Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data
Freight vehicle tours and tour-chains are essential elements of state-the-art agent-based urban freight simulations as well as key units to analyse freight vehicle demand. GPS traces are typically used to extract vehicle tours and tour-chains and became available in a large scale to, for example, fl...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2020
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| Online Access: | https://hdl.handle.net/1721.1/125643 |
| _version_ | 1811090169017663488 |
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| author | Jing, Peiyu Ben-Akiva, Moshe E |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Jing, Peiyu Ben-Akiva, Moshe E |
| author_sort | Jing, Peiyu |
| collection | MIT |
| description | Freight vehicle tours and tour-chains are essential elements of state-the-art agent-based urban freight simulations as well as key units to analyse freight vehicle demand. GPS traces are typically used to extract vehicle tours and tour-chains and became available in a large scale to, for example, fleet management firms. While methods to process this data with the objec-tive of analysing and modelling tour-based freight vehicle operations have been proposed, they were not fully explored with regard to the implication of underlying assumptions. In this context, we test different algorithms of stop-to-tour assignment, tour-type and tour-chain identification, aiming to expose their implications. Specifically, we compare the traditional stop-to-tour assignment algorithm using the location of a “base” as the start/end point of tours, against other algorithms using stop activities or payload capacity usage. Furthermore, we explore high-resolution tour-type/chain identification algorithms, considering stop types and recurrence of visits. For tour-chain identification, we explore two algorithms: one defines the day-level tour-chain-type based on the predominant tour-type identified for the period of 1 day and another defines the tour-chain-type based on the average number of stops per tour by stop type. For a demonstration purpose, we apply the methods to data from a large-scale GPS-based survey conducted during 2017–2019 in Singapore. We compare the algorithms in an assessment of freight vehicle operations day-to-day pattern homogeneity. Our analysis demonstrates that the predictions of tours, tourtypes, and tour-chain-types are highly dependent on the assumptions used, underlining the importance of carefully selecting and disclosing the methods for data processing. Finally, the exploration of day-to-day pattern homogeneity reveals operational differences across vehicle types and industries. |
| first_indexed | 2024-09-23T14:36:18Z |
| format | Article |
| id | mit-1721.1/125643 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:36:18Z |
| publishDate | 2020 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1256432022-09-29T09:56:43Z Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data Jing, Peiyu Ben-Akiva, Moshe E Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Intelligent Transportation Systems Laboratory Freight vehicle tours and tour-chains are essential elements of state-the-art agent-based urban freight simulations as well as key units to analyse freight vehicle demand. GPS traces are typically used to extract vehicle tours and tour-chains and became available in a large scale to, for example, fleet management firms. While methods to process this data with the objec-tive of analysing and modelling tour-based freight vehicle operations have been proposed, they were not fully explored with regard to the implication of underlying assumptions. In this context, we test different algorithms of stop-to-tour assignment, tour-type and tour-chain identification, aiming to expose their implications. Specifically, we compare the traditional stop-to-tour assignment algorithm using the location of a “base” as the start/end point of tours, against other algorithms using stop activities or payload capacity usage. Furthermore, we explore high-resolution tour-type/chain identification algorithms, considering stop types and recurrence of visits. For tour-chain identification, we explore two algorithms: one defines the day-level tour-chain-type based on the predominant tour-type identified for the period of 1 day and another defines the tour-chain-type based on the average number of stops per tour by stop type. For a demonstration purpose, we apply the methods to data from a large-scale GPS-based survey conducted during 2017–2019 in Singapore. We compare the algorithms in an assessment of freight vehicle operations day-to-day pattern homogeneity. Our analysis demonstrates that the predictions of tours, tourtypes, and tour-chain-types are highly dependent on the assumptions used, underlining the importance of carefully selecting and disclosing the methods for data processing. Finally, the exploration of day-to-day pattern homogeneity reveals operational differences across vehicle types and industries. Land and Liveability National Innovation Challenge (L2 NIC) (Singapore) (Award L2 NICTDF1-2016-1) 2020-06-03T15:37:23Z 2020-06-03T15:37:23Z 2019-11 2020-05-14T18:13:11Z Article http://purl.org/eprint/type/JournalArticle 2523-3564 https://hdl.handle.net/1721.1/125643 Alho, André Romano et al. “Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data” Journal of Big Data Analytics in Transportation, vol. 1, no. 2-3, 2019, pp. 175-190 © 2019 The Author(s) en https://dx.doi.org/10.1007/S42421-019-00011-X Journal of Big Data Analytics in Transportation Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Springer |
| spellingShingle | Jing, Peiyu Ben-Akiva, Moshe E Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data |
| title | Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data |
| title_full | Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data |
| title_fullStr | Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data |
| title_full_unstemmed | Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data |
| title_short | Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data |
| title_sort | exploring algorithms for revealing freight vehicle tours tour types and tour chain types from gps vehicle traces and stop activity data |
| url | https://hdl.handle.net/1721.1/125643 |
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