Comparative analysis of trip generation models: results using home-based work trips in the Seoul metropolitan area

Figures & data

1. Conventional approaches and their alternatives

Table 2. Number of households by trip rate

	Data in 2006			Data in 2002
Trip rate	Total	Estimation set	Validation set	Validation set
0	43 521 (20·57)	30 455 (20·57)	13 066 (20·59)	29 237 (18·38)
1	115 358 (54·53)	80 738 (54·52)	34 620 (54·54)	91 139 (57·30)
2	43 714 (20·66)	30 616 (20·67)	13 098 (20·64)	31 504 (19·81)
3	7630 (3·61)	5337 (3·60)	2293 (3·61)	5961 (3·75)
4	1234 (0·58)	872 (0·59)	362 (0·57)	1126 (0·71)
5 or more	107 (0·05)	73 (0·05)	34 (0·05)	101 (0·06)
Total	211 564 (100·00)	148 091 (100·00)	63 473 (100·00)	159 068 (100·00)
Average	1·1130	1·0928	1·0920	1·0926
Standard deviation	0·7702	0·7804	0·7799	0·7802

The number in parentheses refers to the percentage; return-home trips are excluded since origin-destination-based data are only available in 2002 while production-attraction-based data are also built in 2006.

Table 1. Advantages and limitations of the cross-validation methods

	Advantages	Limitations
Hold-out method	Easy to apply	Little reliable rule for the sample classification
K-fold method	Known to involve reasonable levels of errors	Little reliable rule for determining the number K
	Acceptable computational needs
Leave-one-out method	Minimal errors incurred in principle	Too much computational burden

Table 5. Estimation results for regression, tobit, Poisson, and ordered logit models

	Regression	Tobit	Poisson	Ordered logit
Constant	0·0098	−0·3352 (−0·3073)***	−0·9518 (−1·0383)***	−1·8605***
Number of employees	0·5394***	0·6166 (0·5652)***	0·4208 (0·4591)***	1·9776***
Household income	0·0947***	0·1303 (0·1194)***	0·1004 (0·1095)***	0·3307***
Car ownership	0·0072*	0·0323 (0·0296)***	0·0276 (0·0301)***	0·0469***
Population density	0·0022***	0·0031 (0·0028)***	0·0030 (0·0033)***	0·0063***
Annual expenditures per capita per area	0·3014***	0·2972 (0·2724)***	0·2878 (0·3139)**	0·9318***
Number of subway lines	0·0091***	0·0101 (0·0093)***	0·0068 (0·0075)	0·0314***
Number of companies per area	0·0551***	0·074 (0·0678)***	0·0432 (0·0471)***	0·1880***
σ		0·7248 (0·6644)***
Scale factor for marginal effects		0·9167	1·0909
θ1				3·5167***
θ2				6·4983***
θ3				9·0627***
Sample size	148 018	148 018	148 018	148 018
Estimator	OLS	MLE	MLE	MLE
F-statistic	68 367·6***
		75 740·6***	29 981·8***	77 142·6***
R^2	0·4005
ρ^2		0·1765	0·0828	0·2302

* 10% significance level; ** 5% significance level; *** 1% significance level; is the likelihood ratio statistic where L(0) is the value of the log likelihood function when all parameters are zero and is that at its maximum.

Table 3. Independent variables considered

	Variable type	Variables considered
Household	Household composition	Number of telecommuters
characteristics		Number of employees
		Number of household members
		Number of preschool children
	Income level	Household income
		House ownership
	Transportation	Car ownership
		Number of cars owned
		Subway availability
		Number of household members with driving licenses
Regional	Demographics	Total population
characteristics		Population density
		Population moved in
		Population moved out
	Regional economy	Annual expenditures
		Annual expenditures per capita
		Annual expenditures per capita per area
		Annual tax revenues
		Annual local tax revenues
		Annual local tax per capita
		Fiscal self-reliance ratio
	Transport system	Number of car registered
		Number of car registered per capita
		Number of subway stations
		Number of subway lines
		Number of bus routes
		Road density
	Land-use	Gross area
		Residential area
		Commercial area
		Industrial area
		Green area
		Number of employees
		Number of employees per area
		Number of companies
		Number of companies per area

Table 4. The specification for regression, tobit, Poisson, and ordered logit models

Variable	Specification
Dependent variable
Trip rates	Number of trips per household
Independent variables
Number of employees	Number of employees per household
Household income	1 if household income is below 1000 K won
	2 if household income ranges 1000–2000 K won
	3 if household income ranges 2000–3000 K won
	4 if household income is over 3000 K won
Car ownership	1 if a household has cars; 0 otherwise
Population density	1000 persons per square kilometer
Annual expenditures per capita per area	1000 K won per person per square kilometer
Number of subway lines	Number of lines within walking distance
Number of companies per area	1000 companies per square kilometer

Table 6. Marginal effects of ordered logit model

Independent variable	q = 0	q = 1	q = 2	q = 3	q = 4
Number of employees	−0·20249	−0·09550	0·27576	0·02048	0·00175
Household income	−0·03386	−0·01597	0·04611	0·00342	0·00029
Car ownership	−0·00480	−0·00227	0·00654	0·00049	0·00004
Population density	−0·00065	−0·00031	0·00088	0·00007	0·00001
Annual expenditures per capita per area	−0·09540	−0·04500	0·12993	0·00965	0·00082
Number of subway lines	−0·00322	−0·00152	0·00438	0·00033	0·00003
Number of companies per area	−0·01925	−0·00908	0·02621	0·00195	0·00017

Table 7. Average daily commuting trips per household by cross-classification

Population density	Household income	Number of employees
		0	1	2	3 or more
Low	0–1000 K won	0·26	0·47	0·76	1·37
	1000–2000 K won	0·69	0·76	1·27	1·52
	2000–3000 K won	0·87	0·84	1·48	1·98
	3000 K won or more	0·90	0·86	1·58	2·27
Medium	0–1000 K won	0·08	0·73	1·35	1·83
	1000–2000 K won	0·26	0·85	1·54	2·11
	2000–3000 K won	0·32	0·89	1·61	2·22
	3000 K won or more	0·41	0·90	1·66	2·39
High	0–1000 K won	0·11	0·74	1·36	1·93
	1000–2000 K won	0·33	0·85	1·53	2·18
	2000–3000 K won	0·39	0·91	1·60	2·33
	3000 K won or more	0·47	0·92	1·69	2·51

Table 8. The result of one-way ANOVAs for the multiple classification analysis

Candidate variable	Stratification	F-statistic
Number of employees	0, 1, 2, and 3 or more	31541·922***
Household income	0–1000 K won, 1000–3000 K won, and 3000 K won or more	4647·556***
Car ownership	0 and 1 or more	1306·429***
Population density	Low (0–5000), medium (5000–18 000), and high (18 000 or more)	722·164***
Annual expenditures per capita per area	0–100, 100–200, 200–300, and 300 or more	58249·731***
Number of subway lines	0, 1, 2, 3, and 4	33·762***
Number of companies per area	0–500, 500–1000, and 1000 or more	285·863***

Population density, annual expenditures per capita per area, and number of companies per area are represented by persons per square kilometer, 1000 won per capita, and companies per square kilometer, respectively; and *** 1% significance level.

Table 9. Average daily commuting trips per household by multiple classification

Population density	Household income	Number of employees
		0	1	2	3 or more
Low	0–1000 K won	0·07	0·10	0·69	1·18
	1000–3000 K won	0·52	0·54	1·13	1·63
	3000 K won or more	0·66	0·68	1·27	1·77
Medium	0–1000 K won	0·18	0·73	1·45	2·09
	1000–3000 K won	0·36	0·92	1·64	2·27
	3000 K won or more	0·43	0·99	1·71	2·34
High	0–1000 K won	0·31	0·82	1·53	2·25
	1000–3000 K won	0·50	1·00	1·71	2·44
	3000 K won or more	0·59	1·10	1·81	2·53
F-statistic	3237·538***
R^2	0·434

Table 10. The result of validation

	Regression	Tobit	Poisson	Ordered logit	Category	MCA
Cross-validation
RMSE	0·6787	0·6808	0·6865	0·6812	0·6423	0·6466
% RMSE	62·16%	62·34%	62·86%	62·38%	58·82%	59·22%
Correlation coefficient	0·5405	0·5402	0·4899	0·5471	0·6107	0·5975
Coincidence ratio	0·7531	0·7407	0·5640	0·7713	0·7500	0·7430
Back-casting
RMSE	0·6126	0·6391	0·6548	0·6499	0·6456	0·6478
% RMSE	55·04%	57·42%	58·84%	58·39%	58·01%	58·21%
Correlation coefficient	0·6241	0·6037	0·5389	0·5764	0·5761	0·5704
Coincidence ratio	0·6885	0·6805	0·5387	0·6971	0·8188	0·8134