Effect of Land Use and Mixed-use on Traffic Congestion at Urban Intersections: Implications for Transportation Planning

Theme	Core insight	Relevance to proposed study	Citations
Baghdad congestion & intersection performance	Sever persistent at intersections LOS often E-F despite control improvement	Establishes local problem context and need to go beyond operational fixes	(Alobaydi et al., 2020) (Abdulazeez et al., 2025) (Alkaissi, 2023) (Attab et al., 2020) (Arma, 2022) (Abood et al., 2025)
Land use morphology	Uncontrolled densification and changing land uses around intersections significantly worsen volumes, delays, and LOS	Justifies explicit modelling of land-use patterns at intersection scale in Baghdad	(Alobaydi et al., 2020) (Wang et al., 2021) (Bao et al., 2022) (Song et al., 2019) (Zhang et al., 2017) (Li et al., 2020)
Regression and hybrid models in traffic prediction	MLR and multivariate regression remain baseline or embedded components alongside advanced ML for traffic forecasting	Supports use of a refined, interpretable MLR framework tailored to intersection-level congestion in data-limited settings	(Rajeh et al., 2022) (Li, 2020) (Alomari et al., 2022) (Shi et al., 2022) Deepika et al., 2024) (Bratsas et al., 2019)
Land-use mix & congestion via regression	MLR/MLSR and related models link land-use composition (e.g., commercial, residential, mixed use) to congestion indices	Provides methodological foundation for integrating land-use mix diversity into congestion models	(Wang et al., 2021) (Bao et al., 2022) (Song et al., 2019) (Zhang et al., 2017) (Li et al. 2020)

Two intersection locations of Baghdad city

Geographical view of Al-Mawal intersection

Mean ranking result of factors determining land use patterns in Baghdad city

Table 1:

Land use distribution within the study intersections zone

Intersection name	Points of attractive	Type use
Al-Mawal Intersection	Al-Mustansirya university	Education
	Al Rafidian college dentist	Education
	International school	Education
	Al Sakhra intesection	Commercial
	Nakhla intersection	Commercial
	Restaurant	Recreational
	Cafés and parks	Recreational
	Bulding housing	Residential
	Directorate of Russafaa 1^st education	Institutional administrative
	Shahriar gardens and halls	Recreational
	Al Wazereyah industrial	industrial
	Schools	Education
Al Shaab Stadium Intersection	Al Shaab stadium	Recreational
	Ministry of oil	Institutional administrative
	Al Rubaie street	Commercial
	Zayona amusement park	Recreational / commercial
	Sindbad amusement park	Recreational
	Social Security department	Recreational
	Zyona water city	Recreational
	Ministry of youth and sport	Institutional administrative
	Ministry of interior	Institutional administrative
	Schools	Education
	Building housing	Residential
	Police academy	Education

Table 2:

Parameters used in study for evaluating congestion

Condition	Parameters	SIDRA	HCM 2010
Geometric conditions	Area type	1	1
	No. of lane	Input user	Input user
	Average length width	3.5 [m/lane]	3.5 [m/lane]
	Grads	0%	0%
	Parking condition	No	No
	Volume by movement	[Pcu/hr]	[Pcu/hr]
	Ideal saturation flow rate	1950 [Pcu/hr]	1930 [Pcu/hr]
	Peak hour factor	Default	Default
	Per cent heavy vehicle	Default	Default
Traffic conditions	Local buses stopping	Al Mawal intersection: Yes Al Shaab stadium intersection: No	Yes No
	Parking activity	Al Mawal intersection: Yes Al Shaab stadium intersection: No	Yes No
	Approach speed [k/h]	Default	Default
Signalized conditions	Cycle length [sec]	138 sec at Al Mawal 150 sec at Al Sheab	138 sec at Al Mawal 150 sec at Al Shaab
	Green time [sec]	25 sec at Al Mawal 33 sec at Al Shaab	25 sec at Al Mawal 33 sec at Al Shaab
	Amber time, all red, clearance [sec]	5 sec	5 sec
	Interval (intergreen)	2 sec	2 sec
	Actuated operation	A	A
	Phase plane	Yes	Yes
	Analysis period [hr]	AM peak PM peak	AM peak PM peak

Daily traffic volumes in the study sites

Mode transportation at Al Mawal intersection

Table 3:

Percentage of trip purpose at selected intersection

Trip purpose	Percentage [%]	Cumulative percentage [%]
To work	40.56	40.56
Returning	30.33	70.89
Commuting to school or universities	11.88	82.77
Shopping	8.69	91.49
Recreation	5.34	96.60
Picking	30.20	100.00

Table 4:

Result of estimation traffic congestion

Output results	SIDRA model intersection I	SIDRA Model Intersection II	HCM 2010 intersection I	HCM 2010 intersection II	Difference SIDRA&HCM I	Difference SIDRA&HCM II
Back of queue length [m]	1014.5	1277	1832	1645	−38	−30.20
Av. Delay [sec]	115.4	336.7	274	487.3	−58	−30.9
Degree [V/C]	1.48	2.5	2.83	2.81	−47	−11
LOS	F	F	E	E	-	-
Av. speed [k/h]	22.4	18.6	20.12	17.34	70.9	13

Linearity of observed back of queue length and that predicted by SIDRA 9.1.1

Linearity of observed back of queue length and that predicted by HCM2010

Table 5:

Summary for baseline model

Model	Sum of squares	df	Mean square	F	P-value
Regression	5736355.381	5	1,147,271.076	647.55	<.0.001)
Residual	201839.119	114	1770.518
Total	5983194.500	119

Table 6:

ANOVA results for overall model significance

Model	R	R2	Adjusted R square	Std. error of the estimate
1	0.783	0.613	0.565	18.235

Table 7:

Regression coefficients for baseline land-use variables

Variables	Estimate	Std. error	Significance	95% Confidence interval
Variables	Estimate	Std. error	Significance	Lower bound	Upper bound
Commercial	30.402	1.345	.000	27.765	33.038
Education	29.359	.653	.000	28.08	30.64
Recreational	14.152	5.945	.000	2.50	25.80
Institutional	19.234	.552	.000	18.15	20.31
Resident	20.153	1.221	.001	17.76	22.54

Table 8:

Final regression coefficients and multicollinearity diagnostics

Independent variables	Coefficients	Std. error	t-value	Sig. (p-value)	VIF
Constant	115.200	12.450	9.253	.000	-
Residential density	28.450	4.120	6.905	.000	1.42
Commercial density	105.120	8.340	12.604	.000	2.15
Institutional density	22.100	3.560	6.208	.001	1.68
Recreational land use	21.800	4.800	6.108	.000	1.24
Educational land use	29.150	3.200	6.609	.001	2.08
Mixed use	42.600	5.150	8.271	.000	2.45

Table 9:

Modell summary (refined)

Model	R	R2	Adjusted R square	Std. error of the estimate
2	0.916	0.84	0.82	15.42

Table 10:

ANOVA results for the refined regression model

Model	Sum of squares	df	Mean square	F	Sig.
Regression	4,988,083.380	6	831,347.23	98.87	0000
Residual	950,111.120	113	8,408.06
Total	5,98,3194.500	119

Diagnostic plots for regression residuals: (left) Histogram of standardized residuals; (right) Normal P-P plot

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DOI: https://doi.org/10.2478/cee-2026-0120 | Journal eISSN: 2199-6512 (formerly 1336-5835) | Journal ISSN: 1336-5835

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Language: English

Submitted on: Jan 13, 2026

Accepted on: Feb 25, 2026

Published on: Sep 16, 2026

Published by: University of Žilina

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Traffic congestion,

Land use,

Mix use,

Transportation planning,

Urban intersection,

Linear regression model,

SIDRA intersection and HCM 2010,

Entropy index

Related subjects:

Business and economics,

Business management,

Industries,

Environmental management

© 2026 Ishraq Hameed Naser, Fatin Hadi Megtoof, Ahmad Benwan Hassan, published by University of Žilina
This work is licensed under the Creative Commons Attribution 4.0 License.

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Effect of Land Use and Mixed-use on Traffic Congestion at Urban Intersections: Implications for Transportation Planning

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