SARIMAX with Fourier terms for predicting travel times for on-demand public transport in Sofia
This paper presents a complex prediction of travel times of city transport of Sofia (Bulgaria) using the Seasonal autoregressive integrated moving average with exogenous inputs with Fourier terms for the needs of On-demand public transport scheme proposed by INNOAIR project. The model is tested on a...
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| description | This paper presents a complex prediction of travel times of city transport of Sofia (Bulgaria) using the Seasonal autoregressive integrated moving average with exogenous inputs with Fourier terms for the needs of On-demand public transport scheme proposed by INNOAIR project. The model is tested on a particular use-case data. The procedure to implement a prediction model goes through three phases: model identification, parameter estimation/optimization, and prediction, with their corresponding sub-phases. The input data used have two sources – traffic data for times of arrival for 4 consecutive stops of a bus line in Sofia and weather data. For validation the root-mean-square error measured in seconds for each stop is used, calculated on the forecast for the test set (last week of observations). |
| doi_str_mv | 10.1063/5.0179199 |
| format | Conference Proceeding |
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The model is tested on a particular use-case data. The procedure to implement a prediction model goes through three phases: model identification, parameter estimation/optimization, and prediction, with their corresponding sub-phases. The input data used have two sources – traffic data for times of arrival for 4 consecutive stops of a bus line in Sofia and weather data. For validation the root-mean-square error measured in seconds for each stop is used, calculated on the forecast for the test set (last week of observations).</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0179199</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Error analysis ; Meteorological data ; Model testing ; Parameter estimation ; Parameter identification ; Prediction models ; Public transportation ; Traffic information ; Travel time</subject><ispartof>AIP conference proceedings, 2023, Vol.2939 (1)</ispartof><rights>Author(s)</rights><rights>2023 Author(s). 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The model is tested on a particular use-case data. The procedure to implement a prediction model goes through three phases: model identification, parameter estimation/optimization, and prediction, with their corresponding sub-phases. The input data used have two sources – traffic data for times of arrival for 4 consecutive stops of a bus line in Sofia and weather data. 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| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2023, Vol.2939 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_scitation_primary_10_1063_5_0179199 |
| source | AIP Journals Complete |
| subjects | Error analysis Meteorological data Model testing Parameter estimation Parameter identification Prediction models Public transportation Traffic information Travel time |
| title | SARIMAX with Fourier terms for predicting travel times for on-demand public transport in Sofia |
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