Space syntax has been proposed as an alternative to conventional transport models (spatial interaction models) in the case of explaining and predicting vehicle traffic flow. Spatial interaction models are often complex, dataintensive, and cost-ineffective and calculated based on the attraction between origins and destinations within the built environment. Space syntax, on the other hand, studies the physical environment’s ability to generate movement flows based on topological and network measurements of the spatial configuration. Several studies have shown a good correlation between space syntax measurements and observed and measured traffic flow data. However, the literature seems to be divided in this matter, since there are inconsistencies in how well the spatial configuration can predict the traffic flows. A combination of the two models; space syntax and spatial interaction model can be found in the place syntax method of origin-destination betweenness. This approach uses the betweenness centrality measurements in space syntax analysis but allows to specification and weight of origin and destination pairs within the network. This thesis has aimed to study how well space and place syntax measurements can capture vehicle traffic flow and how the incorporation of attraction measurements impacts the results' explanatory power.  This study has been using spatial analysis and statistical methods to compute space and place syntax measurements to test how well these models can capture vehicle flow movement in the Swedish city of Gävle. Through the analysis, the results have shown that the spatial configuration alone can explain a significant part of the movement, but incorporating land use impacts through the place syntax method improves the correlation, providing a more accurate representation of traffic dynamics. When analysing solely the spatial configuration’s ability to capture vehicle traffic, closeness measurement has shown to be a better indicator than betweenness measurement for capturing local vehicle traffic flow. Additionally, when adding land use variables, resident and employment ratios as origins and destination significantly improved the correlation, which underscores its importance as a travel demand indicator. Lastly, a combined approach of using least angular and geometrical shortest path route choices produced improved correlation with traffic flow data, reflecting more accurately the travel paths in the urban environment.