Statistical Modelling of Dwell Time of the public Buses by Bus Stop Type and Time of Day at Six Lane Road of Araniko Highway

Authors

  • Raju Bhele Lecturer, Department of Civil Engineering, Kathmandu University, Dhulikhel, Kavre, Nepal.
  • Bishnu Prasad Devkota Assistant Manager, Department of Grid Development, Nepal Electricity Authority, Nepal.

Keywords:

Dwell Time, bus bays, video graphic survey, boarding and alighting number, regression analysis

Abstract

Dwell time (DT) takes up a significant fraction of the trip time. The variation of the dwell time largely affects the travel time. It is an essential element in the understanding the travel time between the end terminals of the bus routes for improving efficiency and effectiveness of the public buses. The study is aimed at determining the dwell time and developing dwell time model for bus bays along Suryavinayk to Kausaltar road section of Araniko Highway taking into the consideration the bus bay types (Near side, far side and  at mid-block) and time of travel (Morning, Day and evening). The boarding and alighting time of passengers were measured using video graphic methods. The dwell time of public buses were modeled using regression analysis. The models were validated statistically. The geometry of all the bus bays under consideration were found similar in shape and size. In day-time, the average DT was 73.65 seconds for bus bays located near intersection while 51.65 seconds for mid-block. On the other hand the average dwell time near intersection were 56.15 and 45.24 seconds in morning and evening respectively. Similarly, for mid-block it was 48.06 and 34.21 seconds in evening and morning respectively. The dwell time prediction models were generated using alighting and boarding number of passengers as independent variable. From the model it is found that the effects of boarding on dwell time is 11.38% more than that of alighting. The analysis also revealed that the models were found significant based on bus bay type and time of travel. The DT models developed were observed to be statistically significant at 95% confidence level, based on the R2, F-Statistic and model validation tests.

 

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Published

2023-12-07