Estimating Path Choice Models through Floating Car Data
The path choice models play a key role in transportation engineering, especially when coupled with an assignment procedure allowing link flows to be obtained. Their implementation could be complex and resource-consuming. In particular, such a task consists of several stages, including (1) the collec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Forecasting |
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| Online Access: | https://www.mdpi.com/2571-9394/4/2/29 |
| _version_ | 1827660273687724032 |
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| author | Antonio Comi Antonio Polimeni |
| author_facet | Antonio Comi Antonio Polimeni |
| author_sort | Antonio Comi |
| collection | DOAJ |
| description | The path choice models play a key role in transportation engineering, especially when coupled with an assignment procedure allowing link flows to be obtained. Their implementation could be complex and resource-consuming. In particular, such a task consists of several stages, including (1) the collection of a large set of data from surveys to infer users’ path choices and (2) the definition of a model able to reproduce users’ choice behaviors. Nowadays, stage (1) can be improved using floating car data (FCD), which allow one to obtain a reliable dataset of paths. In relation to stage (2), different structures of models are available; however, a compromise has to be found between the model’s ability to reproduce the observed paths (including the ability to forecast the future path choices) and its applicability in real contexts (in addition to guaranteeing the robustness of the assignment procedure). Therefore, the aim of this paper is to explore the opportunities offered by FCD to calibrate a path/route choice model to be included in a general procedure for scenario assessment. The proposed methodology is applied to passenger and freight transport case studies. Significant results are obtained showing the opportunities offered by FCD in supporting path choice simulation. Moreover, the characteristics of the model make it easily applicable and exportable to other contexts. |
| first_indexed | 2024-03-09T23:47:30Z |
| format | Article |
| id | doaj.art-2c0f5378e1e948e4adaf6a7e98bc4d9a |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-09T23:47:30Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-2c0f5378e1e948e4adaf6a7e98bc4d9a2023-11-23T16:39:38ZengMDPI AGForecasting2571-93942022-06-014252553710.3390/forecast4020029Estimating Path Choice Models through Floating Car DataAntonio Comi0Antonio Polimeni1Department of Enterprise Engineering, University of Rome Tor Vergata, 00133 Rome, ItalyDepartment of Engineering, University of Messina, 98166 Messina, ItalyThe path choice models play a key role in transportation engineering, especially when coupled with an assignment procedure allowing link flows to be obtained. Their implementation could be complex and resource-consuming. In particular, such a task consists of several stages, including (1) the collection of a large set of data from surveys to infer users’ path choices and (2) the definition of a model able to reproduce users’ choice behaviors. Nowadays, stage (1) can be improved using floating car data (FCD), which allow one to obtain a reliable dataset of paths. In relation to stage (2), different structures of models are available; however, a compromise has to be found between the model’s ability to reproduce the observed paths (including the ability to forecast the future path choices) and its applicability in real contexts (in addition to guaranteeing the robustness of the assignment procedure). Therefore, the aim of this paper is to explore the opportunities offered by FCD to calibrate a path/route choice model to be included in a general procedure for scenario assessment. The proposed methodology is applied to passenger and freight transport case studies. Significant results are obtained showing the opportunities offered by FCD in supporting path choice simulation. Moreover, the characteristics of the model make it easily applicable and exportable to other contexts.https://www.mdpi.com/2571-9394/4/2/29route choice modelspath choice modeldiscrete choice modelrandom utility theoryscenario assessmentfreight transport |
| spellingShingle | Antonio Comi Antonio Polimeni Estimating Path Choice Models through Floating Car Data Forecasting route choice models path choice model discrete choice model random utility theory scenario assessment freight transport |
| title | Estimating Path Choice Models through Floating Car Data |
| title_full | Estimating Path Choice Models through Floating Car Data |
| title_fullStr | Estimating Path Choice Models through Floating Car Data |
| title_full_unstemmed | Estimating Path Choice Models through Floating Car Data |
| title_short | Estimating Path Choice Models through Floating Car Data |
| title_sort | estimating path choice models through floating car data |
| topic | route choice models path choice model discrete choice model random utility theory scenario assessment freight transport |
| url | https://www.mdpi.com/2571-9394/4/2/29 |
| work_keys_str_mv | AT antoniocomi estimatingpathchoicemodelsthroughfloatingcardata AT antoniopolimeni estimatingpathchoicemodelsthroughfloatingcardata |