Single Occupancy Vehicles Research Articles

Factors Influencing Willingness to Pool in Ride-Hailing Trips

In the past decade, transportation network companies (TNCs) such as Uber, Lyft, and Via have established themselves as a viable transportation alternative to other modes. However, the popularity of these services has come with a fair share of criticism for their negative externalities such as increasing vehicle miles traveled and congestion in cities. Pooled ride-hailing trips, in which all or a part of two individual (or group) trips are combined in and served by a single vehicle, have the potential to reduce these externalities. Pooling of rides is an effective solution to reduce congestion and travel cost, but pooled rides still represent a small percentage of the total trips served (and miles driven) by TNCs relative to single-occupancy (and without customer) vehicle miles. Both TNCs and cities alike will benefit from understanding what factors encourage or deter pooling a ride-hailing trip. In this study, newly available Chicago transportation network provider data were explored to identify the extent to which different socioeconomic, spatiotemporal, and trip characteristics affect willingness to pool (WTP) in ride-hailing trips. Multivariate linear regression and machine-learning models were employed to understand and predict WTP based on location, time, and trip factors. The results show intuitive trends, with income level at drop-off and pickup locations and airport trips as the most important predictors of WTP. Results from this study can help TNCs and cities devise strategies that increase pooled ride-hailing, thereby reducing adverse transportation and energy impacts from ride-hailing modes.

Tackling single-occupancy vehicles to reduce carbon emissions: Actionable model of drivers’ implementation intention to try public buses

When driving a single-occupancy vehicle (SOV) on roads has become ubiquitous for certain drivers, it causes 23% of the global greenhouse gas emissions. The adoption of high-occupancy public bus transport (PT) by the SOV drivers contributes to the lower and more responsible consumption of energy. This paper predicts drivers’ implementation intention to try public buses (implementation intention) as a desirable solution to reduce carbon emissions. It builds on the renaissance of drivers’ cognitive, social, and affective factors to examine their implementation intention. An actionable model investigated their desire for services, perceived influence of referents (referents), worry to try PT (worry), intention to try PT (intention), and habit of driving every day was synthesized. This study employs 9 focus groups and solicits 653 responses from SOV drivers through web surveys. Data is analyzed using variance-based structural equation modeling of partial least squares (PLS-SEM). The empirical results indicate that their desire for comfort and convenience predicted their intention and implementation intention. Although the desire for service information and referents predicted intention, these predictors were found to have no impact on their implementation intention. The intention remains as the primary predictor of implementation intention. Strong driving habit exerts significant lower implementation intention whereas weak habit exerts higher implementation intention. On the contrary, their worry acted as an impediment to intention and implementation intention. The findings indicate that social intervention is imperative to mitigate worry and to break the habit of driving SOVs in order to promote implementation intention.

What we can learn from five naturalistic field experiments that failed to shift commuter behaviour.

Across five field experiments with employees of a large organization (n = 68,915), we examined whether standard behavioural interventions ('nudges') successfully reduced single-occupancy vehicle commutes. In Studies 1 and 2, we sent letters and emails with nudges designed to increase carpooling. These interventions failed to increase carpool sign-up or usage. In Studies 3a and 4, we examined the efficacy of other well-established behavioural interventions: non-cash incentives and personalized travel plans. Again, we found no positive effect of these interventions. Across studies, effect sizes ranged from Cohen's d = -0.01 to d = 0.05. Equivalence testing, using study-specific smallest effect sizes of interest, revealed that the treatment effects observed in four out of five of our experiments were statistically equivalent to zero (P < 0.04). The failure of these well-powered experiments designed to nudge commuting behaviour highlights both the difficulty of changing commuter behaviour and the importance of publishing null results to build cumulative knowledge about how to encourage sustainable travel.

MaaS surveillance: Privacy considerations in mobility as a service

Abstract The concept of Mobility as a Service (MaaS) is seeing increasing attention from researchers, industry, and the public sector. MaaS, which posits that traditional models of car ownership and travel may be supplanted by models focused on packages of shared vehicle access, use of public transport, active transport, and teleworking, is currently viewed as having potential beneficial impacts including reductions in single-occupancy vehicle trips, with concomitant reductions in travel cost, congestion, and environmental concerns. MaaS, however, relies upon a number of social expectations, including trust, reliability, and transparency, each of which is reliant upon both the social network that enables MaaS to work efficiently, and upon the ways in which data are handled within the enabling framework. In light of this, it is anticipated that the recently-enacted General Data Protection Regulation (GDPR) has the potential to significantly impact upon the further implementation of MaaS. MaaS services are predicated upon the sharing of personal travel information (vehicle availability, origins, destinations, financial information, social network data, etc.) that, under GDPR, may be considered personal, subject to the regulations and restrictions this categorisation implies. For MaaS to work in a European context, then, it must be responsive to GDPR requirements related to issues such as Privacy by Design, Consent, and Protection. In this paper, we explore the concept of MaaS in relation to privacy considerations raised by GDPR requirements, with attention to methods and techniques related to relevant data acquisition, sharing, and protection processes. A case study of the Whim application’s privacy policy is presented to demonstrate the potential implications of this policy in an applied context.

Measuring the potential emission reductions from a shift towards public transport

The research presented in this paper investigates the behavioural response and subsequent appraisal of a range of policy incentives devised to encourage greater use of sustainable travel options within the Greater Dublin Area (GDA). Interventions were assessed by employing four-stage modelling techniques, as a tool to identify signs of mode shifting behaviour from single occupancy vehicle (SOV) use to public transport modes. Extensive modelling work was conducted in collaboration with the National Transport Authority (NTA) of Ireland utilising the National Regional Modelling System (RMS) to represent the sustainable policy changes. Parameter modifications in the mode and destination choice and trip assignment stages were made to account for improvements made to frequency, time and cost attributes of public transport modes included in the model. Subsequent changes in vehicle kilometres travelled of various modes modelled were then utilised to estimate the potential emissions savings. In addition to this, the potential monetary savings generated from a reduction in emissions were calculated. The key results produced from this study found that an increase in the mode share of public transport of up to 2.87% could be achieved in the GDA. Up to 1.76% of which was estimated to come directly from private car trips, as a result of implementing the policy incentives tested. Furthermore, daily emissions savings of up to 431.58 tonnes of CO2, 0.95 tonnes of NOx, and 0.046 tonnes of PM2.5 were also estimated.

Mobility Sharing as a Preference Matching Problem

Traffic congestion, dominated by single-occupancy vehicles, reflects not only transportation system inefficiency and negative externalities but also a sociological state of human isolation. Advances in information and communication technology are enabling the growth of real-time ridesharing to improve system efficiency. While most ridesharing algorithms optimize fellow passenger matching based on efficiency criteria (maximum number of paired trips, minimum total vehicle-time, or vehicle-distance traveled), very few explicitly consider passengers’ preference for their peers as the matching objective. The existing literature either considers the bipartite driver–passenger matching problem, which is structurally different from the monopartite passenger–passenger matching, or only considers the passenger–passenger problem in a simplified one-origin–multiple-destination setting. We formulate a general monopartite passenger matching model in a road network and illustrate the model by pairing 301 430 taxi trips in Manhattan in two scenarios: one considering 1000 randomly generated preference orders and the other considering four sets of group-based preference orders. In both scenarios, compared with efficiency-based matching models, preference-based matching improves the average ranking of paired fellow passenger to the near-top position of people’s preference orders with only a small efficiency loss at the individual level and a moderate loss at the aggregate level. The near-top-ranking results fall in a narrow range even with the random variance of passenger preference as inputs.

Modelling the potential benefits of increased active travel

This research examines the behavioural response of commuters within the Greater Dublin Area (GDA), Ireland, to a range of policy incentives designed to encourage travellers to make greater use of sustainable travel modes for work. Several policy measures were evaluated using discrete choice and four stage modelling methods, to estimate the propensity of commuters to shift to active modes use (walking and cycling) rather than single occupancy vehicle (SOV). As a means of appraising the hypothetical introduction of a range of active mode policy incentives, a stated preference experiment (SP) was created as an instrument for gathering decision making, mode choice and socio-demographic data from a sample of commuters in the GDA, based on a number of designed policy scenarios. Extensive transport modelling work was subsequently conducted with the National Transport Authority (NTA) of Ireland using the National Regional Modelling System (RMS). A representation of the policy changes explored in the SP experiment was made in this four-stage transport model in order to produce real-life estimates of trip making behaviour and mode share, to test the behavioural response of the introduction of the policy incentives. Changes to parameters in the Mode Choice and Trip Assignment stages of the four-stage model were made to account for improvements made to the infrastructure and time attributes of walking and cycling in the model. The modifications were made based on ‘Do Nothing/Base’, ‘Do Something’, and ‘Do Maximum’ scenarios, which were determined by attribute level values taken from the SP survey.The findings produced in this study show a marked inelasticity of commuters to react in response to improvements made to the level of service of cycling, while on the contrary exhibiting high levels of elasticity to changes made to pedestrian infrastructure. Outputs from these model scenarios found that pedestrians in the GDA were most sensitive to the policy incentives tested. The results highlight that investing in improvements to the public realm may be a more worthwhile investment in order to encourage a modal shift to walking for commuting trip purposes.

Self-organized ridesharing: Multiperspective annotated review

Most cars traversing the roads for commuting purposes travel as single-occupant vehicles, generating externalities in the form of congestion, pollution, and vehicle-miles traveled. A relatively efficient and sustainable transportation option for addressing these concerns is ridesharing—the practice of sharing a car with other passengers, free-of-charge, or expense sharing. The history of ridesharing spans more than a century and includes different initiatives, some offered by official authorities and some arising from spontaneous bottom-up self-organization. This article presents a qualitative multidata-source review aimed at exploring the knowledge base of self-organized ridesharing. The review reveals that: (1) the literature mainly treats self-emergent ridesharing as separate occurrences; (2) these cases have repeated forms, attributes, and influencing factors; (3) practice theory combined with additional framing theories, mainly grassroots innovation (GI), multilevel perspective (MLP), and sharing economy (SE), have the potential to collectively explain some of these cases; and (4) emergence processes and local context hold the key to the success of these cases, yet this is seldom captured in research. The review offers insights into existing self-emerging cases and demonstrates the applicability of findings in analyzing a particular case. It identifies future work required to establish repeated patterns, or a meta-type, capturing self-organized ridesharing.

Choice of High Occupancy/Toll Lanes under Alternative Pricing Strategies

High occupancy/toll (HOT) lanes typically vary tolls charged to single occupant vehicles, with the toll increasing during congested periods. The toll is usually tied to time of day or to the density of vehicles in the HOT lane. The purpose of raising the toll with congestion is to discourage demand sufficiently to maintain travel speeds in the HOT lane. However, it has been demonstrated that the HOT toll may act as a signal of downstream congestion (in both general purpose (GP) and HOT lanes), causing an increase in demand for the HOT lane, at least at lower prices. This paper develops a model of lane choice to evaluate alternative HOT lane pricing strategies, including the use of GP density, to more accurately reflect the value of the HOT lane. In addition, the paper explores the potential effect these strategies would have on the HOT lane vehicle share through a partial equilibrium analysis. This analysis demonstrates the change in demand elasticity with price, showing the point at which drivers switch from a positive to negative elasticity.

Strengthening proenvironmental intentions: Intrinsic interest may support use of transport alternatives to driving alone

For many reasons, communities encourage citizens to reduce automobile use, often using incentives or competitions to encourage the change. While these programs may be effective in temporarily reducing vehicle use, psychological research suggests that such externally motivated behaviors are not likely to endure once the incentive program ends. This article proposes that use of alternative travel modes might be more durable if participants become internally motivated to maintain the behaviors. The study tests the hypothesis that participants who psychologically transform their commute modes into intrinsically interesting trips are more likely to report they intend to continue using their alternative modes. In a month-long Challenge to reduce single occupant vehicle (SOV) use, participants (N = 197) were randomly assigned to three different instruction groups: cognitive elaboration, cognitive dissonance, or community-based social marketing. Data were collected at 4 different times, including a final questionnaire 2 months after the Challenge ended. For all three groups, analyses supported the hypothesis and showed that the relationship from initial commitment to final attitude was mediated by intrinsic interest, specifically, by their reports that they had made their alternative travel modes interesting/fun or productive. The results encourage additional research asking how people can find ways to make transit use more attractive than driving alone.

Vanpooling and its effect on commuter stress

Vanpools are used in the United States as a form of commuter transportation, with up to 15 people commuting together in one shared vehicle. This research examined commuter behavior to determine if those who use a vanpool to and from work experience a different level of stress than their single-occupant vehicle counterparts. The outcome of this study is important to refining future studies to include vanpool participants as a standalone category to uniquely consider all specific modes individually. Follow-on research must continue to answer the additional questions of behavior choice to determine if knowledge of lowered stress will prompt users to change said behavior. Within the emerging field of Mobility as a Service, it is useful to understand the differences in commute experience to fully support a holistic mobility framework, accounting for multiple modes that address a specific journey requirement.In this study, performed over the course of two weeks, questionnaire respondents rated their personally-perceived level of stress before and after their commute on a Likert scale of 1 and 10. Overall, the aggregated data revealed that vanpoolers experienced a 21% lower rate of self-reported commuting stress than their single-occupant vehicle counterparts. The difference between commute types was strongest for the after-work-to home portion of the commute. In that instance, vanpool commuters experienced a significant decrease in stress compared to their single-occupant counterparts even though their initial stress prior to the evening commute was higher. This indicates that the commute by vanpool helped them overcome a portion of the stress realized as a result of the experiences of their day.

Estimating commute-travel implications of telecommuting by female employees in Kuala Lumpur, Malaysia

A number of Malaysian cities are experiencing severe traffic congestion resulting from rapidly increasing vehicle ownership and usage, and the insufficient supply and inferior quality of public transport. The trend of congestion, frequent traffic accidents and air pollution is nowhere more prevalent in Kuala Lumpur where telecommuting is increasingly being perceived as a viable travel demand management strategy. Due to a number of unique travel characteristics of female commuters and the relatively large participation rate of women in the Malaysian work force, telecommuting by female employees seems a more relevant and potentially more crucial means of alleviating rush hour traffic in the Kuala Lumpur metropolitan area. The aim of this study is to estimate both the potential and actual travel implications of telecommuting by female employees. Therefore, this study is exploratory in nature as it mainly aims to explore the influence of the propagation of a phenomenon (i.e., telecommuting among female employees in Kuala Lumpur) on travel demand, and is thus mainly a quantitative research. The travel implications estimated in this paper include the reduction in commute trips, with a particular emphasis on the reduction in single occupancy vehicles (SOVs) entering the city of Kuala Lumpur during peak hours; vehicle and passenger-kilometers forgone; and time savings as a result of the elimination of the daily commute. The findings clearly show the potential of substantial reduction in vehicle and passenger-kilometers travelled, as well as potential time savings of more than 7000 employee-hours/day under an optimal adoption scenario. Furthermore, telecommuting by female employees could potentially result in a daily reduction of up to 7.8% of all SOVs entering Kuala Lumpur in 2019; however, there is a large gap between that potential reduction and the maximum actual reduction of 0.96%.

ACTIVITY-BASED MODELING FRAMEWORK FOR TRAVEL DEMAND AND BEHAVIOUR

In all developed and developing countries of the world, the government transportation’s policies aimed at controlling aggregate phenomena such as congestion, emissions and land use patterns. These are achieved through the provision of employer- based commute programs, single occupant vehicle regulation, road pricing, multimodal facilities and transit oriented land development. But these policies affect the aggregate phenomen indirectly through the behaviour of individuals. Furthermore, individuals adjust their behaviour in complex ways, motivated by a desire to achieve their activity objectives. This paper examines the activity based modeling framework for travel demand and behaviour, the concepts underlying the methods and modeling approaches. Finally, it identified three classes of model systems, which are econometric model systems, hybrid simulation systems and the theory of planned behaviour model, and also look at some examples in each class, considering how they work, and their particular strengths and weaknesses, and above all, looking at the big picture. Article visualizations:

Quality of Life, Multimodality, and the Demise of the Autocentric Metropolis: A Multivariate Analysis of 148 Mid-Size U.S. Cities

Quality of life has recently gained prominence in the urban affairs, development, and planning debates. A wide-range of factors have been linked to quality of life, including environmental health, commute times, arts and cultural amenities, school quality, housing availability, and economic concerns. The accessibility inherent in multimodal transportation is critical in the functioning of metropolitan areas. What has not been explored is the association between multimodal transportation and urban quality of life. In this paper, we adapt the method of urban sociologists, Harvey Molotch and Richard Appelbaum, to explore the association between multimodality and 12 measures of quality of life. We analyze 148 cities in the United States with populations over 50,000 that are more than 20 miles from other similarly sized cities. Our test measure is the percentage of workers who commute by some means other than a single-occupant vehicle. Using bivariate and multivariate analysis, this study shows a higher quality of life in counties and metropolitan areas with higher levels of multimodal commuting. These findings underscore the positive impact of sustainable transportation policies on quality of life and opens up new directions for research and policy in the built environment.

Examining the potential for car-shedding in the Greater Dublin Area

This paper examines the receptivity of a sample of commuters within the Greater Dublin Area (GDA) to a range of policy incentives that encourage travellers to make more sustainable usage of their private car. Several policy measures are evaluated to identify a means of stimulating a shift from single occupancy vehicle (SOV) use to modes that make sustainable usage of the car, namely carpooling and car-sharing. Consequently, an indication of increased levels of ‘car-shedding’ in the GDA is ascertained. Behavioural indicators such as market elasticities and simulation models are estimated from multinomial logit (MNL) modelling of choices from a stated preference (SP) survey. The analysis determined that reductions in the modal share of SOVs of up to 8% in a ‘best case scenario’, could be achieved if policies are put in place to reduce the time and cost attributes of commuting to work by carpooling and car-sharing. Furthermore, a 1% change in the convenience, time and cost attributes yielded a direct effect or increase in the probability of carpooling and car-sharing being chosen, of up to 0.34%, suggesting that the carpool and car-share modes may be relatively elastic to attribute changes. A comparison of means analysis of other survey data is also included in this paper. Ultimately, this paper offers support to the argument that greater investment in policy incentives alone is worth considering in order to increase the occupancy of vehicles commuting to work and education.

ACTIVITY-BASED MODELING OF TRAVEL DEMAND AND BEHAVIOUR

In all developed and developing countries of the world, the government transportation’s policies aimed at controlling aggregate phenomena such as congestion, emissions and land use patterns. These are achieved through the provision of employer- based commute programs, single occupant vehicle regulation, road pricing, multimodal facilities and transit oriented land development. But these policies affect the aggregate phenomen indirectly through the behaviour of individuals. Furthermore, individuals adjust their behaviour in complex ways, motivated by a desire to achieve their activity objectives. This paper examines the activity based modeling of travel demand and behaviour, the concepts underlying the methods and modeling approaches. Finally, it identified three classes of model systems, which are econometric model systems, hybrid simulation systems and the theory of planned behaviour model, and also look at some examples in each class, considering how they work, and their particular strengths and weaknesses, and above all, looking at the big picture. Article visualizations:

Comparative analyses of sustainable campuses as living laboratories for managing environmental quality

PurposeDespite an emerging trend in the higher education sector toward sustainable campuses, comparative analyses that span multiple themes across multiple campuses are still limited. The purpose of this paper is to reduce such a gap by comparing universities that are members of the International Sustainable Campus Network across themes that are related to environmental quality.Design/methodology/approachIn total, 34 universities are included in the sample. Indicators are systematically reviewed and clustered into ten themes. Common indicators (CIs) are identified in seven themes for at least seven and at most 20 campuses. At the absence of CIs, the given theme is assessed based on the measures applied. The results indicate the average levels of performance in the sample and/or the scope of the measures that are undertaken.FindingsAccording to related values, an average campus spent 233,402 MWh of energy in buildings, 838,317 m3 of water on campus, generated 4,442 tonnes of waste, and emitted 75,354 tonnes of CO2 emissions. The average recycling rate was 50 percent, the average single occupancy vehicle rate in campus commuting was 34 percent, and on average, there were 152 sustainability-oriented courses. Best practices from the measures included energy audits for data centers, retrofit of water intense laboratories, and on-site renewable energy projects.Originality/valueIn addition, a unified monitoring framework is proposed to improve subsequent comparative analyses of campuses. Universities must focus on the use of the campus as a living laboratory to guide society toward a more sustainable future.

Commuter exposure to PM2.5, BC, and UFP in six common transport microenvironments in Sacramento, California

This study was designed to estimate and compare the air pollution exposures experienced by commuters in six common transportation modes utilized by California residents, and to evaluate the impact of practical exposure mitigation strategies in reducing commute exposures. We measured concentrations of fine particle matter (PM2.5), black carbon (BC), and ultrafine particles (UFP) for 161 commutes between April 2014 and November 2015 in Sacramento, CA. We collected measurements for six modes including single occupancy vehicles, high occupancy vehicles (multiple occupants), buses, light rail, train, and bicycling. The largest average concentrations for most pollutants were measured during train commutes and the lowest average concentrations were observed during light-rail commutes. Mitigation options were explored for personal vehicles, bicycling, and train commute modes. We found that ventilation settings of personal vehicles can reduce in-vehicle PM2.5, BC, and UFP concentrations by up to 75%. Similarly, bicycle route choice can reduce exposures by 15–75% with the lowest concentrations observed during commutes on dedicated bicycle paths away from traffic sources. Train commuters experienced UFP concentrations an order of magnitude greater when the locomotive engine was pulling the rail cars versus pushing the rail cars. We found that UFP concentrations during bus, bicycling, and train commutes were 1.6–5.3 times greater than personal vehicle commutes, while light rail commutes had 30% lower UFP concentrations than personal vehicle commutes. The largest exposure per mile occurred during bicycle commutes with PM2.5, BC, and UFP exposures of 1.312 μg/mile, 0.097 μg/mile, and 3.0 × 109 particles/mile, respectively. Train commutes experienced the largest exposure per mile of all of the combustion-derived transportation commute modes. BC accounted for 5–20% of total PM mass across all commute modes with an average fraction of ∼7% of PM2.5.

Sustainable transportation pros, cons, and self-efficacy as predictors of 6-month stage transitions in a Chinese sample

Abstract Sustainable transportation (ST) refers to any form of commuting other than single occupancy vehicle usage. This study prospectively examined the pros, cons, and self-efficacy for ST as predictors of stage transitions over a period of 6 months. Participants included 656 Chinese college students and workers. They completed a translated questionnaire on their ST stage (from Precontemplation to Maintenance), pros, cons, and self-efficacy. Six months later, the participants completed another questionnaire on their stage of change. The data on the participants in the pre-Action and post-Action stages were combined to predict forward transitions out of the pre-Action stages and backward transitions from the post-Action stages. Logistic regression analyses were conducted predicting 6-month stage transitions, either forward or backward, using baseline stage, pros, cons, and self-efficacy. Higher ST pros and, unexpectedly, lower ST self-efficacy predicted forward stage transitions among baseline participants who were not using ST. In contrast, lower ST pros and higher ST cons predicted backward stage transitions (regression) among those who at baseline were using ST. This study provides a preliminary understanding of predictive mechanisms of stage transitions for ST.

Commute mode diversity and public health: A multivariate analysis of 148 US cities

ABSTRACTUrban sprawl has a negative relationship with regional health outcomes, such as high obesity and chronic diseases related to physical inactivity. At the same time, literature has shown that walkable built environments are connected to lower obesity rates and increased physical activity. Less understood is the association of modal diversity with those health outcomes. This research studies a range of public health outcomes to determine their associations with commute mode diversity. Researchers adapted the methodology of urban sociologists Harvey Molotch and Richard Appelbaum to explore twelve different health measures in 148 US counties and their metropolitan statistical areas. The test measure is the percentage of commuters who use some means other than a single-occupant vehicle (SOV). The percentage of such commuters ranges from as low as 11% in Houston County, Alabama to as high as 36% in Honolulu County, Hawaii. Using bivariate and multivariate analyses, we found better health outcomes in counties and metropolitan areas that have fewer workers commuting by SOV. These findings underscore the positive impact of sustainable transportation policies on community health and open up a new direction for public health research and the built environment.