Modeling Bus Dwell Time with Decision Tree-Based Methods

Soroush Rashidi · Prakash Ranjitkar · Yuval Hadas

35 citations0/0 files storedTransportation Research Record Journal of the Transportation Research Board

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Transportation Planning and Optimization Traffic control and management Urban Transport Systems Analysis

Abstract

A large proportion of transit travel time is made up by dwell time for passengers boarding and alighting. More accurate modeling and estimation of bus dwell time (BDT) can enhance the efficiency and reliability of the public transportation system. Multiple linear regression (MLR) has been the most commonly used method in the literature for modeling and estimating BDT. However, the underlying assumptions of the MLR method, such as multicollinearity and normality of random error, cannot always be satisfied for real applications. This study developed and implemented two methods based on decision trees (DTs), namely, classification and regression tree and chi-squared automatic interaction detector, for the first time for BDT modeling and estimation. The models were compared with the traditional MLR model after calibrating and validating the new models against the data collected from four bus stops in Auckland, New Zealand. Various error measurements were used to evaluate the accuracy of the models. The DT-based methods eliminated the limitations of the MLR method and provided reliable and accurate estimation of BDT.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 35 citations exist upstream.

References

2002Model-Dependent Variance Inflation Factor Cutoff Values 2001The valuation of reliability for personal travel 2009Using GPS Data to Gain Insight into Public Transport Travel Time Variability 2004Determinants of Bus Dwell Time 1983ANALYZING TRANSIT TRAVEL TIME PERFORMANCE 2003Estimation of Bus Dwell Times with Automatic Passenger Counter Information

Cited by

2022Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership 2022Estimating bus dwell time: A review of the literature 2021Social norms as a cost-effective measure of managing transport demand: Evidence from an experiment on the London underground 2017Using Automatic Vehicle Location Data to Model and Identify Determinants of Bus Bunching 2020Subway station dwell time prediction and user-induced delay 2024Bus dwell time estimation and overtaking maneuvers analysis: A stochastic process approach 2021Diagnosing Obstacles to Speed and Reliability with High-Resolution Automatic Vehicle Locator Data: Bus Time Budgets

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.