Exploring the Role of Socio-Economic and Built Environment Driving Factors in Shaping the Commuting Modal Share: A Path-Analysis-Based Approach

Soufiane Boukarta; Ewa Berezowska-Azzag

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Exploring the Role of Socio-Economic and Built Environment Driving Factors in Shaping the Commuting Modal Share: A Path-Analysis-Based Approach

Soufiane Boukarta

Ewa Berezowska-Azzag

| 04 dic 2020

Quaestiones Geographicae

Volumen 39 (2020): Edición 4 (December 2020)

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Publicado en línea: 04 dic 2020

Páginas: 87 - 107

Recibido: 16 jun 2020

DOI: https://doi.org/10.2478/quageo-2020-0032

Palabras clave
commuting, questionnaire, sensitivity analysis, path analysis, Djelfa (Algeria)

© 2020 Soufiane Boukarta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 3.0 Public License.

Location of the city of Djelfa on highlands band (Benslimane et al. 2009).Source: own elaboration.

Conceptual model explaining the need of commuting.Source: own study.

Maximum values (B standardised) for the impact of the drivers related to: (a) – socio-economic characteristics of households, (b) – the built environment. P – positive; N – negative.Source: own compilation.

Study area and types of housings in the municipality of Djelfa. 1 – Very compact individual housing, 2 – Identical individual housing, 3 – Individual housing of housing estate type, 4 – Discontinuous collective housing, 5 – High continuous collective housing, and 6 – Village housing. The discontinuous line represents the study area.Source: own study.

Comparison between survey's and census data.Source: own compilation.

Mesh representing home-to-work trips. Green colour – homes; yellow colour – workplaces; grey arrows – the main directions for commuting. Markings are carried out in Google earth (2015).Source: own study.

Hypothetical causal pathways between variables and modal share.Source: own compilation.

Hypothetical causal pathways between variables and the modal share of the car.Source: own compilation.

Hypothetical causal pathways between variables and the modal share of walking and transit.Source: own compilation.

Path analysis of the modal share of the car.Source: own compilation.

Path analysis of the modal share of public transit.Source: own compilation.

Path analysis of the modal share of walking.Source: own compilation.

Synthesis of the effects of the built environment and socio-economic drivers in shaping the modal share of the car, public transit and walking.Source: own compilation.

Number of studies selected per country.

Country	Algeria	Belgium	Canada China	Denmark	international	Norway	Netherlands	UK	USA
Number of studies	1	2	1 6	1	2	1	3	2	9

Presentation of the papers chosen for the literature review.

Authors	Country	Period	Data sources	Study scale	Sensitivity analysis method	Sample size	Mobility type			Drivers		Explanatory power of modal
Authors	Country	Period	Data sources	Study scale	Sensitivity analysis method	Sample size	C	S	L	SE	BE	Explanatory power of modal
Acker and Witlox (2010)	Belgium	2000–2001	Survey on behaviour of travellers in Ghent on people aged 18 and over	City	Structural equation modelling	2,500 households	X	X	X	X	X	R² = 20.1%
Breheny (1995)	Wales, UK	1961–1991	Aggregated data from Ecotec project (1993)	National	Interpolation from data from Ecotec project (1993)	Ecotec project sample (1993)	X	X	X		X	–
Brownstone and Golob (2008)	California, USA	2001	National Household Transportation Survey. Aggregate data	National	Structural equation modelling	2,079 households	X	X	X	X	X	R² = 0.37 and 0.42
Calabrese et al. (2012)	Massachusetts, USA	2011	Deducted by detecting signal of mobile phones carried out by AirSag	Metropolitan area	Multiple linear regression	1,101 households	X	X	X	X	X	R² = 49.40% and 56.48%
Newman and Kenworthy (1989)	32 cities of different countries	1980	Collection of fuel consumption data and calculation of density excluding rural areas. Urban planning agency of different countries. Aggregate data	City	Bivariate correlation analysis	32 cities	X	X	X	X	X	/
Cervero and Murakami (2010)	USA	2003	Data collected from Highway Statistics. Department of Commerce	National	Structural equation modelling	370 urban areas	X	X	X		X	CFI (>0:900) 0.969NFI (>0:950) 0.961NNFI (>0:900) 0.942
Cervero and Radisch (1995)	USA	1990–1991	Bay Area Travel questionnaire survey	Neighbourhood	Binary logistic regression	2 neighbourhoods: 620 households for non-commuting and 840 households for commuting	X	X	X	X	X	Pseudo R² = 0.29, Predicted cases = 88.6%
Chen et al. (2007)	NY, USA	1997/1998	Household survey	Metropolitan area	Structural equation modelling	2,089 trips	X			X	X	R² = 0.45 and 0.58
Dargay (2004)	UK	1970–1995	Survey of family spending	National	Semi-logistic regression	256 pseudo panels	X	X	X	X	X	R² = 0.989
Dieleman et al. (2002)	Netherlands	1996	National Mobility Survey in Netherlands	National	Multinomial logistic regression	70,000 households	X	X	X	X	X	R² = 0.31
Ding et al. (2017)	Baltimore, USA	2001	Household survey	Metropolitan area	Structural equation modelling	3,519 households	X			X	X	/
Feng et al. (2013)	China and Netherlands	2008	Household survey on mobility in both countries	City	Multiple linear regression	2,989 respondents for 10 districts in China and 1,322 respondents for Randstad	X	X	X	X	X	China: R² = 0.115Randstad: R² = 0.124
Handy et al. (2005)	California, USA	2003	e-mail questionnaire carried out on 8 neighbourhoods	District in metropolitan area	Linear regression	1,466 respondents				X	X	R² = 0.16R² adjusted = 154
Holden and Norland (2005)	Oslo, Norway	2003	Questionnaire distributed by mail	Regional	Linear regression	650 for daily trips, 778 for leisure travel, and <100 respondents per zone (eight zones selected for the study)	X		X	X	X	R² = 0.231 for commuting
Karathodorou et al. (2010)	42 countries	1995	Millennium Cities Database for Sustainable Transport (1999) for 100 countries and car occupancy from Mobility in Cities database (2006)	Cities	Linear regression	84 cities	X		X	X	X	R² = 0.61
Khan et al. (2014)	Seattle, USA	2006	Questionnaire/Puget Sound Regional Council	Metropolitan area	Regression modelling	10,510 respondents of 4,741 households	X		X	X	X	/
Kitamura et al. (1997) Attitude is very important more than the others.	San Francisco, USA	1994	Questionnaire. And land use information is obtained from Metropolitan Transportation Commission	Neighbourhood	Multiple linear regression	5 neighbourhoods, 640 respondents	X		X	X	X	R² = 0.2125
Limtanakool et al. (2006)	Netherlands	1996	National Mobility Survey conducted by telephone interview and questionnaire	Regional	Binary logistic regression	Commuting:2,326Shopping: 932Leisure: 3,072	X	X	X	X	X
Ma et al. (2014)	China	2007	Questionnaire	Neighbourhood	Logistic regression	60 households, 699 trips of 10 neighbourhoods	X	X	X	X	X	Pseudo R² = 0.16
Manaugh et al. (2009)	Montréal, Canada	2003	Origin-destination survey	Neighbourhood	Linear regression	17,000 trips	X			X	X	SE: R² = 0.06; SE+BE modal: R²=0.40
Marique (2013)	Belgium	2001	2001 Socio-economic survey	National	Multiple linear regression	966,247 respondents	X			X	X	R² = 0.457
Næss (2010)	Hangzhou, China	2005	Qualitative interview and questionnaire in 40 urban areas	Urban zone	Multiple linear regression	28 interviews3,150 questionnaire respondents	X			X	X	R² = 0.189
Naess (2014)	Hangzhou, China and Copenhagen, Denmark	2005	Interview and questionnaire	Regional	Linear regression	1,932 questionnaires for Copenhagen and 3,150 for Hangzhou	X			X	X	Copenhagen:R² = 0.233Hangzhou: = 095
Pan et al. (2009)	Shanghai, China	2001	Questionnaire	Neighbourhood	Multiple logistic regression	1,709 respondents in 4 neighbourhoods	X		X	X	X	Pseudo R² = 0.2714
Zhang et al. (2014)	Zhongshan, China	2010	Questionnaire	Neighbourhood	Linear regression	25,618 respondents	X	X	X	X	X	Pseudo R² = 0.2823
Baouni et al. (2013)	Algiers, Algeria	2013	Questionnaire	Regional	Bivariate correlation	175 respondents						–
Bakour (2016)	Algiers, Algeria	2004	Household survey conducted by an organisation	City	Linear regression	1,200 respondents	X					R² = 0.5–0.9

Descriptive statistics.

Variables	N	Type	Min	Max	AVG	Std dev
	Accessibility
Outward journey time (min.)	175	Continuous	2	200	20.59	18.26
Home-to-work distance (m)	162	Continuous	50	18,000	2,206.91	1,966.96
Number of bus rotations	150	Continuous	0	3	1.20	0.556
	Density
Plot ratio^*	139	Continuous	0.17	1.00	0.59	0.32
Built density^*	139	Continuous	0.60	3.20	2.02	0.74
	Design
Distance to centre (m)^*	148	Continuous	17.59	2,659.18	1,219.54	696.57
Distance from national road (m)^*	151	Continuous	25.62	2,569.23	1,163.83	629.94
Average number of floors (n)^*	139	Continuous	2.00	6.00	3.79	1.08
Block's area (m²)^*	139	Continuous	770	36,061	7,398.83	9,150.32
Housing type (1: collective, 2: individual)	184	Nominal	1	2	1.54	0.50
	Distance to public transit
Distance to public transit (housing zone) (0: < 300, 4: > 1 km)	173	Ordinal	1.00	4.00	2.3237	0.98
Distance to public transit (work zone) (0: < 300, 4: > 1 km)	172	Ordinal	0.00	4.00	1.6919	0.97
Bus frequency	184	Continuous	0.00	5.00	2.4620	1.56
	Diversity
Mixed use index (from 5 to 40)^*	175	Continuous	12	36	24.23	5.38
	Households’ socio-economic characteristics
Household's average age^*	42	Continuous	16.33	43.80	27.1681	8.44
Respondent age	120	Continuous	27	70	43.95	10.82
Round trip frequency	172	Continuous	1	4	1.66	0.51
Household's education level	46	Continuous	2.00	5.00	3.5230	0.77
Respondent's education level	174	Ordinal	0	4	3.26	1.06
Number of cars owned	184	Continuous	0	2	0.60	0.57
Profession (1: public, 2: liberal)	181	Nominal	1	3	1.19	0.52
Income (from 15,000 to +60,000 Da)	181	Ordinal	1	4	2.15	0.95
Occupancy rate per housing	139	Continuous	2	12	5.34	1.87
	Modal share
Public transit (1: TC, 0: others)	184	Nominal	0.00	1.00	0.31	0.46
Car (1: vehicle, 0: others)	184	Nominal	0.00	1.00	0.26	0.44
Walking (1: MAP, 0: others)	184	Nominal	0.00	1.00	0.42	0.49

Models fit indices.

Modal share	df	χ²	Probability level (> 0.05)	NFI(> 0.9 or > 0.95)^***	CFI(> or closer to 0.95)^***
Car	59	39.35	0.977	0.954	1.000
Transit	34	21.20	0.957	0.964	1.000
Walking	34	28.03	0.755	0.949	1.000

Projection of explanatory variables for the modal share of walking and transit.

	Axes
	1	2	3	4	5	6	7
Home-to-work distance	−0.019	0.787	−0.122	−0.079	0.198	−0.252	−0.022
Outward journey time	−0.113	0.032	0.058	0.046	0.837	0.305	−0.062
Number of bus rotations	−0.018	0.318	−0.114	0.053	0.827	−0.231	0.002
Built density	0.935	0.027	−0.003	0.043	−0.061	−0.022	−0.020
Plot ratio	0.974	−0.022	0.014	0.109	−0.012	−0.010	−0.005
Housing type	0.885	0.016	0.088	0.239	−0.103	0.076	0.141
Distance from national road	−0.167	0.779	−0.135	0.146	−0.067	0.308	0.053
Distance to centre	−0.447	0.676	−0.160	0.133	−0.017	0.394	0.044
Block's area	−0.576	0.201	0.029	−0.114	0.079	0.479	−0.063
Average number of floors	−0.874	0.283	0.035	−0.085	−0.012	0.028	0.017
Mixed use index	−0.406	−0.220	0.007	−0.565	−0.095	0.189	0.128
Distance to public transit (housing)	0.147	−0.003	0.068	0.715	0.324	−0.075	−0.273
Bus frequency	0.228	−0.092	0.045	0.773	−0.193	−0.062	0.124
Distance to public transit (working zone)	−0.279	−0.391	−0.170	0.478	0.403	0.177	0.244
Profession	−0.009	−0.220	0.024	0.093	−0.037	−0.524	0.122
Respondent age	−0.091	−0.173	0.855	0.141	−0.026	0.201	−0.119
Respondent's education level	−0.046	0.141	−0.733	−0.085	0.090	0.399	−0.075
Income	0.001	−0.111	0.470	−0.102	0.058	0.609	0.470
Number of cars owned	0.098	0.213	0.004	−0.077	0.069	0.019	0.880
Occupancy rate per housing	0.070	0.100	0.752	−0.094	0.022	0.063	−0.039
Walking's modal share	0.009	−0.724	−0.100	0.132	−0.198	−0.238	0.025
Transit's modal share	0.018	0.382	0.179	−0.021	0.221	0.245	−0.695

Projection of variables in PCA space.

	Axes
	1	2	3	4	5	6	7
Home-to-work distance	0.253	0.651	0.074	0.036	−0.263	0.131	−0.394
Outward journey time	0.259	0.181	−0.391	0.505	0.218	−0.499	−0.085
Number of bus rotations	0.168	0.536	−0.396	0.274	0.096	−0.298	−0.495
Built density (COS)	−0.833	0.295	0.187	0.056	−0.158	−0.183	0.075
Plot ratio (CES)	−0.889	0.295	0.145	0.113	−0.110	−0.193	0.083
Housing type	−0.829	0.258	0.246	0.212	0.021	−0.004	0.153
Distance from national road	0.436	0.597	0.174	0.274	−0.304	0.220	0.222
Distance to centre	0.677	0.450	0.081	0.245	−0.177	0.211	0.276
Block's area	0.709	−0.057	0.018	0.214	−0.053	−0.065	0.237
Average number of floors	0.870	−0.115	−0.057	−0.004	−0.041	0.253	−0.093
Mixed use index	0.452	−0.424	0.283	−0.226	0.074	−0.292	0.021
Distance to public transit (housing)	−0.297	0.202	−0.629	0.344	0.098	0.237	0.055
Bus frequency	−0.459	0.063	−0.197	0.165	0.291	0.571	0.248
Distance to public transit (work zone)	0.131	−0.044	−0.395	0.194	0.707	0.015	0.165
Profession	−0.218	−0.182	−0.114	−0.279	0.270	0.263	−0.352
Respondent age	−0.065	−0.596	−0.026	0.654	−0.217	0.119	−0.025
Respondent's education level	0.287	0.471	−0.041	−0.318	0.148	−0.292	0.465
Income	0.096	−0.308	0.483	0.596	0.274	−0.180	0.201
Number of cars owned	0.032	0.234	0.616	0.161	0.532	0.070	−0.204
Occupancy rate per housing	−0.079	−0.327	0.165	0.551	−0.331	0.016	−0.230
Car's modal share	0.194	0.334	0.633	0.059	0.398	0.177	−0.232

Results of Pearson's bivariate correlation.

	Car		Walking		Public transit
	Correlation	Sig	Correlation	Sig.	Correlation	Sig
	Accessibility
Home-to-work distance (m)	0.247^**	0.002	−0.407^**	0.000	0.214^**	0.006
Outward journey time (min.)	−0.100	0.187	−0.308^**	0.000	0.431^**	0.000
Number of bus rotations	0.128	0.118	−0.329^**	0.000	0.237^**	0.004
	Density
Built density (COS)	−0.071	0.408	0.087	0.306	−0.026	0.757
Plot ratio (CES)	−0.037	0.662	0.082	0.336	−0.052	0.546
	Design
Housing type	0.021	0.802	0.035	0.673	−0.055	0.501
Distance from national road	0.205^*	0.011	−0.287^**	0.000	0.111	0.175
Distance to centre	0.149	0.072	−0.336^**	0.000	0.212^**	0.010
Block's area	0.140	0.100	−0.280^**	0.001	0.164	0.054
Average number of floors	0.047	0.584	−0.119	0.162	0.082	0.339
	Diversity
Mixed use index	−0.069	0.362	0.126	0.096	−0.064	0.399
	Distance to public transit
Distance to public transit (housing)	−0.125	0.100	0.008	0.914	0.099	0.194
Bus frequency	−0.070	0.343	0.113	0.126	−0.051	0.492
Distance to public transit (work zone)	0.045	0.559	−0.010	0.898	−0.031	0.687
	Households’ socio-economic characteristics
Profession	0.198^**	0.008	−0.115	0.123	−0.060	0.421
Respondent age	−0.086	0.349	0.158	0.084	−0.079	0.388
Respondent's education level	−0.004	0.962	−0.037	0.628	0.079	0.302
Income	0.204^**	0.006	−0.086	0.247	−0.124	0.097
Number of cars owned	0.459^**	0.000	−0.182^*	0.013	−0.267^**	0.000
Occupancy rate per housing	−0.086	0.310	0.077	0.368	−0.004	0.967

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Exploring the Role of Socio-Economic and Built Environment Driving Factors in Shaping the Commuting Modal Share: A Path-Analysis-Based Approach

Publicado en línea: 04 dic 2020

Páginas: 87 - 107

Recibido: 16 jun 2020

DOI: https://doi.org/10.2478/quageo-2020-0032

Palabras clave
commuting, questionnaire, sensitivity analysis, path analysis, Djelfa (Algeria)

© 2020 Soufiane Boukarta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 3.0 Public License.

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Number of studies selected per country.

Presentation of the papers chosen for the literature review.

Descriptive statistics.

Models fit indices.

Projection of explanatory variables for the modal share of walking and transit.

Projection of variables in PCA space.

Results of Pearson's bivariate correlation.

Exploring the Role of Socio-Economic and Built Environment Driving Factors in Shaping the Commuting Modal Share: A Path-Analysis-Based Approach

Publicado en línea: 04 dic 2020

Páginas: 87 - 107

Recibido: 16 jun 2020

DOI: https://doi.org/10.2478/quageo-2020-0032

Palabras clavecommuting, questionnaire, sensitivity analysis, path analysis, Djelfa (Algeria)

© 2020 Soufiane Boukarta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 3.0 Public License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Fig. 14

Fig. 15

Number of studies selected per country.

Presentation of the papers chosen for the literature review.

Descriptive statistics.

Models fit indices.

Projection of explanatory variables for the modal share of walking and transit.

Projection of variables in PCA space.

Results of Pearson's bivariate correlation.

Palabras clave
commuting, questionnaire, sensitivity analysis, path analysis, Djelfa (Algeria)