2026
Uncovering latent urban mobility patterns via smart-card and survey data fusion
Vo, K. D., Ham, S. W., Roy, M., Mishra, S., & Bansal, P. Nature Communications, 17 (2026): 7102.
AI + Statistical Machine Learning + Big DataPEER-REVIEWED RESEARCH
Our Publications
MULTIMEDIA
Representative Papers with Multimedia
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.
AI + Statistical Machine Learning + Big Data2026
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, 17(1) (2026): 43.
Energy Behavior and Systems2026
Variational inference for hierarchical models with conditional scale and skewness corrections
Kock, L., Tan, L. S., Bansal, P., & Nott, D. J. Journal of Computational and Graphical Statistics (2026): 1–11.
AI + Statistical Machine Learning + Big Data2026
Causal inference in conjoint analysis: Logit models vs. potential outcomes
Wu, Z., Zhai, G., & Bansal, P. Journal of Choice Modelling, 59 (2026): 100610.
Causal Inference2025
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.
Energy Behavior and Systems2025
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.
Eye-Tracking and VR2024
A new flexible and partially monotonic discrete choice model
Kim, E. J., & Bansal, P. Transportation Research Part B: Methodological, 183 (2024): 102947.
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2026
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).
AI + Statistical Machine Learning + Big Data2025
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).
AI + Statistical Machine Learning + Big Data2025
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).
AI + Statistical Machine Learning + Big Data2025
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).
AI + Statistical Machine Learning + Big Data2025
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).
AI + Statistical Machine Learning + Big Data2025
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.
AI + Statistical Machine Learning + Big Data2025
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).
AI + Statistical Machine Learning + Big Data2023
Robust discrete choice models with t-distributed kernel errors
Krueger, R., Bierlaire, M., Gasos, T., & Bansal, P. Statistics and Computing, 33(1) (2023).
AI + Statistical Machine Learning + Big Data2023
A deep generative model for feasible and diverse population synthesis
Kim, E. J., & Bansal, P. Transportation Research Part C: Emerging Technologies, 148 (2023).
AI + Statistical Machine Learning + Big Data2022
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).
AI + Statistical Machine Learning + Big Data2022
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.
AI + Statistical Machine Learning + Big Data2022
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.
AI + Statistical Machine Learning + Big Data2021
Fast Bayesian estimation of spatial count data models
Bansal, P., Krueger, R., & Graham, D. J. Computational Statistics & Data Analysis, 157 (2021).
AI + Statistical Machine Learning + Big Data2025
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).
New Methods in Transportation Science2024
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).
New Methods in Transportation Science2023
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).
New Methods in Transportation Science2022
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.
New Methods in Transportation Science2022
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.
New Methods in Transportation Science2022
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).
New Methods in Transportation Science2021
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.
New Methods in Transportation Science2020
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.
New Methods in Transportation Science2020
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.
New Methods in Transportation Science2020
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).
New Methods in Transportation Science2019
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.
New Methods in Transportation Science2018
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.
New Methods in Transportation Science2018
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.
New Methods in Transportation Science2025
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).
Energy Behavior and Systems2025
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.
Energy Behavior and Systems2025
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).
Energy Behavior and Systems2024
Inverse product differentiation logit model: Holy grail or not?
Huo, J., Dua, R., & Bansal, P. Energy Economics, 131 (2024).
Energy Behavior and Systems2024
Understanding consumers’ non-compensatory and heterogeneous preferences for electric vehicles
Huo, J., Kim, E. J., & Bansal, P. Energy Policy, 192 (2024).
Energy Behavior and Systems2024
Are American electric vehicle owners quitting?
Dua, R., Edwards, A., Anand, U., & Bansal, P. Transportation Research Part D: Transport and Environment, 133 (2024).
Energy Behavior and Systems2022
Fuel consumption elasticities, rebound effect and feebate effectiveness in the Indian and Chinese new car markets
Bansal, P., & Dua, R. Energy Economics, 113 (2022).
Energy Behavior and Systems2021
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).
Energy Behavior and Systems2021
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).
Energy Behavior and Systems2026
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.
Eye-Tracking and VR2025
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).
Eye-Tracking and VR2025
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).
Eye-Tracking and VR2026
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).
Eye-Tracking and VR2024
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).
Eye-Tracking and VR2024
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).
Eye-Tracking and VR2025
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).
Causal Inference2024
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).
Causal Inference2023
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).
Causal Inference2023
Surge pricing and consumer surplus in the ride-hailing market: Evidence from China
Xu, M., Anupriya., & Bansal, P. Travel Behaviour and Society, 33 (2023).
Causal Inference2021
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.
Causal Inference2020
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.
Causal Inference2020
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.
Causal Inference2024
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).
Public Health2022
Modelling the propagation of infectious disease via transportation networks
Anupriya., Bansal, P., & Graham, D. J. Scientific Reports, 12(1) (2022).
Public Health2022
COVID-19 vaccine preferences in India
Bansal, P., Raj, A., Mani Shukla, D., & Sunder, N. Vaccine, 40(15) (2022): 2242–2246.
Public Health2019
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.
Public Health