Unavailable Transit Feed Specification: Making It Available With Recurrent Neural Networks

Iovino, Ludovico; Nguyen, Phuong T.; Salle, Amleto Di; Gallo, Francesco; Flammini, Michele

doi:10.1109/tits.2021.3053373

Cited by 8 publications

(4 citation statements)

References 28 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the diagnosis of operational bottlenecks, an additional solution could be proposed to indicate solutions or corrective actions to address the unidentified causes. In predicting routes and stopping points to build graphs for public transportation, Iovino et al ( 46 ) indicated that efforts could be made to refine the results.…”

Section: Discussionmentioning

confidence: 99%

“…The solution proposed by Iovino et al ( 46 ) also focused on providing information to passengers about the itinerary, stopping points, and the average travel time of the bus routes. For this, the proposed solution built a graphical representation of bus routes.…”

Section: Problems and Solutions Retrieved From The Literature Related...mentioning

confidence: 99%

“…Iovino et al ( 46 ) presented a ML approach to predict bus routes and stopping points to build traffic graphs. For this, the authors used GPS data and the LSTM algorithm to train a model capable of deducing missing information and exporting adequate general transit feed specification (GTFS) transit feeds.…”

Section: Categorizing the Retrieved Solutionsmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Applied to Public Transportation by Bus: A Systematic Literature Review

Alexandre

Bernardini

Viterbo

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Machine learning (ML) solutions have been proposed to make public transportation more attractive. Works that employ ML in bus transportation focus on various problems, such as travel time prediction or passenger flow prediction. These solutions look to improve elements of transportation services, such as the availability of information on passengers’ travel time and the reliability and regularity of the service. An analysis of the solutions proposed in the literature for public transportation by bus can reveal opportunities for data scientists and transportation professionals, and highlight problems that have been only slightly explored. In addition, mapping information about modeling these solutions (e.g., types of data produced by devices on the transportation network, which can be used in modeling a solution) could help beginner data scientists develop public transportation solutions. Transportation professionals can benefit from an overview of possible transportation solutions to improve transportation problems and direct government agency efforts to implement these solutions. This paper presents a survey of ML-based solutions for public bus transportation and details the modeling of these solutions (e.g., data types, ML algorithms). In addition, the problems tackled in the literature are categorized into four themes, and the solutions proposed to deal with them are schematized, highlighting problems that are little explored.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Problems and Solutions Retrieved From The Literature Related...mentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Applied to Public Transportation by Bus: A Systematic Literature Review

Alexandre

Bernardini

Viterbo

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…In terms of the application of technology, recurrent neural network technology is applied to GPS trajectory mining to reconstruct a complete public transportation network [2], promoting the development of public transportation. Multi-input and multioutput (MIMO) communication technology is used to solve communication capacity in rail transit [3].…”

Section: Introductionmentioning

confidence: 99%

Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics

Chen

Cai

Huang

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

To study the classification and evolution of key technologies in the transportation field, the data of 36 authoritative SCI journals in the transportation field were collected from the Web of Science core collection database from 2001 to 2020. Based on the bibliometric method, this study used Python to process and visualize data, combined with bibliometric software VOSviewer to assist data visualization. Firstly, a preprocessing data algorithm was designed to deduplicate the collected data, merge synonyms, and extract key technologies. Then the paper records that contained the key technology lexicon were filtered out. Next, the annual number of publications and the distribution of key technologies over time were counted. The least squares method was used to fit the distribution of the annual proportion of the publications, and the slope k1 of the fitted linear regression equation was used to determine the research interest trend of key technologies. The key technologies were divided into “hot technology,” “cold technology,” and “other technologies,” according to the research heat trend. In order to further explore the research hotspots, the least squares method was also used to fit the citations of all technologies to obtain the slope k2. We use the Gaussian mixture model (GMM) algorithm to cluster k1 and k2 of each technology. As a result, the 144 technologies were divided into 13 super-key technologies, 60 key technologies, 59 relative key technologies, and 12 lower-key technologies. Then, the evolution of key technologies was analyzed from two perspectives of weighted evolution and cumulative evolution. And the technology evolution trend in the transportation field in the past 20 years was explored. Finally, the cooccurrence clustering method was adopted to divide key transportation technologies into five categories: vehicle technology and control, optimization algorithms and simulation techniques, artificial intelligence and big data, Internet of Things and computing, and communication technology. The research results can provide references for different people in the transportation field, including but not limited to researchers, journal editors, and funding agencies.

show abstract

Effectiveness of Recent Methodologies of Intelligent Transportation System

Appaji,

Raviraj

2023

Int. J. ITS Res.

View full text Add to dashboard Cite

Unavailable Transit Feed Specification: Making It Available With Recurrent Neural Networks

Cited by 8 publications

References 28 publications

Machine Learning Applied to Public Transportation by Bus: A Systematic Literature Review

Machine Learning Applied to Public Transportation by Bus: A Systematic Literature Review

Classification and Evolution Analysis of Key Transportation Technologies Based on Bibliometrics

Effectiveness of Recent Methodologies of Intelligent Transportation System

Contact Info

Product

Resources

About