When is getting there half the fun? Modeling the liking for travel

This paper analyzes empirically measured values of Travel Liking––how much individuals like to travel, in various overall, mode-, and purpose-based categories. The study addresses two questions: what types of people enjoy travel, and under what circumstances is travel enjoyed? We first review and augment some previously hypothesized reasons why individuals may enjoy travel. Then, using data from 1358 commuting residents of three San Francisco Bay Area neighborhoods, a total of 13 ordinary least-squares linear regression models are presented: eight models of short-distance Travel Liking and five models of long-distance Travel Liking. The results indicate that travelers’ attitudes and personality (representing motivations) are more important determinants of Travel Liking than objective travel amounts. For example, while those who commute long distances do tend to dislike commute travel (as expected), the variables entering the models that hold the most importance relate to the personality and attitudes of the traveler. Most of the hypothesized reasons for liking travel are empirically supported here.     

When is big data big enough? Implications of using GPS-based surveys for travel demand analysis

A number of studies in the last decade have argued that Global Positioning Systems (GPS) based survey offer the potential to replace traditional travel diary surveys. GPS-based surveys impose lower respondent burden, offer greater spatiotemporal precision and incur fewer monetary costs. However, GPS-based surveys do not collect certain key inputs required for the estimation of travel demand models, such as the travel mode(s) taken or the trip purpose, relying instead on data-processing procedures to infer this information. This study assesses the impact that errors in inference can have on travel demand models estimated using data from GPS-based surveys and proposes ways in which these errors can be controlled for during both data collection and model estimation. We use simulated datasets to compare performance across different sample sizes, inference accuracies, model complexities and estimation methods. Findings from the simulated datasets are corroborated with real data collected from individuals living in the San Francisco Bay Area, United States. Results indicate that the benefits of using GPS-based surveys will vary significantly, depending upon the sample size of the data, the accuracy of the inference algorithm and the desired complexity of the travel demand model specification. In many cases, gains in the volume of data that can potentially be retrieved using GPS devices are found to be offset by the loss in quality caused by inaccuracies in inference. This study makes the argument that passively collected GPS-based surveys may never entirely replace surveys that require active interaction with study participants.     

Hubris or humility? Accuracy issues for the next 50 years of travel demand modeling

This study reviews the 50-year history of travel demand forecasting models, concentrating on their accuracy and relevance for public decision-making. Only a few studies of model accuracy have been performed, but they find that the likely inaccuracy in the 20-year forecast of major road projects is ±30 % at minimum, with some estimates as high as ±40–50 % over even shorter time horizons. There is a significant tendency to over-estimate traffic and underestimate costs, particularly for toll roads. Forecasts of transit costs and ridership are even more uncertain and also significantly optimistic. The greatest knowledge gap in US travel demand modeling is the unknown accuracy of US urban road traffic forecasts. Modeling weaknesses leading to these problems (non-behavioral content, inaccuracy of inputs and key assumptions, policy insensitivity, and excessive complexity) are identified. In addition, the institutional and political environments that encourage optimism bias and low risk assessment in forecasts are also reviewed. Major institutional factors, particularly low local funding matches and competitive grants, confound scenario modeling efforts and dampen the hope that technical modeling improvements alone can improve forecasting accuracy. The fundamental problems are not technical but institutional: high non-local funding shares for large projects warp local perceptions of project benefit versus costs, leading to both input errors and political pressure to fund projects. To deal with these issues, the paper outlines two different approaches. The first, termed ‘hubris’, proposes a multi-decade effort to substantially improve model forecasting accuracy over time by monitoring performance and improving data, methods and understanding of travel, but also by deliberately modifying the institutional arrangements that lead to optimism bias. The second, termed ‘humility’, proposes to openly quantify and recognize the inherent uncertainty in travel demand forecasts and deliberately reduce their influence on project decision-making. However to be successful either approach would require monitoring and reporting accuracy, standards for modeling and forecasting, greater model transparency, educational initiatives, coordinated research, strengthened ethics and reduction of non-local funding ratios so that localities have more at stake.     

How much is too much for tolled road users: Toll saturation and the implications for car commuting value of travel time savings?

The current practice of forecasting the demand for new tolled roads typically assumes that car users are prepared to pay a higher toll for a shorter journey, and they will keep doing so as long as the toll cost is not higher than their current value of travel time savings. Practice ignores the possibility that there could be a point when motorists stop driving on toll roads due to a toll budget constraint. The unconstrained toll budget assumption may be valid in networks where the addition of a new toll road does not result in a binding budget constraint that car users may have for using toll roads (although it could also be invoked for existing tolled routes through a reduction in use of a tolled route). In a road network like Sydney which offers a growing number of (linked) tolled roads, the binding budget constraint may be invoked, and hence including additional toll links might in turn reduce the car users’ willingness to pay for toll roads to save the same amount of travel time. When this occurs, car users are said to reach a toll saturation point (or threshold) and begin to consider avoiding one or more toll roads. Whilst toll saturation has important implications for demand forecasting and planning of toll roads, this type of behaviour has not been explored in the literature. We investigate the influence that increasing toll outlays has on preferences of car commuters to use one or more tolled roads as the number of tolled roads increases. The Sydney metropolitan area offers a unique laboratory to test this phenomenon, with nine tolled roads currently in place and another five in planning. The evidence supports the hypothesis that the value of travel time savings decreases as a consequence of toll saturation.     

Is generalised cost justified in travel demand analysis?

Transportation - Generalised cost (GC) has long been widely used as a measure of the attractiveness of travel alternatives. We argue that its limitations have been forgotten, overlooked or not...     

Intrazonal or interzonal? Improving intrazonal travel forecast in a four-step travel demand model

Transportation - Conventional four-step travel demand models, used by most metropolitan planning organizations (MPOs), state departments of transportation, and local planning agencies, are the...     

How perceptions mediate the effects of the built environment on travel behavior?

This study provides a better understand the mechanism underlying the built environment-behavior connection by systematically exploring the relationships between the objective (actual) environment and people’s perceptions of the environment, and their relative effects on travel behavior using the Stimuli-Organism-Response framework. Based on data for the Twin Cities, this study explores (1) How do perceptions mediate the effects of the objective environment on travel behavior? (2) How do travel attitudes influence the effects of perceptions on travel behavior? Among the eight empirical models tested here, six are consistent with the framework: objective built environment affects travel behavior through its influence on perceptions. Moreover, the framework fits walking and bicycling behavior better than transit and driving behavior. Furthermore, travel attitudes greatly moderate the influences of perceptions on travel behavior.

     

Employer travel plans,cycling and gender: will travel plan measures improve the outlook for cycling to work in the UK?

Cycling is a ‘green’ alternative to commuting by car yet it makes up only a small percentage of journeys in the UK. Here we examine the commuter habits of three companies in Hertfordshire, UK. These provide contrasting case studies allowing examination of travel behaviour in relation to gender and employer travel plans. Women are known to commute shorter distances, yet are less likely to cycle. A variety of cultural and trip characteristics can account for this yet more detailed analysis reveals that some generalisations do not apply. Organisational initiatives to increase cycle commuting were perceived more positively by men than women and this suggests provision of cycling facilities in travel plans will not be effective for organisations employing a large proportion of women. However, this hides a subgroup of women who have access to a cycle and live near enough to cycle who are more positive about cycle facilities. A variety of cultural and societal constraints on cycle use are considered. Measures to encourage cycling in employer travel plans must reflect the gender balance in the organisation as well as recognised geographical and organisational factors.     

Cycleways and footpaths: What separation is needed for equivalent air pollution dose between travel modes?

Cycling and walking are being promoted in many urban areas as alternatives to motorised transport for health, environmental, and financial reasons. The reduced congestion and resulting decrease in the overall amount of pollution reduced can be expected to result in health benefits for the community. However, active commuters, due to their increased respiration rates and often increased travel times can expect to receive larger doses of air pollution compared with those using motorised forms of transport. However, given the large dropoff in concentrations away from a road, it can be expected that significant reductions can be achieved even with relatively small increases in separation between the path of cyclists/pedestrians and motor vehicles.This study presents a simple methodology for calculating the separation needed for cyclists and pedestrians to experience the same air pollution dose as car commuters. An example is given based on carbon monoxide (CO) data collected in a field campaign consisting of a car driver, a cyclist and a pedestrian travelling on a 2600 metre loop of road in Auckland. For this case study, the estimated distance from the centreline needed for cyclists and pedestrians to receive an equivalent dose of CO as motorists was found to range from 5.8 to 14.2 m depending on the commuting mode and the dispersion state of the atmosphere at the site. This was equal to a CO concentration reduction of 0.1–0.14 ppm per metre. Recommendations on facility modifications and route selections have been made to make active mode commuting safer.     

How does travel satisfaction sum up? An exploratory analysis in decomposing the door-to-door experience for multimodal trips

Transportation - Understanding how satisfaction with individual trip legs aggregates to the overall travel experience for different types of trips will enable the identification of the trip legs...     

Personal travel management: the adoption and consideration of travel‐related strategies

Traveler behavior plays a role in the effectiveness of travel demand management (TDM) policies. Personal travel management is explored in this paper by analyzing individuals' adoption and consideration of 17 travel‐related alternatives in relation to socio‐demographic, mobility, travel‐related attitude, personality and lifestyle preference variables. The sample comprises 1282 commuters living in urban and suburban neighborhoods of the San Francisco Bay Area. Among the findings: females were more likely to have adopted/considered the more ‘costly’ strategies; those with higher mobility were more likely to have adopted/considered travel‐maintaining as well as travel‐reducing strategies; and those who like travel and want to do more are less likely to consider travel‐reducing strategies. These findings, when combined with those of earlier work on this subject, present a compelling argument for the need to further understand traveler behavior – particularly in response to congestion and TDM policies.     

A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks

Urban travel demand, consisting of thousands or millions of origin–destination trips, can be viewed as a large-scale weighted directed graph. The paper applies a complex network-motivated approach to understand and characterize urban travel demand patterns through analysis of statistical properties of origin–destination demand networks. We compare selected network characteristics of travel demand patterns in two cities, presenting a comparative network-theoretic analysis of Chicago and Melbourne. The proposed approach develops an interdisciplinary and quantitative framework to understand mobility characteristics in urban areas. The paper explores statistical properties of the complex weighted network of urban trips of the selected cities. We show that travel demand networks exhibit similar properties despite their differences in topography and urban structure. Results provide a quantitative characterization of the network structure of origin–destination demand in cities, suggesting that the underlying dynamical processes in travel demand networks are similar and evolved by the distribution of activities and interaction between places in cities.     

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

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

Uncertain socioeconomic projections used in travel demand and emissions models: could plausible errors result in air quality nonconformity?

A sensitivity analysis of plausible errors in population, employment, fuel price, and income projections is conducted using the travel demand and emissions models of the Sacramento, CA, USA, region for their transportation plan. The results of the analyses indicate that plausible error ranges for household income and fuel prices are not a significant source of uncertainty with respect to the region's travel demand and emissions projections. However, plausible errors in population and employment projections (within approximately one standard deviation) may result in the region's transportation plan not meeting the conformity test for nitrogens of oxides (NO_x) in the year 2005 (i.e., an approximately 16% probability). This outcome is also possible in the year 2015 but less likely (within approximately two standard deviations or a 2.5% probability). Errors in socioeconomic projections are only one of many sources of error in travel demand and emissions models. These results have several policy implications. First, regions like Sacramento that meet their conformity tests by a very small margin should rethink new highway investment and consider contingency transportation plans that incorporate more aggressive emissions reduction policies. Second, regional transportation planning agencies should conduct sensitivity analyses as part of their conformity analysis to make explicit significant uncertainties in the methods and to identify the probability of their transportation plan not conforming. Third, the US Environmental Protection Agency (EPA) should clarify the interpretation of “demonstrate” conformity of transportation plans; that is, specify the level of certainty that it considers a sufficient demonstration of conformity.     

Activity‐based approaches to travel analysis: conceptual frameworks,models, and research problems

Recent policy discussions about information technology in transport and traffic demand management have increased interest in activity‐based approaches to the analysis of travel behaviour, in particular in the modelling of household activity scheduling which is at the core of many of the required changes in travel behaviour. This paper is a state‐of‐the‐art review of conceptualizations and models of activity scheduling with special regard to issues raised by the new policy instruments. In the course of the review, the validity of behavioural assumptions is examined critically and several needs for future research identified.     

What do commuters think travel time reliability is worth? Calculating economic value of reducing the frequency and extent of unexpected delays

The measurement of transportation system reliability has become one of the central topics of travel demand studies. A growing literature concerns the measurement of value of travel time reliability which provides a monetary cost of avoiding unpredictable travel time. The goal of this study is to measure commuters’ sensitivities to travel time reliability and their willingness to pay (WTP) to avoid unreliable routes. The preferences are elicited through a pivoted stated preference survey technique. To circumvent the issue of presenting numerical distributions and statistical terms to day-to-day commuters, we use the frequency of delay days as a means of measuring traveler’s sensitivities to travel time reliability. The advantage of using simplified measures to elicit traveler preferences for travel time reliability is that these methods simply compare days with high delay to days with usual travel time. It was found that travelers are not only averse to the amount of unexpected delay but also to the frequency of days with unexpected delays. The paper presents WTP findings for three measures: travel time, frequency embedded travel time, and travel time reliability. The ‘reliability’ increase in WTP for travel time is found to be nearly proportional to the frequency of experiencing unexpected delays. For example, the WTP for mean travel time is calculated at $6.98/h; however, reliability adds $3.27 (about 50 % of $6.98) to avoid unexpected delays ‘5 out of 10 days’. The results of the study would provide valuable inputs to cost-benefit analyses and traffic and revenue studies required for road tolling investment projects.     

Is the intention to use public transport for different travel purposes determined by different factors?

The objective of this study was to examine the psychological predictors of the intention to use public transport for three travel purposes: work or study, shopping, and leisure. An expanded version of the theory of planned behaviour (TPB) which contains overall image and past behaviour is used. Data were gathered through the survey of 392 residents living in the central parts of Kuala Lumpur in Malaysia. These data were analysed using the partial least squares technique. The results indicate that attitude and perceived behavioural control are significant predictors of the intention to use public transportation for various purposes. Further, they explain between 34.6% and 49.8% of the intention variance. By adding the overall image and past behaviour to the original predictors in the TPB, the explained variance, with regard to work or study, shopping, and leisure purposes, increased by 5.6%, 5.1%, and 6.8%, respectively.     

Towards a microeconomic framework for modelling the joint choice of activity–travel behaviour and ICT use

The rapid development of information and communication technologies (ICT) has been argued to affect time use patterns in a variety of ways, with consequent impacts on travel behaviour. While there exists a significant body of empirical studies documenting these effects, theoretical developments have lagged this empirical work and in particular, microeconomic time allocation models have not to date been fully extended to accommodate the implications of an increasingly digitised society. To address this gap, we present a modelling framework, grounded in time allocation theories and the goods–leisure framework, for joint modelling of the choice of mode of activity (physical versus tele-activity), travel mode and route, and ICT bundle. By providing the expression for a conditional indirect utility function, we use hypothetical scenarios to demonstrate how our framework can conceptualise various activity–travel decision situations. In our scenarios we assume a variety of situations such as the implications of severe weather, the introduction of autonomous vehicles, and the interaction between multiple decision makers. Moreover, our approach lays the microeconomic foundations for deriving subjective values of ICT qualities such as broadband speed or connection reliability. Finally, we also demonstrate the means by which our framework could be linked to various data collection protocols (stated preference exercises, diaries of social interactions, laboratory experiments) and modelling approaches (discrete choice modelling, hazard-based duration models).     

Analysis of long-distance vacation travel demand in the United States: a multiple discrete–continuous choice framework

This study analyzes the annual vacation destination choices and related time allocation patterns of American households. More specifically, an annual vacation destination choice and time allocation model is formulated to simultaneously predict the different vacation destinations that a household visits in a year, and the time (no. of days) it allocates to each of the visited destinations. The model takes the form of a multiple discrete–continuous extreme value (MDCEV) structure. Further, a variant of the MDCEV model is proposed to reduce the prediction of unrealistically small amounts of vacation time allocation to the chosen destinations. To do so, the continuously non-linear utility functional form in the MDCEV framework is replaced with a combination of a linear and non-linear form. The empirical analysis was performed using the 1995 American Travel Survey data, with the United States divided into 210 alternative destinations. The model estimation results provide several insights into the determinants of households’ vacation destination choice and time allocation patterns. Results suggest that travel times and travel costs to the destinations, and lodging costs, leisure activity opportunities (measured by employment in the leisure industry), length of coastline, and weather conditions at the destinations influence households’ destination choices for vacations. The annual vacation destination choice model developed in this study can be incorporated into a larger national travel modeling framework for predicting the national-level, origin–destination flows for vacation travel.     

How could the collaboration between airport and high speed rail affect the market?

This paper discusses the possibility of collaboration between airport and high speed rail (HSR) to improve the airport’s gateway function. We apply the model which consists of three players, carriers, passengers, and the airport. In order to estimate the impact of the collaboration, we carry out some numerical computations with the model under the twin hub three zones network. Our results show that the collaboration between HSR and the smaller demand airport can reduce the congestion at the bigger demand airport, which suggests the collaboration can be preferable for carriers and passengers; it would improve the social welfare.