Work2017

Electronic ticket machine data analytics for public bus transport planning

Anila Cyril · Varghese George · Raviraj H. Mulangi

16 citations0/0 files storedInternational Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)

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 Human Mobility and Location-Based Analysis Urban Transport Systems Analysis

Abstract

This paper investigates various aspects related to demand modeling and line-planning for bus transport systems based on data elicited from Electronic Ticket Machine (ETM). The ETM data has not been explored thoroughly for transportation planning although it is nowadays collected and compiled by public transport undertakings on a regular basis. The data used in the study is part of transactions on ticket sales by Kerala State Road Transport Corporation (KSRTC) maintained at 6 bus depots in Trivandrum city for the period between 2010 and 2013. The data collected through ETM is immensely huge with average monthly passenger transactions of approximately one million. The database can be audited and compiled to determine the passenger demand, operator's performance, and effectiveness of the service provided. It is possible to determine the origin-destination (OD) matrix of the bus commuters by querying the ETM database using a specially developed program in MATLAB®. The OD data will assist in travel demand modelling, decision-making, and formulation of strategies for future preplanning of the transit system. The work presented in this paper provides details on the block-diagram developed for the formulation of the programme, and a demonstration of its capabilities. In a similar manner, it is also possible to determine the link-volume in terms of passenger flow on the transit network using a specially developed program. Additionally, it is also possible to elicit details on the load-rate with information on boarding and alighting of passengers at bus stops in addition to performance-related statistical details can be elicited from the ETM database. It is proposed to develop MATLAB® based programs for the same. The work described herein also includes description on the use of the time-series approach in short-term demand forecasting. The present work proposes a number of analytical methods that can be employed to derive information from ETM data for travel demand modeling, and strategic and operational planning of public transport.

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 — 16 citations exist upstream.

References

2011Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection Systems 2006Using Archived AVL-APC Data to Improve Transit Performance and Management 2006Bus passenger Origin-Destination Matrix estimation using Automated Data Collection systems 2015Passenger Journey Destination Estimation From Automated Fare Collection System Data Using Spatial Validation 2016Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal 2015Short-Term Prediction of Ridership on Public Transport with Smart Card Data 2006A MULTIVARIATE ARIMA MODEL TO FORECAST AIR TRANSPORT DEMAND 2014Forecasting public transport demand for the Sydney Greater Metropolitan Area: a comparison of univariate and multivariate methods

Cited by

2024Deep-learning-based model for prediction of crowding in a public transit system 2020Demand-Based Model for Line Planning in Public Transport 2018Public Transit Travel Time Analysis Using GPS Data: A Case Study of Mysore ITS 2021Bus Demand Forecasting for Rural Areas Using XGBoost and Random Forest Algorithm

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.