Articles
Importance of Spatiotemporal Evaluation of Public Transportation Metrics
The European Transport Conference 2020
Aim and Scope: Public transit (PT) is a necessary but very challenging component of sustainable transport, as PT operations are very costly (and often subsidized) and have traditionally been planned according to land use and travel demand forecasts. Smart Card (SC) data holds great potential to reveal insights regarding the performance of PT services; however, it needs efficient analysis and visualisation tools and selection of appropriate metrics to evaluate and portray PT characteristics. This study focuses on portraying the importance of spatiotemporal evaluation of PT demand characteristics as opposed to aggregate values using some selected measures. The numeric results are obtained using SC data from the metropolitan city of Konya, Turkey. The study includes evaluation of aggregate and hourly disaggregated data for selected ridership metrics of total trips (R), transfer trips (TR), and discounted trips (DR) in a Geographical Information Systems (GIS) environment.
Lessons Learnt from an Assessment of Public Transit Data: Case Study of Konya, Turkey
The European Transport Conference 2020
Aim and Scope: This study aims to present an evaluation of the smartcard and GPS track data quality and lessons learned during this process, which is a part of Newton-Katip Celebi project funded by Royal Academy of Engineering, UK. In the scope of this study, first, overall data continuity in smartcard data was checked in terms of availability of data for each line, each vehicle, each day. Secondly, missing data was detected for smartcard data in terms of missing cells in the row data (ie. Smartcard transaction time, bus line ID, driver ID, etc.). Then, data consistency was evaluated in order to detect inconsistencies in stop sequence, declared bus line route versus bus GPS track data.
Cloud-Based Scalable Public Transport Analysis
6. High Performance Computing Conference - BAŞARIM 2020
Abstract: All kinds of events, actions or similar events that take place during life can be seen as a potential source of data. By analyzing such data, we can learn insight into the facts. The situation is no different in public transport. Researchers working in the field of transport and traffic have pointed out for some time that such analyzes will be invaluable in designing urban transport and, in particular, adapting to current changes. In this study, the smart boarding pass and vehicle location data collected in the field of public transportation, which is classified as big data especially with its production speed, is introduced to a cloud-based public transportation analysis platform that can analyze the vehicle location data in near real time. The performance of the platform, following the Apache Beam model and supported by high scalability on Google Cloud Dataflow cloud service, is shown on Konya Metropolitan Municipality data.
(2:18:00-)