Journal Publications and Book Chapters

  1. Sharma, A., Zheng, Z., Kim, J., Bhaskar, A., & Haque, M. M. (2021). Assessing traffic disturbance, efficiency, and safety of the mixed traffic flow of connected vehicles and traditional vehicles by considering human factors. Transportation Research Part C: Emerging Technologies, 124, 102934. https://doi.org/10.1016/j.trc.2020.102934
  2. Jia, D., Sun, J., Sharma, A., Zheng, Z., & Liu, B. (2021). Integrated simulation platform for conventional, connected and automated driving: A design from cyber–physical systems perspective. Transportation Research Part C: Emerging Technologies, 124, 102984. https://doi.org/10.1016/j.trc.2021.102984
  3. Haque, M. M., Oviedo-Trespalacios, O., Sharma, A., & Zheng, Z. (2021). Examining the driver-pedestrian interaction at pedestrian crossings in the connected environment: A Hazard-based duration modelling approach. Transportation Research Part A: Policy and Practice, 150, 33-48. https://doi.org/10.1016/j.tra.2021.05.014
  4. Banerjee, I., Deepa, L., and Pinjari A.R. (2021). Public Transit Ridership Forecasting Models. In: Vickerman, Roger (eds.) International Encyclopedia of Transportation. Vol. 4, pp. 459-467. UK: Elsevier Ltd. http://dx.doi.org/10.1016/B978-0-08-102671-7.10367-7
  5. Nirmale, S.K., Pinjari, A.R., and Sharma. A.  (2021). A discrete-continuous multi­-vehicle anticipation model of driving behaviour in heterogeneous disordered traffic conditions. Transportation Research Part C.  Vol. 128,  103144. https://doi.org/10.1016/j.trc.2021.103144
  6. Saxena, S., and Pinjari, A.R., Roy, A., and Paleti, R.  (2021). Multiple discrete-continuous choice models with bounds on consumptions. Transportation Research Part A.  Vol. 149, pp. 237-265. https://doi.org/10.1016/j.tra.2021.03.016
  7. Pellegrini, A., Pinjari, A.R., and Maggi, R.  (2021). A multiple discrete continuous model of time use that accommodates non-additively separable utility functions along with time and monetary budget constraints. Transportation Research Part A.  Vol. 144, pp. 37-53. https://doi.org/10.1016/j.tra.2020.11.009
  8. Pinjari, A.R., and Bhat, C.R.  (2021). Computationally Efficient Forecasting Procedures for Kuhn-Tucker Consumer Demand Model Systems: Application to Residential Energy Consumption Analysis. Journal of Choice Modelling.  Vol. 39, 100283. https://doi.org/10.1016/j.jocm.2021.100283
  9. Balusu, S., Mannering, F.L., and Pinjari, A.R.  (2021). Hazard-based duration analysis of the time between motorcyclists’ initial training and their first crash. Analytic Methods in Accident Research.  Vol.28, 100143. https://doi.org/10.1016/j.amar.2020.100143
  10. Dias, F.F., T. Kim, C.R. Bhat, R.M. Pendyala, W.H.K. Lam, A.R. Pinjari, K.K.Srinivasan, Ramadurai, ​G. (2021). Modeling the evolution of ride-hailing adoption and usage: A case study of the Puget Sound region. Transportation Research Record: Journal of the Transportation Research Board.  Vol. 2675(3), pp. 81-97. https://doi.org/10.1177%2F0361198120964788
  11. Calastri, C., Hess, S., Pinjari, A.R., Daly, A. (2020). Accommodating correlation across days in multiple-discrete continuous models for time use. Transportmetrica B: Transport Dynamics,  Vol. 8(1), pp. 108-128. https://doi.org/10.1080/21680566.2020.1721379
  12. Devaraj, A., G.A. Ramakrishnan, G.S. Nair, K.K. Srinivasan, C.R. Bhat, A.R. Pinjari, G. Ramadurai, Pendyala, R.M. (2020). Joint Model of App-Based Ridehailing Adoption, Intensity of Use and Intermediate Public Transport (IPT) Consideration among Workers in Chennai City. Transportation Research Record: Journal of the Transportation Research Board.  Vol. 2674(4), pp. 152-164. https://doi.org/10.1177%2F0361198120912237
  13. Dias, F.F., P.S. Lavieri, S. Sharda, S. Khoeini, C.R. Bhat, R.M. Pendyala, A.R. Pinjari, G. Ramadurai, Srinivasan, K.K. (2020). A comparison of online and in-person activity engagement: The case of shopping and eating meals. Transportation Research Part C,  Vol. 114, pp. 643-656. https://doi.org/10.1016/j.trc.2020.02.023
  14. Momtaz, S.U., N. Eluru, S. Anowar, N. Keenya, B. Dey, A.R. Pinjari, F. Tabatabaee (2020). Fusing Freight Analysis Framework and Transearch Data: An Econometric Data Fusion Approach with Application to Florida. ASCE Journal of Transportation Engineering, Part A: Systems.  146(2). https://doi.org/10.1061/JTEPBS.0000294
  15. Chen, T.T., Sze, N.N., Saxena, S., Pinjari, A.R., Bhat, C.R., and Bai,L. (2020). Evaluation of penalty and enforcement strategies to combat speeding offences among professional drivers: A Hong Kong stated preference experiment Accident Analysis & Prevention, Vol. 135, 105366 https://doi.org/10.1016/j.aap.2019.105366
  16. Rambha, T., Nozick, L.K., and Davidson, R. (2021). Modeling Hurricane Evacuation Behavior using a Dynamic Discrete Choice Framework. Transportation Research Part B: Methodological, 150, pp.75-100. https://doi.org/10.1016/j.aap.2019.105366
  17. Rambha, T., Nozick, L.K., Davidson, R., Yi, W. and Yang, K. (2021).A stochastic optimization model for staged hospital evacuation during hurricanes. Transportation Research Part E: Logistics and Transportation Review, 151, p.102321. https://doi.org/10.1016/j.aap.2019.105366
  18. Simmhan, Y., Rambha, T., Khochare, A., Ramesh, S., Baranawal, A., George, J.V., Bhope, R.A., Namtirtha, A., Sundararajan, A., Bhargav, S.S. and Thakkar, N., (2020). GoCoronaGo: Privacy Respecting Contact Tracing for COVID-19 Management. Journal of the Indian Institute of Science, pp.1-24. https://doi.org/10.1016/j.aap.2019.105366
  19. Menon, N., Zhang, Y., Pinjari, A.R., & Mannering, F. (2020). A statistical analysis of consumer perceptions towards automated vehicles and their intended adoption. Transportation Planning and Technology, 43, 253-278. https://doi.org/10.1080/03081060.2020.1735740
  20. Tahlyan, D., & Pinjari, A. R. (2020). Performance evaluation of choice set generation algorithms for analysing truck route choice: insights from spatial aggregation for the breadth first search link elimination (BFS-LE) algorithm. Transportmetrica A: Transport Science, 16(3), 1030-1061. https://doi.org/10.1080/23249935.2020.1725790
  21. Zhao, D., Balusu, S. K., Sheela, P. V., Li, X., Pinjari, A. R., & Eluru, N. (2020). Weight-categorized truck flow estimation: A data-fusion approach and a Florida case study. Transportation Research Part E: Logistics and Transportation Review, 136, 101890. https://doi.org/10.1016/j.tre.2020.101890
  22. Tahlyan, D., Balusu, S. K., Sheela, P. V., Maness, M., & Pinjari, A. R. (2020). An empirical assessment of the impact of incorporating attitudinal variables on model transferability. In K.G. Goulias & A.W. Davis (Eds.). Mapping the Travel Behavior Genome (pp. 145-165). Elsevier. https://doi.org/10.1016/B978-0-12-817340-4.00009-7
  23. Mohan, R., & Ramadurai, G. (2020). Field data application of a non-lane-based multi-class traffic flow model. IET Intelligent Transport Systems 14(7), 657-667. http://dx.doi.org/10.1049/iet-its.2019.0583
  24. Nath, R. B., & Rambha, T. (2019). Modelling Methods for Planning and Operation of Bike-Sharing Systems. Journal of the Indian Institute of Science, 99(4), 621-645. https://doi.org/10.1007/s41745-019-00134-8
  25. Rambha, T., E., Jafari., & Boyles, S.D. (2019). Transportation Network Issues in Evacuation. In K. K. Stephens (Eds.), New Media in Times of Crisis (pp. 144-161). New York, NY: Routledge.
  26. Menon, N., Barbour, N., Zhang, Y., Pinjari, A. R., & Mannering, F. (2019). Shared autonomous vehicles and their potential impacts on household vehicle ownership: An exploratory empirical assessment. International Journal of Sustainable Transportation, 13(2), 111-122. https://doi.org/10.1080/15568318.2018.1443178
  27. Gurram, S., Stuart, A. L., & Pinjari, A. R. (2019). Agent-based modeling to estimate exposures to urban air pollution from transportation: Exposure disparities and impacts of high-resolution data. Computers, Environment and Urban Systems, 75, 22-34. https://doi.org/10.1016/j.compenvurbsys.2019.01.002
  28. Ma, J., Ye, X., & Pinjari, A. R. (2019). Practical Method to Simulate Multiple Discrete-Continuous Generalized Extreme Value Model: Application to Examine Substitution Patterns of Household Transportation Expenditures. Transportation Research Record, 2673(8), 145-156. https://doi.org/10.1177/0361198119842819
  29. Pinjari, A. R. (2019). Recent Advances in Transportation Research. Journal of the Indian Institute of Science, 99(4), 549-551. https://doi.org/10.1007/s41745-019-00136-6
  30. Mohan, R. (2019). Multi-class AR model: comparison with microsimulation model for traffic flow variables at network level of interest and the two-dimensional formulation. International Journal of Modeling and Simulation. https://doi.org/10.1080/02286203.2019.1675015
  31. Mohan, R., & Ramadurai, G. (2019). Numerical Study with Field Data for Macroscopic Continuum Modelling of Indian Traffic. Transportation in Developing Economies, 5(2), 16. https://doi.org/10.1007/s40890-019-0081-9
  32. Balusu, S. K., Pinjari, A. R., Mannering, F. L., & Eluru, N. (2018). Non-decreasing threshold variances in mixed generalized ordered response models: A negative correlations approach to variance reduction. Analytic Methods in Accident Research, 20, 46-67. https://doi.org/10.1016/j.amar.2018.09.003
  33. Mayakuntla, S. K., & Verma, A. (2018). A novel methodology for construction of driving cycles for Indian cities. Transportation Research Part D: Transport and Environment, 65, 725–735. https://doi.org/10.1016/j.trd.2018.10.013
  34. Munigety, C. R. (2018). A spring-mass-damper system dynamics-based driver-vehicle integrated model for representing heterogeneous traffic. International Journal of Modern Physics B, 32(11). https://doi.org/10.1142/S0217979218501357
  35. Munigety, C. R. (2018). Modelling behavioural interactions of drivers’ in mixed traffic conditions. Journal of Traffic and Transportation Engineering, 5(4), 284-295. https://doi.org/10.1016/j.jtte.2017.12.002
  36. Rahul, T. M., & Verma, A. (2017). The influence of stratification by motor-vehicle ownership on the impact of built environment factors in Indian cities. Journal of Transport Geography, 58, 40–51. https://doi.org/10.1016/j.jtrangeo.2016.11.008
  37. Rahul, T. M., & Verma, A. (2018). Sustainability analysis of pedestrian and cycling infrastructure – A case study for Bangalore. Case Studies on Transport Policy. https://doi.org/10.1016/j.cstp.2018.06.001
  38. Rambha, T., Boyles, S. D., Unnikrishnan, A., & Stone, P. (2018). Marginal cost pricing for system optimal traffic assignment with recourse under supply-side uncertainty. Transportation Research Part B: Methodological, 110, 104–121. https://doi.org/10.1016/j.trb.2018.02.008
  39. Sharon, G., Albert, M., Rambha, T., Boyles, S., & Stone, P. (2018). Traffic Optimization for a Mixture of Self-Interested and Compliant Agents. In AAAI Conference on Artificial Intelligence. https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16414
  40. Verma, A., Raturi, V., & Kanimozhee, S. (2018). Urban Transit Technology Selection for Many-to-Many Travel Demand Using Social Welfare Optimization Approach. Journal of Urban Planning and Development, 144(1), 4017021. https://doi.org/10.1061/(asce)up.1943-5444.0000409
  41. Verma, A., Tahlyan, D., & Bhusari, S. (2018). Agent based simulation model for improving passenger service time at Bangalore airport. Case Studies on Transport Policy. https://doi.org/10.1016/j.cstp.2018.03.001


Conference Publications



  1. Agarwal, P. and T. Rambha. (2021). An Empirical Analysis of the Effect of Travel Time Variability on Transit Routing. International Conference on COMmunication Systems & NETworkS, Bangalore, India.
  2. Rambha, T., M. Albert, G. Sharon, S. D. Boyles, and P. Stone. (2019). Identifying Compliant Users Needed for Social Optimum Routing in Traffic Networks. TRISTAN X, Hamilton Island, Australia.
  3. Singh, N. and T. Rambha. (2019). Offline Optimization of Cab Supply for Ride-Sharing Applications using Hypergraph Matching. 15th World Conference on Transport Research, Mumbai, India.
  4. Kushwaha, V., Pinjari, A.R., and Sundaresan R. (2021). Evaluating the Benefit of Collaboration between Rideshare and Transit Service Providers COMSNETS 2021: International Conference on COMmunication Systems & NETworkS
  5. Biswas, M., Pinjari, A.R., and Ghosh, S. (2020). A Choice Modeling Framework with Stochastic Variables and Random Coefficients COMSNETS 2020: International Conference on COMmunication Systems & NETworkS
  6. Nirmale, S.K., and Pinjari, A.R. (2020). Discrete-Continuous Choice Framework to Model Driver Behaviour in Heterogeneous Traffic Conditions COMSNETS 2020: International Conference on COMmunication Systems & NETworkS
  7. Nirmale, S.K., Pinjari, A.R., and Munigety, C. R. (2019). A copula-based joint multinomial discrete-continuous choice framework to model driver behaviour in mixed traffic conditions WCTR 2019: World Conference on Transport Research
  8. Nirmale, S.K., and Pinjari, A.R. (2020). Driver Behaviour Models with Perception Errors: A Choice Modelling Framework with Stochastic Variables TRB 2020: Annual Meeting of the Transportation Research Board
  9. Saxena, S., Pinjari, A.R., Roy, A., and Paleti, R (2021). Multiple Discrete-Continuous Choice Models with Bounds on Consumption: Application to Episode-level Activity Participation and Time use Analysis. TRB 2021: Annual Meeting of the Transportation Research Board
  10. Saxena, S., Pinjari, A.R., and Paleti, R (2020). A Multiple Discrete-Continuous Modelling Framework for Disaggregate Activity Participation and Time-Use Analysis TRB 2020: Annual Meeting of the Transportation Research Board
  11. Saxena, S., Pinjari, A.R., and Paleti, R (2019). Multiple Discrete Continuous Choice Models with Conditional Constraints on Budget Allocations: An Application to Disaggregate Time-Use Analysis. ICMC 2019: International Choice Modelling Conference
  12. Pellegrini, A., Saxena, S., Pinjari, A.R., and, Dekker, T. (2019). Alternative non-additively separable utility functions for random utility maximization-based multiple discrete continuous models. ICMC 2019: International Choice Modelling Conference
  13. Saxena, S., Pinjari, A.R., Paleti, R., and Tahlyan, D. (2019). A Rank Ordered Logit Multivariate Count Data Framework for Analysing Route Choice Portfolios WCTR 2019: World Conference on Transport Research
  14. Banerjee, I., Kala, J. V., Bhat, T.M., Pinjari, A.R. (2019). Transit ridership forecasting models: design considerations and a case study for Bangalore [Accepted for presentation] 5th Conference of Transportation Research Group of India, CTRG, Bhopal, India.
  15. Shankari, K., Yedavalli P., Rashidi, T.H., Banerjee, I. (2019). e-mission: a platform for reproducible and extensible human travel data collection. World Conference on Transport Research, WCTR 2019, Mumbai.
  16. Mohan, R., & Gupta, R. K. (2020). Multi-class DTA framework for non-lane-based traffic scenario. [Accepted for presentation] 8th International Symposium on Dynamic Traffic assignment, University of Washington, Seattle.
  17. Mohan, R. (2019). Development of dynamic traffic assignment framework for heterogeneous traffic lacking lane discipline. 5th Conference of Transportation Research Group of India, Bhopal, India.
  18. Mohan, R., & Ramadurai, G. (2019). Field data application of a non-lane based multi-class traffic flow model. 15th World Conference on Transport Research, Mumbai, India.
  19. Mohan, R., Eldhose, S., & Manoharan, G. (2019). Choice of applicability of VISSIM at network level in heterogeneous traffic scenario. 15th World Conference on Transport Research, Mumbai, India.
  20. Mohan, R., & Ramadurai, G. (2019). Multi-class Traffic Flow Model Based on Three-Dimensional Flow-Concentration Surface (No. 19-04534). 98th Annual meeting of the Transportation Research Board, Washington DC.
  21. Rambha, T., Nozick, L., & Davidson, R. (2019). Modeling Departure Time Decisions During Hurricanes Using a Dynamic Discrete Choice Framework. Transportation Research Board Annual Meeting. (No. 19-06045)
  22. Nirmale, S. K., Pinjari, A. R., & Biswas, M. (2019). Multi-stimuli driver behaviour models with perception errors: An integrated latent variable and discrete-continuous framework with empirical applications to heterogeneous and homogeneous traffic conditions. International Choice Modelling Conference 2019.
  23. Biswas, M., Pinjari, A. R., & Dubey, S. K. (2019). Travel time variability and route choice: An integrated modelling framework. 11th International Conference on Communication Systems & Networks (COMSNETS) (pp. 737-742). IEEE.
  24. Gurram, S., A.L. Stuart, & Pinjari, A.R. (2018). Impacts of Transit-Oriented Compact-Growth on Air Pollutant Concentrations and Exposures in the Tampa Region. 7th International Conference on Innovations in Travel Modeling, Atlanta.
  25. Munigety, C.R., & Naidu, Y. K. (2018). A driver-vehicle integrated model using car-following and engine dynamics. 97th Annual Meeting of Transportation Research Board, Washington D.C., USA.
  26. Munigety, C.R., & Vishnoi, S.C. (2018). A hybrid socio-physical system-based driver behavioral model for representing traffic dynamics. 97th Annual Meeting of Transportation Research Board, Washington D.C., USA.
  27. Munigety, C.R., Ramesh, A. K., & Vishnoi, S.C. (2018). A multi-regime car-following model for representing vehicle-type dependent driving behavior in mixed traffic. 97th Annual Meeting of Transportation Research Board, Washington D.C., USA.
  28. Nirmale, S., & Pinjari, A.R. (2018). Influence Zone, Multi-Stimuli, and Two-Dimensional (IZMS-2D) Driving Behavior in Heterogenous Traffic Conditions: An Econometric Framework and Exploratory Analysis of Driving Behaviours in India. 15th International Conference of Travel Behaviour Research, Santa Barbara.
  29. Tahlyan, D., Sheela, P.V., Maness, M., & Pinjari, A.R. (2018). Improving the Spatial Transferability of Travel Demand Forecasting Models: An Empirical Assessment of the Impact of Incorporating Attitudes on Model Transferability. 7th International Conference on Innovations in Travel Modeling, Atlanta.

Reports


  1. Centre for infrastructure, Sustainable Transport, and Urban Planning. (2019) "Development of a Traffic Modelling Framework for Analysis of Strategies Aimed at Decongesting Phase I, Electronics City, Bangalore." Electronics City Industrial Township Authority