Multiclass Speed-Density Relationship for Pedestrian Traffic

Abstract

We introduce a modeling approach for pedestrian speed-density relationship. It is motivated by a high scatter in real data that precludes the use of traditional equilibrium relationships. To characterize the observed pattern we relax the homogeneity assumption of equilibrium relations and propose a multi-class model. In addition to the general modeling framework, we also present some concrete model specifications. Real data is utilized to test the performance of the approach. The approach is able to reveal fundamental properties causing the heterogeneity in population and describe their impact on pedestrian movement. We also show the advantages of the proposed approach compared to approaches from the literature. The proposed model is flexible, and it provides better fit and richer information than traditional models.

From dawn to dusk: daily fluctuations in pedestrian traffic in the city center

“…They found a significant negative correlation between these two variables, and our model confirms this finding, with a correlation coefficient of –0.96 (see Figure 6). This dependency is consistent with the results of Nikolić et al 65 and aligns with fundamental diagrams of pedestrian flows as observed in studies by Campanella et al, 66 and Vanumu et al 67…”

“…They found a significant negative correlation between these two variables, and our model confirms this finding, with a correlation coefficient of -0.96 (see Figure 6). This dependency is consistent with the results of Nikolic ´et al 65 and aligns with fundamental diagrams of pedestrian flows as observed in studies by Campanella et al, 66 and Vanumu et al 67 During rush hours, we identified two significant bottlenecks occurring in the areas connecting the Old Market Square and the narrow historic streets. These bottlenecks 6 presents a comparison between the results of these studies and our model output.…”

Impact of COVID-19 on the mobility patterns: An investigation of taxi trips in Chicago

“…Earlier research estimating pedestrian demand within a train station uses various indicators such as train timetable data, ridership data, link flow data, and Origin-Destination flow data [ 32 ]. Further research involving a multiclass speed-density relationship in pedestrian movement utilizes real-life data to test the performance of the approach [ 33 ]. Again, research involving a dynamic discrete choice-based demand model replicates timing decisions, trip length, and trip duration for daily travel activity [ 34 ].…”