakamoto, 2005;Kunieda & Gauthier, 2007;Garrard et al., 2008;Krizek et al., 2009;Lusk et al., 2014). Though still an emerging area of research, studies of women in developing countries find pronounced mobility challenges related to lower levels of income, tenuous social and legal statuses, strained infrastructural conditions resulting from rapid urbanization (including lacking public transportation options), cultural and religious norms, and threats of harassment and violence in public spaces (Astrop, 1996;Peters, 2013;Rosenbloom & Plessis-Fraissard, 2010;Tran & Schlyter, 2010). ...
This paper explores the question of how to promote cycling among women who face disproportionate mobility and accessibility barriers in rapidly urbanizing contexts by analyzing empirical findings from a multi‐method research study based in Solo, Indonesia. Building on and applying a combination of critical gender, geography, and development perspectives, it focuses on the perceptions, attitudes, and behaviors of women residing in low‐income neighbourhoods with little access to public transportation networks in Solo as an ‘indicator species for bike‐friendly cities’. Based on research and analytic findings, the paper further contemplates alternative policy and planning approaches to promoting cycling in more gender‐inclusive and responsive terms.
... Nodes 1 and 2 are the origins, and Nodes 5 and 6 are the destinations. Node 3 is an intermediate activity point that must be passed between Origin 2 and Destination 5. We adopted an FHWA (Federal Highway Administration) cost function for the link costs, shown in Eq. (14). The free-flow travel time for each link was set as 1, and Tables 1 and 2 Next, based on the solution of trip-chain user equilibrium, we put a small perturbation on the green time. ...
Trip chaining is explored in this chapter, where individuals combine multiple activities into one trip to save travel time and cost. The study focuses on commuter choices and identifying the link between trip chain and mode choice. A questionnaire survey was conducted in Kota, Rajasthan, to collect data on travel patterns, trip chain behaviour, mode choice, and sociodemographic variables from December 2022 to February 2023. This complex relationship was examined using Structural Equation Modelling (SEM). The study identifies factors influencing trip chaining, including mode choice, income, travel expenditure, trip distance, vehicle ownership, education level, and age. Walking was the most significant mode, highlighting the importance of pedestrian infrastructure. The findings have implications for policymakers and urban planners to improve efficiency and sustainability by understanding these factors and promoting walkability and public transportation options.
School trips of schoolchildren usually require family escort and accompaniment. Only by conducting a comprehensive analysis from the perspective of the trip chain can we understand the similarities and differences in school transportation. A series of statistical analyses were conducted on the school escorting travel behaviors. Accordingly, sub-classification of the two trip chain types was defined and their trip chain lengths were compared. Subsequently, gender and age group differences for escort guardians were studied. The results suggest that: (1) Significant differences exist in activities and trips between escorting to and from school. (2) There is a significant association between escort choices, duration of escort activities, and escort modes with the trip chain type. (3) The average trip chain length of the household-care trip chain and its sub-types are significantly greater than those of the work trip chain. (4) Gender, age group, and escort choice significantly influence the trip chain type.
Child care travel is differentiated by sex: who makes such trips shapes the mode and distance to child care in relation to home and, for working parents, to jobs. To better understand the relationship between sex and child care travel, we analyze child care trips in California by sex while controlling for a variety of demographic and socio-economic factors. We find women are responsible for over 70% of all child care trips. Though most child care trips are taken by automobile, women are more likely to walk kids to child care centers than men. We also find that households choose child care centers that are closer to home than workplaces.
Bu çalışmada kadınların ve erkeklerin farklı seyahat davranışları araştırılmıştır. Farklı seyahat davranışına neden olan toplumsal cinsiyet konularına değinilmiş ve konu ile ilgili yapılmış bilimsel araştırmaların bibliyometrik analizi sunulmuştur. Kadınların ve erkeklerin ulaşım sistemlerinden nasıl faydalandıklarının anlaşılması için Türkiye geneli İstatistiki ve İstatistiki Bölge Birimleri Sınıflandırması 2. Düzey (İBBS-2) ölçeğinde yapılmış olan bir anket çalışmasından sonuçlar paylaşılmıştır. Türkiye’de kadın ve erkeklerin seyahat aracı kullanarak gerçekleştirecekleri aktivitelerde hangi ulaşım türünden faydalandıkları araştırılmıştır. Çalışmanın sonucunda kadınların erkeklerden daha fazla kent içi toplu ulaşım araçları kullandığı görülmüştür. Erkeklerin toplamının yüzde 41’i kent içi toplu ulaşım araçlarından faydalanırken kadınların toplamının yüzde 54’ünün kent içi ulaşım sistemlerinden faydalandığı ortaya koyulmuştur. Kadınların ve erkeklerin hangi ulaşım aracından hangi oranda faydalandıkları grafik yöntemi kullanılarak açıkça ifade edilmiştir. Yapılan bilimsel araştırmalar ve anket çalışmaları sonucunda kadınların ve erkeklerin seyahat davranışlarının farklılığı üzerine tartışılmış ve ulaşım planlama politikalarına toplumsal cinsiyet perspektifinin entegre edilmesi ile ilgili öneriler geliştirilmiştir.
The COVID-19 pandemic has exacerbated the food insecurity issue across the nation, especially for vulnerable population groups. As an important way to access food, online grocery shopping has gained substantial popularity during COVID-19. However, it is not clear how COVID-19 impacted both the in-store and online grocery shopping simultaneously and how the impacts varied among different population groups. To gain a comprehensive understanding of the impacts of COVID-19 on food access, we conducted two rounds of online survey during the initial and middle stages of COVID-19 in Maricopa County, Arizona, United States. In addition to the factors that have been examined in the literature, we examined how travel behavior, food environment, and lifestyle change affected both in-store and online shopping during COVID-19 when compared with before COVID-19. Analysis results suggest that the methods people used for grocery shopping varied with different stages of COVID-19 and among different population groups. Changes of people’s daily life and travel behavior during COVID-19 are found to be associated with people’s grocery shopping method selection. While people with better food environment were more likely to keep in-store grocery shopping, as the pandemic progressed, food environment became less important as a factor influencing the adoption of online grocery shopping. Food assistance program participants had a higher demand for the online grocery shopping service. However, the barriers associated with redeeming their benefits online prohibited them from becoming new online grocery shoppers during COVID-19. The study provides important insights into planning of food provision and formulating effective intervention strategies during future shocks.
India's burgeoning population has led to an increased demand for transportation services, particularly in urban areas. To address the growing mobility needs and mitigate the adverse effects of personal vehicle usage, efficient public transit services are imperative. Several Indian cities have implemented bus rapid transit (BRT) systems to address this problem, but ridership for the majority of the BRT systems remains below breakeven (this refers to the point at which the revenue generated from ridership equals or surpasses the costs associated with operating and maintaining the transportation system). Thus, it is necessary to assess the determinants of commuter satisfaction with BRT. The present study aims to examine factors affecting travel satisfaction among BRT commuters. By using structural equation modeling (SEM), the effect of demographic (age and gender) and travel-related variables (fare price, travel time, trip frequency, reliability, ease of using bus service, and comfort level) on BRT commuters' satisfaction and usage of BRT services were analyzed. It was found that while ease of using the service, comfort level, and reliability positively affect BRT travel satisfaction, trip frequency is significantly affected by the ease of using the service only. Furthermore, demographic variables (age, and gender of respondents) were not found to have significant effect of travel satisfaction and trip frequency. The findings from this can study serve as a base for policymakers, city officials, and urban planners to identify commuters' priorities for a well-functioning BRT in India and formulate policies to attract riders by enhancing their preferences.
Telecommuting has boomed in popularity during the pandemic and is expected to remain at elevated levels persistently. Using 2009 and 2017 U.S. National Household Travel Surveys, we investigate if there exist consistent modification influences of telecommuting on trip-chaining behavior in the decade prior to the pandemic. We find telecommuting significantly increases people’s propensity to chain trips, raises trip chaining frequency, and encourages more complex trip chains. Furthermore, these impacts are significant on commuting days, which suggests that telecommuters still have different trip chaining behavior than non-telecommuters on the days when they commute to the workplace. While trip chaining has been encouraged under pandemic conditions to minimize health risks, heightened health concerns will fade as the pandemic recedes. With telecommuting likely to persist, unraveling how trip chaining behavior had changed in response to telecommuting before the pandemic helps policymakers better understand the long-term changes in travel behavior in the post-pandemic world.
Private car dependence is widely recognised as problematic. While we have the tools, technologies and trends to promote more sustainable ways of travelling, our cities and routines remain defined by the car. To challenge this status quo, we must understand the complexities of car use. This positioning piece provides a research agenda to challenge one particularly complex expression of private car use in cities: the way we travel as families. Targeting two simple interventions in practice, the paper prescribes an evidence base for effective policy change to lessen the negative impacts of private car use.
This study explores gender differences in commute behavior with a focus on two-earner households using data from the 2001, 2009 and 2017 U.S. National Household Travel Surveys (NHTS). To understand whether gender differences are shrinking or persistent in terms of commute distances, we first analyze these differences by assessing descriptive statistics and t-test across multiple population sub-categories and trip purposes. We then employ Seemingly Unrelated Regression (SUR) models on the pooled data in order to analyze the determinants of the household total commute distance and share of women’s commute distance. Our study reveals: (1) gender gap in commute distances has narrowed over the years, however the magnitude of change is small; (2) women continue to have shorter commute distances; (3) commute mode, presence of children, and occupation-related characteristics affect gender gaps in commuting.
To improve local roadway safety, the diverse nature of municipalities must be considered given their differing needs associated with their unique population characteristics and physical environments. Yet, these differences are not accounted for in the current funding frameworks that support local highway departments. This paper explores the factors that impede the ability of municipalities to efficiently and equitably distribute highway funding to improve local road safety. Data envelopment analysis is conducted by fusing municipal highway staff data, municipal highway expenditure data, crash data, geographic data, and demographic data. Among other findings, this study concludes that non-white and rural regions require more financial support to equitably improve safety. Further, data envelopment analysis is demonstrated to be a beneficial method to consider equity disparities at a local level. The results suggest where local highway resources should be invested to efficiently and equitably improve local roadway safety.
The paper investigates the non-commuting travel demand of car commuters using Automatic Number Plate Recognition (ANPR) trip chain data in Cambridge, UK. A novel rule-based algorithm is developed for identifying commuting vehicles and the associated non-commuting trips. Identification results are validated with external data. Non-commuting travel demand is investigated in terms of trip probability, average trip frequency, duration and demand elasticity. The study finds that, first, non-commuting trips represent a significant source of travel demand for car commuters – car commuters who engage in non-commuting activities in their daily trip chains would on average spend approximately 2.7hr on those activities including travel time on a typical workday in Cambridge. Second, longer working hours are associated with a lower probability of engaging in non-commuting trips, implying a substitution effect within the daily travel time budget. Last, in terms of travel demand elasticity, non-commuting trips starting in the early morning (6–9am) are less elastic than those starting in the morning (9–12am) and during the lunch break (12-3pm). The varying demand elasticities are likely to be attributed to the different travel constraints associated with certain trip purposes. Implications for post-pandemic traffic demand and management are drawn.
Commuting, like other types of human travel, is complex in nature, such as trip-chaining behavior involving making stops of multiple purposes between two anchors. According to the 2001 National Household Travel Survey, about one half of weekday U.S. workers made a stop during their commute. In excess commuting studies that examine a region's overall commuting efficiency, commuting is, however, simplified as nonstop travel from homes to jobs. This research fills this gap by proposing a trip-chaining-based model to integrate trip-chaining behavior into excess commuting. Based on a case study of the Tampa Bay region of Florida, this research finds that traditional excess commuting studies underestimate both actual and optimal commute, while overestimate excess commuting. For chained commuting trips alone, for example, the mean minimum commute time is increased by 70 percent from 5.48 minutes to 9.32 minutes after trip-chaining is accounted for. The gaps are found to vary across trip-chaining types by a disaggregate analysis by types of chain activities. Hence, policymakers and planners are cautioned of omitting trip-chaining behavior in making urban transportation and land use policies. In addition, the proposed model can be adopted to study the efficiency of non-work travel.
The addiction to technology of older persons is an emerging field, because the literature tends to focus only on the benefits of the use of technology in this age group. Along with this, there is interest in how participation improves the quality of life of older persons. In this context, the present study aims to examine the association between the level of participation of older individuals and their addictive behaviors to Internet, including lack of control and emotional deregulation. All this, considering the social influence for the use of the Internet as a mediator of this relationship. For this, 151 older Internet users answered a set of questions about internet addiction, level of participation, and social influence for the use of technology. A structural equation modeling was carried out to evaluate the mediation model. The results show that the level of participation is indirectly associated with the two dimensions of Internet addiction, via the social influence that promotes the use of technology. This has important implications in the development of interventions that encourage Internet use in older persons, decreasing addictive behaviors that could emerge as the use of technology becomes more common.
The aim of this paper is to identify effective factors on motorcycle usage time in work-tours. To do so, a survey on motorcyclists whose workplaces are in the Central Business District (CBD) of Tehran is conducted. Due to the connected nature of daily activities, travel pattern usually has tour shape, so tour-based approach is employed in this study. For identifying and comparison of variables reflecting motorcycle usage time in simple and complex work-tours, two Accelerated Failure Time (AFT) models are developed. The results indicate that in simple tours model “home location” and “tour start time” are significant while in complex tours model “trip number”, “motorcycle age” and “driving experience” are the most significant factors. Moreover, it has been shown that trip distance and monthly fuel cost are two effective factors on motorcycle usage time in both simple and complex tours. However, trip distance has greater effect in complex tours.
In the U.S., households with less than one car per driver (auto-deficit households) are more than twice as common as zero-vehicle households. Yet we know very little about these households and their travel behavior. In this study, therefore, we examine whether car deficits, like carlessness, are largely a result of financial constraint or of other factors such as built environment characteristics, household structure, or household resources. We then analyze the mobility outcomes of car-deficit households compared to the severely restricted mobility of carless households and the largely uninhibited movement of fully-equipped households, households with at least one car per driver. Data from the California Household Travel Survey show that car-deficit households are different than fully-equipped households. They have different household characteristics, travel less, and are more likely to use public transit. While many auto-deficit households have incomes that presumably enable them to successfully manage with fewer cars than adults, low-income auto-deficit households are—by definition—income constrained. Our analysis suggests that low-income car-deficit households manage their travel needs by carefully negotiating the use of household vehicles. In so doing, they travel far more than carless households and use their household vehicles almost as much as low-income households with at least one car per driver. These results suggest that the mobility benefits of having at least one car per driver are more limited than we had anticipated. Results also indicate the importance of transportation and employment programs to ease the potential difficulties associated with sharing cars among household drivers.
Does a stop that a commuter makes on the way to or from work adversely affect peak period traffic? This question is addressed by examining the effects of stops made as part of commute journeys on daily, as well as peak period, travel time and distance, in a 6-week travel diary data set from Germany. Increments in travel time and distance due to stops made in commute journeys are evaluated, and a panel regression analysis is performed to determine contributions of stops of different types to travel time and distance. It was found that the travel time and distance of a commute journey increase with the number of stops and that incorporating stops as part of commute journeys tends to make daily travel more efficient, with more distances traversed in relatively less time than when the same number of stops are all made outside the commute journey. Nonetheless, stops in commute journeys do add to peak period travel time and distance. Stops made before work do not produce much incremental travel in the morning peak period. Stops made after work, however, substantially add to peak period travel time and distance as well as daily travel time and distance. In particular, a home-based journey made after returning home contributes to afternoon peak traffic more than does a stop in a work-to-home commute journey.
Commuting comes with costs, in terms of money, the opportunity cost of time, emotional burdens, and danger. Yet Americans take on considerably longer commutes than are strictly necessary. This suggests that longer commutes must have benefits, or that many people who take on long commutes are not maximizing their utility. This research seeks evidence for compensation for longer-duration commuting. It finds four possible sources. First, longer commutes are associated with higher wages. Second, longer commutes are associated with higher rates of homeownership, possibly in part because they facilitate suburban living. Third, long commutes may benefit spouses, since marriage is associated with longer commutes, although there is no association between commute duration and the presence of children in the household. Fourth, spouses of those with longer commutes are less likely to work, which appears to be due in part to higher wages for the worker. However, there is no evidence that a longer commute is associated with higher wages for the commuter's spouse when the spouse works. Longer commute trips are not associated with poorer mood during the trip, but also are not associated with more emotionally fulfilling work. Finally, commute duration is not associated with life satisfaction, perhaps because the net benefits and costs of commutes are roughly equal across varying commute durations, or because the burdens and benefits of the commute are not strong enough to impact as broad a construct as life satisfaction. The absence of an association between well-being and commute duration suggests that people are doing a reasonable job of maximizing their utility when selecting home and work locations.
This work studies the relationships between the number of complex tours (with one or more intermediate stops) and simple home-based tours, total distances traveled by mode, and land-use patterns both at the residence and at the workplace using path analysis. The model includes commuting distance, car ownership and motorcycle ownership, which are intermediate variables in the relationship between land use, tour complexity and distances traveled by mode. The dataset used here was collected in a region comprising four municipalities located in the north of Portugal that are made up of urban areas, their sprawling suburbs, and surrounding rural hinterland. The results confirm the association between complex tours and higher levels of car use. Land-use patterns significantly affect travelled distances by mode either directly and indirectly via the influence of longer-term decisions like vehicle ownership and commuting distance. The results obtained highlight the role of socioeconomic variables in influencing tour complexity; in particular, households with children, household income, and workers with a college degree tend to do more complex tours. Land-use patterns mediate the effects of tour complexity on the kilometers travelled by different modes. Increasing densities in central areas, and particularly the concentration of jobs, have relevant benefits by reducing car kilometers driven.
Gender and household life-cycle together affect daily travel behavior. While this makes intuitive sense, transportation planners and policy makers/shapers have done little to understand what effect and impact these factors have on daily transportation choices. This paper uses the 1995 Nationwide Personal Transportation Survey (NPTS) to examine trip chaining behavior of adult men and women traveling Monday through Friday. The data show that women continue to make more trips to perform household-sustaining activities such as shopping and family errands to a greater extent than men. Women, especially with children in the household, are more likely to chain these household sustaining trips to the trip to and from work. Women's participation in the labor force is at an all time high, but women's patterns in travel to work are different from men's patterns, and vary with family and life cycle status. The type and location of jobs that women take are likely affected by their greater household and family responsibilities. The biggest question for the future is whether and how the changes in women's status in the workplace, and perhaps the concomitant change in the household dynamics and responsibilities, will affect travel behavior of both men and women. These changes will deeply impact the development of programs related to transit, land-use planning, work schedules, telecommuting, and other programs related to auto use.
Transportation infrastructure demand is driven by a need to replace existing infrastructure and to provide capacity to meet future travel demands. Thus, long-term transportation policy and financial planning benefit from an understanding of future investment needs. The hypothesis that the United States has reached a critical juncture in underlying sociodemographic conditions and travel behavior that will result in more moderate rates of future vehicle miles of travel (VMT) growth is explored. Two simple model formulations for predicting VMT are proposed, and historical trends for establishing inputs for scenario forecasts of future VMT are examined. An understanding of future VMT demand is critical for policy decision making and infrastructure investment planning. The VMT predicting formulas enable National Household Travel Survey information to be used to predict input components in forecasting future VMT. Two VMT predictions are provided and compared with a forecast reported in "2002 Status of the Nations Highways, Bridges, and Transit: Conditions and Performance." While this study builds a case for slowing VMT growth, it hypothesizes that there may continue to be declining system performance despite slower VMT growth because more of the roadway system is at or near critical congestion levels and hence more susceptible to performance deterioration with modest increases in travel demand. It also suggests that land use pattern effects on travel behavior and person travel time budget growth are the least understood factors and possibly weak links in reaching conclusions about the ultimate pace of VMT growth.
The day-to-day variability of journey-to-work trips, including depar-ture time, route choice, and trip-chaining behavior, was examined with Global Positioning System-based disaggregate morning commute data for 56 drivers during a 1-week period. Data were collected from the ongoing instrumented vehicle projects in Atlanta sponsored by NHTSA and FHWA. The study examines alternative measurements of the day-to-day variability of the commute pattern. While commuting trips are often thought to be highly repetitious and therefore highly predictable trips, research results indicate that commuters change departure times more frequently than routes, and trip chaining significantly affects commuters' departure time and route choice behavior. This study begins to explore definitions and relationships that will be necessary to better understand the day-to-day commute dynamics.
This study operationalizes the conceptual analysis presented in a companion paper, to examine the effects of objective and subjective variables on the consideration of 16 travel-related strategies reflecting a range of individuals' potential reactions to congestion. Using 1283 commuting respondents to a 1998 survey conducted in the San Francisco Bay Area, binary logit models were developed for the consideration of each individual strategy. The proportion of information explained by these models ranges from 0.18 to 0.63. It was found that the consideration of travel-related strategies is affected not only by the amounts of travel that individuals actually do, but also by their subjective assessments, desires and affinities with respect to travel, as well as their travel attitudes, personality and lifestyle. The previous adoption of these strategies greatly affects their current consideration, demonstrating an effect of past experience. Mobility constraints and socio-economic and demographic characteristics exhibit distributional effects with respect to the options individuals consider. These findings imply that policies designed to alleviate congestion may be less effective than expected, because individuals' responses to the travel-related strategies analyzed here—many of them directly tied to public policies intended to reduce vehicle travel—are influenced by a large variety of qualitative and experiential variables that are seldom measured and incorporated into demand models. Therefore, understanding the role of such variables will improve our ability to design effective policies and to accurately forecast the response to policy interventions as well as natural trends.
This paper analyzes 1968 and 1987-88 metropolitan Washington, DC household travel surveys to understand the daily allocation of time among different activities of individuals classified by work status and gender. The increase in female labor force participation rates has produced an increase in overall time spent at work per person. The increase in work trips and the simultaneous increase in nonwork trips has resulted in less time spent at home. People are substituting money for time spent at home, buying household services outside the home. The group of individuals who work at home is analyzed separately to obtain an understanding of this growing segment. .
Trip-Chaining, Childcare, and Personal Safety in Women's Travel Behavior
- M Bianco
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