(*student or postdoc trainee at the time of work)

2025

• Pena-Bastidas*, J., Liu, J., Jones, S. and Lee, H.Y., 2025. The Role of Emerging Mobility Solutions in Shaping Care-Seeking Behaviors in Rural Communities: A National Survey with Stated Choice Experiment. Transport Policy, 162, pp. 313-324. https://doi.org/10.1016/j.tranpol.2024.12.016

• Zhao, D, Xu*, N., and Liu, J., 2025. Robotaxis in China: Firsthand perspectives of traffic law enforcement officers from Chinese cities with operational fleets. Journal of Intelligent Transportation Systems. https://doi.org/10.1080/15472450.2025.2497503

• Xu*, N., Liu, J., Zhang*, Z., and Jones, S., 2025. How do motorists' pre-crash behaviors contribute to the injury severity of police officers? Using interpretable machine learning to untangle the behavioral pathways in crashes involving police vehicle. Forthcoming in Journal of Safety Research.

• Yang*, C., Liu, J., Zhang*, Z., Adanu, E., Penmetsa, P., and Jones, S., 2025. A Machine Learning Approach to Understanding the Road and Traffic Environments of Crashes Involving Driver Distraction and Inattention (DDI) on Rural Multilane Highways. Journal of Safety Research, 92, pp.14-26. https://doi.org/10.1016/j.jsr.2024.11.011

• Sanni*, T., Chen, J., Shao, W., Liu, J. and Shi, Y., 2025. Investigating the Feasibility of Adopting Virtual Reality as a Method for Natural Hazard Risk Communication. International Journal of Disaster Risk Reduction. p.105296. https://doi.org/10.1016/j.ijdrr.2025.105296

• Bhuiya*, M.M.R, Liu, J., Jones, S. and Nie, Q., 2025. Is There Any Association of Local Characteristics with Traffic Signal and Stop Sign Violation Induced Crashes? A hierarchical modeling based study from Alabama, USA. Case Studies on Transport Policy, p.101390. https://doi.org/10.1016/j.cstp.2025.101390

• Islam, R., Adanu, E., Liu, J. and Jones, S., 2025. The potential of using SAVs in evacuating vulnerable population during tornado early warning: A case study of Tuscaloosa County, Alabama. Transportation Planning and Technology, pp.1-23. https://doi.org/10.1080/03081060.2024.2445646

2024

• Pena-Bastidas*, J., Liu, J. and Jones, S., 2024. Measuring Traffic Speed Change After the Reallocation of Road Space for Cycling: A Data-Driven Analysis for Bogotá. Cities, 153, p.105296. https://doi.org/10.1016/j.cities.2024.105296

• Zhang*, Z., Liu, J., Bastidas, J.P. and Jones, S., 2024. Charging infrastructure assessment for shared autonomous electric vehicles in 374 small and medium-sized urban areas: An agent-based simulation approach. Transport Policy, 155, pp.58-78. https://doi.org/10.1016/j.tranpol.2024.06.017

• Zhang*, Z., Xu, N., Liu, J. and Jones, S., 2024. Exploring spatial heterogeneity in factors associated with injury severity in speeding-related crashes: An integrated machine learning and spatial modeling approach. Accident Analysis & Prevention, 206, p.107697. https://doi.org/10.1016/j.aap.2024.107697

• Zhang*, Z., Liu, J., Nie, Q. and Jones, S., 2024. Shared low-speed autonomous vehicles for short-distance trips: agent-based modeling with mode choice analysis. Transportation Planning and Technology, pp.1-29. https://doi.org/10.1080/03081060.2024.2373322

• Yang*, C., Liu, J., Zhang*, Z., Adanu, E., Penmetsa, P., and Jones, S., 2024. A Machine Learning Approach to Understanding the Road and Traffic Environments of Crashes Involving Driver Distraction and Inattention (DDI) on Rural Multilane Highways. Journal of Safety Research, in-press. 

• Xu*, N., Liu, J., Zhang*, Z. and Jones, S., 2024. Injury severity of police officers involved in traffic crashes: A spatial analysis of Alabama. Safety science, 172, p.106406. https://doi.org/10.1016/j.ssci.2023.106406

• Xu*, N., Nie*, Q., Liu, J., and Jones, S., 2024. Linking short- and long-term impacts of the COVID-19 pandemic on travel behavior and travel preferences in Alabama: A machine learning-supported path analysis. Forthcoming in Transport Policy. 

• Zhang*, Z., Nie*, Q., Liu, J., Hainen, A., Islam*, N. and Yang*, C., 2024. Machine learning based real-time prediction of freeway crash risk using crowdsourced probe vehicle data. Journal of Intelligent Transportation Systems, 28(1), pp.84-102.. https://doi.org/10.1080/15472450.2022.2106564

2023

• Yang*, C., Liu, J., Li*, X. and Barnett, T., 2023. Analysis of First Responder-Involved Traffic Incidents by Mining News Reports. Accident Analysis & Prevention, 192, p.107261. https://doi.org/10.1016/j.aap.2023.107261

• Liu, J., Xu*, N., Shi, Y., Rahman, M., Barnett, T. and Jones, S., 2023. Do First Responders Trust Connected and Automated Vehicles? A National Survey. Transport Policy, 140, pp.85-99. https://doi.org/10.1016/j.tranpol.2023.06.012​

• Liu, J., Fu*, X., Hainen, A., Yang*, C., Villavicencio, L. and Horrey, W.J., 2023. Evaluating the impacts of vehicle-mounted Variable Message Signs on passing vehicles: implications for protecting roadside incident and service personnel. Journal of Intelligent Transportation Systems, pp.1-21. http://dx.doi.org/10.1080/15472450.2023.2227968

• Xu*, N., Nie*, Q., Liu, J. and Jones, S., 2023. Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data. Travel Behaviour and Society, 32, p.100584. https://doi.org/10.1016/j.tbs.2023.100584

• Fu*, X., Liu, J., Huang, Z., Hainen, A. and Khattak, A.J., 2023. LSTM-based lane change prediction using Waymo open motion dataset: The role of vehicle operating space. Digital Transportation and Safety 2(2):1−12. https://doi.org/10.48130/DTS-2023-0009

• Liu, J., Xu*, N., Shi, Y., Barnett, T. and Jones, S., 2023. Are first responders prepared for electric vehicle fires? A national survey. Accident Analysis & Prevention, 179, p.106903. https://doi.org/10.1016/j.aap.2022.106903

• Zhang*, Z., Liu, J., Li*, X., Fu*, X., Yang*, C. and Jones, S., 2023. Localizing safety performance functions for two-way STOP-controlled (TWST) three-leg intersections on rural two-lane two-way (TLTW) roadways in Alabama: A geospatial modeling approach with clustering analysis. Accident Analysis & Prevention, 179, p.106896. https://doi.org/10.1016/j.aap.2022.106896

• Bullard, C., Jones, S., Adanu, E.K., and Liu, J., 2023. Crash severity analysis of single-vehicle rollover crashes in Namibia: A mixed logit approach. IATSS Research, 47(3), pp.318-324.

• Okafor, S., Liu, J., Adanu, E.K. and Jones, S., 2023. Behavioral pathway analysis of pedestrian injury severity in pedestrian-motor vehicle crashes. Transportation research interdisciplinary perspectives, 18, p.100777. https://doi.org/10.1016/j.trip.2023.100777

• Adanu, E.K., Lidbe, A., Liu, J. and Jones, S., 2023. A comparative study of factors associated with motorcycle crash severities under different causal scenarios. Journal of Transportation Safety & Security, 15(4), pp.376-396.

2022

• Fu*, X., Nie*, Q., Li*, X., Liu, J., Nambisan, S. and Jones, S., 2022. The role of the built environment in emergency medical services delays in responding to traffic crashes.  Journal of transportation engineering, Part A: Systems, 148(10), p.04022085. https://ascelibrary.org/doi/full/10.1061/JTEPBS.0000726

• Agyemang, W., Adanu, E.K., Liu, J. and Jones, S., 2022. A latent class multinomial logit analysis of factors associated with pedestrian injury severity of inter-urban highway crashes. Journal of Transportation Safety & Security, pp.1-21. https://doi.org/10.1080/19439962.2022.2153952

• Lu*, W., Liu, J., Fu*, X., Yang, J., Jones, S., 2022. Integrating machine learning into path analysis for quantifying behavioral pathways in bicycle-motor vehicle crashes. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2022.106622

• Fu*, X., Liu, J., Jones, S., Barnett, T., and Khattak, A., 2022. From the past to the future: Modeling the temporal instability of safety performance functions. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2022.106592

• Fu*, X., Nie*, Q., Liu, J., Zhang, Z. and Jones, S., 2022. How do college students perceive future shared mobility with autonomous Vehicles? A survey of the University of Alabama students. International Journal of Transportation Science and Technology, 11(2), pp.189-204. https://doi.org/10.1016/j.ijtst.2021.11.006

• Wali, B., Khattak, A.J. and Liu, J., 2022. Heterogeneity assessment in incident duration modelling: Implications for development of practical strategies for small & large scale incidents. Journal of Intelligent Transportation Systems, 26(5), pp.586-601.vhttps://doi.org/10.1080/15472450.2021.1944135

• Fu*, X., Nie*, Q., Liu, J., Khattak, A., Hainen, A. and Nambisan, S., 2022. Constructing spatiotemporal driving volatility profiles for connected and automated vehicles in existing highway networks. Journal of Intelligent Transportation Systems, 26(5), pp.572-585. https://doi.org/10.1080/15472450.2021.1944133

• Li*, X., Hu, Q., Liu, J., Nambisan, S., Khattak, A., Lidbe, A., Lee, H. Y., 2022. Pathway analysis of relationships among community development, active travel behavior, body mass index, and self-rated health. International Journal of Sustainable Transportation, 16(4), pp.340-356. https://doi.org/10.1080/15568318.2021.1872123

2021

• Liu, J., Jones, S. L., Adanu, E., Li*, X., 2021. Behavioral pathways in bicycle-motor vehicle crashes: From contributing factors, pre-crash actions, to injury severities. Journal of Safety Research. https://doi.org/10.1016/j.jsr.2021.02.015

• Adanu, E., Hu, Q., Liu, J. and Jones, S., 2021. Better rested than sorry: data-driven approach to reducing drowsy driving crashes on interstates. Journal of transportation engineering, Part A: Systems, 147(10), p.04021067. https://doi.org/10.1061/JTEPBS.0000569

• Mohammadnazar, A., Mahdinia, I., Ahmad, N., Khattak, A.J. and Liu, J., 2021. Understanding how relationships between crash frequency and correlates vary for multilane rural highways: Estimating geographically and temporally weighted regression models. Accident Analysis & Prevention, p.106146. https://doi.org/10.1016/j.aap.2021.106146

• Hu, Q., Li*, X., Liu, J. and Adanu, E.K., 2021. A low-cost approach to identify hazard curvature for local road networks using open-source data. Transportation Research Interdisciplinary Perspectives, 10, p.100393. https://doi.org/10.1016/j.trip.2021.100393

• Zhang*, Z., Liu, J., Li*, X., Khattak, A., 2021. Do Larger Sample Sizes Increase the Reliability of Traffic Incident Duration Models? A Case Study of East Tennessee Incidents. Transportation Research Record. https://doi.org/10.1177/0361198121992063

• Adanu, E.K., Li*, X., Liu, J. and Jones, S., 2021. An Analysis of the Effects of Crash Factors and Precrash Actions on Side Impact Crashes at Unsignalized Intersections. Journal of Advanced Transportation. https://doi.org/10.1155/2021/6648523

• Li*, X., Liu, J., Yang*, C. and Barnett, T., 2021. Bayesian approach to developing context-based crash modification factors for medians on rural four-lane roadways. Transportation research record, 2675(9), pp.1316-1330.

• Li*, X., Penmetsa, P., Liu, J., Hainen, A. M., Nambisan, S., 2021. Severity of emergency natural gas distribution pipeline incidents: Application of an integrated spatio-temporal approach fused with text mining. Journal of Loss Prevention in the Process Industries, 69. https://doi.org/10.1016/j.jlp.2020.104383

• Li*, X., Liu, J., Zhang*, Z., Parrish, A. S., Jones, S. L., 2021. A spatiotemporal analysis of motorcyclist injury severity: findings from 20 years of crash data from Pennsylvania. Accident Analysis & Prevention, 151. https://doi.org/10.1016/j.aap.2020.105952

2020

​

• Liu, J., Li*, X. and Khattak, A.J., 2020. An integrated spatio-temporal approach to examine the consequences of driving under the influence (DUI) in crashes. Accident Analysis & Prevention, 146, p.105742. https://doi.org/10.1016/j.aap.2020.105742

• Liu, J. and Khattak, A., 2020. Informed decision-making by integrating historical on-road driving performance data in high-resolution maps for connected and automated vehicles. Journal of Intelligent Transportation Systems, 24(1), pp.11-23.

• Liu, J., Khattak, A.J., Li*, X., Nie, Q.* and Ling, Z., 2020. Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates. Journal of Safety Research, 73, pp.25-35. DOI: 10.1016/j.jsr.2020.02.006

• Liu, J., Khattak, A., Han, L. and Yuan, Q., 2020. How much information is lost when sampling driving behavior data? Indicators to quantify the extent of information loss. Journal of Intelligent and Connected Vehicles. Vol. 3 No. 1, pp. 17-29.

• Liu, J., Jones, S. and Adanu, E.., 2020. Challenging human driver taxis with shared autonomous vehicles: a case study of Chicago. Transportation Letters, 12(10), pp.701-705.

• Liu, J., Nambisan, S., Li*, X. and Fu*, X., 2020. Are young Americans carless across the United States? A spatial analysis. Transportation research part D: transport and environment, 78, p.102197.

• Zhai, G., Yang, H. and Liu, J., 2020. Is the front passenger seat always the “death seat”? An application of a hierarchical ordered probit model for occupant injury severity. International journal of injury control and safety promotion, 27(4), pp.438-446.

• Lu*, W., Liu, J., Mao, J., Hu, G., Gao, C. and Liu, L., 2020. Macroscopic Fundamental Diagram Approach to Evaluating the Performance of Regional Traffic Controls. Transportation Research Record, p.0361198120923359.

• Li*, X., Liu, J., Khattak, A. and Nambisan, S., 2020. Sequential prediction for large-scale traffic incident duration: application and comparison of survival models. Transportation research record, 2674(1), pp.79-93.

2019

• Liu, J., Khattak, A.J., Li*, X. & Fu*, X., 2019. A spatial analysis of the ownership of alternative fuel and hybrid vehicles. Transportation Research Part D: Transport and Environment, 77, pp.106-119. DOI: 10.1016/j.trd.2019.10.018

• Liu, J., Hainen, A., Li, X., Nie, Q. and Nambisan, S., 2019. Pedestrian injury severity in motor vehicle crashes: an integrated spatio-temporal modeling approach. Accident Analysis & Prevention, 132, p.105272. DOI:10.1016/j.aap.2019.105272.

• Rios-Torres, J., Liu, J. and Khattak, A., 2019. Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization. International Journal of Sustainable Transportation, 13(2), pp.123-137. DOI: 10.1080/15568318.2018.1445321

• Wali, B., Khattak, A.J., Greene, D.L. and Liu, J., 2019. Fuel economy gaps within and across garages: A bivariate random parameters seemingly unrelated regression approach. International Journal of Sustainable Transportation, 13(5), pp.324-339. DOI: ​10.1080/15568318.2018.1466222

​2018

• Liu, J. & Khattak, A., 2018. Are gates at rail grade crossings always safe? Examining motorist gate-violation behaviors using path analysis. Transportation Research Part F: Traffic Psychology and Behaviour, 55, pp.314-324. DOI: 10.1016/j.trf.2018.03.014

• Liu, J., Khattak, A.J., Chen, C., Wan, D., Ma, J. and Hu, J., 2018. Revisiting hit-and-run crashes: a geo-spatial modeling method. Transportation Research Record, 2672(38), pp.81-92. DOI: 10.1177/0361198118773889.

• Wali, B., Greene, D.L., Khattak, A.J. and Liu, J., 2018. Analyzing within garage fuel economy gaps to support vehicle purchasing decisions–A copula-based modeling & forecasting approach. Transportation Research Part D: Transport and Environment, 63, pp.186-208. DOI: 10.1016/j.trd.2018.04.023

• Zhang, M., Khattak, A.J., Liu, J. and Clarke, D., 2018. A comparative study of rail-pedestrian trespassing crash injury severity between highway-rail grade crossings and non-crossings. Accident Analysis & Prevention, 117, pp.427-438. DOI: 10.1016/j.aap.2018.02.001

• Loeb, B., Kockelman, K.M. and Liu, J., 2018. Shared autonomous electric vehicle (SAEV) operations across the Austin, Texas network with charging infrastructure decisions. Transportation Research Part C: Emerging Technologies, 89, pp.222-233.Transportation Research Part C: Emerging Technologies, 89, 222-233. DOI: 10.1016/j.trc.2018.01.019.

2017​

• Liu, J., Kockelman, K.M., Boesch, P.M. and Ciari, F., 2017. Tracking a system of shared autonomous vehicles across the Austin, Texas network using agent-based simulation. Transportation, 44(6), pp.1261-1278. DOI: 10.1007/s11116-017-9811-1.

• Liu, J., Khattak, A.J. and Wali, B., 2017. Do safety performance functions used for predicting crash frequency vary across space? Applying geographically weighted regressions to account for spatial heterogeneity. Accident Analysis & Prevention, 109, pp.132-142. DOI: 10.1016/j.aap.2017.10.012.

• Liu, J., Khattak, A. and Wang, X., 2017. A comparative study of driving performance in metropolitan regions using large-scale vehicle trajectory data: Implications for sustainable cities. International Journal of Sustainable Transportation, 11(3), pp.170-185. DOI: 10.1080/15568318.2016.1230803.

• Greene, D.L., Liu, J., Khattak, A.J., Wali, B., Hopson, J.L. and Goeltz, R., 2017. How does on-road fuel economy vary with vehicle cumulative mileage and daily use?. Transportation Research Part D: Transport and Environment, 55, pp.142-161. DOI: 10.1016/j.trd.2017.06.004.

• Liu, J. & Khattak, A., 2017. Gate-Violation Behavior at Highway-Rail Grade Crossings and the Consequences: Using Geo-Spatial Modeling Integrated with Path Analysis. Accident Analysis & Prevention, 109, pp.99-112. DOI: 10.​1016/​j.​aap.​2017.​10.​010.

• Greene, D.L., Khattak, A.J., Liu, J., Wang, X., Hopson, J.L. and Goeltz, R., 2017. What is the evidence concerning the gap between on-road and Environmental Protection Agency fuel economy ratings?. Transport Policy, 53, pp.146-160. DOI: 10.1016/j.tranpol.2016.10.002. The work in this paper was reported by Reuters, available at: http://www.reuters.com/article/usa-mileage-idUSL1N1212XV20151001  

2016​

• Liu, J. and Khattak, A.J., 2016. Delivering improved alerts, warnings, and control assistance using basic safety messages transmitted between connected vehicles. Transportation Research Part C: Emerging Technologies, 68, pp.83-100. DOI: 10.1016/j.trc.2016.03.009.

• Liu, J., Wang, X. and Khattak, A., 2016. Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users. Transportation Research Part C: Emerging Technologies, 67, pp.280-298. DOI: 10.1016/j.trc.2016.02.016.

• Liu, J., Khattak, A. and Zhang, M., 2016. What role do precrash driver actions play in work zone crashes?: Application of hierarchical models to crash data. Transportation Research Record, 2555(1), pp.1-11. DOI: 10.3141/2555-01.

• Wan, D., Kamga, C., Liu, J., Sugiura, A. and Beaton, E.B., 2016. Rider perception of a “light” bus rapid transit system-The New York City select bus service. Transport Policy, 49, pp.41-55. DOI: 10.1016/j.tranpol.2016.04.001.

• Liu, J., Bartnik, B., Richards, S.H. and Khattak, A.J., 2016. Driver behavior at highway–rail grade crossings with passive traffic controls: A driving simulator study. Journal of Transportation Safety & Security, 8(sup1), pp.37-55. DOI: 10.1080/19439962.2015.1043478.

• Liu, J., Wang, X., Khattak, A.J., Hu, J., Cui, J. and Ma, J., 2016. How big data serves for freight safety management at highway-rail grade crossings? A spatial approach fused with path analysis. Neurocomputing, 181, pp.38-52. DOI: 10.1016/j.neucom.2015.08.098.

• Wang, X., Liu, J., Khattak, A.J. and Clarke, D., 2016. Non-crossing rail-trespassing crashes in the past decade: A spatial approach to analyzing injury severity. Safety Science, 82, pp.44-55. DOI: 10.1016/j.ssci.2015.08.017.

• Khattak, A.J., Liu, J., Wali, B., Li, X. and Ng, M., 2016. Modeling traffic incident duration using quantile regression. Transportation Research Record, 2554(1), pp.139-148.. DOI: 10.3141/2554-15.

​2015​

• Liu, J., Khattak, A.J., Richards, S.H. and Nambisan, S., 2015. What are the differences in driver injury outcomes at highway-rail grade crossings? Untangling the role of pre-crash behaviors. Accident Analysis & Prevention, 85, pp.157-169. DOI: 10.1016/j.aap.2015.09.004.

• Liu, J., Khattak, A. and Wang, X., 2015. The role of alternative fuel vehicles: Using behavioral and sensor data to model hierarchies in travel. Transportation Research Part C: Emerging Technologies, 55, pp.379-392. DOI: 10.1016/j.trc.2015.01.028.

• Wang, X., Khattak, A.J., Liu, J., Masghati-Amoli, G. and Son, S., 2015. What is the level of volatility in instantaneous driving decisions?. Transportation Research Part C: Emerging Technologies, 58, pp.413-427. DOI: 10.1016/j.trc.2014.12.014.