Travel demand and the 3Ds: Density, diversity, and design R Cervero, K Kockelman Transportation research part D: Transport and environment 2 (3), 199-219, 1997 | 5669 | 1997 |
Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations DJ Fagnant, K Kockelman Transportation Research Part A: Policy and Practice 77, 167-181, 2015 | 4044 | 2015 |
The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios DJ Fagnant, KM Kockelman Transportation Research Part C: Emerging Technologies 40, 1-13, 2014 | 1526 | 2014 |
Assessing public opinions of and interest in new vehicle technologies: An Austin perspective P Bansal, KM Kockelman, A Singh Transportation Research Part C: Emerging Technologies 67, 1-14, 2016 | 1133 | 2016 |
Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies P Bansal, KM Kockelman Transportation Research Part A: Policy and Practice 95, 49-63, 2017 | 885 | 2017 |
Travel behavior as function of accessibility, land use mixing, and land use balance: evidence from San Francisco Bay Area K Maria Kockelman Transportation research record 1607 (1), 116-125, 1997 | 846 | 1997 |
Driver injury severity: an application of ordered probit models KM Kockelman, YJ Kweon Accident Analysis & Prevention 34 (3), 313-321, 2002 | 791 | 2002 |
Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas DJ Fagnant, KM Kockelman Transportation 45, 143-158, 2018 | 715 | 2018 |
Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions TD Chen, KM Kockelman, JP Hanna Transportation Research Part A: Policy and Practice 94, 243-254, 2016 | 660 | 2016 |
Operations of shared autonomous vehicle fleet for Austin, Texas, market DJ Fagnant, KM Kockelman, P Bansal Transportation Research Record 2563 (1), 98-106, 2015 | 521 | 2015 |
A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods J Ma, KM Kockelman, P Damien Accident Analysis & Prevention 40 (3), 964-975, 2008 | 493 | 2008 |
Locating Electric Vehicle Charging Stations: Parking-Based Assignment Method for Seattle, Washington TD Chen, KM Kockelman, M Khan Transportation Research Record, 28-36, 2013 | 484* | 2013 |
Are we ready to embrace connected and self-driving vehicles? A case study of Texans P Bansal, KM Kockelman Transportation 45, 641-675, 2018 | 335 | 2018 |
Economic effects of automated vehicles LM Clements, KM Kockelman Transportation research record 2606 (1), 106-114, 2017 | 327 | 2017 |
Carsharing’s life-cycle impacts on energy use and greenhouse gas emissions TD Chen, KM Kockelman Transportation Research Part D: Transport and Environment 47, 276-284, 2016 | 318 | 2016 |
The propagation of uncertainty through travel demand models: an exploratory analysis Y Zhao, KM Kockelman The Annals of regional science 36, 145-163, 2002 | 305 | 2002 |
Credit-based congestion pricing: a policy proposal and the public’s response KM Kockelman, S Kalmanje Transportation Research Part A: Policy and Practice 39 (7-9), 671-690, 2005 | 299 | 2005 |
Analysis of large truck crash severity using heteroskedastic ordered probit models JD Lemp, KM Kockelman, A Unnikrishnan Accident Analysis & Prevention 43 (1), 370-380, 2011 | 280 | 2011 |
Overall injury risk to different drivers: combining exposure, frequency, and severity models YJ Kweon, KM Kockelman Accident Analysis & Prevention 35 (4), 441-450, 2003 | 279 | 2003 |
A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application MW Levin, KM Kockelman, SD Boyles, T Li Computers, Environment and Urban Systems 64, 373-383, 2017 | 278 | 2017 |