Real-Time Public Transportation Prediction with Machine Learning Algorithms

Panovski, Dancho; Zaharia, Titus

doi:10.1109/icce46568.2020.9043077

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…This section presents the experimental results obtained for the CDM (Client Data Model) [72]. Let us underline that in this case, the prediction window is very narrow in the moment of the observation (real-time).…”

Section: B Experimental Results For Cdmmentioning

confidence: 99%

Long and Short-Term Bus Arrival Time Prediction With Traffic Density Matrix

Panovski

Zaharia

2020

IEEE Access

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Section: B Experimental Results For Cdmmentioning

confidence: 99%

Long and Short-Term Bus Arrival Time Prediction With Traffic Density Matrix

Panovski

Zaharia

2020

IEEE Access

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“…In Mendes-Moreira et al ( 20 ), a heterogeneous ensembles approach can mitigate some instability observed by algorithms when dealing with seasonal data. More complex approaches (e.g., LSTM and deep CNN) can consider non-linear phenomena (e.g., traffic jams) and provide closer predictions of travel time ( 41 , 42 ). However, the prediction of travel time in these scenarios is still considered a challenge and can be explored by further work.…”

Section: Discussionmentioning

confidence: 99%

“…Panovski and Zaharia ( 41 ) presented a real-time prediction method of arrival time at bus stop points on an itinerary. Because of the difficulty in obtaining data (governmental restrictions and legislation), artificial data from loop detectors were created with the SUMO simulator.…”

Section: Categorizing the Retrieved Solutionsmentioning

confidence: 99%

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

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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.

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