Bus Journey and Arrival Time Prediction based on Archived AVL/GPS data using Machine Learning

Taparia, Ankit; Brady, Michael

doi:10.1109/mt-its49943.2021.9529328

Cited by 7 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In travel time prediction, further research could be conducted to verify which algorithms might better predict total travel time and the weather between stopping points. Only the work proposed by Taparia and Brady ( 43 ) proposed this investigation. In passenger destination forecasts, Jung and Sohn ( 22 ) verified that the DL model did not obtain satisfactory matching accuracy and pointed out that using more reliable data on socioeconomic activities around the candidate destination can considerably increase predictor performance.…”

Section: Discussionmentioning

confidence: 99%

“…Taparia and Brady ( 43 ) proposed predictors to predict total travel time and arrival times using historical data from AVL systems, bus routes, and bus stop information. The authors focused on developing models capable of estimating reliable travel and arrival times using minimum attributes for situations where attributes are not accessible.…”

Section: Categorizing the Retrieved Solutionsmentioning

confidence: 99%

“…In travel time prediction, we observe that the MLP, GBDT, and SVR algorithms were the most used. Only Taparia and Brady ( 43 ) explored the use of different algorithms for the prediction of total travel time and the prediction time between stopping points.…”

Section: Algorithms Used For Constructing the Machine Learning Modelsmentioning

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: Categorizing the Retrieved Solutionsmentioning

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

“…GB methods have been utilised in a number of studies. In [8], A. Taparia and M. Brandy use GPS data collected from a Brazilian bus route in order to compare Linear Regression, NNs, LSTM and XGBoost, using RMSE, MAE and MAPE as metrics. Moreover, in [9] and [10], the authors use data from flight transportation to compare LightGBM, XGBoost and traditional GB Trees [9], and CatBoost with Multilayer Perceptron and Bagging Regression Methods [10], respectively, with XGBoost and CatBoost outperforming the traditional methods.…”

Section: Related Workmentioning

confidence: 99%

“…Concurrently, predicting the arrival time remains a predominant challenge for other means of PT [8], i.e bus itineraries, or demand-responsive transport (DRT), whether taxi routes [16] [17] or private trips [18], with applications even in the field of logistics supply chains [19]. Considering DRT, the authors of [16] collected GPS taxi data from New York and developed prediction models based on both regression and classification.…”

Section: Related Workmentioning

confidence: 99%

Estimated Time of Arrival in Autonomous Vehicles Using Gradient Boosting: Real-life case study in public transportation

Antypas

Spanos

Lalas

et al. 2022

2022 IEEE International Smart Cities Conference (ISC2)

View full text Add to dashboard Cite

Autonomous Vehicles (AVs) are expected to revolutionise the methods of transportation. Research and innovation in this field is making huge leaps in the last few years, whether it considers vehicles used for private or public transport. Predicting the Estimated Time of Arrival (ETA) is a very important attribute associated with Public Transport (PT). Especially with the rise of AVs' adoption, PT is expected to follow this trend. Therefore, ETA prediction is deemed to be a service that interests the majority of PT stakeholders. PT is a field that automation benefits both stakeholders and commuters, and this research aims to provide a benchmark considering AVs in PT. Within this work, Gradient Boosting (GB) techniques for ETA prediction were employed, namely eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost) and Light Gradient Boosting Machines (LightGBM). This study proposes competitive ETA prediction methods in Autonomous Buses, while the results of this research are very encouraging and aim to contribute to the overall investigations in the field of autonomous and automated PT.

show abstract

Spatiotemporal Bus Arrival Prediction Using ConvLSTM and CTGANs-augmented Data

Nigam

2024

Int. J. ITS Res.

View full text Add to dashboard Cite

Bus Journey and Arrival Time Prediction based on Archived AVL/GPS data using Machine Learning

Cited by 7 publications

References 11 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

Estimated Time of Arrival in Autonomous Vehicles Using Gradient Boosting: Real-life case study in public transportation

Spatiotemporal Bus Arrival Prediction Using ConvLSTM and CTGANs-augmented Data

Contact Info

Product

Resources

About