Subjective well-being and travel: retrospect and prospect

Although the improvement of well-being is often an implicitly-assumed goal of many, if not most, public policies, the study of subjective well-being (SWB) and travel has so far been confined to a relatively small segment of the travel behavior community. Accordingly, one main purpose of this paper is to introduce a larger share of the community to some fundamental SWB-related concepts and their application in transportation research, with the goal of attracting others to this rewarding area of study. At the same time, however, I also hope to offer some useful reflections to those already working in this field. After discussing some basic issues of terminology and measurement of SWB, I present from the literature four conceptual models relating travel and subjective well-being. Following one of those models, I review five ways in which travel can influence well-being. I conclude by examining some challenges associated with assessing the impacts of travel on well-being, as well as challenges associated with applying what we learn to policy.

     

Subjective well-being related to satisfaction with daily travel

Previous research demonstrates an impact on subjective well-being (SWB) of affect associated with routine performance of out-of-home activities. A primary aim of the present study is to investigate whether satisfaction with daily travel has a positive impact on SWB, either directly or indirectly through facilitating the performance of out-of-home activities. A secondary aim is to determine whether emotional-symbolic or instrumental reasons for car use results in higher satisfaction with daily travel than other travel modes. A survey of a population-based sample of 1,330 Swedish citizens included measures of car access and use, satisfaction with daily travel, satisfaction with performance of out-of-home routine activities, and affective and cognitive SWB. The results confirmed that the effect on affective and cognitive SWB of satisfaction with daily travel is both direct and indirect via satisfaction with performance of activities. Percent weekly car use had a small effect on satisfaction with daily travel and on affective SWB, although fully mediating the effect of satisfaction with performance of the activities. This suggests that car use plays a minor role for satisfaction with daily travel and its effect on SWB. This role may be larger if investigated after a forced reduced car use.     

Out-of-home activities,daily travel,and subjective well-being

It is argued that utility theory that underpins current cost-benefit analyses of daily travel needs to be complemented. An alternative theoretical framework is to this end proposed which applies subjective well-being (SWB) to travel behaviour analysis. It is posited in this theoretical framework that participation in goal-directed activities, facilitated or hindered by travel, contributes to SWB, that the degree of travel-related stress in participating in these activities reduces SWB, and that positive affect associated with travel in itself has an impact on SWB.     

Some features of non-linear travel time models for dynamic traffic assignment

Abstract

This paper investigates some features of non-linear travel time models for dynamic traffic assignment (DTA) that adopt traffic on the link as the sole determinant for the calculation of travel time and have explicit relationships between travel time and traffic on the link. Analytical proofs and numerical examples are provided to show first-in-first-out (FIFO) violation and the behaviour of decreasing outflow with increasing traffic in non-linear travel time models. It is analytically shown that any non-linear travel time model could violate FIFO in some circumstances, especially when inflow drops sharply, and some convex non-linear travel time models could show behaviour with outflow decreasing as traffic increases. It is also shown that the linear travel time model does not show these behaviours. A non-linear travel time model in general form was used for analytical proofs and several existing non-linear travel time models were adopted for numerical examples. Considering the features addressed in this study, non-linear travel time models seem to have limitations for use in DTA in practical terms and care should be taken when they are used for modelling time-varying transportation networks.     

What Moves Us? An Interdisciplinary Exploration of Reasons for Traveling

Abstract

We review a number of theories of motivation, and typologies of motivations, in psychological theory and in application to a variety of specific contexts, including shopping, eating, leisure, tourism, and travel. A recurring theme is the distinction between extrinsic (instrumental, utilitarian, functional) and intrinsic (autotelic, hedonic, experiential) motivations. We suggest that travel is a behavior to which intrinsic motivations apply, and that focusing exclusively on the extrinsic motivations to travel runs the risk of substantially underestimating the demand for travel, and the resistance to policies attempting to reduce it or to technologies (notably, information and communication technologies) expected to (partly) replace it. We offer a number of suggestions for improving standard travel surveys to help obtain the data needed to explore intrinsic motivations more fully. As better data become available, travel behavior models can be refined to partly account for such motivations. We believe that the resulting insights will be extremely valuable to policy-makers, planners, and behavioral scholars.     

Assessing network vulnerability of heavy rail systems with the impact of partial node failures

Much of the literature in recent years has examined the vulnerability of transportation networks. To identify appropriate and operational measures of nodal centrality using connectivity in the case of heavy rail systems, this paper presents a set of comprehensive measures in the form of a Degree of Nodal Connection (DNC) index. The DNC index facilitates a reevaluation of nodal criticality among distinct types of transfer stations in heavy rail networks that present a number of multiple lines between stations. Specifically, a new classification of transfer stations—mandatory transfer, non-mandatory transfer, and end transfer—and a new measure for linkages—link degree and total link degree—introduces the characteristics of heavy rail networks when we accurately expose the vulnerability of a node. The concept of partial node failure is also introduced and compare the results of complete node failure scenarios. Four local and global indicators of network vulnerability are derived from the DNC index to assess the vulnerability of major heavy rail networks in the United States. Results indicate that the proposed DNC indexes can inform decision makers or network planners as they explore and compare the resilience of multi-hubs and multi-line networks in a comprehensive but accurate manner regardless of their network sizes.

     

Strava Metro data for bicycle monitoring: a literature review

ABSTRACT

Monitoring bicycle trips is no longer limited to traditional sources, such as travel surveys and counts. Strava, a popular fitness tracker, continuously collects human movement trajectories, and its commercial data service, Strava Metro, has enriched bicycle research opportunities over the last five years. Accrued knowledge from colleagues who have already utilised Strava Metro data can be valuable for those seeking expanded monitoring options. To convey such knowledge, this paper synthesises a data overview, extensive literature review on how the data have been applied to deal with drivers’ bicycle-related issues, and implications for future work. The review results indicate that Strava Metro data have the potential—although finite—to be used to identify various travel patterns, estimate travel demand, analyse route choice, control for exposure in crash models, and assess air pollution exposure. However, several challenges, such as the under-representativeness of the general population, bias towards and away from certain groups, and lack of demographic and trip details at the individual level, prevent researchers from depending entirely on the new data source. Cross-use with other sources and validation of reliability with official data could enhance the potentiality.     

Commuting and wellbeing: a critical overview of the literature with implications for policy and future research

ABSTRACT

This review provides a critical overview of what has been learnt about commuting’s impact on subjective wellbeing (SWB). It is structured around a conceptual model which assumes commuting can affect SWB over three time horizons: (i) during the journey; (ii) immediately after the journey; and (iii) over the longer term. Our assessment of the evidence shows that mood is lower during the commute than other daily activities and stress can be induced by congestion, crowding and unpredictability. People who walk or cycle to work are generally more satisfied with their commute than those who travel by car and especially those who use public transport. Satisfaction decreases with duration of commute, regardless of mode used, and increases when travelling with company. After the journey, evidence shows that the commute experience “spills over” into how people feel and perform at work and home. However, a consistent link between commuting and life satisfaction overall has not been established. The evidence suggests that commuters are generally successful in trading off the drawbacks of longer and more arduous commute journeys against the benefits they bring in relation to overall life satisfaction, but further research is required to understand the decision making involved. The evidence review points to six areas that warrant policy action and research: (i) enhancing the commute experience; (ii) increasing commute satisfaction; (iii) reducing the impacts of long duration commutes; (iv) meeting commuter preferences; (v) recognising flexibility and constraints in commuting routines and (vi) accounting for SWB impacts of commuting in policy making and appraisal.     

Does Bus Rapid Transit Influence Urban Land Development and Property Values: A Review of the Literature

Abstract

Despite the proliferation of bus rapid transit (BRT) systems over the past few decades across developed and developing world cities, the impacts across these systems on the urban spatial development and property markets have not been comprehensively studied. The current paper attempts to fill this gap in the literature by reviewing the methodologies, underlying theories, and findings presented in the individual academic studies on BRT land-use and price impacts, mostly drawing on those that have focused on Latin American and Asian systems. The review shows that the land-use and value impacts have been less uniform across systems compared to such operational performance metrics as speed and travel time improvements. While predominantly relying on cross-sectional modeling techniques, the approaches used to evaluate land-use changes induced by the transit systems are not uniform either, with only some studies explicitly measuring changes in types of land use as opposed to simply land or rental price. The study also concludes that more rigorous evaluation is needed as to whether the BRT systems have improved accessibility for the populations that inhabited the corridors previously or whether, instead, the desired land value increases have in fact resulted in significant population displacement.     

Do supra-modal traveller types exist? A travel behaviour market segmentation using Goal framing theory

Travel behaviour market segmentations have become a popular method of identifying different types of car users, bicyclists or public transport users. However, while previous studies have looked at different types of users within single modes, such as the car, little research has explored the existence of traveller types transcending modes. The study presented here is an extension of an earlier segmentation study that distinguished travellers based on their individual preferences, yet did so independent of their current mode choice. The data came from a travel survey at a middle-sized UK university and were analysed using a combination of hierarchical and iterative partitioning methods. Crucially, however, the current study uses a different theoretical framework to previous segmentation research—goal framing theory—which may more adequately explain the findings than models used in the past such as the theory of planned behaviour. The findings supported earlier work, suggesting the presence of seemingly stable traveller types that cut across modes and can be distinguished based on gain, hedonic and normative goals. This has important implications for policies aimed at encouraging mode change which may have been too preoccupied with changing people’s attitudes rather than paying attention to people’s underlying travel preferences.

     

A demand model for recreational travel

A model is developed to describe and to predict the patterns of regional recreational travel. The model is designed in such a manner to allow its calibration and use without the need to conduct extensive travel surveys in a large region. To allow its use for prediction, the model is based on a causal structure and attempts to derive recreational travel demand from behavioural variables. The main hypothesis of the model is that the amount of recreational travel a recreation area attracts is affected by the accessibility of this area to points of demand potential and by its attractiveness relative to the recreation areas.

The calibration is founded on actual data on recreational travel to national forests in California, U.S.A. It is found in the calibration that accessibility to demand potential is the single most important determinant of recreational travel attraction. A simple relationship is derived to relate travel to each national forest to the relative accessibility of the forest. The model is calibrated and statistically validated.

It is suggested that when constructing travel demand models simplicity be sought, even at the risk of the loss of some explanatory power. In the calibration of such models statistical significant is more important than the ability to reproduce observed patterns.     

Discussing the “positive utilities” of autonomous vehicles: will travellers really use their time productively?

ABSTRACT

Autonomous vehicles (AVs) are expected to reshape travel behaviour and demand in part by enabling productive uses of travel time—a primary component of the “positive utility of travel” concept—thus reducing subjective values of travel time savings (VOT). Many studies from industry and academia have assumed significant increases in travel time use and reductions in VOT for AVs. In this position paper, I argue that AVs’ VOT impacts may be more modest than anticipated and derive from a different source. Vehicle designs and operations may limit activity engagement during travel, with AV users feeling more like car passengers than train riders. Furthermore, shared AVs may attenuate travel time use benefits, and productivity gains could be limited to long-distance trips. Although AV riders will likely have greater activity participation during travel, many in-vehicle activities today may be more about coping with commuting burdens than productively using travel time. Instead, VOT reductions may be more likely to arise from a different “positive utility”—subjective well-being improvements through reduced stresses of driving or the ability to relax and mentally transition. Given high uncertainty, further empirical research on the experiential, time use, and VOT impacts of AVs is needed.     

Estimating origin‐destination matrices from link volume counts: a simple intercity auto travel demand model for Sri Lanka

The cost of nation wide travel surveys is high. Hence in many developing countries, planners have found it difficult to develop intercity transportation plans due to the non availability of origin‐destination trip matrices. This paper will describe a method for the intercity auto travel estimation for Sri Lanka with link traffic volume data.

The paper outlines the rationale of selecting the district capitals of Sri Lanka as its “cities,” the methodology for selecting the intercity road network, determination of link travel times from express bus schedules and the location of link volume counting positions.

Initially, the total auto travel demand model is formulated with various trip purpose sub‐models. This model is finally modified to a simple demand model with district urban population and travel times between city pairs as the exogenous variables, to overcome statistical estimation difficulties. The final demand model has statistics within the acceptable regions.

The advantages of a simple model are discussed and possible extensions are proposed.     

Misclassification in travel surveys and implications to choice modeling: application to household auto ownership decisions

Travel surveys that elicit responses to questions regarding daily activity and travel choices form the basis for most of the transportation planning and policy analysis. The response variables collected in these surveys are prone to errors leading to mismeasurement or misclassification. Standard modeling methods that ignore these errors while modeling travel choices can lead to biased parameter estimates. In this study, methods available in the econometrics literature were used to quantify and assess the impact of misclassification errors in auto ownership choice data. The results uncovered significant misclassification rates ranging from 1 to 40% for different auto ownership alternatives. Also, the results from latent class models provide evidence for variation in misclassification probabilities across different population segments. Models that ignore misclassification were not only found to have lower statistical fit but also significantly different elasticity effects for choice alternatives with high misclassification probabilities. The methods developed in this study can be extended to analyze misclassification in several response variables (e.g., mode choice, activity purpose, trip/tour frequency, and mileage) that constitute the core of advanced travel demand models including tour and activity-based models.

     

Comparing whole-link travel time models

In a model commonly used in dynamic traffic assignment the link travel time for a vehicle entering a link at time t is taken as a function of the number of vehicles on the link at time t. In an alternative recently introduced model, the travel time for a vehicle entering a link at time t is taken as a function of an estimate of the flow in the immediate neighbourhood of the vehicle, averaged over the time the vehicle is traversing the link. Here we compare the solutions obtained from these two models when applied to various inflow profiles. We also divide the link into segments, apply each model sequentially to the segments and again compare the results. As the number of segments is increased, the discretisation refined to the continuous limit, the solutions from the two models converge to the same solution, which is the solution of the Lighthill, Whitham, Richards (LWR) model for traffic flow. We illustrate the results for different travel time functions and patterns of inflows to the link. In the numerical examples the solutions from the second of the two models are closer to the limit solutions. We also show that the models converge even when the link segments are not homogeneous, and introduce a correction scheme in the second model to compensate for an approximation error, hence improving the approximation to the LWR model.     

Making mode detection transferable: extracting activity and travel episodes from GPS data using the multinomial logit model and Python

The increasing popularity of global positioning systems (GPSs) has prompted transportation researchers to develop methods that can automatically extract and classify episodes from GPS data. This paper presents a transferable and efficient method of extracting and classifying activity episodes from GPS data, without additional information. The proposed method, developed using Python®, introduces the use of the multinomial logit (MNL) model in classifying extracted episodes into different types: stop, car, walk, bus, and other (travel) episodes. The proposed method is demonstrated using a GPS dataset from the Space-Time Activity Research project in Halifax, Canada. The GPS data consisted of 5127 person-days (about 47 million points). With input requirements directly derived from GPS data and the efficiency provided by the MNL model, the proposed method looks promising as a transferable and efficient method of extracting activity and travel episodes from GPS data.     

Traffic Performance Models for Dynamic Traffic Assignment: An Assessment of Existing Models

Abstract

This paper provides a review of the traffic performance models for dynamic traffic assignment (DTA) and it identifies the strength and weakness of existing models. Requirements for traffic performance models are identified and various forms of existing traffic performance models for DTA are reviewed and analysed according to the requirements. Non‐linear travel time models are shown to have some deficiencies that make them unsuitable for the analysis of time‐varying transportation networks. Even though linear‐type travel time models are identified as good candidates for the analysis of dynamic transportation networks, they have limitations from the practical point of view that travel time increases only linearly with the amount of traffic on the link. This poses a dilemma and it seems to be one whose resolution is an imminent precondition for DTA modelling to progress in a way that is both theoretically coherent and plausible in practical terms.     

Impacts of highly automated vehicles on travel demand: macroscopic modeling methods and some results

Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.