Impact of Carriage Crowding Level on Bus Dwell Time: Modelling and Analysis

Yiming Bie,Le Zhang,Yunhao Wang

doi:10.1155/2020/6530530

Abstract

This paper develops two types of estimation models to quantify the impacts of carriage crowding level on bus dwell time. The first model (model I) takes the crowding level and the number of alighting and boarding passengers into consideration and estimates the alighting time and boarding time, respectively. The second model (model II) adopts almost the same regression method, except that the impact of crowding on dwell time is neglected. The analysis was conducted along two major bus routes in Harbin, China, by collecting 640 groups of dwell times under crowded condition manually. Compared with model II, the mean absolute error (MAE) of model I is reduced by 137.51%, which indicates that the accuracy of bus dwell time estimation could be highly improved by introducing carriage crowding level into the model. Meanwhile, the MAE of model I is about 3.9 seconds, which is acceptable in travel time estimation and bus schedule.

Highlights

The use of advanced traffic detection technique, such as the vehicle navigation system based on GPS and automatic passenger counter, offers a far more convenient and efficient data source, which makes it feasible to conduct the transit travel pattern and reliability analysis reasonably [1,2,3,4,5]
From the perspective of static bus schedule, bus dwell time at stops is a major component of vehicle travel time, while bus travel time plays an important role in determining the departure frequency and route design for public transit planners and operators. e proportion of bus dwell time in total running time can consume up to 26% for some highfrequency, high-ridership bus transit routes, especially in some high-density areas [9]. us, estimating the bus dwell time accurately contributes a lot to planning bus schedule reasonably and efficiently, including the departure headway and the fleet size required to provide service
The bus dwell time estimation is essential for improving the service quality as well as reliability of the public transit system [13,14,15,16]

Summary

Introduction

Before joining into the traffic, the bus has to spend some time in finding an acceptable gap between consecutive vehicles on the left lane, resulting in longer dwell time In this case, the real moment for closing door is set as the sum of the moment of the last passenger boarding or alighting the vehicle plus the average duration for closing door, where the average duration for closing a door can be calculated by previous data in general cases; that is, nonpassenger related delays are not included in bus dwell time in our study. 6 types of regression models are developed to fit the collected 640 data sets and their performance measured by the adjusted coefficient of determination (Adj R2) is shown, where independent variables include the number of boarding passengers (X1) and bus crowding level (C); boarding time (Y1) is the dependent variable. To test for a constant variance, an auxiliary regression analysis is conducted, which regresses the squared residuals

Linear Quadratic Cubic Exponential Semilogarithmic Double logarithmic

Estimated value

Findings

Data type