Weighted Regression Method for the Study of Pedestrian Flow Characteristics in Dhaka, Bangladesh

Khalidur Rahman, Noraida Abdul Ghani, Anton Abdulbasah Kamil, Adli Mustafa

Abstract


The proper estimation of pedestrian speed-flow-density relationships is of vital importance, because such relationships play an important role in developing useful tools for analysing and improving pedestrian facilities in terms of efficiency and safety. One of the major problems with previous macroscopic studies of pedestrian flow characteristics is that the relationships were established based on a model with specification errors that had been estimated by ordinary least squares (OLS). Thus, the validity of the relationships and conclusions drawn from those studies is open to question and should be examined further. In this study, pedestrian speed-flow-density relationships in Dhaka, Bangladesh, are estimated using a weighted regression method. The flows and speeds generated by the derived flow-density and speed-flow relationships based on the weighted regression method and the OLS method, separately, are compared with empirical values. The root mean square error is used as an evaluation criterion. In addition, the pedestrian characteristics of Dhaka are compared with those of other studies. The results indicate the existence of a probable bias in previous studies and an improvement in predictive power with the use of the weighted regression method. Pedestrian flows on the sidewalks in Dhaka have some particular characteristics that are not similar to the uninterrupted pedestrian flows in other countries. Since the weighted regression estimation techniques can mitigate a part of the OLS bias, such techniques could be incorporated in simulation packages to predict pedestrian flows and speeds as well as to design and analyse the capacity of a pedestrian facility precisely. The study also recommends refraining from the direct adoption of foreign design and parameters for pedestrian facilities in Dhaka.


Full Text: PDF DOI: 10.5539/mas.v7n4p17

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This work is licensed under a Creative Commons Attribution 3.0 License.

Modern Applied Science   ISSN 1913-1844 (Print)   ISSN 1913-1852 (Online)

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