Joint RP–SP data in a mixed logit analysis of trip timing decisions

In the current paper, a departure time choice model including travel time variability is estimated, combining stated preference and revealed preference data. We account for response scale differences between RP and SP data and, applying the mixed logit model, test for correlation of scheduling sensitivity across RP and SP choices within individuals. The analysis implies systematic differences in the RP and SP data. With support of the evaluation from the Stockholm trial, this indicates that SP is less trustworthy for trip timing analysis and forecasting, presumably because there are temporal differences in RP and SP choice situations.     

The built environment and vehicle ownership modeling: Evidence from 32 diverse regions in the U.S.

Vehicle ownership is an important determinant of the travel demand forecasting process. Vehicle ownership models are used by policy makers to identify factors that affect vehicle miles traveled, and therefore address problems related to energy consumption, air pollution, and traffic congestion. For the conventional travel demand forecasting, it logically follows land use forecasting, before trip generation, which is commonly treated as step one. The most critical limitation of the vehicle ownership models, especially in the conventional process, is that they are often related mainly to sociodemographic variables, not so much to built environmental variables. In this study, by pooling regional household travel survey data from 32 diverse regions (almost 92,000 households) of the U.S., and by controlling for socio-demographic and the built environmental variables, we estimated a vehicle ownership model that contributes to the understanding of vehicle ownership and improves the accuracy of travel demand forecasts. Two main findings of this research are: 1) The number of vehicles owned by a household increases with socio-demographic variables and decreases with almost all of the built environmental variables. For the urban planning and design practices, this finding suggests that car shedding occurs as built environments become more dense, mixed, connected, and transit-served. 2) We used both count regression and discrete choice models, and the results suggest that count regression models have better predictive accuracy. The model developed in this study can be directly used for travel demand modeling and forecasting by metropolitan planning organizations.     

Inter-regional and inter-temporal analysis of travel behaviour for Asian metropolitan cities: Case studies of Bangkok, Kuala Lumpur, Manila, and Nagoya

The unprecedented demand for travel experienced in Asia, in conjunction with the economic development of the1980s, has resulted in a number of detrimental effects on urban systems. Economic development has certainly intensified per capita income enhancing personal mobility. In Asia, private vehicle ownership and usage have continued to be recognised as an obligatory element of travel for many. Undoubtedly there is a direct relationship between vehicle ownership and public transport usage. Inter-regional and inter-temporal investigations of travel behaviour in Asian cities are therefore necessary to develop an understanding of the future transportation system including suitability and the role of public transport. Since travel data are scarce in Asian countries, inter-regional or inter-temporal travel behaviour investigations do not exist to date. Several travel demand models are developed using discrete choice modelling techniques and Bangkok, Kuala Lumpur, Manila, and Nagoya as case studies. Estimation results of the mode choice models are successfully incorporated to compare travel behaviour trends in selected cities in Asia. The developed models are tested for spatial and temporal transferability.     

The sensitivity of the valuation of travel time savings to the specification of unobserved effects

The behavioural value of travel time savings (VTTS) remains a controversial data item in the evaluation of transport projects. Over the last 40 years we have seen numerous empirical studies on the amount that an individual or firm is willing to pay to save a unit of travel time (or more precisely transfer a fixed amount of time from one activity to another). With a few exceptions, the majority of empirical studies have used essentially the same data metric and model estimation procedure. The most popular approaches have used either revealed preference (RP) or stated preference (SP) data and a multinomial logit (MNL) choice model to identify marginal rates of substitution between travel time and price of a trip. A few more advanced studies have integrated RP and SP data and have explored the gains in behavioural power of models that relax, to varying degrees, the underlying assumptions that produce the MNL model. This paper highlights the potential gains from a more carefully considered structure of the unobserved effects which condition the form of a discrete choice model, and hence the possible misinference from the simpler MNL specification. We demonstrate the implication for VTTS estimates of serial correlation between the SP treatments, covariance amongst alternatives, presence of individual specific (random) effects or heterogeneity, and differential variance of the unobserved effects.     

Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai,China

As the propensity to link multiple intermediate stops in a trip chain (a sequence of journeys that starts and ends at home, includes visiting one or more locations) is more prevalent, the relationship between travel mode choice and trip chain pattern aroused the attention of academics. This paper examines two distinct structures to identify the decision process of travelers between travel mode choice and trip chain pattern: one structure in which trip chain pattern organization precedes travel mode choice, another structure in which travel mode choice decision precedes the organization of trip chain pattern. To accommodate multi-day behavioral variability and unobserved heterogeneity in personal characteristics ignored by traditional travel surveys, multi-day GPS data collected in Shanghai is employed to estimate these two structures within Nested Logit (NL) model. The Monte Carlo (MC) method simulates the switch of trip-chaining and mode choice under possible Transportation Demand Management (TDM) strategies based on estimation results. The findings of this study are as follows: (1) trip chain pattern decision precedes travel mode choice, which means trip-chaining is organized first and affects travel mode choice; (2) complex trip chain is related to higher automobile dependency, and it is a barrier to the tendency to adopt public transit; (3) people who generally travel by automobiles might switch to public transit when private cars are unavailable, and an increase in household bicycle ownership enhances competition between the bicycle and public transit which leads people to turn to cycling. These findings help implement TDM strategies to develop sustainable transportation systems and optimize the urban trip structure.     

An examination of the factors that impact upon multiple vehicle ownership: The case of Dublin, Ireland

This paper examines the characteristics of households with multiple car ownership in Dublin, Ireland. Data from the 2006 Census of Ireland are analysed to ascertain the characteristics of these households. The analysis of multiple car ownership presented herein examines individual specific, transport availability, and household characteristics to provide an indication of the individuals most likely to have access to more than one vehicle. Understanding the characteristics of households with more than one car is important for many reasons, such as how policies for emissions reductions or pricing regimes might affect households. Ireland, like many countries, has recently launched a number of electric vehicle and car sharing schemes. Traditionally these schemes have been aimed at reducing multiple car ownership, therefore it is important to develop an understanding of the households that would most likely give up an extra car and use a car sharing scheme or an electric vehicle. Also from a sustainability point of view, greater levels of car ownership can result in unsustainable transport patterns.This paper examines the Census data using a multinomial logit regression model to determine the relationships between multiple car ownership levels and several household characteristics. The findings of the paper demonstrate that occupation, public transport availability and residential density all have an impact upon the decision to own more than one vehicle.     

Modelling airport and airline choice behaviour with the use of stated preference survey data

The majority of studies of air travel choice behavior make use of revealed preference (RP) data, generally in the form of survey data collected from departing passengers. While the use of RP data has certain methodological advantages over the use of stated preference (SP) data, major issues arise because of the often low quality of the data relating to the un-chosen alternatives, in terms of explanatory variables as well as availability. As such, studies using RP survey data often fail to recover a meaningful fare coefficient, and are generally not able to offer a treatment of the effects of airline allegiance. In this paper, we make use of SP data for airport and airline choice collected in the US in 2001. The analysis retrieves significant effects relating to factors such as airfare, access time, flight time and airline and airport allegiance, illustrating the advantages of SP data in this context. Additionally, the analysis explores the use of non-linear transforms of the explanatory variables, as well as the treatment of continuous variations in choice behavior across respondents.     

Disentangling the behavioural side of the first and last mile problem: the role of modality style and the built environment

The first and last mile (FLM) problem, namely the poor connection between trip origins or destination and public transport stations, is a significant obstacle to sustainable transportation as it is likely to encourage the use of cars for FLM travel, if not for the entire trip. This study examines the role of modality style and built environment in FLM mode choice behaviour, in order to identify the key features that might invoke a travel mode shift from cars to more sustainable travel options for both mandatory and discretionary trips. More specifically, this study draws on disaggregate data from the South East Queensland household travel survey and presents a latent class choice model to unravel modality style groups. Results reveal two distinct individual-level modality style groups: (1) driving and walking oriented; (2) multimodal travellers. Individuals in the second modality style group were found to be relatively inelastic to FLM travel time for mandatory trips, while individuals in the first group were largely unaffected by built environment characteristics and highly habitual in their mode choice behaviour for both mandatory and discretionary trips. Home residence environments with high road intersection density and public transport accessibility, and home residence environments with diverse land use mix, respectively encourage individuals within the second modality style to walk for mandatory trips, and discretionary trips. To this end, when place-based policies seek to change certain built environment features, individuals in the second modality style are more likely to shift their preference from cars to more sustainable modes. Finally, our findings have practical planning implications in targeting mode shift through highlighting the importance of considering the intersection of individual modality style in a given locale and mode choice behaviour. More specifically, our findings advocate for place-based policies that seek to target particular locales with the certain modality style deemed to be more predisposed to adopting a mode shift.     

A dynamic hazard-based system of equations of vehicle ownership with endogenous long-term decision factors incorporating group decision making

The transportation system affects all aspects of our daily lives including relatively long-term decisions on work and home location choice and automobile ownership decisions. The interdependency existing among these three decisions jointly influences household mobility and overall travel patterns. Therefore, a dynamic modeling framework that can account for the effects of interdependencies between vehicle transaction behavior and residential and job location choices is highly desirable. These decisions are made in the household level while individuals’ decisions influence the overall outcome; therefore, it is also important to incorporate a group decision making process within such modeling frameworks.This study introduces a dynamic model for vehicle ownership, residential mobility, and employment relocation timing decisions. These decisions are modeled at the individual level and then sequentially aggregated to the household level if it is required. A hazard-based system of equations is formulated and applied in which work location and residential location changes are included as endogenous variables in the vehicle transaction model while other important factors such as land-use and built environment variables, household dynamics, and individuals’ socio-demographics are also considered.     

Accounting for differences in modelled estimates of RP,SP and RP/SP direct petrol price elasticities for car mode choice: A warning

Using a sample of 245 direct petrol price elasticities of car travel collected from 52 published mode choice studies, a random coefficient regression model is estimated to account for heterogeneity in the influence of the type of data used in the various studies (RP, SP and a combination of RP/SP). The focus on the type of data is designed to highlight a concern that has emerged in the way that an increasing number of researchers and consultants derive elasticities from stand-alone stated preference studies, and apply them. It is well known that this is not valid without model calibration (usually via the mode-specific constants), since the elasticity formula uses the probability of an alternative being chosen. To understand the extent of possible behavioural response bias when calibration is ignored is important, signalling a possible adjustment process to correct for the absence of calibration relative to calibrated RP and or RP/SP derived elasticities.     

Determinants and spatial variability of after-school travel by teenagers

This research work targeted teenagers to investigate after-school travel patterns and factors affecting their modal choice characteristics in Okinawa, Japan, where teenagers are not allowed to drive. The analysis is based on the cross-sectional data collected for discrete choice modeling of students’ travel in the prefecture. In this study, high school students’ trips from school to home have been analysed using a multinomial logit model. This has revealed the impact of individual, modal, and spatial variables on the mode choice decisions and return trip patterns. A comparative analysis made of downtown and suburb schools exposed differences in travel patterns induced by spatial factors and some existing constraints in modal choice options.     

Shared autonomous vehicles and their potential impacts on household vehicle ownership: An exploratory empirical assessment

Emerging transportation technologies have the potential to significantly reshape the transportation systems and household vehicle ownership. Key among these transportation technologies are the autonomous vehicles, particularly when introduced in shared vehicle fleets. In this paper, we focus on the potential impact that fleets of shared autonomous vehicles might have on household vehicle ownership. To obtain initial insights into this issue, we asked a sample of university personnel and members of the American Automobile Association as to how likely they would consider relinquishing one of their household's personal vehicles if shared autonomous vehicles were available (thus reducing their household vehicle ownership level by one). For single-vehicle households, this would be relinquishing their only vehicle, and for multivehicle households (households owning two or more vehicles) this would be relinquishing just one of their vehicles. Possible responses to the question about relinquishing a household vehicle if shared autonomous vehicles are present are: extremely unlikely, unlikely, unsure, likely, and extremely likely. To determine the factors that influence this response, random parameters ordered probit models are estimated to account for the likelihood that considerable unobserved heterogeneity is likely to be present in the data. The findings show that a wide range of socioeconomic factors affects people's likelihood of vehicle relinquishment in the presence of shared autonomous vehicles. Key among these are gender effects, generational elements, commuting patterns, and respondents' vehicle crash history and experiences. While people's opinions of shared autonomous vehicles are evolving with the continual introduction of new autonomous vehicle technologies and shifting travel behavior, the results of this study provide important initial insights into the likely effects of shared autonomous vehicles on household vehicle ownership.     

An explanation of the relationship between adults’ work trip mode and children’s school trip mode through the Heckman approach

Most studies on children’s travel hypothesize that the characteristics of children, households, schools, and neighborhoods exogenously affect their travel mode to school. This study makes an additional assumption that children’s mode to school and household adults’ travel mode to work are interrelated because of a lifestyle choice made by parents and caregivers. With this assumption, Heckman probit models were used with data from the 2009 US National Household Travel Survey to predict household adults’ travel mode to work and children’s travel mode to school jointly. It found strong evidence that household adults’ decision to drive to work significantly increases the probability of children being dropped off at school and decreases their likelihood of walking and bicycling, but not vice versa. As adults’ mode choice is more fundamental in the household decision-making process, the study suggests that children’s mode choice studies should not ignore how parents or caregivers travel to work.     

Employment centers and travel behavior: exploring the work commute of Mumbai’s rapidly motorizing middle class

In the Greater Mumbai Region (GMR), jobs and housing are agglomerating in nodes in the periphery of Mumbai City. However, current transportation investments focus on strengthening connections within Mumbai City, while these outlying nodes have received less attention. As housing and jobs move out, given limited travel choices, the need for mobility nudges many middle class Indian households into owning private vehicles. Using household travel survey data from the GMR, this paper develops an understanding of how worker’s trips are different for those who commute to the city versus the exurbs. Socio-economic and transportation indicators for middle class workers going to the city versus the exurbs show that these populations are quite similar demographically. However, those traveling to the exurbs, on average, tend to be at a socio-economic disadvantage with respect to income, education and out-of-pocket travel burdens. Those traveling to exurban work locations have shorter travel times and trip distances, and make much higher use of walking, biking, rickshaws, and motorized two-wheelers compared to commuters to Mumbai City. Across the GMR, car users travel longer and farther compared to motorized two-wheeler users. On average, traveling by a private vehicle is faster than bus or rickshaw travel revealing advantages of private vehicle use. These mode choices in the middle class have resulted in rapid motorization and negative externalities such as traffic congestion and emissions. Evidence of large increases in motorized two-wheelers and cars in India suggests that these modes will likely keep growing, unless competing efficient travel options are supplied.     

Modeling green vehicle adoption: An integrated approach for policy evaluation

This article employs an integrated discrete-continuous car ownership model to jointly forecast households’ future preferences on vehicle type, quantity and use, and to estimate greenhouse gas (GHG) emissions. The model system is estimated on a dataset collected from a web-based stated preference survey conducted in Maryland in 2014. The data contain vehicle purchase decisions and sociodemographic information of 456 households who were requested to state their future preferences over a 9-year period (2014–2022). In each time period, a respondent is faced to four alternatives that include the current vehicle, a new gasoline vehicle, a new hybrid electric vehicle, and a new battery electric vehicle. Intertemporal choices between conventional and “green” vehicles such as hybrid and electric cars capture dynamics in vehicle purchase decisions. Short run and medium-long run situations were predicted and compared based on the first 4-year data and the entire 9-year data of the dynamic panel. Vehicle GHG emissions were calculated correspondingly. We find the introduction of “green” vehicles makes a positive impact on car ownership and use, especially in a medium-long run. Two “green” taxation policies, gasoline tax and ownership tax, were proposed and their impact on vehicle use and emission reductions was evaluated. Results indicate that: (a) gasoline tax is a more effective way to reduce vehicle miles traveled and GHG emissions and (b) gasoline tax makes a higher impact on car use and emission reductions in the medium-long run, while ownership tax makes a higher impact in the short run.     

Spatial and socio-demographic determinants of South East Queensland students’ school cycling

The low prevalence of Australian students’ utilitarian school cycling could be attributed to the varied and context specific demographic, socio-economic and spatial school travel mode choice determinants. Travel distance is universally important for school cycling and is reliant amongst related home and school spatial proximity factors on a student’s choice of schools. This paper, primarily based on school students’ travel data extracted from the 2009 South East Queensland Household Travel Survey (SEQHTS), examines comparative school cycling travel patterns, school catchment choices and the significant analytical determinants of cycling mode choices from within an urban regional Australian context. Students’ choice of a school external to that located within their designated State school catchment zone was associated with household attributes of parent/guardian employment location, number of income earners and ownership of private vehicles coupled with State school and catchment attributes. Noteworthy variations in school travel distances and modal splits were allied with school catchment choices. Adolescent male students from two parent households owning fewer than two cars and more than two bicycles, with parents/guardians commuting using non-motorised travel modes and resident in Census Collection Districts with conducive cycling environments, contributed largely to the one-way bi-directional primary and secondary school cycling mode shares. This prevalent student cyclist profile indicates the need for enhancements in student school cycling participation, through policies addressing both the spatial (built) and social environment which impact students’ personal and traffic safety coupled with utilitarian cycling image enhancements. Concerted efforts to bolster cycling amongst student segments with current low cycling participation inclusive of females, students from single parent households and those with adequate access to private motorised travel modes may be necessary to further enhance school cycling mode shares. The paper makes a case for individualised targeted travel interventions informed by segregated mode choice determinant analysis for respective primary and secondary school types and directions of school travel.     

Exploring transport to arts and cultural activities as a facilitator of social inclusion

This paper explores the relationship between travel and social inclusion in relation to a relatively rarely examined group of travel destinations—arts and cultural activities. This paper examines travel behaviour to arts and cultural activities and how this relates to social inclusion. Research literature associated with these issues is examined and then an analysis of a household travel survey in Melbourne, Australia, is undertaken to explore how travel to arts and cultural activities varies by income, car ownership and location. The paper outlines a range of evidence linking participation in arts and cultural activities and positive outcomes for social inclusion. Arts and cultural activities do not fit well into traditional household travel survey definitions of trip purposes. There is also no definitional difference between travel to activities and ‘participation’ or ‘attendance’ in arts and cultural activities. This is unfortunate since social outcomes may vary by participation or attendance. Travel survey analysis shows that like other activities trip rates to arts and cultural activities increase with income. However higher participation is demonstrated for zero- and one-car households, which contrasts with previous research of work, education and social travel. Higher participation is also demonstrated for those living in inner parts of the city. The paper suggests that most travel to arts and cultural activity is quite localised and hence much travel may be led by the diversity and range of local opportunities provided. These are particularly high in inner parts of the city. A high share of travel is also demonstrated for older people, who are thought to have the time and desire for greater participation in arts and cultural activities.     

Activity-travel behaviour of non-workers belonging to different income group households in Bangalore,India

Non-workers – homemakers, unemployed individuals and the retired – generally have more discretionary time than workers do, and hence, their travel behaviour and response to infrastructure improvements and travel demand management measures are different from that of workers. However, much remains unknown about activity-travel behaviour of non-workers in developing countries. Can we expect similarity between activity participation and travel behaviour of non-workers in different income group households in developing countries? How do the socio-demographic settings in India, for e.g., traditional gender roles and multi-member households, and land use context influence the activity-travel choices of non-workers in different income group households? The present study attempts to answer these questions by presenting a comparative analysis of activity-travel behaviour of non-workers in low-, medium-, and high-income households in Bangalore city, India. Using a primary activity-travel survey data, the study mainly compares the activity-travel behaviour of the three income-groups with respect to various activity-travel indicators. In addition, statistical models of daily out-of-home time allocation, trip chaining, and mode choice behaviour are estimated separately for the three groups to investigate the potential sensitivity of the groups to various factors influencing these behaviour indicators. The study findings suggest that the low-income group non-workers are more mobility-constrained than others, apparent from longer walk trip length and the lower dependency on other modes of transport. The low-income group appears to make more social stops than the other two groups, probably a reflection of social-connections due to the increased dependency on walking. However, the number of recreational and shopping stops made by this group appears to be significantly lower than the other two groups. Further, the influence of mixed residential development on low-income group individuals' maintenance and discretionary activity time allocation decision appears to be insignificant. Overall, the study suggests that future land use planning for Bangalore might ensure that all groups of individuals have equal access (in terms of travel time/distance) to basic facilities.     

Built environment diversities and activity–travel behaviour variations in Beijing, China

The association between built environment and travel behaviour has received considerable research attention in recent years. In an attempt to contribute to this growing literature, this paper investigates the connections between urban built environments and activity–travel patterns in Beijing, the capital city of China. We characterize the built environment in Beijing and establish associations between built environment and activity–travel behaviour in terms of car ownership, time spent for out-of-home activities, and daily trip frequencies and travel time. Activity diaries from 1119 respondents living in ten different neighbourhoods were collected by face-to-face interviews. A household-level structure equations model incorporating intra-household interactions is developed to analyse this data. The empirical results show that residents of different types of neighbourhoods in Beijing demonstrate significant differences in car ownership, time spent for out-of-home activities, trip rate, and travel time. Further, the characteristics of the built environment are found to have more significant impacts on the activity–travel behaviour of the male head than that of the female head.