Exploring an Interaction Model for Land Used Intensity-traffic Congestion

Asmael, Noor Moutaz; Al-Taweel, Hayder Mohammed; Waheed, Mohanned · 2024 · Crossref

DOI: 10.3311/pptr.23305

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Summary

This study investigates the correlation between land use patterns and traffic congestion, addressing a gap in urban planning where the interaction between derived travel demand and land use intensity is often overlooked, particularly in developing countries like Iraq. The research is motivated by the increasing prevalence of traffic congestion and the frequent neglect of Traffic Impact Analysis (TIA) in urban development projects. Specifically, the authors aim to quantify how different land use types, particularly commercial developments such as shopping malls, influence traffic volume and road service levels. The methodology employed statistical modeling, specifically linear regression, to analyze the relationship between land use variables and traffic congestion. The study area focused on four major shopping malls in Baghdad, Iraq, located on primary roads: Baghdad Mall, Mansour Mall, Dream City Mall, and Babylon Mall. Data collection involved two phases: first, land use data were gathered by interviewing building owners and using ArcGIS to measure the area of eight land use classes within a 2 km buffer around each mall. Second, roadside traffic surveys were conducted between 8:00 PM and 9:00 PM on weekends to capture peak evening traffic volumes. Additional variables included road characteristics such as the number of lanes, intersections, and the presence of parking. The data, comprising 50 sample points, were analyzed using SPSS to generate regression models linking independent variables (land use percentages and road properties) to the dependent variable (traffic volume). The results demonstrate a strong statistical relationship between land use and traffic congestion. While road property variables (lanes, parking, intersections) explained only 12% of the variance in traffic volume (R-Squared = 0.12), land use variables proved to be significantly more influential. Commercial land use emerged as the strongest predictor, showing a strong negative correlation with traffic volume (R-Squared = 0.87 in the land-use-only model, improving to 0.90 when combined with road properties). The authors clarify that this negative correlation indicates that as commercial land use intensity increases, road capacity and speed decrease, thereby increasing congestion. Empirical data from the study sites confirmed this, showing significant speed reductions and delays during evening peak hours compared to morning periods. For instance, speeds near Mansour Mall and Babylon Mall dropped to 20 km/h and 15 km/h respectively in the evening, with delays reaching 13 and 12 minutes. The study concludes that commercial land use intensity is the primary driver of traffic congestion in the studied urban areas. The findings underscore the critical need for mandatory Traffic Impact Studies before approving new commercial or residential developments. The authors recommend that urban planners integrate land use and transportation planning to mitigate congestion, suggesting interventions such as improving intersection geometry, optimizing access points, and promoting alternative transportation modes. This research provides evidence-based support for stricter regulatory frameworks regarding land use changes to ensure sustainable traffic management.

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• traffic density