Connected and automated vehicles (CAVs) represent a transformative technology that can revolutionize how people and goods move. The private sector is at the forefront of developing this technology, and many municipalities are attempting to prepare for a more connected and automated future. At the same time, as the CAV technology is not mature yet, academics are directing most of their attention to research on CAVs and their impact on the transportation system, overlooking the need for workforce development. The objective of this paper is to assess the needs for workforce development in CAVs, to identify potential obstacles that educators face in fulfilling those needs, and to propose ways to overcome the obstacles. Toward this end, a workshop was designed to bring together experts to identify the best ways to meet the demand for a workforce skilled in CAVs. As the field of CAVs can be diverse, a survey was distributed ahead of the workshop to identify the main themes around which the workshop was designed: (1) next generation infrastructure for CAVs, (2) human factors with CAVs, (3) modeling, simulation, and testing of CAVs, and (4) travel behavior in the context of CAVs.

Perceptions that Electric Vehicles (EVs) hold symbolic value-expressing the owners' identity, values, and status-are positively associated with willingness to purchase an EV. However, is this relationship between symbolic value and purchase intent fixed over time or does it change as the purchase decision draws nearer? In two cross-sectional survey studies, we assess whether and how purchase timing moderates the association between EV symbolic value and adoption intent. Study 1 (N = 565; Columbus, Ohio) finds symbolic value is more closely related to EV adoption intent when a purchase decision is near in time. Study 2 (N = 709; Los Angeles, California) replicates these findings and yields evidence of symbolic value as a mechanism. Specifically, the association between EV symbolic value and identity is stronger among individuals nearer to a purchase decision. This identity-enhanced symbolism, in turn, is positively associated with EV adoption intent. Though our samples were relatively small to detect moderation, this work provides initial evidence that the relationship between symbolic value and adoption intent is stronger when individuals are near (vs. distant) in time to a vehicle purchase decision. We employ two psychological theories, Identity-Based Motivation Theory and Construal Level Theory, to interpret our findings and suggest that an identity-based motivational pull toward products may be partly explained by enhanced symbolic value as one nears a product purchase.

Large-scale adoption of telemobility, such as teleworking and online shopping, has affected travel patterns significantly. The impacts of teleworking and online shopping on travel have been studied separately and with trip-level analyses, thereby ignoring tour complexity, trip chaining, and activity scheduling. We aim to address this gap by investigating the interactions between online shopping, teleworking, and travel at a tour level, considering trip chaining and the importance of the activities involved. We classify tours into mandatory (e.g., travel for work, school), maintenance (e.g., travel for grocery shopping, appointments, errands), and discretionary (e.g., travel for non-grocery shopping, leisure, religious activities) tours according to the primary activity purpose. We then estimate a structural equation model using a one-week activity-travel diary from the 2019 Puget Sound Regional Travel Study. The results indicate that teleworking reduced mandatory and maintenance tours while increasing online shopping. Mandatory tours were negatively associated with both maintenance tours and online shopping, whereas the number of maintenance tours was positively associated with the number of discretionary tours. We did not find a statistically significant relationship between online shopping, maintenance tours, and discretionary tours. Overall, this study offers new insights into the effect of teleworking and online shopping on travel, with potential implications for travel demand modeling and management, as well as for the design of travel surveys that take such virtual activities into account.

To reduce the environmental footprint of urban transportation, it is important to understand mechanisms that can help or hinder the transition to more sustainable travel behavior. Prior research on social influence suggests that the influence of people in one's social network may be one such mechanism. This may be why researchers and public transportation agencies alike have focused on encouraging transit users to recommend service to others as part of efforts to increase ridership in transit systems. However, there is little empirical evidence on the effectiveness of this strategy, and while those in our social networks often share stories with us about their experiences using transit, the impact of these stories on our own willingness to use transit has not yet been explored. Our research is the first to evaluate how hearing about someone else's transit experience influences one's own willingness to use transit. Using survey data from N = 291 transit riders in the central Ohio region, we investigate the influences of others' transit experiences and one's own transit experiences on future transit use intentions. We also examine the roles of experience valence (positivity vs. negativity), transit experience forecast, and predicted transit use satisfaction in this relationship. Through a series of sequential mediation models, we show that hearing about someone's (good or bad) transit experience affects a traveler's own forecasts of how pleasant their transit experience will be, which in turn influences their predicted satisfaction with transit, and ultimately, their intentions to use transit. The models replicate using the traveler's own experiences. Implications for policy and practice are discussed.

The large-scale adoption of Information and Communication Technologies (ICT) such as teleworking and online shopping affects mobility behavior on many dimensions. While a large body of research investigates the relationship between ICT use and various travel outcomes, the consideration of mode use has been limited, with most studies only focusing on a subset of modes and overlooking differences across travel purposes. Therefore, there remains a shortage of literature taking a comprehensive view of the multiple dimensions of mode use in the context of ICT use and capturing all principal modes that travelers may use. We investigate the relationship between an individual’s ICT use and their habitual mode use, termed modality style, which is considered an indicator of latent preferences for certain transportation modes. Using a one-week travel-activity diary from the 2019 Puget Sound Regional Household Travel dataset, latent class cluster analyses of work, grocery shopping, and non-grocery shopping tours are performed to distinguish modality styles for the respective travel purposes. The analysis finds a relationship between ICT use and modality style, and a distinct class of Non-travelers is found in the case of work and non-grocery shopping travel, consisting of individuals with high levels of teleworking and online shopping. A key outcome is the identification of modality styles that are characterized by moderate car use, frequent carpooling, and high levels of ICT use. Furthermore, the respective work travel modality style has lower household car ownership than the more car-centric modality style. Thus, this study not only contributes to unraveling the relationship between ICT use and mode use but also the relationship between ICT use and car ownership.

Demand-responsive, pooled, app-based transportation services, often known as microtransit, fill a gap in providing public transportation where fixed-route transit services are weak. While prior research mostly focused on public-access microtransit services, little is known about the potential of restricted-access, employer-sponsored services to achieve mode shifts away from driving. This study investigates the possible use of employer-sponsored microtransit service by commuters who currently drive to work, using data from a stated choice experiment conducted at a major medical center in Columbus, Ohio. The results reveal a considerable interest in a hypothetical microtransit commuter service among medical center employees, with on average 29.6% of them shifting from car to microtransit. Overall, relatively few sociodemographic characteristics are found to correlate with interest in employer-sponsored microtransit use, but income, status as a shift worker, and a desire to work while commuting are found to affect choice. Valuations of in-vehicle travel time, flexibility in drop-off/pick-up time, and stop location are calculated and compared to prior results from the transit literature. Such valuations can serve as inputs for optimization models to design microtransit systems. Furthermore, respondents' potential concerns about a microtransit service and reactions to proposed incentive schemes are analyzed. The study results highlight the value of combining employer-sponsored microtransit implementations with transportation demand management strategies that reduce the attractiveness of commuting by car. The findings suggest that employer-sponsored microtransit represents an opportunity to reduce greenhouse gas emissions and congestion in an industry sector that employs 6.6 million workers in the US.

Many policies to support battery electric vehicle (BEV) adoption involve roll-out of public charging stations. While greater density of public charging stations is correlated with higher BEV adoption, the mechanisms underlying this effect are not well understood. We use a sample of 1467 online survey respondents living in the metropolitan areas of Los Angeles, Dallas/Fort Worth, and Atlanta in the United States to investigate three potential mechanisms through which greater public charging station density could shape BEV adoption intent. These three potential mechanisms are lower range anxiety, lower perceived mobility restriction, and more positive pro-BEV subjective norms. Multiple regression with ordinary least squares is used to investigate associations between charging station density and adoption intent. Multiple mediation analysis is then used to evaluate the three potential mechanisms for impact of charging station density on BEV adoption intent, and indicates that greater perceived subjective norms in support of BEVs explain much of the association between charger density and adoption intent. Range anxiety plays a smaller and less robust role as a mechanism, while perceived mobility restriction has no direct or indirect effect on BEV adoption intent. That is, we found no indication that BEV adoption intent is influenced by expectations that BEVs are unable to meet mobility needs. Findings indicate that norms are particularly important for investments in charging infrastructure to translate to more BEVs on the road.

The COVID-19 pandemic has severely impacted public transit services through plummeting ridership during the lockdown and subsequent budget cuts. This study investigates the equity impacts of reductions in accessibility due to transit service cuts during COVID-19 and their association with urban sprawl. We evaluated transit access to food and health care services across 22 US cities in three phases during 2020. We found stark socio-spatial disparities in access to basic services and employment in food and health care. Transit service cuts worsened accessibility for communities with multiple social vulnerabilities, such as neighborhoods with high rates of poverty, low-income workers, and zero-vehicle households, as well as poor neighborhoods with high concentrations of black residents. Moreover, sprawled cities experienced greater access loss during COVID-19 than compact cities. Our results point to policies and interventions to maintain social equity and sustainable urban development while benefiting diverse social groups during disruptions.

Broader adoption of battery electric vehicles (BEVs) and reductions in household car ownership are key to meeting climate goals. Yet, BEV market shares remain low in most countries, and car sharing systems tend to appeal to a limited demographic. In this research we examine a novel ownership option that allows households more flexibility in car ownership: short-term vehicle subscriptions that provide users with exclusive access to a vehicle for 6–12 months and incorporate soft costs such as insurance and maintenance. We investigate whether subscriptions can appeal to distinct segments of the population and whether preferences for subscriptions compared to purchases differ between fuel types. Among other factors, we consider the role of drivers’ self-identity as technology enthusiasts and environmentalists. Data are collected through a discrete choice experiment with 1,567 individuals in three US states. An integrated latent class and latent variable choice model indicates the presence of three distinct classes: one motivated by preference for ownership, one motivated by a combination of enthusiasm for new technology and care for the environment, and one motivated by cost concerns. While the subscription option is never appealing to the ownership-oriented class, we find a preference for subscribing to a BEV rather than purchasing it among the class that is both technology-oriented and environmentally conscious. Furthermore, while the cost-oriented class would be averse to subscribing to a conventional vehicle, it is not averse to a BEV or hybrid vehicle subscription. This suggests that subscription models could allow consumers who are unwilling to commit to purchasing a BEV to gather experience with one.

The rise of e-commerce has led to substantial changes in personal travel and activities. We systematically reviewed empirical studies on the relationship between online shopping and personal travel behaviour. We synthesised and assessed the evidence for four types of effects on various travel outcomes, including trip frequency, travel distance, trip chaining, mode choice, and time use. In 42 articles reviewed, we found more evidence that online shopping substitutes for shopping travel. Most studies to date have focused on trip frequency but neglected other travel outcomes. Very few studies have considered the modification effect, which has significant implications for travel demand management. In sum, previous studies have not reached a consensus on the dominant effect of online shopping, in part due to the diversity in variable measurements, types of goods, study areas, and analytic methods. A limitation of previous studies is the reliance on cross-sectional surveys, which hinders the distinction between short- and long-term behaviours and between modification, complementarity, and substitution effects. Our study provides an agenda for future research on this topic and discusses policy implications related to land use, behavioural changes, data collection, and modelling for practitioners who wish to incorporate e-commerce in planning for sustainable urban systems.

Battery electric vehicles (BEVs) have received increasing attention in recent years as BEV technical capabilities have rapidly developed. While many studies have attempted to investigate the societal impacts of BEV adoption, there is still a limited understanding of the extent to which widespread adoption of BEVs may affect both environmental and economic variables simultaneously. This study intends to address this research gap by conducting a comprehensive impact assessment of BEV adoption. Using demand estimates derived from a discrete choice experiment, the impact of various scenarios is evaluated using a computable general equilibrium model. Three drivers of BEV total cost of ownership are considered, namely, subsidy levels, cash incentives by manufacturers, and fuel costs. Furthermore, in light of current trends, improvements in BEV battery manufacturing productivity are considered. This research shows that changes in fuel price and incentives by manufacturers have relatively low impacts on GDP growth, but that the effect of subsidies on GDP and on BEV adoption is considerable, due to a stimulus effect on both household expenditures and on vehicle-manufacturing-related sectors. Productivity shocks moderately impact GDP but only affect BEV adoption in competitive markets. Conversely, the environmental impact is more nuanced. Although BEV adoption leads to decreases in tailpipe emissions, increased manufacturing activity as a result of productivity increases or subsidies can lead to growth in non-tailpipe emissions that cancels out some or all of the tailpipe emissions savings. This demonstrates that in order to achieve desired emissions reductions, policies to promote BEV adoption with subsidies should be accompanied by green manufacturing and green power generation initiatives.

Inequality to food access has always been a serious problem, yet it became even more critical during the COVID-19 pandemic, which exacerbated social inequality and reshaped essential travel. This study provides a holistic view of spatio-temporal changes in food access based on observed travel data for all grocery shopping trips in Columbus, Ohio, during and after the state-wide stay-at-home period. We estimated the decline and recovery patterns of store visits during the pandemic to identify the key socio-economic and built environment determinants of food shopping patterns. The results show a disparity: during the lockdown, store visits to dollar stores declined the least, while visits to big-box stores declined the most and recovered the fastest. Visits to stores in low-income areas experienced smaller changes even during the lockdown period. A higher percentage of low-income customers was associated with lower store visits during the lockdown period. Furthermore, stores with a higher percentage of white customers declined the least and recovered faster during the reopening phase. Our study improves the understanding of the impact of the COVID-19 crisis on food access disparities and business performance. It highlights the role of COVID-19 and similar disruptions on exposing underlying social problems in the US.

Maintaining transit infrastructure in a state of good repair (SGR) is key to improving the sustainability of urban transportation. Insufficient SGR investments lead to the deterioration of transit agency assets and to declining service quality and a loss of ridership. Thus, a significant portion of the benefits of SGR investments are due to travel demand impacts, but the literature on this subject is extremely sparse. As a result, evidence and demand modeling tools to properly determine benefits of transit SGR investments are lacking. This paper quantifies the impact that this gap of knowledge has on SGR investment benefit forecasts. It presents a comprehensive sensitivity analysis to determine first-order and higher-order effects of travel demand variables on the variance in SGR benefit estimates, using 37 hypothetical investment scenarios. The results show that the variance attributable to uncertainty in travel demand forecasts is considerable and may skew investment decisions. It varies by SGR project type and may be reduced by addressing some of the variables with which travel demand forecasts interact. (C) 2021 American Society of Civil Engineers.

Food access has always been a serious problem, yet it became even more critical during the COVID-19 pandemic, which exacerbated social inequality and reshaped essential travel.This study provides a holistic view of spatio-temporal changes in food access based on observed travel data for all grocery shopping trips in Columbus, Ohio, during and after the state-wide lockdown.We examined customers' shopping travel patterns by demographic segments, including low-income populations and racial minorities.By considering four types of food retailers, segmented by price levels and business size, we visualized and estimated the decline and recovery patterns of store visits during the pandemic.We developed two hurdle models to identify the key socio-economic and built environment determinants of the store traffic changes.The results show a clear disparity: during the lockdown, store visits to dollar stores declined the least, while visits to big-box stores declined the most and recovered the fastest, albeit mostly based on visits from nearby neighborhoods.Visits to stores in low-income areas, and especially in food deserts, experienced smaller changes even during the lockdown period.Percentage of low-income and white customers were negatively associated with the magnitude of traffic decline to a store during the lockdown period.Meanwhile, stores with a higher percentage of white customers recovered faster during the reopening phase.Our study contributes to developing a better understanding of the impact of the COVID-19 crisis on food access disparities and business performance.Quantifying how the pandemic disrupted food shopping travel can illuminate the social dimensions of access to healthy foods and support the development of resilient and equitable food systems, as well as strategies for the economic revival of affordable and healthy food retailers.

The COVID-19 pandemic has severely impacted public transit services through a combination of plummeting ridership during the lockdown and subsequent budget cuts. This study investigates the equity impacts of reductions in accessibility due to public transit service cuts during COVID-19 and their association with urban sprawl. We evaluated accessibility to essential services such as grocery stores and both urgent and non-urgent health care across 22 cities across the United States in three phases during 2020: pre-lockdown, lockdown, and post-lockdown. We estimated the spatio-temporal coverage of transit service during the peak and off-peak periods in each phase. We found stark disparities in food and health care access for various socio-economic groups. Economically disadvantaged and suburban neighborhoods were more likely to lose food and health care access by public transit during COVID-19. In particular, transit service cuts worsened accessibility for population groups with multiple social vulnerabilities, such as low-income workers with zero vehicle ownership, poor households living in urban neighborhoods, and non-white populations residing in suburban neighborhoods. Moreover, our study suggests that sprawled cities experienced greater losses in access to food and health care during COVID-19 than compact cities, highlighting the influence of urban form on the functionality of transit services during crises.

Due to constraints on pack sizes in which products are shipped to retail stores, excess inventory can accumulate in stores. In order to optimize the allocation of store space between storage and customer-facing areas, simple expressions are required for backroom inventory levels that can be inserted into optimization models. This paper systematically investigates the effect of pack size constraints on in-store inventory and storage space needs. The context and problem definition are based on a limited service restaurant setting. An approximation for the distribution of inventory positions after replenishment is proposed, and its accuracy is compared to results obtained from simulation. Furthermore, the effect of pack size constraints on the probability of stock-outs is derived. The expressions are found to be good approximations that are usable in complex optimization models for store space allocation. Building on these results, we perform exploratory analyses and demonstrate how increasing pack sizes increases service levels but also removes revenue-generating frontroom space because by increasing backroom space requirements.

Although online shopping has been shown to interact with travel behavior in several ways, most studies to date focused on individual-level behavior in non-representative, geographically limited samples, making it difficult to derive clear profiles of shoppers. Using the 2017 US National Household Travel Survey, which included information on online shopping frequency, we estimated a latent class model to identify different shopper types (classes) that exhibited distinct travel and online shopping behavior. We found four classes: time-pressured shoppers, dual-channel shoppers, traditional shoppers, and infrequent shoppers and travelers, that differed in terms of sociodemographic characteristics and stages of life. Our results suggest that this heterogeneity may extend to the effects of online shopping on shopping travel, for instance, with substitution effects being dominant for one class whereas complementarity effects may be dominant for another. These findings can inform the design of tailored policies to mitigate the sustainability impacts of online shopping while also addressing the various classes' needs. Further research is needed to untangle the complex relationships between online shopping and travel behavior, especially considering this heterogeneity and the modification effect.

The transportation engineering field is currently experiencing a profound transformation driven by technological evolution, which highlights the importance of preparing students for the types of careers that will be available to them in the future. Although transportation engineering programs in the United States are typically at the graduate level, the majority of existing research has focused on undergraduate courses. This study focuses on master's-level transportation engineering curricula, with the goal of investigating how changes in employment opportunities and day-to-day work responsibilities of transportation engineers over the coming 5-10 years will inform the topics that graduate-level curricula should include to set students up for future success. The study consists of in-depth interviews with a range of academics and practitioners and subsequent analyses of interview transcripts using thematic analysis methods. Seven themes were derived, pertaining to three categories: future opportunities, identified skills, and program structure observations. The three thematic categories are not independent, and their interactions with one another hold information that can lead to recommendations for the design of transportation engineering master's programs.

Accessibility measures are necessary for evaluating the benefits of proposed transportation improvements. However, they often do not account for travel time unreliability, but instead incorporate deterministic and time-invariant travel times. This approach risks mischaracterizing the accessibility experienced by travelers. In this paper, we review recent literature on accessibility and travel time reliability with a focus on transit and introduce an approach to joint accessibility-reliability measurement that relies on a behavioral perspective. Using this behavioral perspective, we propose that existing accessibility measures be implemented using travelers’ total travel time budget as a measure of travel time, and that varying departure time strategies depending on service characteristics be considered. The total travel time budget can reasonably be quantified with a high percentile of the total travel time distribution. However, we note that different percentiles may be more appropriate for different traveler types, as these percentiles correspond to varying tolerances for late arrivals. This behavioral perspective can be operationalized with commonly used accessibility measures, such as the cumulative opportunity measure, and with real-time vehicle location data. We include a demonstration of the potential changes in accessibility estimates when accounting for travel time unreliability, with a simplified case study of a transit route in San Francisco. The results show a considerable reduction of the number of opportunities available to travelers when the calculation is based on the latter—between 5.9% and 37.9% less, depending on various factors. Such differences have the potential to significantly affect the accessibility benefits of transit capital investments.

Transit use in many major US cities has recently been declining, often entailing shifts from transit to less sustainable modes of travel, such as private automobiles or ride-hailing. To address this challenge, it is critical to reduce the attrition of existing riders, which requires understanding the determinants of transit user satisfaction and the link between dissatisfaction and attrition. To date, few studies have examined the effect of satisfaction on observed transit use over time. We use a unique panel data set where satisfaction with various factors and changes in transit use were measured one year apart, allowing us to quantify revealed behavior changes and determine which aspects of satisfaction were most predictive. We present two integrated choice and latent variable models (a panel model and a predictive model) to describe the relationship between satisfaction and transit user loyalty, measured as transit use frequency and retention rate over a year. We found that satisfaction with operations significantly affect the level of transit use, but satisfaction with the travel environment and life event do not have a significant impact. Users' self-reported reasons for attrition corroborates the above findings and offers additional insights on observed mode shifts, such as the effect of competing ride-hailing services and bicycling on transit use. Our predictive model, together with an accompanying sensitivity analysis, can be used to forecast attrition as a function of satisfaction. We conclude by recommending strategies to increase user retention and reduce shifts to less sustainable modes.