Full Random Coefficients Multilevel Modeling of the Relationship between Land Use and Trip Time on Weekdays and Weekends
Abstract
:1. Introduction
2. Empirical Relationship between Land Use and Weekend Travel
3. Multilevel Modeling
4. Data
5. Results
5.1. Null Model
5.2. Random Intercepts Model: Inserting Level-One Variables
5.3. Random Intercepts Model: Inserting Level-Two Variables
5.4. (Full) Random Coefficients Model
5.5. Level-One Variables: Individual Characteristics
5.6. Level-Two Variables: Land Use Characteristics
6. Conclusions
Acknowledgments
Conflicts of Interest
References
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Authors | Years | Data | Methods | Results |
---|---|---|---|---|
Lanzendorf | 2002 | Weekend data: two-weekend-day survey in four neighborhoods in Cologne, Germany | Binomial logistic regression and multiple linear regression | Urban form variables such as residential neighborhood and garden ownership affect automobile use for weekend leisure travel. |
Bhat and Gossen | 2004 | Weekend data: weekend subsample of the 2000 BATS (Bay Area Travel Survey) | Mixed multinomial logistic regression | Land use balance and density do not differentiate weekend recreation trips. |
Bhat and Srinivasan | 2005 | Weekend data: weekend subsample of the 2000 BATS | Mixed ordered-response logistic regression | Land use balance and density do not affect weekend non-work trips. |
Troped et al. | 2000 | Weekday and weekend data: accelerometer and GPS records for four consecutive days (including two weekend days) in Massachusetts | One model without a weekday–weekend difference dummy: multiple linear regression | Population and housing density, land use balance, and intersection density positively affect physical activity levels. |
Forsyth et al. | 2007 | Weekday and weekend data: travel diary and accelerometer records for one week in Twin Cities, Minnesota | One model without a weekday–weekend difference dummy: t-test | Residential density has a modest association with walking and physical activity. |
Cervero and Duncan | 2003 | Weekday and weekend data: entire 2000 BATS data (two days = one weekday + one weekend day) | One model with a weekday–weekend difference dummy: binomial logistic regression | Land use diversity and design factors exert a very week effect on walking and biking choices. |
Ogilvie et al. | 2008 | Weekday and weekend data: a survey in deprived neighborhoods in Glasgow, Scotland | One model with a weekday–weekend difference dummy: multinomial logistic regression | Except for local destination access, environmental variables generally have a limited effect on active travel and physical activity. |
Lin and Yu | 2011 | Weekday and weekend data: a survey of students at three elementary schools in Taipei, Taiwan | Weekday–weekend separate models: negative binomial regression and multinomial logistic regression | Among residential land use variables, land use mix has a significant effect on leisure trips and intersection and building densities on transit and non-motorized trips. |
Written et al. | 2012 | Weekday and weekend data: survey and seven-day accelerometer records in 48 neighborhoods in New Zealand | Weekday–weekend separate models: multiple linear regression | In relation to the leisure-time physical activity, destination accessibility, street connectivity, and residential density are significant both in the weekday and weekend models (the other two variables, land use mix and streetscape quality, are insignificant). |
Lee et al. | 2009 | Weekday and weekend data: SMARTRAQ (Strategies for Metropolitan Atlanta’s Regional Transportation and Air Quality) household travel survey | Weekday–weekend separate models: Tobit models | Regarding the total travel time (not just leisure travel time), its reduction is associated with housing and commercial district densities and rail proximity in the weekday model, whereas no variables are significant in the weekend model. |
Variables: Sources (Dates) | Definitions/Units | Mean | S.D. | |
---|---|---|---|---|
Group Level | ||||
Population density: Ministry of the Interior (31 December 2006) | Persons/mi2 | 60,776.341 | 34,905.569 | |
Land use balance: The Seoul Institute (2007) | Shannon entropy (0–1) | 0.632 | 0.174 | |
Road connectivity: Highway Management System (2007) | Street intersection points/mi2 | 888.368 | 516.713 | |
Subway availability: New Address System (2007) | Subway station points/mi2 | 1.549 | 1.002 | |
Individual Level * | ||||
Weekday trip time | Minutes | 37.281 | 31.499 | |
Weekend trip time | Minutes | 45.229 | 45.603 | |
Birth year | Year | 1969.486 | 16.141 | |
Household size | Household members | 3.799 | 0.990 | |
Children | Household members under six | 0.115 | 0.373 | |
Automobiles | Sedans + vans + trucks + taxis + motorbikes + others | 1.026 | 0.631 | |
Sedans/vans | Sedans + vans | 0.856 | 0.578 | |
Categories | Freq. (%) | Categories | Freq. (%) | |
Intra-neighborhood trip | Yes | 4198 (81.0) | No | 982 (19.0) |
Intra-district trip | Yes | 2839 (54.8) | No | 2341 (45.2) |
Alternative-mode trip | Yes (trip not by automobile as driver + as passenger) | 1282 (24.7) | No | 3898 (75.3) |
Job | (1) Student | 1309 (25.5) | (2) Homemaker/unemployed/under school age | 883 (17.2) |
(3) Professional/engineer | 630 (12.3) | (4) Admin/office/manager | 761 (14.8) | |
(5) Sales | 266 (5.2) | (6) Customer service | 329 (6.4) | |
(7) Agriculture/fisheries + manufacturing/transportation/general labor † | 253 (4.9) | (8) Others | 706 (13.7) | |
Housing type | (1) Condominium | 2192 (42.3) | (2) Row house | 689 (13.3) |
(3) Multi-family house | 985 (19.0) | (4) Single-family house | 1150 (22.2) | |
(5) Officetel + others † | 164 (3.2) | |||
Home ownership | (1) Ownership | 3934 (75.9) | (2) Jeonse (two-year lease) | 923 (17.8) |
(3) Tenancy | 206 (4.0) | (4) Others | 117 (2.3) | |
Income | (1) <1 million won | 291 (5.7) | (2) 1–2 million won | 1288 (25.1) |
(3) 2–3 million won | 1381 (26.9) | (4) 3–5 million won | 1791 (34.9) | |
(5) 5–10 million won | 328 (6.4) | (6) ≥10 million won | 56 (1.1) |
Null Model | Random Intercepts Model (Including Only Level-1 Variables) | Random Intercepts Model (Also Including Level-2 Variables) * | Full Random Coefficients Model * | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Coef. | p | Coef. | p | Coef. | p | Coef. | p | |||
Fixed effects | ||||||||||
INTRCPT2 (γ00) | 3.332439 | 0.000 | 3.810316 | 0.000 | 3.799161 | 0.000 | 3.795070 | 0.000 | ||
Group level (level 2) | Population density | POP2_D (γ01) ‡ | −0.000001 | 0.033 | −0.000001 | 0.024 | ||||
Land use balance | ENT (γ02) ‡ | −0.091994 | 0.193 | −0.091923 | 0.191 | |||||
Road connectivity | CNN_D (γ03) ‡ | −0.000053 | 0.031 | −0.000053 | 0.034 | |||||
Subway availability | AVL_MET_D (γ04) ‡ | 0.026379 | 0.036 | 0.027598 | 0.029 | |||||
Individual level (level 1) | Intra-neighborhood trip | TIntMi (γ10) | −0.329049 | 0.000 | −0.327185 | 0.000 | −0.321752 | 0.000 | ||
Intra-district trip | TIntMa (γ20) | −0.750560 | 0.000 | −0.747672 | 0.000 | −0.744683 | 0.000 | |||
Alternative-mode trip (not by automobile as driver + as passenger) | TModA (γ30) | 0.061358 | 0.007 | 0.063422 | 0.005 | 0.064001 | 0.005 | |||
Birth year | MBirth (γ40) † | 0.000989 | 0.241 | 0.001012 | 0.229 | 0.001149 | 0.172 | |||
Job: homemaker/unemployed/under school age | MJobRD2 (γ50) | 0.004761 | 0.896 | 0.007171 | 0.842 | 0.010878 | 0.764 | |||
Job: professional/engineer | MJobRD3 (γ60) | 0.135904 | 0.000 | 0.136189 | 0.000 | 0.135160 | 0.000 | |||
Job: administrative/office/manager | MJobRD4 (γ70) | 0.195824 | 0.000 | 0.196864 | 0.000 | 0.191045 | 0.000 | |||
Job: sales | MJobRD5 (γ80) | 0.079907 | 0.098 | 0.087049 | 0.070 | 0.085388 | 0.072 | |||
Job: customer service | MJobRD6 (γ90) | 0.004898 | 0.921 | 0.008525 | 0.863 | 0.004616 | 0.927 | |||
Job (two categories were combined): agriculture/fisheries + manufacturing/transportation/general labor | MJobRD7 (γ100) | 0.157631 | 0.001 | 0.160962 | 0.001 | 0.149816 | 0.002 | |||
Job: others | MJobRD9 (γ110) | 0.065844 | 0.087 | 0.067916 | 0.076 | 0.073028 | 0.053 | |||
Household size | HMemb (γ120) † | −0.018707 | 0.116 | −0.018402 | 0.122 | −0.016769 | 0.163 | |||
Children | HChil (γ130) † | 0.004967 | 0.859 | 0.004735 | 0.865 | 0.002827 | 0.920 | |||
Automobiles (sedans + vans + trucks + taxis + motorbikes + others) | HAuto (γ140) † | −0.029270 | 0.310 | −0.030334 | 0.291 | −0.022842 | 0.432 | |||
Sedans/vans | HPriv (γ150) † | 0.008107 | 0.803 | 0.011434 | 0.724 | 0.007560 | 0.812 | |||
Housing type: row house | HHouTypRD2 (γ160) | −0.077421 | 0.009 | −0.067014 | 0.023 | −0.062939 | 0.031 | |||
Housing type: multi−family house | HHouTypRD3 (γ170) | −0.074794 | 0.013 | −0.066457 | 0.030 | −0.070255 | 0.019 | |||
Housing type: single−family house | HHouTypRD4 (γ180) | −0.022230 | 0.383 | −0.014646 | 0.574 | −0.018184 | 0.480 | |||
Housing type (two categories were combined): officetel + others | HHouTypRD5 (γ190) | −0.021687 | 0.698 | −0.007248 | 0.899 | −0.001098 | 0.985 | |||
Home ownership: Jeonse (two-year lease) | HHouOwnD2 (γ200) | −0.050395 | 0.063 | −0.050273 | 0.065 | −0.050604 | 0.062 | |||
Home ownership: tenancy | HHouOwnD3 (γ210) | −0.051465 | 0.367 | −0.055372 | 0.335 | −0.061015 | 0.279 | |||
Home ownership: others | HHouOwnD4 (γ220) | −0.057576 | 0.264 | −0.059050 | 0.249 | −0.069941 | 0.176 | |||
Income: 1–2 million won | HIncomeD2 (γ230) | −0.119903 | 0.004 | −0.119815 | 0.004 | −0.117085 | 0.006 | |||
Income: 2–3 million won | HIncomeD3 (γ240) | −0.174290 | 0.000 | −0.177915 | 0.000 | −0.172727 | 0.000 | |||
Income: 3–5 million won | HIncomeD4 (γ250) | −0.175353 | 0.000 | −0.179371 | 0.000 | −0.176584 | 0.000 | |||
Income: 5–10 million won | HIncomeD5 (γ260) | −0.130346 | 0.017 | −0.136805 | 0.013 | −0.125636 | 0.022 | |||
Income: ≥10 million won | HIncomeD6 (γ270) | −0.144842 | 0.100 | −0.146652 | 0.094 | −0.180462 | 0.037 | |||
Random effects | ||||||||||
Level-1 variance (eij) | 0.52957 | 0.34895 | 0.34888 | 0.32834 | ||||||
Level-2 variance (u0j) | 0.06667 | 0.000 | 0.01329 | 0.000 | 0.01225 | 0.000 | 0.01404 | 0.000 | ||
MBirth variance (u4j) | 0.00003 | 0.019 | ||||||||
HMemb variance (u12j) | 0.00921 | 0.001 | ||||||||
HAuto variance (u14j) | 0.03325 | 0.000 | ||||||||
Deviance (−2LL) | 11,776.499745 | 9361.714076 | 9405.194477 | 9359.800033 | ||||||
Pseudo R12 | 0.34107 | 0.34120 | 0.37999 | |||||||
Pseudo R22 | 0.80066 | 0.81626 | 0.78941 | |||||||
Pseudo R2 | 0.39246 | 0.39432 | 0.42577 |
Null Model | Random Intercepts Model (Including Only Level-1 Variables) | Random Intercepts Model (Also Including Level-2 Variables) * | Full Random Coefficients Model * | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Coef. | p | Coef. | p | Coef. | p | Coef. | p | |||
Fixed effects | ||||||||||
INTRCPT2 (γ00) | 3.486306 | 0.000 | 3.850539 | 0.000 | 3.847121 | 0.000 | 3.849297 | 0.000 | ||
Group level (level 2) | Population density | POP2_D (γ01) ‡ | −0.000001 | 0.171 | −0.000001 | 0.105 | ||||
Land use balance | ENT (γ02) ‡ | −0.155377 | 0.088 | −0.172984 | 0.056 | |||||
Road connectivity | CNN_D (γ03) ‡ | −0.000036 | 0.240 | −0.000026 | 0.387 | |||||
Subway availability | AVL_MET_D (γ04) ‡ | −0.001697 | 0.909 | −0.002298 | 0.877 | |||||
Individual level (level 1) | Intra-neighborhood trip | TIntMi (γ10) | −0.134581 | 0.004 | −0.134039 | 0.004 | −0.121428 | 0.008 | ||
Intra-district trip | TIntMa (γ20) | −0.790325 | 0.000 | −0.791172 | 0.000 | −0.793413 | 0.000 | |||
Alternative-mode trip (not by automobile as driver + as passenger) | TModA (γ30) | −0.003038 | 0.908 | 0.000020 | 0.999 | 0.001625 | 0.949 | |||
Birth year | MBirth (γ40) † | −0.000191 | 0.839 | −0.000209 | 0.824 | 0.000035 | 0.971 | |||
Job: homemaker/unemployed/under school age | MJobRD2 (γ50) | 0.054128 | 0.155 | 0.052674 | 0.166 | 0.051406 | 0.171 | |||
Job: professional/engineer | MJobRD3 (γ60) | 0.039638 | 0.247 | 0.038902 | 0.256 | 0.035802 | 0.292 | |||
Job: administrative/office/manager | MJobRD4 (γ70) | 0.069268 | 0.037 | 0.069645 | 0.036 | 0.069300 | 0.032 | |||
Job: sales | MJobRD5 (γ80) | 0.047839 | 0.359 | 0.045265 | 0.384 | 0.046589 | 0.362 | |||
Job: customer service | MJobRD6 (γ90) | −0.001374 | 0.976 | 0.000152 | 0.997 | 0.003069 | 0.945 | |||
Job (two categories were combined): agriculture/fisheries + manufacturing/transportation/general labor | MJobRD7 (γ100) | 0.110787 | 0.023 | 0.110859 | 0.023 | 0.108292 | 0.022 | |||
Job: others | MJobRD9 (γ110) | 0.075592 | 0.061 | 0.075510 | 0.061 | 0.072786 | 0.061 | |||
Household size | HMemb (γ120) † | −0.003569 | 0.752 | −0.003543 | 0.753 | −0.001596 | 0.892 | |||
Children | HChil (γ130) † | 0.030203 | 0.437 | 0.030858 | 0.426 | 0.037577 | 0.339 | |||
Automobiles (sedans + vans + trucks + taxis + motorbikes + others) | HAuto (γ140) † | −0.038681 | 0.132 | −0.038988 | 0.129 | −0.046038 | 0.113 | |||
Sedans/vans | HPriv (γ150) † | 0.015202 | 0.664 | 0.017027 | 0.628 | 0.013277 | 0.722 | |||
Housing type: row house | HHouTypRD2 (γ160) | 0.033147 | 0.369 | 0.037794 | 0.311 | 0.043425 | 0.249 | |||
Housing type: multi-family house | HHouTypRD3 (γ170) | −0.004025 | 0.903 | 0.001160 | 0.972 | −0.004632 | 0.889 | |||
Housing type: single-family house | HHouTypRD4 (γ180) | 0.005029 | 0.865 | 0.009188 | 0.759 | 0.006970 | 0.820 | |||
Housing type (two categories were combined): officetel + others | HHouTypRD5 (γ190) | 0.083100 | 0.245 | 0.088474 | 0.219 | 0.092240 | 0.198 | |||
Home ownership: Jeonse (two-year lease) | HHouOwnD2 (γ200) | 0.068442 | 0.056 | 0.069910 | 0.051 | 0.066958 | 0.054 | |||
Home ownership: tenancy | HHouOwnD3 (γ210) | −0.044102 | 0.481 | −0.050841 | 0.424 | −0.052840 | 0.384 | |||
Home ownership: others | HHouOwnD4 (γ220) | 0.141862 | 0.030 | 0.139677 | 0.031 | 0.112346 | 0.108 | |||
Income: 1–2 million won | HIncomeD2 (γ230) | −0.064683 | 0.174 | −0.064966 | 0.173 | −0.062922 | 0.189 | |||
Income: 2–3 million won | HIncomeD3 (γ240) | −0.073149 | 0.137 | −0.075770 | 0.123 | −0.071268 | 0.151 | |||
Income: 3–5 million won | HIncomeD4 (γ250) | −0.123373 | 0.015 | −0.125815 | 0.013 | −0.133888 | 0.009 | |||
Income: 5–10 million won | HIncomeD5 (γ260) | −0.132253 | 0.028 | −0.134832 | 0.026 | −0.133813 | 0.030 | |||
Income: ≥10 million won | HIncomeD6 (γ270) | 0.043801 | 0.654 | 0.043647 | 0.657 | 0.033884 | 0.734 | |||
Random effects | ||||||||||
Level-1 variance (eij) | 0.59537 | 0.44601 | 0.44581 | 0.41696 | ||||||
Level-2 variance (u0j) | 0.06495 | 0.000 | 0.03823 | 0.000 | 0.03788 | 0.000 | 0.04059 | 0.000 | ||
MBirth variance (u4j) | 0.00004 | 0.001 | ||||||||
HMemb variance (u12j) | 0.00957 | 0.001 | ||||||||
HAuto variance (u14j) | 0.05299 | 0.000 | ||||||||
Deviance (−2LL) | 17,869.808912 | 15,532.279516 | 15,579.442605 | 15,490.205456 | ||||||
Pseudo R12 | 0.25087 | 0.25121 | 0.29966 | |||||||
Pseudo R22 | 0.41139 | 0.41678 | 0.37506 | |||||||
Pseudo R2 | 0.26666 | 0.26749 | 0.30708 |
© 2017 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gim, T.-H.T. Full Random Coefficients Multilevel Modeling of the Relationship between Land Use and Trip Time on Weekdays and Weekends. Sustainability 2017, 9, 1824. https://doi.org/10.3390/su9101824
Gim T-HT. Full Random Coefficients Multilevel Modeling of the Relationship between Land Use and Trip Time on Weekdays and Weekends. Sustainability. 2017; 9(10):1824. https://doi.org/10.3390/su9101824
Chicago/Turabian StyleGim, Tae-Hyoung Tommy. 2017. "Full Random Coefficients Multilevel Modeling of the Relationship between Land Use and Trip Time on Weekdays and Weekends" Sustainability 9, no. 10: 1824. https://doi.org/10.3390/su9101824