Translation software: An alternative to transit data standards

Lawson, Catherine T.; Tomchik, Paul; Muro, Alex; Krans, Eric

doi:10.1016/j.trip.2019.100028

Cited by 6 publications

(7 citation statements)

References 47 publications

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“…The main result of the research and some previous research supports that machine translation has a weakness in translating, especially from a translation quality point of view. The accuracy is categorized less because machine translation is not able to recognize the situation (Lawson et al, 2019). In addition, machine translation prefers to reduce information from the source to the target language.…”

Section: Target Text (Final Version)mentioning

confidence: 99%

The Impact of Machine’s and Students’ Translation on Accuracy of Roda Kehidupan

Sipayung

2023

Lingua Cultura

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The research aimed to describe the impact of machine translation on translation accuracy. Machine translation was widely used to translate the original language to the target. Accuracy was a crucial thing that the translator needed to restructure in the target language. The research applied a qualitative method with sampling based on its criteria. In addition, the research had two types of data: objective and effective. There were two instruments used to collect data; the first was instruction for translating a short film entitled “Roda Kehidupan”. The students were asked to translate a short film with the help of a machine, without machine translation, and the final version of the translation. The second instrument was the translation accuracy indicator, formulated in indicator form. The translation accuracy indicator (questionnaire) was distributed to inter-raters. The research shows that the accuracy of translation without machine translation (first version) is inaccurate (1,5); however, the accuracy of translation with machine translation (second version) is categorized as less accurate (2,4), and the translation accuracy on the final version of the translation is 2,3 (less accurate). The researcher suggests that the translator and lecturer need to use machine translation in translating, but a human touch (post-editing of translation) is really important to achieve high translation quality.

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Section: Target Text (Final Version)mentioning

confidence: 99%

The Impact of Machine’s and Students’ Translation on Accuracy of Roda Kehidupan

Sipayung

2023

Lingua Cultura

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“…In these cases, the use of free and open-source software and reproducible management models, within the collaboration between public transport authorities and mobility research groups, has become an interesting strategy to analyse this specific type of data [33]. More recently, some studies have pointed out that the use of standards in AFCs and the subsequent data analysis could provide significant advantages and favour the use of this type of data in all types of applications [13,34], such as those seen in the previous sections.…”

Section: Experiences Using Different Technological Solutionsmentioning

confidence: 99%

“…Despite the growing list of identified benefits that has demonstrated the value of analysing AFC data in detail, little progress has been made in importing this type of analysis to most public transportation agencies [13]. Before considering the analysis of AFC data, public transport organisations and mobility researchers must consider the data analysis costs (in-house or third-party), additional surveying costs and marketing costs when changes are introduced in response to the patterns found in data [7].…”

Section: Introductionmentioning

confidence: 99%

Development of a Common Framework for Analysing Public Transport Smart Card Data

et al. 2021

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The data generated in public transport systems have proven to be of great importance in improving knowledge of public transport systems, being very valuable in promoting the sustainability of public transport through rational management. However, the analysis of this data involves numerous tasks, so that when the value of analysing the data is finally verified, the effort has already been very great. The management and analysis of the collected data face some difficulties. This is the case of the data collected by the current automated fare collection systems. These systems do not follow any open standards and are not usually designed with a multipurpose nature, so they do not facilitate the data analysis workflow (i.e. acquisition, storage, quality control, integration and quantitative analysis). Intending to reduce this workload, we propose a conceptual framework for analysing data from automated fare collection systems in mobility studies. The main components of this framework are (1) a simple data model, (2) scripts for creating and querying the database and (3) a system for reusing the most useful queries. This framework has been tested in a real public transport consortium in a Spanish region shaped by tourism. The outcomes of this research work could be reused and applied, with a lower initial effort, in other areas that have data recorded by an automated fare collection system but are not sure if it is worth investing in exploiting the data. After this experience, we consider that, even with the legal limitations applicable to the analysis of this type of data, the use of open standards by automated fare collection systems would facilitate the use of this type of data to its full potential. Meanwhile, the use of a common framework may be enough to start analysing the data.

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“…A number of recent advancements in geographic information science (e.g., modern processing techniques developed for Netflix and Facebook using open source code) provide web-based platforms with the capabilities to meet the special needs of transit planning (see Lawson et al 2019). Modern processing using leaflet (http://leafl etjs.com/) and D3.js (http://d3js.org/), both open source software, facilitate the creation of interactive maps organized by Census tract geographies.…”

Section: Data Ecosystem Elements and Toolsmentioning

confidence: 99%

“…These challenges include the use of proprietary software, the lack of data sharing agreements, the lack of data standards, and the lack of internal workforce data-handling skills. At the same time, modern data processing opportunities and new forms of data offer many cost-effective approaches to tackle some of these challenges (Lawson et al 2019). There is a growing interest in identifying opportunities for the use of emerging data sources (often referred to as "Big Data") in combination with, or in place of, traditional transportation data sources, for transit planning.…”

Section: Introductionmentioning

confidence: 99%

Forecasting bus ridership using a “Blended Approach”

2019

Self Cite

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As sources of "Big Data" continue to grow, transportation planners and researchers seek to utilize these new resources. Given the current dependency on traditional transportation data sources and conventional tools (e.g., spreadsheets and propriety models), how can these new resources be used? This research examines a "blended data" approach, using a web-based, open source platform to assist transit agencies to forecast bus ridership. The platform is capable of incorporating new Big Data sources and traditional data sources, using modern processing techniques and tools, particularly Application Programming Interfaces (APIs). This research demonstrates the use of APIs in a transit demand methodology that yields a robust model for bus ridership. The approach uses the Census Transportation Planning Products data, modified with American Community Survey data, to generate origin-destination tables for bus trips in a designated market area. Microsimulation models us a transit scheduling specification (General Transit Feed Specification) and an open source routing engine (OpenTripPlanner). Local farebox data validates the microsimulation models. Analyses of model output and farebox data for the Atlantic City transit market area, and a scenario analysis of service reduction in the Princeton/Trenton transit market area, illustrate the use a "blended approach" for bus ridership forecasting.

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Translation software: An alternative to transit data standards

Cited by 6 publications

References 47 publications

The Impact of Machine’s and Students’ Translation on Accuracy of Roda Kehidupan

The Impact of Machine’s and Students’ Translation on Accuracy of Roda Kehidupan

Development of a Common Framework for Analysing Public Transport Smart Card Data

Forecasting bus ridership using a “Blended Approach”

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