Centre for infrastructure, Sustainable Transportation and Urban Planning
Indian Institute of Science (IISc), Bangalore
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Journal Publications and Book Chapters

  1. Banerjee, I., Deepa, L., and Pinjari A.R. (2020) "Public Transit Ridership Forecasting Models." Encyclopedia of Transportation. edited by Roger Vickerman et al. Forthcoming
  2. 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
  3. 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
  4. 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, 8(1), 108-128. https://doi.org/10.1080/21680566.2020.1721379
  5. Chen, T., Sze, N. N., Saxena, S., Pinjari, A. R., Bhat, C. R., & 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, 135, 105366. https://doi.org/10.1016/j.aap.2019.105366
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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.
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. Menon, N., Barbour, N., Zhang, Y., Pinjari, A. R., & Mannering, F. (2018). Shared autonomous vehicles and their potential impacts on household vehicle ownership: An exploratory empirical assessment. Forthcoming, International Journal of Sustainable Transportation. https://doi.org/10.1080/15568318.2018.1443178
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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. Banerjee, I., Kala, J. V., Bhat, T.M., Pinjari, A.R. (2019, December 18-21). "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.
  2. Shankari, K., Yedavalli P., Rashidi, T.H., Banerjee, I. (2019, May 26-31). e-mission: a platform for reproducible and extensible human travel data collection. World Conference on Transport Research, WCTR 2019 Mumbai.
  3. Mohan, R., & Gupta, R. K. (2020). Multi-class DTA framework for non-lane-based traffic scenario. Accepted for presentation at the 8th International Symposium on Dynamic Traffic assignment, University of Washington, Seattle.
  4. Mohan, R. (2019). Development of dynamic traffic assignment framework for heterogeneous traffic lacking lane discipline. Presented at the 5th Conference of Transportation Research Group of India, Bhopal, India.
  5. Mohan, R., & Ramadurai, G. (2019). Field data application of a non-lane based multi-class traffic flow model. Presented at the 15th World Conference on Transport Research, Mumbai, India.
  6. Mohan, R., Eldhose, S., & Manoharan, G. (2019). Choice of applicability of VISSIM at network level in heterogeneous traffic scenario. Presented at the 15th World Conference on Transport Research, Mumbai, India.
  7. Mohan, R., & Ramadurai, G. (2019). Multi-class Traffic Flow Model Based on Three-Dimensional Flow-Concentration Surface (No. 19-04534). Presented at the 98th Annual meeting of the Transportation Research Board, Washington DC.
  8. 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)
  9. Saxena, S., Pinjari, A. R., & Paleti, R. (2019, March). Multiple Discrete Continuous Choice Models with Conditional Constraints on Budget Allocations: An Application to Disaggregate Time-Use Analysis. In International Choice Modelling Conference 2019.
  10. Nirmale, S. K., Pinjari, A. R., & Biswas, M. (2019, March). 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. In International Choice Modelling Conference 2019.
  11. Biswas, M., Pinjari, A. R., & Dubey, S. K. (2019, January). Travel time variability and route choice: An integrated modelling framework. In 2019 11th International Conference on Communication Systems & Networks (COMSNETS) (pp. 737-742). IEEE. https://doi.org/10.1109/COMSNETS.2019.8711185
  12. 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. Proceedings of the 7th International Conference on Innovations in Travel Modeling, Atlanta.
  13. Munigety, C.R., & Naidu, Y. K. (2018). A driver-vehicle integrated model using car-following and engine dynamics. Presented at the 97th Annual Meeting of Transportation Research Board, Washington D.C., USA.
  14. Munigety, C.R., & Vishnoi, S.C. (2018). A hybrid socio-physical system-based driver behavioral model for representing traffic dynamics. Presented at the 97th Annual Meeting of Transportation Research Board, Washington D.C., USA.
  15. 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. Presented at the 97th Annual Meeting of Transportation Research Board, Washington D.C., USA.
  16. 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. Proceedings of the15th International Conference of Travel Behaviour Research, Santa Barbara.
  17. Rambha, T., Nozick, L., & Davidson, R. (2019) Modeling departure time decisions during hurricanes during a dynamic discrete choice framework. Accepted for presentation at the 98th Annual Meeting of Transportation Research Board, Washington D.C., USA.
  18. 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. Proceedings of the 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