Our Publications

AI + Statistical Machine Learning + Big Data

Scalable variational inference for multinomial probit models under large choice sets and sample sizes

Kim, G., Kang, Y., Kock, L., Bansal, P., & Sohn, K. Statistics and Computing, 36(1) (2026).

From biases to opportunities: Leveraging location-based service (LBS) data for next-generation transportation planning

Chen, C., Wang, R., Bansal, P., Chen, L., Ugurel, E., Zhang, Y., & Wu, X. Transportation Research Part C: Emerging Technologies, 182 (2026): 105416.

Multi-objective Bayesian optimization for likelihood-free inference in sequential sampling models of decision making

Chen, D., Li, X., Kim, E. J., Bansal, P., & Nott, D. Transactions on Machine Learning Research (2025).

A novel data fusion method to leverage passively-collected mobility data in generating spatially-heterogeneous synthetic population

Vo, K. D., Kim, E. J., & Bansal, P. Transportation Research Part B: Methodological, 191 (2025).

Collaborative generative adversarial networks for fusing household travel survey and smart card data to generate heterogeneous activity schedules in urban digital twins

Lee, H., Bansal, P., Vo, K. D., & Kim, E. J. Transportation Research Part C: Emerging Technologies, 176 (2025).

Physics informed deep generative model for vehicle trajectory reconstruction at arterial intersections in connected vehicle environment

Xu, M., Fang, J., Bansal, P., Kim, E. J., & Qiu, T. Z. Transportation Research Part C: Emerging Technologies, 171 (2025).

Coordinated Multi-Agent Reinforcement Learning Method for Integrating Transit Signal Priority and Speed Guidance Control

Fang, J., Cai, S., You, Y., Xu, M., Bansal, P., & Kim, E.-J. IEEE Transactions on Intelligent Transportation Systems (2025): 1–13.

Nonparametric mixed logit model with market-level parameters estimated from market share data

Ren, X., Chow, J. Y. J., & Bansal, P. Transportation Research Part B: Methodological, 196 (2025).

A new flexible and partially monotonic discrete choice model

Kim, E. J., & Bansal, P. Transportation Research Part B: Methodological, 183 (2024): 102947.

Robust discrete choice models with t-distributed kernel errors

Krueger, R., Bierlaire, M., Gasos, T., & Bansal, P. Statistics and Computing, 33(1) (2023).

A deep generative model for feasible and diverse population synthesis

Kim, E. J., & Bansal, P. Transportation Research Part C: Emerging Technologies, 148 (2023).

Detecting metro service disruptions via large-scale vehicle location data

Zhang, N., Graham, D. J., Bansal, P., & Hörcher, D. Transportation Research Part C: Emerging Technologies, 144 (2022).

Designed quadrature to approximate integrals in maximum simulated likelihood estimation

Bansal, P., Keshavarzzadeh, V., Guevara, A., Li, S., & Daziano, R. A. Econometrics Journal, 25(2) (2022): 301–321.

A dynamic choice model to estimate the user cost of crowding with large-scale transit data

Bansal, P., Hörcher, D., & Graham, D. J. Journal of the Royal Statistical Society: Series A, 185(2) (2022): 615–639.

Fast Bayesian estimation of spatial count data models

Bansal, P., Krueger, R., & Graham, D. J. Computational Statistics & Data Analysis, 157 (2021).

New Methods in Transportation Science

Revisiting McFadden’s correction factor for sampling of alternatives in multinomial logit and mixed multinomial logit models

Dekker, T., Bansal, P., & Huo, J. Transportation Research Part B: Methodological, 192 (2025).

A tactical planning framework to integrate paratransit with formal public transport systems

Gadepalli, R., Bansal, P., Tiwari, G., & Bolia, N. Transportation Research Part D: Transport and Environment, 136 (2024).

A data fusion approach for ride-sourcing demand estimation: A discrete choice model with sampling and endogeneity corrections

Krueger, R., Bierlaire, M., & Bansal, P. Transportation Research Part C: Emerging Technologies, 152 (2023).

A multinomial probit model with Choquet integral and attribute cut-offs

Dubey, S., Cats, O., Hoogendoorn, S., & Bansal, P. Transportation Research Part B: Methodological, 158 (2022): 140–163.

A general framework to forecast the adoption of novel products: A case of autonomous vehicles

Dubey, S., Sharma, I., Mishra, S., Cats, O., & Bansal, P. Transportation Research Part B: Methodological, 165 (2022): 63–95.

Modelling animal–vehicle collision counts across large networks using a Bayesian hierarchical model with time-varying parameters

Gurumurthy, K. M., Bansal, P., Kockelman, K. M., & Li, Z. Analytic Methods in Accident Research, 36 (2022).

A new spatial count data model with time-varying parameters

Buddhavarapu, P., Bansal, P., & Prozzi, J. A. Transportation Research Part B: Methodological, 150 (2021): 566–586.

A generalized continuous–multinomial response model with a t-distributed error kernel

Dubey, S., Bansal, P., Daziano, R. A., & Guerra, E. Transportation Research Part B: Methodological, 133 (2020): 114–141.

Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

Bansal, P., Krueger, R., Bierlaire, M., Daziano, R. A., & Rashidi, T. H. Transportation Research Part B: Methodological, 131 (2020): 124–142.

A new spatial count data model with Bayesian additive regression trees for accident hot spot identification

Krueger, R., Bansal, P., & Buddhavarapu, P. Accident Analysis and Prevention, 144 (2020).

A framework to integrate mode choice in the design of mobility-on-demand systems

Liu, Y., Bansal, P., Daziano, R. A., & Samaranayake, S. Transportation Research Part C: Emerging Technologies, 105 (2019): 648–665.

Minorization–Maximization (MM) algorithms for semiparametric logit models: Bottlenecks, extensions, and comparisons

Bansal, P., Daziano, R. A., & Guerra, E. Transportation Research Part B: Methodological, 115 (2018): 17–40.

Extending the logit–mixed logit model for a combination of random and fixed parameters

Bansal, P., Daziano, R. A., & Achtnicht, M. Journal of Choice Modelling, 27 (2018): 88–96.

Energy Behavior and Systems

Lithium-ion battery recycling relieves the threat to material scarcity amid China’s electric vehicle ambitions

Zhang, B., Xin, Q., Chen, S., Wang, B., Li, H., Wang, Z., & Bansal, P. Nature Communications, 16(1) (2025).

Behavioral uncertainty in EV charging drives heterogeneous grid load variability under climate goals

Zhang, B., Xin, Q., Chen, S., Wang, Z., Lu, Y., Niu, N., Zhang, F., Liu, G., & Bansal, P. Nature Communications (Forthcoming, 2025).

Strategic planning of charging infrastructure for shared electric vehicles: A multi-phase stochastic approach using reinforcement learning

Ye, Q., Bansal, P., & Adey, B. T. Sustainable Cities and Society, 133 (2025): 106850.

Coordinating demand response strategies for electric vehicles and second-life battery energy storage to optimize renewable energy absorption under demand and supply uncertainties

Xin, Q.-Y., Zhang, B., Zhang, F., & Bansal, P. Transportation Research Part A: Policy and Practice, 202 (2025): 104725.

Can decoy effects nudge ride-hailing drivers’ preferences for electric vehicles?

Ding, J., Kim, E. J., Maksimenko, V., & Bansal, P. Transportation Research Part A: Policy and Practice, 196 (2025).

Inverse product differentiation logit model: Holy grail or not?

Huo, J., Dua, R., & Bansal, P. Energy Economics, 131 (2024).

Understanding consumers’ non-compensatory and heterogeneous preferences for electric vehicles

Huo, J., Kim, E. J., & Bansal, P. Energy Policy, 192 (2024).

Are American electric vehicle owners quitting?

Dua, R., Edwards, A., Anand, U., & Bansal, P. Transportation Research Part D: Transport and Environment, 133 (2024).

Fuel consumption elasticities, rebound effect and feebate effectiveness in the Indian and Chinese new car markets

Bansal, P., & Dua, R. Energy Economics, 113 (2022).

Willingness to pay and attitudinal preferences of Indian consumers for electric vehicles

Bansal, P., Kumar, R. R., Raj, A., Dubey, S., & Graham, D. J. Energy Economics, 100 (2021).

Fuel economy valuation and preferences of Indian two-wheeler buyers

Bansal, P., Dua, R., Krueger, R., & Graham, D. J. Journal of Cleaner Production, 294 (2021).

Eye-Tracking and VR

Do Pedestrians Respond Differently to Perceived Vehicle Automation in Monitor-based and Head-Mounted immersive Experiments?

Maksimenko, V., Yuan, L., & Bansal. P. Travel Behaviour and Society, 43 (2026): 101210.

In-lab versus web-based eye-tracking in decision-making: A systematic comparison on multiple display-size conditions mimicking common electronic devices

Muñoz, S., Maksimenko, V., Henríquez-Jara, B., Bansal, P., & Perez, O. D. Behavior Research Methods, 57 (2025): 339.

Video-based experiments better unveil societal biases towards ethical decisions of autonomous vehicles

Maksimenko, V., Li, X., Kim, E. J., & Bansal, P. Transportation Research Part C: Emerging Technologies, 179 (2025).

Preferences for electric vehicles under uncertain charging prices: An eye-tracking study

Ding, J., Yu, J., & Bansal, P. Transportation Research Part D: Transport and Environment, 140 (2025).

Relative impact of pedestrian’s age and gender on ethical decisions of autonomous vehicles: Insights from neuro-behavioural analysis

Maksimenko, V., Li, X., Pandey, H., & Bansal, P. AI and Ethics, 6(15) (2026).

Discrete choice experiments with eye-tracking: How far we have come and ways forward

Bansal, P., Kim, E. J., & Ozdemir, S. Journal of Choice Modelling, 51 (2024).

Do people need guidance to estimate a food portion size? Evidence from an exploratory eye-tracker study

Maksimenko, V., Labbe, D., Eldridge, A. L., Mak, T. N., Huo, J., Bansal, P., & Thoresen, J. C. Food Quality and Preference, 120 (2024).

Causal Inference

Understanding the capacity of airport runway systems

Chen, K., Anupriya., Bansal, P., Anderson, R. J., Findlay, N. S., & Graham, D. J. Transportation Research Part C: Emerging Technologies, 173 (2025).

Examining the causal impacts of the built environment on cycling activities using time-series street view imagery

Ito, K., Bansal, P., & Biljecki, F. Transportation Research Part A: Policy and Practice, 190 (2024).

Congestion in cities: Can road capacity expansions provide a solution?

Anupriya., Bansal, P., & Graham, D. J. Transportation Research Part A: Policy and Practice, 174 (2023).

Surge pricing and consumer surplus in the ride-hailing market: Evidence from China

Xu, M., Anupriya., & Bansal, P. Travel Behaviour and Society, 33 (2023).

A causal inference approach to measure the vulnerability of urban metro systems

Zhang, N., Graham, D. J., Hörcher, D., & Bansal, P. Transportation, 48(6) (2021): 3269–3300.

Quantifying the ex-post causal impact of differential pricing on commuter trip scheduling in Hong Kong

Anupriya., Graham, D. J., Hörcher, D., Anderson, R. J., & Bansal, P. Transportation Research Part A: Policy and Practice, 141 (2020): 16–34.

Understanding the costs of urban rail transport operations

Anupriya., Graham, D. J., Carbo, J. M., Anderson, R. J., & Bansal, P. Transportation Research Part B: Methodological, 138 (2020): 292–316.

Public Health

Getting it right with discrete choice experiments: Are we hot or cold?

Ozdemir, S., Gonzalez, J. M., Bansal, P., Huynh, V. A., Sng, B. L., & Finkelstein, E. Social Science and Medicine, 348 (2024).

Modelling the propagation of infectious disease via transportation networks

Anupriya., Bansal, P., & Graham, D. J. Scientific Reports, 12(1) (2022).

COVID-19 vaccine preferences in India

Bansal, P., Raj, A., Mani Shukla, D., & Sunder, N. Vaccine, 40(15) (2022): 2242–2246.

Arriving at a decision: A semi-parametric approach to institutional birth choice in India

Bansal, P., Daziano, R. A., & Sunder, N. Journal of Choice Modelling, 31 (2019): 86–103.