Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo

Greedy function approximation: A gradient boosting machine

Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works

Classification and Regression Trees.

Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,834 · see citing works

Classification and Regression Trees.

Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,028 · see citing works

A working guide to boosted regression trees

Elith, J., Leathwick, J. R., & Hastie, T. (2008) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2008.01390.x · Cited by 6,484 · see citing works

Travel and the Built Environment

Ewing, R., & Cervero, R. (2010) · Journal of the American Planning Association · doi:10.1080/01944361003766766 · Cited by 5,421 · see citing works

Travel demand and the 3Ds: Density, diversity, and design

Cervero, R., & Kockelman, K. M. (1997) · Transportation Research Part D Transport and Environment · doi:10.1016/s1361-9209(97)00009-6 · Cited by 4,601 · see citing works

The Elements of Statistical Learning

Hastie, T. J., Tibshirani, R. J., & Friedman, J. H. (2009) · Springer series in statistics · doi:10.1007/b94608 · Cited by 4,111 · see citing works

Central Places in Southern Germany

Berry, B. J. I., Baskin, C. W., & Christaller, W. (1967) · Economic Geography · doi:10.2307/143299 · Cited by 2,528 · see citing works

Travel and the Built Environment: A Synthesis

Ewing, R., & Cervero, R. (2001) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1780-10 · Cited by 1,884 · see citing works

Is Los Angeles-Style Sprawl Desirable?

Ewing, R. (1997) · Journal of the American Planning Association · doi:10.1080/01944369708975728 · Cited by 1,330 · see citing works

BOOSTED TREES FOR ECOLOGICAL MODELING AND PREDICTION

De’ath, G. (2007) · Ecology · doi:10.1890/0012-9658(2007)88[243:btfema]2.0.co;2 · Cited by 1,293 · see citing works

A gradient boosting method to improve travel time prediction

Zhang, Y., & Haghani, A. E. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.02.019 · Cited by 844 · see citing works

Methodologies for exploring the link between urban form and travel behavior

Handy, S. (1996) · Transportation Research Part D Transport and Environment · doi:10.1016/s1361-9209(96)00010-7 · Cited by 568 · see citing works

The impact of residential density on vehicle usage and energy consumption

Brownstone, D. M., & Golob, T. F. (2008) · Journal of Urban Economics · doi:10.1016/j.jue.2008.09.002 · Cited by 554 · see citing works

Three Challenges for the Compact City as a Sustainable Urban Form: Household Consumption of Energy and Transport in Eight Residential Areas in the Greater Oslo Region

Holden, E., & Norland, I. T. (2005) · Urban Studies · doi:10.1080/00420980500332064 · Cited by 518 · see citing works

Growing cooler evidence on urban development and climate change

Ewing, R. (2008) · Cited by 467 · see citing works

Does Compact Development Make People Drive Less?

Stevens, M. R. (2016) · Journal of the American Planning Association · doi:10.1080/01944363.2016.1240044 · Cited by 406 · see citing works

TTB or not TTB, that is the question: a review and analysis of the empirical literature on travel time (and money) budgets

Mokhtarian, P. L., & Chen, C. (2004) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2003.12.004 · Cited by 397 · see citing works

Growing Cooler: The Evidence on Urban Development and Climate Change

Handy, S., & Mokhtarian, P. L. (2008) · Journal of the American Planning Association · doi:10.1080/01944360802540364 · Cited by 372 · see citing works

Sub-centring and Commuting: Evidence from the San Francisco Bay Area, 1980-90

Cervero, R., & Wu, K. (1998) · Urban Studies · doi:10.1080/0042098984484 · Cited by 319 · see citing works

Location Efficiency: Neighborhood and Socio-Economic Characteristics Determine Auto Ownership and Use - Studies in Chicago, Los Angeles and San Francisco

Holtzclaw, J., Clear, R. D., Dittmar, H., et al. (2002) · Transportation Planning and Technology · doi:10.1080/03081060290032033 · Cited by 290 · see citing works

Relationships between Land Use, Socioeconomic Factors, and Travel Patterns in Britain

Stead, D. (2001) · Environment and Planning B Planning and Design · doi:10.1068/b2677 · Cited by 274 · see citing works

Residential Choice, the Built Environment, and Nonwork Travel: Evidence Using New Data and Methods

Chatman, D. G. (2009) · Environment and Planning A Economy and Space · doi:10.1068/a4114 · Cited by 272 · see citing works

Prioritizing Influential Factors for Freeway Incident Clearance Time Prediction Using the Gradient Boosting Decision Trees Method

Ma, X., Ding, C., Luan, S., Wang, Y., & Wang, Y. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2635719 · Cited by 248 · see citing works

Gradient boosting trees for auto insurance loss cost modeling and prediction

Guelman, L. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.09.058 · Cited by 247 · see citing works

“Does Compact Development Make People Drive Less?” The Answer Is Yes

Ewing, R., & Cervero, R. (2017) · Journal of the American Planning Association · doi:10.1080/01944363.2016.1245112 · Cited by 226 · see citing works

Varying influences of the built environment on household travel in 15 diverse regions of the United States

Ewing, R., Tian, G., Goates, J. P., et al. (2015) · doi:10.1177/0042098014560991 · Cited by 185 · see citing works

Urban Form and Household Activity‐Travel Behavior

Buliung, R. N., & Kanaroglou, P. (2006) · Growth and Change · doi:10.1111/j.1468-2257.2006.00314.x · Cited by 175 · see citing works

Accessibility, Activity Participation and Location of Activities: Exploring the Links between Residential Location and Travel Behaviour

Næss, P. (2006) · Urban Studies · doi:10.1080/00420980500534677 · Cited by 173 · see citing works

Examining the effects of the built environment and residential self-selection on commuting trips and the related CO 2 emissions: An empirical study in Guangzhou, China

Cao, X., & Yang, W. (2017) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2017.02.003 · Cited by 170 · see citing works

Key research themes on urban space, scale, and sustainable urban mobility

van Wee, B., & Handy, S. (2014) · International Journal of Sustainable Transportation · doi:10.1080/15568318.2013.820998 · Cited by 159 · see citing works

Travel Distances in Daily Travel and Long-Distance Travel: What Role is Played by Urban Form?

Holz‐Rau, C., Scheiner, J., & Sicks, K. (2014) · Environment and Planning A Economy and Space · doi:10.1068/a4640 · Cited by 146 · see citing works

Residential location, commuting and non-work travel in two urban areas of different size and with different center structures

Næss, P., Strand, A., Wolday, F., & Stefánsdóttir, H. (2017) · Progress in Planning · doi:10.1016/j.progress.2017.10.002 · Cited by 135 · see citing works

Exploring the connections among residential location, self-selection, and driving: Propensity score matching with multiple treatments

Cao, X., Xu, Z., & Fan, Y. (2010) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2010.07.010 · Cited by 126 · see citing works

A gradient boosting logit model to investigate driver’s stop-or-run behavior at signalized intersections using high-resolution traffic data

Ding, C., Wu, X., Yu, G., et al. (2016) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2016.09.016 · Cited by 112 · see citing works

Driving and the built environment: the effect of compact development on motorized travel, energy use, and CO2 emissions

Gómez-Ibáñez, J. A., & Humphrey, N. (2010) · TR news · Cited by 111 · see citing works

Driving and the built environment : the effects of compact development on motorized travel, energy use, and CO2 emissions

Systems, E. (2009) · Transportation Research Board eBooks · Cited by 110 · see citing works

Factor complexity of crash occurrence: An empirical demonstration using boosted regression trees

Chung, Y.-S. (2012) · Accident Analysis & Prevention · doi:10.1016/j.aap.2012.08.015 · Cited by 101 · see citing works

Prioritizing Highway Safety Manual’s crash prediction variables using boosted regression trees

Saha, D., Alluri, P., & Gan, A. C. (2015) · Accident Analysis & Prevention · doi:10.1016/j.aap.2015.03.011 · Cited by 94 · see citing works

Exploring the Influences of Density on Travel Behavior Using Propensity Score Matching

Cao, X., & Fan, Y. (2012) · Environment and Planning B Planning and Design · doi:10.1068/b36168 · Cited by 94 · see citing works

Thoughts on the Meaning of Mark Stevens’s Meta-Analysis

Handy, S. (2017) · Journal of the American Planning Association · doi:10.1080/01944363.2016.1246379 · Cited by 83 · see citing works

Residential location, transport rationales and daily-life travel behaviour: The case of Hangzhou Metropolitan Area, China

Næss, P. (2012) · Progress in Planning · doi:10.1016/j.progress.2012.05.001 · Cited by 81 · see citing works

Oslo's Farewell to Urban Sprawl

Næss, P., Næss, T., & Strand, A. (2010) · European Planning Studies · doi:10.1080/09654313.2011.530395 · Cited by 78 · see citing works

Residential location, travel, and energy use in the Hangzhou Metropolitan Area

Næss, P. (2010) · Journal of Transport and Land Use · doi:10.5198/jtlu.v3i3.98 · Cited by 70 · see citing works

Heterogeneity in the relationship between the built environment and driving: Focus on neighborhood type and travel purpose

Salon, D. (2015) · Research in Transportation Economics · doi:10.1016/j.retrec.2015.10.008 · Cited by 65 · see citing works

Are Short Daily Trips Compensated by Higher Leisure Mobility?

Næss, P. (2006) · Environment and Planning B Planning and Design · doi:10.1068/b31151 · Cited by 62 · see citing works

The relationships between land use measures and travel behavior: a meta-analytic approach

Gim, T. T. (2013) · Transportation Planning and Technology · doi:10.1080/03081060.2013.818272 · Cited by 56 · see citing works

A meta-analysis of the relationship between density and travel behavior

Gim, T. T. (2011) · Transportation · doi:10.1007/s11116-011-9373-6 · Cited by 52 · see citing works

Changing Effects of Job Accessibility on Employment and Commute: A Case Study of Los Angeles

Hu, L. (2014) · The Professional Geographer · doi:10.1080/00330124.2014.886920 · Cited by 46 · see citing works

Which D’s are the important ones? The effects of regional location and density on driving distance in Oslo and Stavanger

Næss, P., Cao, X., & Strand, A. (2017) · Journal of Transport and Land Use · doi:10.5198/jtlu.2017.1183 · Cited by 39 · see citing works

Commute responses to employment decentralization: Anticipated versus actual mode choice behaviors of new town employees in Kunming, China

Yang, X. C., Day, J. E., Langford, B. C., et al. (2016) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2016.11.012 · Cited by 37 · see citing works

Driving and Compact Growth: A Careful Look in the Rearview Mirror

Knaap, G., Avin, U., & Fang, L. (2017) · Journal of the American Planning Association · doi:10.1080/01944363.2017.1251276 · Cited by 12 · see citing works

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works

Gradient Boosting Machine: A SurveyPreprint

He, Z., Lin, D., Lau, T., & Wu, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.06951 · Cited by 32 · see citing works

GBMVis: Visual Analytics for Interpreting Gradient Boosting Machine

Xia, Y., Cheng, K., Cheng, Z., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88207-5_7 · Cited by 6 · see citing works

On Application of Machine Learning Models for Prediction of Failures in Production Lines

Mokeev, V. V. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-71119-1_13 · Cited by 1 · see citing works

Proximal boosting: aggregating weak learners to minimize non-differentiable lossesPreprint

Fouillen, E., Boyer, C., & Sangnier, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.09670

Accelerated proximal boostingPreprint

Fouillen, E., Boyer, C., & Sangnier, M. (2018) · arXiv (Cornell University)