Many rapidly developing countries around the world are at a crossroads when it comes to transportation, air quality, and sustainability. Indeed, the challenges presented by vehicular growth in India have motivated the search for sustainable transportation solutions. One solution constitutes ridehailing services, which are expected to reduce car ownership and provide affordable means of transportation. Another key solution is the rise of electric vehicles (EVs), which are expected to reduce greenhouse gas emission and address the growing demand for sustainable urban mobility. Using a unique survey data set collected in 2018 from a sample of 43,000 respondents spread across 20 cities in India, this paper attempts to shed light on the factors that affect adoption of on-demand transportation services and EVs in India. In particular, not only does this paper consider the socio-economic and demographic variables that affect these behavioral choices, but the modeling framework adopted in this study places a special emphasis on representing the important role played by attitudes, values, and perceptions in determining adoption of on-demand transportation services and EVs. It is observed that attitudes and values significantly affect the use of on-demand transportation services and EV ownership, suggesting that information campaigns and free trials/demonstrations would help advance the adoption of sustainable transportation modes. The model results help in the identification of policy options and infrastructure investments that can advance a sustainable transportation future in India.

Agent-based models (ABMs) in transportation modeling simulate activity and travel decisions at the disaggregate level of households and individuals. To do this, ABMs require detailed and realistic information on agents’ socioeconomic and demographic characteristics. Various synthetic population generators have been proposed to address this need. However, most of those currently in practice are cross-sectional in nature and do not account for the dynamics within households and individuals as they progress through life events over time. This is a major shortcoming, as literature has shown that transportation decisions are affected by the transition between and co-occurrence of life cycle events. While some demographic evolution simulators have been proposed to address this issue, they are developed using cross-sectional data and capture only a small set of life cycle events and their interdependence. Addressing these drawbacks, we propose a demographic microsimulator (DEMOS) that captures the “continuum of life” by considering a range of household- and individual-level life cycle events. DEMOS is developed using the Panel Survey of Income Dynamics, one of the world’s longest-running longitudinal surveys. The DEMOS submodels consider key life cycle events that are influenced by agents’ demographic variables. DEMOS is applied to evolve the population of the San Francisco Bay Area over a 9-year horizon. Results demonstrate how DEMOS generates life trajectories and how DEMOS outputs match the observed demographic trends. DEMOS is expected to enable longitudinal analysis in the context of ABMs and expand ABMs analyses relating to dynamic processes such as household-level vehicle transactions.

Prior to the market uptake of autonomous vehicles (AVs), it is critical to understand the consumer segments of AV adopters and quantify the impact of AV adoption on transportation systems and the environment, such as annual vehicle miles travelled (VMT) and greenhouse gas (GHG) emission. In this study, using the 2019 California Vehicle Survey (CVS) a statistical model correlating people’s intention to buy an AV and their socioeconomic and built environment attributes at the household level is estimated and a sensitivity analysis is used to understand the importance of factors impacting AV adoption. This is followed by a microsimulation of market penetration and five vehicle replacement in the household scenario analysis using the entire population in the San Francisco Bay Area region to assess how the annual vehicle miles traveled (VMT) and Carbon Dioxide (CO 2 ) emissions shift at the regional. It is found that adopting electric AVs can potentially reduce more than 5 megatons of CO 2 , which is approximately 30% of the total CO 2 emitted by internal combustion engine cars in the regional resident population.

Autonomous electric vehicles (AEVs) can potentially revolutionize the transportation landscape, offering a safer, contact-free, easily accessible, and more eco-friendly mode of travel. Prior to the market uptake of AEVs, it is critical to understand the consumer segments that are most likely to adopt these vehicles. Beyond market adoption, it is also important to quantify the impact of AEVs on broader transportation systems and the environment, such as impacts on the annual vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. In this pilot study, using survey data, a statistical model correlating AEV adoption intention and socioeconomic and built environment attributes was estimated, and a sensitivity analysis was conducted to understand the importance of factors impacting AEV adoption. We found that the market segments range from early adopters who are wealthy, technologically savvy, and relatively young to non-adopters who are more cautious to new technologies. This is followed by a synthetic population microsimulation of market penetration for the San Francisco Bay Area. With five household vehicle replacement scenarios, we assessed the annual VMT and tailpipe carbon dioxide (CO₂) emissions change associated with vehicle replacement. It is found that adopting AEVs can potentially reduce more than 5 megatons of CO₂ yearly, which is approximately 30% of the total CO₂ emitted by internal combustion engine (ICE) cars in the region.