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.     

The rational locator reexamined: Are travel times still stable?

The rational locator hypothesis posits that individuals can, if they choose, maintain approximately steady journey-to-work travel times by adjusting their home and workplace. This hypothesis was coupled with the observation of long-term stability in drive alone journey-to-work times in metropolitan Washington (those times were unchanged from 1957 through 1968 to 1988). Despite the increase of average commuting distance and congestion, trip duration remained constant or even declined when controlling for travel purpose and travel mode because of shifting a share of traffic from slow urban routes to faster suburban routes. This observation has significance, as it is important to know for travel demand analysis if there is an underlying budget, or even a regularity, as this helps us determine whether our forecasts are reasonable. To re-test the underlying rationale for the hypothesis that travel times are stable, intra-metropolitan comparisons of travel times are made using Washington DC data from 1968, 1988, and 1994, and Twin Cities data from 1990 and 2000. The results depend upon geography. For the larger Washington DC region, keeping the same geography shows little change in commute times, but using the larger 1994 area suggests an increase in commute times. However, the Twin Cities, starting from a much shorter commute time, shows a marked increase over the decade, using either the smaller or the larger geography. Despite the remarkable continuing observation of stability in drive alone commuting times in metropolitan Washington, we reject the theory of personal commuting budgets, as we find that not only are commuting times not generally stable over time at the intra-metropolitan area, but that commuting time clearly depends on metropolitan spatial structure.     

Subjective well-being in China: how much does commuting matter?

With rapidly increasing urbanization and motorization in China, the effect of commuting on residents’ subjective well-being (SWB) is likely growing. We used 13,261 individual, 124 city, and 401 neighbourhood samples from the 2014 China Labour-Force Dynamics Survey (CLDS 2014) and applied multilevel mixed-effects ordered probit regressions to investigate the relationship between commuting and SWB. We found huge differences between urban and rural areas in relation to commuting. Urban respondents’ daily average commuting time was 0.56 h while rural respondents’ daily average commuting time was 0.41 h. Further, the daily average commute time for residents living in cities with high urbanization rates (> 70%) was longer than for those living in cities with low urbanization rates (< 70%). The subjective well-being of residents who commute by walking or cycling was significantly lower than that of those who commute by other transportation modes. The regression results indicated that the longer the commute time, the lower the subjective well-being. Among residents who live in rural areas or cities with low urbanization, subjective well-being was more easily affected by commuting time. Commuting time was also found to influence residents’ job satisfaction and family life satisfaction, which in turn influence SWB. China’s current development mode ignores the traffic needs of vulnerable groups. Therefore, future traffic construction should increase its prioritization of these vulnerable transportation groups.

     

Neighborhoods,cars, and commuting in New York City: A discrete choice approach

Cities around the world are trying out a multitude of transportation policy and investment alternatives with the aim of reducing car-induced externalities. However, without a solid understanding of how people make their transportation and residential location choices, it is hard to tell which of these policies and investments are really doing the job and which are wasting precious city resources. The focus of this paper is the determinants of car ownership and car use for commuting. Using survey data from 1997 to 1998 collected in New York City, this paper uses discrete choice econometrics to estimate a model of the choices of car ownership and commute mode while also modeling the related choice of residential location.The main story told by this analysis is that New Yorkers are more sensitive to changes in travel time than they are to changes in travel cost. The model predicts that the most effective ways to reduce both auto ownership and car commuting involve changing the relative travel times for cars and transit, making transit trips faster by increasing both the frequency and the speed of service and making auto trips slower – perhaps simply by allowing traffic congestion. Population density also appears to have a substantial effect on car ownership in New York.     

Evaluation of congestion trends on chicago expressways

The growth in congestion on Chicago area expressways has had a major effect on almost all aspects of the region's economy, travel behavior and land-use development patterns. The objective of this study was to evaluate congestion trends on Chicago's expressways during the last decade (1980 to 1989) by quantifying the magnitude and tendencies of congestion for the various expressways. This study concentrated on an analysis of the minute-miles of congestion (MMC), a measure that provides an averaged section congestion during a given time period and that is continuously collected by the Illinois Department of Transportation for all Chicago-area expressways. The MMC offers a relatively accurate and unbiased estimate of the quantity and duration of congestion. In order to compare congestion trends on the area's ten major expressways in the 1980's it was decided to evaluate the yearly Cumulative Distribution Function of congestion. This analysis was conducted for both the inbound and outbound directions and for the A.M. and P.M. daily peak periods. The magnitude of congestion was found to increase considerably with the years from 1980 to 1989. The increase in congestion for “reverse commuting”, trips to the suburbs in the morning peak and from the suburbs in the evening peak, grew more substantially than the increase in the regular “city commute.” The reason for this phenomenon was the rise in job opportunities in the suburbs, resulting partially from increased congestion and reduced accessibility. Additional analysis showed that the duration of the peak period, defined by the time in which congestion exceeded a predetermined threshold level, was generally longer for the “city commuting” than for the “reverse commuting.” Comparison of congestion among seasons, showed that the most congested season was usually the spring (May 1 to June 15). The system analysis showed that the by 2000, the expected system congestion will be more than twice the 1980 system congestion.     

The positive utility of the commute: modeling ideal commute time and relative desired commute amount

Two measures of commute time preferences – Ideal Commute Time and Relative Desired Commute amount (a variable indicating the desire to commute "much less" to "much more" than currently) – are modeled, using tobit and ordered probit, respectively. Ideal Commute Time was found to be positively related to Actual Commute Time and to a liking and utility for commuting, and negatively related to commute frequency and to a family/community-oriented lifestyle. Relative Desired Commute, on the other hand, was negatively related to amounts of actual commute and work-related travel, but positively related to travel liking and a measure of commute benefit. Overall, commute time is not unequivocally a source of disutility to be minimized, but rather offers some benefits (such as a transition between home and work). Most people have a non-zero optimum commute time, which can be violated in either direction – i.e. it is possible (although comparatively rare, occurring for only 7% of the sample) to commute too little. On the other hand, a large proportion of people (52% of the sample) are commuting longer than they would like, and hence would presumably be receptive to reducing (although usually not eliminating) that commute.     

Using a stages of change approach to explore opportunities for increasing bicycle commuting

Given the potential benefits of bicycling to the environment, the economy, and public health, many U.S. cities have set ambitious goals for increasing the bicycle share of commute trips. The Transtheoretical Model of Behavior Change, which seeks to describe how positive and permanent change can be fostered in individuals, may shed light on how cities can most effectively increase bicycle commuting. We use the model’s “stages of change” framework to explore the potential for increased bicycle commuting to the UC Davis campus in Davis, California. Our analysis uses data from the 2012 to 2013 UC Davis Campus Travel Survey, an annual online survey that is randomly administered to students and employees at UC Davis. Based on their responses to questions about current commute mode and contemplation of bicycle commuting, respondents are divided into five stages of change: Pre-contemplation, Contemplation, Preparation, Action, and Maintenance. We construct a Bayesian multilevel ordinal logistic regression model to understand how differences in socio-demographic characteristics, travel attributes, and travel attitudes between individuals explain their membership in different stages of change. In addition, we use this model to explore the potential of various intervention strategies to move individuals through the stages of change toward becoming regular bicycle commuters. Our results indicate that travel attitudes matter more to progression toward regular commute bicycling than travel attributes, tentatively supporting the efficacy of “soft” policies focused on changing travel attitudes.     

Marginal costs of freeway traffic congestion with on-road pollution exposure externality

The health cost of on-road air pollution exposure is a component of traffic marginal costs that has not previously been assessed. The main objective of this paper is to introduce on-road pollution exposure as an externality of traffic, particularly important during traffic congestion when on-road pollution exposure is highest. Marginal private and external cost equations are developed that include on-road pollution exposure in addition to time, fuel, and pollution emissions components. The marginal external cost of on-road exposure includes terms for the marginal vehicle’s emissions, the increased emissions from all vehicles caused by additional congestion from the marginal vehicle, and the additional exposure duration for all travelers caused by additional congestion from the marginal vehicle. A sensitivity analysis shows that on-road pollution exposure can be a large portion (18%) of marginal social costs of traffic flow near freeway capacity, ranging from 4% to 38% with different exposure parameters. In an optimal pricing scenario, excluding the on-road exposure externality can lead to 6% residual welfare loss because of sub-optimal tolls. While regional pollution generates greater costs in uncongested conditions, on-road exposure comes to dominate health costs on congested freeways because of increased duration and intensity of exposure. The estimated marginal cost and benefit curves indicate a theoretical preference for price controls to address the externality problem. The inclusion of on-road exposure costs reduces the magnitudes of projects required to cover implementation costs for intelligent transportation system (ITS) improvements; the net benefits of road-pricing ITS systems are increased more than the net benefits of ITS traffic flow improvements. When considering distinct vehicle classes, inclusion of on-road exposure costs greatly increases heavy-duty vehicle marginal costs because of their higher emissions rates and greater roadway capacity utilization. Lastly, there are large uncertainties associated with the parameters utilized in the estimation of health outcomes that are a function of travel pollution intensity and duration. More research is needed to develop on-road exposure modeling tools that link repeated short-duration exposure and health outcomes.     

Enjoyment of commute: A comparison of different transportation modes

This study investigates how socio-demographic and attitudinal variables of university students affect their desire to increase or decrease their daily commute. The case study is McMaster University in Hamilton, Canada, and data was obtained by means of a web-based survey that included questions regarding travel behavior, socio-demographic information, and attitudes toward travel, land use, and the environment. The objective variable is defined as the ratio of ideal to actual commute time, and regression analysis is implemented to test the relationship between this variable and socio-demographic variables and attitudinal scores. The impact of different attitudes on the gap between ideal and actual commute time is expanded to include three different modes, active travel (walk/cycle), transit, and personal automobile. Interestingly, the results indicate that active travelers tend to be less dissatisfied with their commute, followed by those who travel in a personal vehicle and transit users. A number of attitudinal responses are shown to impact the desire to travel more or less, including variables that relate to the social environment, availability of local activities, quality of facilities, productive use of the commute, and the intrinsic value found in the commute travel. The picture emerges of a traveler who would like to spend more time commuting, as someone who is an active traveler, thinks that getting there is half the fun, dislikes traveling alone, but rather likes to live in an active neighborhood where there is a sense of community. The results suggests that enjoyment of commuting, while a challenge from the perspective of motorized mobility, may provide valuable policy opportunities from the perspective of active transportation.     

Population exposure to ultrafine particles: Size-resolved and real-time models for highways

Prior research on ultrafine particles (UFP) emphasizes that concentrations are especially high on-highway, and that time on highways contribute disproportionately to total daily exposures. This study estimates individual and population exposure to ultra-fine particles in the Minneapolis – St. Paul (Twin Cities) metropolitan area, Minnesota. Our approach combines a real-time model of on-highway size-resolved UFP concentrations (32 bins, 5.5–600 nm); individual travel patterns, derived from GPS travel trajectories collected in 144 individual vehicles (123 h at locations with UFP estimates among 624 vehicle-hours of travel); and, loop-detector data, indicating real-time traffic conditions throughout the study area. The results provide size-resolved spatial and temporal patterns of exposure to UFP among freeway users. On-highway exposures demonstrate significant variability among users, with highest concentrations during commuting peaks and near highway interchanges. Findings from this paper could inform future epidemiological studies in on-road exposure to UFP by linking personal exposures to traffic conditions.     

Travel to work and household responsibility: new evidence

A persistent empirical finding in the research on travel patterns is that women tend to engage in shorter work commutes than men. Research evaluating the degree to which this gender differential in commuting may be explained by the division of labor in the household has produced decidedly mixed findings. This paper presents a critical review of the methods and results of recent research on the gender differential in commuting along with the associated implications for the household responsibility hypothesis (HRH). While all of the articles reviewed are informative and unique, not all conclusions are well supported. The paper then tests the HRH using the 1990 NPTS travel data. The new evidence confirms that women continue to exhibit shorter commute times and distances than men and provides support for the HRH. The paper concludes with a summary of findings and recommendations for further research.     

Travel satisfaction effects of changes in public transport usage

We study the effects of public transport use incentives on changes in travel satisfaction of participants that undergo behavior change. We use the results of an experiment conducted recently at the Massachusetts Institute of Technology and requiring a temporary use of public transport for commuting to work by habitual car drivers. We observe an increase in the average level of satisfaction with the commute to work of participants who switch to public transport after the temporary intervention. This increase is sustained to some extent several months after the intervention. Moreover, participants’ dissatisfaction is almost eliminated. We conclude that public transport use incentives are promising not only for encouraging sustainable travel behavior but also for increasing people’s satisfaction.     

Pricing scheme design of ridesharing program in morning commute problem

This paper examines the dynamic user equilibrium of the morning commute problem in the presence of ridesharing program. Commuters simultaneously choose departure time from home and commute mode among three roles: solo driver, ridesharing driver, and ridesharing rider. Considering the congestion evolution over time, we propose a time-varying compensation scheme to maintain a positive ridesharing ridership at user equilibrium. To match the demand and the supply of ridesharing service over time, the compensation scheme should be set according to the inconvenience cost functions and the out-of-pocket cost functions. When the price charged per time unit is higher than the inconvenience cost per time unit perceived by the ridesharing drivers, the ridesharing participants will travel at the center of peak hours and solo drivers will commute at the two tails. Within the feasible region with positive ridership, the ridesharing program can reduce the congestion and all the commuters will be better off. To support system optimum (SO), we derive a time-varying toll combined with a flat ridesharing price from eliminating queuing delay. Under SO toll, the ridesharing program can attract more participants and have an enlarged feasible region. This reveals that the commuters are more tolerant to the inconvenience caused by sharing a ride at SO because of the lower travel time. Compared with no-toll equilibrium, both overall congestion and individual travel cost are further reduced at SO.     

An investigation of commuting trip timing and mode choice in the Greater Toronto Area: Application of a joint discrete-continuous model

The trip timing and mode choice are two critical decisions of individual commuters mostly define peak period traffic congestion in urban areas. Due to the increasing evidence in many North American cities that the duration of the congested peak travelling periods is expanding (peak spreading), it becomes necessary and natural to investigate these two commuting decisions jointly. In addition to being considered jointly with mode choice decisions, trip timing must also be modelled as a continuous variable in order to precisely capture peak spreading trends in a policy sensitive transportation demand model. However, in the literature to date, these two fundamental decisions have largely been treated separately or in some cases as integrated discrete decisions for joint investigation. In this paper, a discrete-continuous econometric model is used to investigate the joint decisions of trip timing and mode choice for commuting trips in the Greater Toronto Area (GTA). The joint model, with a multinomial logit model for mode choice and a continuous time hazard model for trip timing, allows for unrestricted correlation between the unobserved factors influencing these two decisions. Models are estimated by occupation groups using 2001 travel survey data for the GTA. Across all occupation groups, strong correlations between unobserved factors influencing mode choice and trip timing are found. Furthermore, the estimated model proves that it sufficiently captures the peak spreading phenomenon and is capable of being applied within the activity-based travel demand model framework.     

Prioritizing bicycle paths in Belo Horizonte City,Brazil: Analysis based on user preferences and willingness considering individual heterogeneity

Using bicycles as a commuting mode has proven to be beneficial to both urban traffic conditions and travelers’ health. In order to efficiently design facilities and policies that will stimulate bicycle use, it is necessary to first understand people’s attitudes towards bicycle use, and the factors that may influence their preferences. Such an understanding will enable reliable predictions of bicycle use willingness level, based on which cycling facility construction can be reasonably prioritized.As people often have different perceptions on exercising, green transportation, and traffic conditions, effects of potentially influencing factors on people’s willingness of using bicycles tend to be highly heterogeneous. This paper uses a random parameter ordered probit model to analyze how travelers’ willingness of using bicycles is influenced by various socio-economic factors in Belo Horizonte City, Brazil, with the consideration of individual heterogeneity. The data was collected through the 2010 bicycle use survey in Belo Horizonte City. Results show that, first, the willingness of using bicycle is favored by middle income class household, and negatively related with commuting time. Second, people who rent apartments tend to be more willing to use bicycles. Third, if a person is currently walking a long time to work, he/she would be most willing to commute with a bicycle in the future. Those currently commuting a relatively short distance by motorcycle and bus follow this group in terms of willingness to commute by bicycle in the future. Car users seem to be difficult to convert to bicycle users. Moreover, the estimation shows clear evidence that significant individual heterogeneity indeed exists, especially for education level, necessitating the consideration of such an effect. With the calibrated model, residents’ willingness of using bicycle commuting is then estimated for the entire Belo Horizonte City using the 2010 Census and the 2012 O/D survey data. The results are cross validated using the bicycle path preference information, also obtained from the 2010 bicycle use survey.     

The effect of social comparisons on commute well-being

We study the effect of social comparisons on travel happiness and behavior. Social comparisons arise from exchanges of information among individuals. We postulate that the social gap resulting from comparisons is a determinant of “comparative happiness” (i.e. happiness arising from comparisons), which in turn affects subsequent behavior. We develop a modeling framework based on the Hybrid Choice Model that captures the indirect effect of social comparisons on travel choices through its effect on comparative happiness.We present an empirical analysis of one component of this framework. Specifically, we study how perceived differences between experienced commute attributes and those communicated by others affect comparative happiness and consequently overall commute satisfaction. We find that greater comparative happiness arising from favorable comparisons of one’s commute to that of others (e.g. shorter commute time than others, same mode as others for car commuters, and different mode than others for non-motorized commuters) increases overall commute satisfaction or utility.The empirical model develops only the link between social comparisons and happiness in the comparisons-happiness-behavior chain. It is anticipated that the theoretical framework that considers the entire chain will enhance the behavioral realism of “black box” models that do not account for happiness in the link between comparisons and behavior.     

Capturing value of reliability through road pricing in congested traffic under uncertainty

Empirical studies showed that travel time reliability, usually measured by travel time variance, is strongly correlated with travel time itself. Travel time is highly volatile when the demand approaches or exceeds the capacity. Travel time variability is associated with the level of congestion, and could represent additional costs for travelers who prefer punctual arrivals. Although many studies propose to use road pricing as a tool to capture the value of travel time (VOT) savings and to induce better road usage patterns, the role of the value of reliability (VOR) in designing road pricing schemes has rarely been studied. By using road pricing as a tool to spread out the peak demand, traffic management agencies could improve the utility of travelers who prefer punctual arrivals under traffic congestion and stochastic network conditions. Therefore, we could capture the value of travel time reliability using road pricing, which is rarely discussed in the literature. To quantify the value of travel time reliability (or reliability improvement), we need to integrate trip scheduling, endogenous traffic congestion, travel time uncertainty, and pricing strategies in one modeling framework. This paper developed such a model to capture the impact of pricing on various costs components that affect travel choices, and the role of travel time reliability in shaping departure patterns, queuing process, and the choice of optimal pricing. The model also shows the benefits of improving travel time reliability in various ways. Findings from this paper could help to expand the scope of road pricing, and to develop more comprehensive travel demand management schemes.     

Motorway crash duration and its determinants: do durations vary across motorways?

Traffic congestion has been a growing issue in many metropolitan areas during recent years, which necessitates the identification of its key contributors and development of sustainable strategies to help decrease its adverse impacts on traffic networks. Road incidents generally and crashes specifically have been acknowledged as the cause of a large proportion of travel delays in urban areas and account for 25% to 60% of traffic congestion on motorways. Identifying the critical determinants of travel delays has been of significant importance to the incident management systems, which constantly collect and store the incident duration data. This study investigates the individual and simultaneous differential effects of the relevant determinants on motorway crash duration probabilities. In particular, it applies parametric Accelerated Failure Time (AFT) hazard‐based models to develop in‐depth insights into how the crash‐specific characteristic and the associated temporal and infrastructural determinants impact the duration. AFT models with both fixed and random parameters have been calibrated on one year of traffic crash records from two major Australian motorways in South East Queensland, and the differential effects of determinants on crash survival functions have been studied on these two motorways individually. A comprehensive spectrum of commonly used parametric fixed parameter AFT models, including generalized gamma and generalized F families, has been compared with random parameter AFT structures in terms of goodness of fit to the duration data, and as a result, the random parameter Weibull AFT model has been selected as the most appropriate model. Significant determinants of motorway crash duration included traffic diversion requirement, crash injury type, number and type of vehicles involved in a crash, day of week and time of day, towing support requirement and damage to the infrastructure. A major finding of this research is that the motorways under study are significantly different in terms of crash durations; such that motorway 1 exhibits durations that are on average 19% shorter compared with the durations on motorway 2. The differential effects of explanatory variables on crash durations are also different on the two motorways. The detailed presented analysis confirms that looking at the motorway network as a whole, neglecting the individual differences between roads, can lead to erroneous interpretations of duration and inefficient strategies for mitigating travel delays along a particular motorway.     

Surveying commuter behavior: Designing motorist information systems

A “Motorist Information Survey” was conducted by University of Washington investigators as part of a project sponsored by the Washington State Department of Transportation (WSDOT) and the Federal Highway Administration under the State's Freeway Arterial Management Effort (FAME). This survey, with responses on 62 variables from 3, 893 Seattle commuters, gathered information about motorist behavior and decision processes, particularly as they relate to the design and delivery of motorist information. Results showed that motorists have greater flexibility as to the ime they leave work than the time they leave home, experience some stress during their commute, and most value saving commute time. Motorists are more likely to change their routes from work than from home, to divert to known routes sooner than to unknown routes, and to be influenced by traffic information, congestion, and time of day. Motorists prefer to receive traffic information before entering the freeway as traffic information has the greatest influence on route choice and departure time and all motorists rely most on commercial radio for receipt of traffic information and find it most useful. A majority of motorists want to see highway advisory radio developed first, followed by a phone hot-line, and a dedicated cable TV station.     

Area- and gender-based commuting differentials in India’s largest urban-rural region

In the countries of the Global South such as India, rapid urbanization and the increase in individual motorization may lead to a predominance of unsustainable commuting patterns. However, urbanization also has important positive effects, including the empowerment of women. This paper examines newly released, spatially disaggregated data on home-to-work commuting by non-agricultural workers in the National Capital Region of India. It aims to understand and compare commuting patterns in urban and rural areas, including choice of travel modes, commuting distances, and gender differentials.The results reveal a tendency observable in urban residents to use individual motorized transport more often both for short and for long trips, although the proportion of individual motorization is far from what it is in the industrial world. Rural areas are characterized by the predominance of non-motorized travel modes and a large share of long trips. The mobility gap between men and women does not appear to increase with literacy. In urban areas, women often choose to commute by car rather than using green modes of transportation (especially in higher-income districts). The paper stresses the importance of the area and gender differentials that need to be taken into account when formulating regional transport policies.