Automobile Commuters Research Articles

Joint Impacts of Pricing Strategies and Persuasive Information on Habitual Automobile Commuters’ Travel Mode Shift Responses

Persuasive information developed by smartphone applications is a potential tool that can be utilized in order to increase the effectiveness of the impact of pricing strategies on triggering sustainable travel mode choice behavior. In order to address the joint impacts of pricing strategies and persuasive information on habitual automobile commuters’ travel mode shift responses, a stated-preference survey was conducted in Beijing’s inner district, from which over 1000 responses were collected. Four separate multivariable multilevel logistic regression models were estimated for more and less habitual automobile commuters when subjected to congestion pricing and reward strategies. The model estimation results showed that the influence of persuasive information was more effective in promoting travel mode shifts among more habitual automobile commuters with regard to reward strategies compared to congestion pricing. The results also showed that the impact of sociodemographic characteristics, commuter travel characteristics, the amount of congestion pricing or monetary award, and types of persuasive information on travel mode shift decisions under these strategies were deemed to be significantly different between more and less habitual automobile commuters. These findings suggest that more effective reward strategies can be explored by providing personalized and differentiated travel feedback information (e.g., pollution emission information and physical activity information), particularly for less habitual automobile commuters. This study also provides some degree of insight regarding the question as to how to design future congestion pricing, i.e., with respect to formulating differentiated charge rates according to the travel characteristics of habitual automobile commuters, as well as in developing complementary persuasive information that focuses on addressing public acceptability and fairness rather than travel feedback information.

Health perception and commuting choice: a survey experiment measuring behavioral trade-offs between physical activity benefits and pollution exposure risks

Previous literature suggests that active commuting has substantial health benefits. Yet, in polluted regions, it can also cause additional health risks by increasing riders’ pollution exposure and raising their inhalation rate. We examine the effect of perceived air pollution on stated commuting choices using an on-site survey experiment for 2285 non-automobile commuters in Zhengzhou, a heavily polluted city in central China. We integrate a sequential randomized controlled trial in a survey where individuals in the treatment group received tailored information on their commuting-related pollution exposure, based on our 2 week peak-hour pollution monitoring campaign across transportation modes in the city. We find that travelers in Zhengzhou have already adopted pollution prevention actions by favoring indoor commuting modes on polluted days. Individuals receiving personalized pollution exposure information by mode further decrease active commuting by 8.4 percentage points (95% CI: 5.1, 11.6), accompanied by a 14.7 percentage points (95% CI: 10.7, 18.3) increase in automobile commuting. Travellers make sub-optimal, overly risk averse choices by reducing active commuting even for trips where epidemiological research suggests the exercise benefits outweigh pollution exposure risks. This pollution avoidance tendency significantly attenuates the effect of policies encouraging active commuting. Our findings show the intricately intertwined relationships between the public health targets of promoting active lifestyles and reducing pollution exposure, and between individual pollution avoidance and societal pollution mitigation.

A new approach to estimating flood-affected populations by combining mobility patterns with multi-source data: A case study of Wuhan, China

Estimating flood impacts on daily activities is vital to disaster risk reduction and urban resilience. Human mobility on complex road networks has caused citizens, especially automobile commuters, to suffer from remarkable impacts on rainy days. However, quantitative studies of these harms have been limited. In this paper, we develop a new approach to estimating flood-affected populations, including their quantity, structure, and spatial distribution, by integrating commuter mobility patterns and road inundation records using multi-source data. Mobility patterns were built by the Gravity model with point-of-interest (POI) data and then downscaled to individual routes by Monte Carlo simulation. Inundation locations and impassable roads were retrieved by combining flood hazard maps with social media data. This approach was applied to Wuhan, a large city in Central China, to estimate the affected automobile commuters under a typical flood event (July 6, 2016) and three flood scenarios (10-year, 20-year, and 50-year). Our approach produces an estimate of the affected population that is more accurate and detailed than existing approaches. It also provides high-resolution impact information for urban studies and can be applied easily to other cities and disasters that include road closures. It will help governments to estimate affected populations rapidly and assess indirect economic losses caused by labor disruptions and work delays.

Gender differences in commuting travel in the U.S.: interactive effects of race/ethnicity and household structure

This research investigates the interactive effects of the household structure and race/ethnicity on gender differences in commuting travel in the United States. Existing research has established that both the household structure and race/ethnicity affect the gender differences, but little has examined if and how the effects of the household structure differ across racial/ethnic groups. Using the 2017 U.S. National Household Travel Survey, I estimate gender differences in commuting distance and in the probability of automobile commute in five household types and in four racial/ethnic groups. Results suggest differing effects of household types across racial/ethnic groups: the effects are particularly large in Hispanic people and small in Black people; the effects are moderate in white and Asian people. The research provides an analytical framework that jointly considers the two important factors that explain gendered commuting travel, and it reveals nuanced findings that can inform people-based and context-sensitive transport policies.

Automobile Commuting in Suburban High-Rise Condominium Apartments: Examining Transitions toward Suburban Sustainability in Toronto

While North American suburbs remain largely dispersed and auto-dependent, they are also increasingly heterogeneous. Although some suburbs have long been punctuated with high-rise developments, for instance rental apartments in the Canadian context, there are now a growing number of new high-rise condominium developments in suburban settings in both the US and Canada. While much is known about downtown high-rise condominium developments, there has of yet been little to no analysis of this trend in the suburbs. We offer such an analysis using Statistics Canada census data from 2016 in the Toronto metropolitan area. We focus on commuting patterns as an indicator of auto-dependence to test whether suburbs with larger shares of new high-rise condominium apartments (high-rise condo clusters) exhibit lower shares of auto commuting. The focus on auto-dependence is important because development and land use plans commonly use environmental concerns arising from heavy automobile use as a rationale for high-rise development. Our findings suggest that in Toronto suburban high-rise condo clusters offer a less auto-intensive way of living in the suburbs than traditionally has been the case in the suburban ownership market. However, this seems to be limited to particular demographic groups, such as smaller households; and suburban high-rise condos are not an evident sign of a broader transition toward suburban sustainability among the population as a whole in the Toronto case. The potential for transitions toward suburban sustainability could be enhanced with greater investments in transit infrastructure and building higher density mid-rise and ground-oriented dwellings that accommodate larger households still commonly found in low-density, auto-dependent suburbs.

Exploring the importance of detailed environment variables in neighborhood commute mode share models

This paper analyzes the relationship between detailed neighborhood environment variables and commute mode share using a dataset drawn from across the United States and includes model validation results. Representing one of the first studies of its kind, we use United States journey-to-work data to explore the following questions: 1) Which detailed environment variables have significant associations with the proportion of people in a neighborhood who take public transit, walk, or bicycle to work? 2) Does adding detailed environment variables to existing, nationally available neighborhood variables improve the predictive accuracy of work commute mode share models? We use a set of 120 randomly selected census tracts to estimate fractional multinomial logit models that predict walk, bicycle, transit, and automobile commute mode shares. The Base Model includes a set of nine significant, nationally available variables identified from a previous analysis of 5,000 tracts. We test 18 additional detailed neighborhood environment variables and identify five variables that have significant associations with commute mode share: sidewalk coverage (positive association with transit and walk), proximity to a rail station (positive association with transit), bicycle facility density (positive association with bicycle), freeway presence (negative association with walk), and mixed land use (positive association with transit, walk, and bicycle). While these detailed environment variables add clarity to our understanding of factors that influence travel behavior, our validation analysis using 50 separate census tracts does not provide conclusive evidence that these variables improve model accuracy. Further studies with larger sample sizes are needed to determine the optimal set of variables to include to predict automobile, transit, pedestrian, and bicycle commute mode shares.

Just Breathe

Motivated by the idea that slow breathing practices could transform the automobile commute from a depleting, mindless activity into a calming, mindful experience, we introduce the first guided slow breathing intervention for drivers. We describe a controlled in-lab experiment (N=24) that contrasts the effectiveness and impact of haptic and voice guidance modalities at slowing drivers' breathing pace, which is a known modulator of stress. The experiment was conducted in two simulated driving environments (city, highway) while driving in one of two driving modes (autonomous, manual). Results show that both haptic and voice guidance systems can reduce drivers' breathing rate and provide a sustained post-intervention effect without affecting driving safety. Subjectively, most participants (19/24) preferred the haptic stimuli as they found it more natural to follow, less distracting, and easier to engage and disengage from, compared to the voice stimuli. Finally, while most participants found guided breathing to be a positive experience, a few participants in the autonomous driving condition found slow breathing to be an unusual activity inside the car. In this paper, we discuss such considerations, offer guidelines for designing in-car breathing interventions, and propose future research that extends our work to on-road studies. Altogether, this paper serves as foundational work on guided breathing interventions for automobile drivers.

Impact of traveller information on mode choice behaviour

This paper analysed automobile commuters' mode choice behaviour under the influence of simulated multimodal traveller information by developing two logit models. A combined revealed preference (RP)/stated preference (SP) travel behaviour survey was administered to drivers to gather individual commuters' travel decisions under integrated multimodal traveller information. Two SP scenarios were designed where the first scenario is to test the mode choice preference in a basic situation involving a congested work/school trip with information on several travel options, and the second scenario is to investigate the mode choice decision when certain incentives are given for public transport. Results showed that integrated multimodal traveller information can influence a traveller's mode choice decision. The influencing factors that significantly affect the mode choice decision include socio-economic characteristics – for example, gender, age, level of education and level of income – and multimodal traveller information attributes – for example, access mode to mass rapid transit (MRT) station, access time to MRT station and transit seat availability. The findings are useful to traffic management agencies for designing better operational policy and information publication strategies.

Strategic Planning for Employment Self-Containment in Metropolitan Sub-Regions

Metropolitan strategic plans often focus on strengthening local employment opportunities to address the congestion and commuting issues threatening city sustainability. The success of such strategies relies on a more equitable distribution of jobs between sub-regions and can be comparatively benchmarked through one of the three related measures of employment self-sufficiency, self-containment or jobs-housing balance. However, in practice, planning policy implementation to meet these targets seldom reduces automobile commuting. This paper investigates self-containment across a range of occupation and industry types to highlight large differences in commuting and employment patterns through a case study of Perth’s Northwest sub-region, Western Australia. Its findings suggest the application of current sub-regional policy and targets within Perth may reinforce the wage and skill disadvantage of outer metropolitan sub-regions over the inner core. It recommends a more nuanced understanding of these measures taking into account the complex dynamics of both employment opportunities and commuting patterns across sub-regions of a city.

Evaluating the impacts of the clean cities program.

The Department of Energy's Clean Cities program was created in 1993 to reduce petroleum usage in the transportation sector. The program promotes alternative fuels such as biofuels and fuel-saving strategies such as idle reduction and fleet management through coalitions of local government, non-profit, and private actors. Few studies have evaluated the impact of the program because of its complexity that include interrelated strategies of grants, education and training and diversity of participants. This paper uses a Difference-in-Differences (DiD) approach to evaluate the effectiveness of the program between 1990 and 2010. We quantify the effectiveness of the Clean Cities program by focusing on performance measures such as air quality, number of alternative fueling stations, private vehicle occupancy and transit ridership. We find that counties that participate in the program perform better on all these measures compared to counties that did not participate. Compared to the control group, counties in the Clean Cities program experienced a reduction in days with bad air quality (3.7%), a decrease in automobile commuters (2.9%), an overall increase in transit commuters (2.1%) and had greater numbers of new alternative fueling stations (12.9). The results suggest that the program is a qualified success.

Estimating Active Transportation Behaviors to Support Health Impact Assessment in the United States.

Health impact assessment (HIA) has been promoted as a means to encourage transportation and city planners to incorporate health considerations into their decision-making. Ideally, HIAs would include quantitative estimates of the population health effects of alternative planning scenarios, such as scenarios with and without infrastructure to support walking and cycling. However, the lack of baseline estimates of time spent walking or biking for transportation (together known as “active transportation”), which are critically related to health, often prevents planners from developing such quantitative estimates. To address this gap, we use data from the 2009 US National Household Travel Survey to develop a statistical model that estimates baseline time spent walking and biking as a function of the type of transportation used to commute to work along with demographic and built environment variables. We validate the model using survey data from the Raleigh–Durham–Chapel Hill, NC, USA, metropolitan area. We illustrate how the validated model could be used to support transportation-related HIAs by estimating the potential health benefits of built environment modifications that support walking and cycling. Our statistical model estimates that on average, individuals who commute on foot spend an additional 19.8 (95% CI 16.9–23.2) minutes per day walking compared to automobile commuters. Public transit riders walk an additional 5.0 (95% CI 3.5–6.4) minutes per day compared to automobile commuters. Bicycle commuters cycle for an additional 28.0 (95% CI 17.5–38.1) minutes per day compared to automobile commuters. The statistical model was able to predict observed transportation physical activity in the Raleigh–Durham–Chapel Hill region to within 0.5 MET-hours per day (equivalent to about 9 min of daily walking time) for 83% of observations. Across the Raleigh–Durham–Chapel Hill region, an estimated 38 (95% CI 15–59) premature deaths potentially could be avoided if the entire population walked 37.4 min per week for transportation (the amount of transportation walking observed in previous US studies of walkable neighborhoods). The approach developed here is useful both for estimating baseline behaviors in transportation HIAs and for comparing the magnitude of risks associated with physical inactivity to other competing health risks in urban areas.

Commuting practices: New insights into modal shift from theories of social practice

The automobile commute makes an important contribution to carbon emissions but has proven stubbornly resistant to modal shift policy initiatives. In this paper we use theories of social practice to develop insights into why this stubbornness might exist, and what might help accelerate transitions to bus- and cycle-commuting. By analyzing qualitative data about everyday mobility in two UK cities, we examine how the availability of the constituent elements of bus- and cycle-commuting practices is crucial for modal shift to occur, but they are often absent. We also draw attention to time-space contingencies that render recruitment to low-carbon commuting practices more or less likely, including how commuting is sequenced with other social practices and how the sites of these practices interact with the affordances, and spatial infrastructure, of bus- and cycle-commuting. These insights lead us to argue that choice and land use planning focussed policy initiatives designed to invoke modal shift need to coexist in integrated policy configurations with initiatives designed to reshape both mobility and non-mobility practices. This means addressing the structural barriers caused by the lack of availability of the elements that constitute bus- and cycle-commuting, and intervening in the timing and spatiality of a range of social practices so as to reduce the tendency for commutes to have spatial and temporal characteristics that militate against the use of bus and cycle modes.

Behavior Insights for an Incentive-Based Active Demand Management Platform

ABSTRACT Most current Travel Demand Management (TDM) programs such as vanpooling, ridesharing, or transit focus on managing travel demand of specific groups of commuters but are limited in effectively managing demand for automobile drivers, who are unable or unwilling to participate in such programs. This paper highlights results from a pilot field study conducted in a large west coast city experiencing major traffic congestion, and documents results of the use of an incentive-based active demand management (ADM) system focusing on automobile commuters. The system, called “Metropia,” predicts future traffic conditions, applies a proprietary routing algorithm to find time-dependent shortest paths for different departure times, and, based on user request, provides automobile travelers with multiple departure times and route choices. Each of these travel choices are assigned points values, with higher points (and thus more valuable rewards) available for travelling during off-peak times and less congested routes, and lower points available for peak traffic travel times. The goal of this ADM system is to improve traffic flow and commuter travel times citywide, alleviating heavily congested areas without the use of new roadway construction by incentivizing travelers to change their travel behavior and avoid traffic congestion. The level of rewards points available to users (commuters) by the system depends on the travelers’ behavioral change degree and their contributions to traffic congestion alleviation. This system was implemented in Los Angeles, Calif., USA, as a small scale pilot field study carried out beginning April 2013 and lasting for 10 weeks. Results from this field study show the system is able to accurately predict travel time with Relative Mean Absolute Error (RMAE) as low as 15.20%. Significant travel behavior changes were observed which validate the concept of using incentives to influence people's travel behavior. Furthermore, field study results show 20% travel time can be saved for people who changed their travel behavior.

モビリティ・マネジメントによる「エコ通勤」の効果分析：平成20年度国土交通省の取り組み

Ministry of Land, Infrastructure, Transport and Tourism (MLIT) implemented nationwide workplace mobility management project, which was called “eco commuting.” In this paper, we showed outlines and results of the project. This paper showed that the project provides reduction of CO2 emissions from participants' automobile commuting. This paper conducted multivariate analyses using attitude and behavioral data which were collected from participants of the project. Several factors which affected on effectiveness of workplace mobility management measures were indicated from the analyses.

Changes in Physical Activity from Walking to School

Changes in Physical Activity from Walking to School

Effects of Automobile Commute Characteristics on Affect and Job Candidate Evaluations

The current study assesses the effects of the commuting environment on affective states and hiring decisions. A total of 136 undergraduate females were randomly assigned to one of four conditions based on the length (10 km vs. 30 km) and level of congestion (low vs. high) during a commute. Multivariate analyses of variance indicate that affective states were differentially influenced before and during the commute between the conditions. Even the anticipatory prospect of encountering congestion led to elevations of anxiety. Subjective impedance was also found to act as a partial, and at times full, mediator that contributed to negative emotional states. Hiring decisions for unqualified candidates were determined in part by the commute that a person drove, indicating that commuting can influence subsequent work behavior.

Congestion Charge and Return Schemes on Modal Choice between Road and Railroad

Recently, “Congestion charging” policy is considered as the quickest and most effective policy to solve traffic congestion problem, and some metropolises have introduced it into their downtown roads. Most of these metropolises have alternate transport systems, such as subways or commuter rail, then the congestion charging influences modal split. It is also important to decide how to return the revenue from the charging to the commuters, because return policy affects on the agreement of the commuters. In this paper, we propose and solve optimal control models simultaneously determining the distribution of home departure time and the modal choice between road and railroad with/without the congestion charging. We theoretically make clear the optimal congestion charge each time of day for each transport mode. Furthermore, we analyze influence of return scheme on the modal choice. From results of numerical simulations using above models, charging and return to railroad commuters is proved to be effective scheme to improve commuters' utility and to reduce the number of automobile commuters.

Does Travel Information Influence Commuter and Noncommuter Behavior?: Results from the San Francisco Bay Area TravInfo Project

Improved information received from public and private advanced traveler information systems can help travelers make more informed decisions, shorten times spent in traffic congestion, and reduce anxiety and stress. The behavioral responses of automobile and transit commuters as well as those of noncommuters to travel information received from radio, television, and telephone are analyzed. The influence of information has seldom been studied in terms of these different users. The data were collected through a computer-aided telephone interview conducted in the San Francisco Bay Area ( n = 947). The study analyzes the impacts of socioeconomic, context, and information variables on individuals’ decisions to adjust travel before beginning their trips; given an adjustment, the frequency of trip changes; and the type of most recent trip changes in terms of route, departure time, or mode shifts. The statistical analysis involves estimation of probit models with and without sample selectivity and a multinomial logit model. The results of the study indicate that a sample selectivity model is more appropriate for understanding respondents’ frequency of trip changes only when members of a subset of the sample population change their trips. Travel time uncertainty and travel information received from the electronic media increase the pretrip adjustment propensity. Furthermore, information from the most widely available and accessed medium, the radio, is highly likely to result in behavioral adjustments. Noncommuters have a high receptivity to canceling their trips in response to travel information. This has important implications for congestion relief in transportation networks.

Comparative Analysis of Spatial Knowledge and En Route Diversion Behavior in Chicago and San Francisco: Implications for Advanced Traveler Information Systems

Drivers’ spatial knowledge and en route response to unexpected delay information are examined. Compared are behavioral responses to information across two metropolitan areas in the United States: Chicago and the San Francisco Bay Area. Comparable behavioral data were collected through handout-mailback questionnaires targeting peak-period automobile commuters. The Chicago respondents perceived higher traffic congestion levels, and more of them knew one or more alternate routes. Among the Chicago respondents, fewer received information about unexpected delays from the radio (as opposed to self-observation of congestion) and more automobile commuters diverted to their alternate routes. To understand the factors that influence knowledge of alternate routes and en route diversion in response to unexpected congestion, a full information maximum likelihood nested logit model is estimated. Results of the model indicate that longer duration of residence, higher propensity for discovering new routes, and locational characteristics tend to increase drivers’ spatial knowledge. Propensity for en route diversion increases with higher than usual route travel time plus delay and shorter alternate route travel times. Drivers with higher propensity for taking risks to avoid unexpected delays are more likely to divert. The model indicates that route knowledge and en route diversion propensity is higher in Chicago than San Francisco. This implies that information-sensitive behavioral models may be context dependent. Importantly, delay information received through radio traffic reports, as opposed to other sources such as self-observation of congestion, increases en route diversion propensity in unexpected delay situations. This implies a more dynamic readjustment of commuters’ route selection decisions. At the same time, the potential benefits from Advanced Traveler Information Systems (ATIS) must compete with the benefits already accruing from radio traffic information.

The impact of adverse weather conditions on the propensity to change travel decisions: A survey of Brussels commuters

New developments in transportation systems and the increasing complexities of urban living are creating challenges and opportunities to comprehensively address transportation problems. The key to exploiting new opportunities lies in understanding how travelers would respond to changes in the transportation system. This study aims to understand traveler behavior under normal and unexpected travel conditions in real-life situations. The results of a comprehensive behavioral survey conducted in Brussels are reported. Commuters' mode, departure time and route selection decisions, and the factors that influence them, including travel conditions, personal and household characteristics, and situational constraints, are explored. The paper develops a deeper understanding of automobile commuters' response to adverse weather. Automobile commuters constitute more than 50% of the total sample ( N = 1218). They were asked about adverse weather impacts on their mode, route and departure time changes. Among those who changed their travel decisions in adverse weather (about 50%), more than one-quarter reported that adverse weather was either very important or important in changing their mode; and 60% changed their departure time due to adverse weather whereas 35% diverted to alternate routes. Furthermore, close to 75% kept themselves informed about weather through secondary information sources such as radio and television. To analyze relationships empirically, ordered probit models of mode and departure time were estimated. The models show that commuters change their travel patterns in systematic ways when adverse weather occurs. Finally, the implications of the survey results for dealing with adverse weather problems are discussed.