<p>Access to reliable data is crucial, particularly in emerging economies, where data may be scarce. Various collection methods, such as commercial Floating Car Data (FCD) and Bluetooth (BT) traffic data, which are low-cost and widely used methods making them ideal for monitoring and analyzing networks, provide valuable insights. This study presents "Link-Based Generalized Data Fusion Method (LB-GDFM)", which integrates and fuses commercial FCD with BT data to assess and manage traffic congestion on urban arterial links. By leveraging the strengths of both data sources, this fusion method provides a more accurate speed estimation and traffic state assessment of urban links and offers a robust comparative method for determining average link speed using fused FCD and BT data, along with performance results. The method, which involves determining a BT-based fused FCD speed, was applied to selected links of an arterial road in Mersin, Turkiye. Two data collection methods were compared to assess the effectiveness of measuring speed reliability across the links over a corridor. Performance metrics such as <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13369_2025_10088_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="90" /> </InlineMediaObject> <EquationSource Format="TEX">\({\text{MAE}}_{FCD, BT}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mtext>MAE</mtext> <mrow> <mi>F</mi> <mi>C</mi> <mi>D</mi> <mo>,</mo> <mi>B</mi> <mi>T</mi> </mrow> </msub> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13369_2025_10088_Article_IEq2.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="107" /> </InlineMediaObject> <EquationSource Format="TEX">\(MAP{\text{E}}_{FCD, BT}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>M</mi> <mi>A</mi> <mi>P</mi> <msub> <mtext>E</mtext> <mrow> <mi>F</mi> <mi>C</mi> <mi>D</mi> <mo>,</mo> <mi>B</mi> <mi>T</mi> </mrow> </msub> </mrow> </math></EquationSource> </InlineEquation>, and <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13369_2025_10088_Article_IEq3.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="99" /> </InlineMediaObject> <EquationSource Format="TEX">\({\text{RMSE}}_{FCD, BT}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mtext>RMSE</mtext> <mrow> <mi>F</mi> <mi>C</mi> <mi>D</mi> <mo>,</mo> <mi>B</mi> <mi>T</mi> </mrow> </msub> </math></EquationSource> </InlineEquation> were used to evaluate the accuracy of both data sources, demonstrating that BT-based fused FCD and BT data provide reliable speed estimates in both heavy and normal traffic scenarios. Results indicated that even with limited data sources, effective traffic management strategies can be developed in emerging economies. This study, therefore, provides a valuable data fusion model for traffic monitoring and emphasizes the potential to enhance the used approach by incorporating additional data sources and broader traffic patterns in developing countries.</p>