Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing
Abstract Connected vehicles (CVs) can capture and transmit detailed data such as vehicle position and speed through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data provides new opportunities to improve safety and mobility of transportation systems, which can o...
| المؤلفون الرئيسيون: | , , |
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| مؤلفون آخرون: | |
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
Springer Singapore
2021
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| الوصول للمادة أونلاين: | https://hdl.handle.net/1721.1/131430 |
| _version_ | 1826193942416195584 |
|---|---|
| author | Lin, Lei Li, Weizi Peeta, Srinivas |
| author2 | Massachusetts Institute of Technology. Institute for Data, Systems, and Society |
| author_facet | Massachusetts Institute of Technology. Institute for Data, Systems, and Society Lin, Lei Li, Weizi Peeta, Srinivas |
| author_sort | Lin, Lei |
| collection | MIT |
| description | Abstract
Connected vehicles (CVs) can capture and transmit detailed data such as vehicle position and speed through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data provides new opportunities to improve safety and mobility of transportation systems, which can overburden storage and communication systems. To mitigate this issue, we propose a compressive sensing (CS) approach that allows CVs to capture and compress data in real-time and later recover the original data accurately and efficiently. We evaluate our approach using two case studies. In the first study, we use our approach to recapture 10 million CV basic safety message (BSM) speed samples as well as other BSM variables. The results show that we can recover the original speed data with root-mean-squared error as low as 0.05 MPH. In the second study, a freeway traffic simulation model is built to evaluate the impact of our approach on travel time estimation. Multiple scenarios with various CV market penetration rates, On-board unit (OBU) capacities, compression ratios, arrival rate patterns, and data capture rates are simulated for our experiments. As a result, our approach provides more accurate estimation than conventional data collection methods by achieving up to 65% relative reduction in travel time estimation error. With a low compression ratio, our approach can still provide accurate estimation, therefore reducing OBU hardware costs. Lastly, our approach can improve travel time estimation accuracy when CVs are in traffic congestion as it provides a broader spatial–temporal coverage of traffic conditions and can accurately and efficiently recover the original CV data. |
| first_indexed | 2024-09-23T09:47:42Z |
| format | Article |
| id | mit-1721.1/131430 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:47:42Z |
| publishDate | 2021 |
| publisher | Springer Singapore |
| record_format | dspace |
| spelling | mit-1721.1/1314302023-02-24T19:57:46Z Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing Lin, Lei Li, Weizi Peeta, Srinivas Massachusetts Institute of Technology. Institute for Data, Systems, and Society Abstract Connected vehicles (CVs) can capture and transmit detailed data such as vehicle position and speed through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data provides new opportunities to improve safety and mobility of transportation systems, which can overburden storage and communication systems. To mitigate this issue, we propose a compressive sensing (CS) approach that allows CVs to capture and compress data in real-time and later recover the original data accurately and efficiently. We evaluate our approach using two case studies. In the first study, we use our approach to recapture 10 million CV basic safety message (BSM) speed samples as well as other BSM variables. The results show that we can recover the original speed data with root-mean-squared error as low as 0.05 MPH. In the second study, a freeway traffic simulation model is built to evaluate the impact of our approach on travel time estimation. Multiple scenarios with various CV market penetration rates, On-board unit (OBU) capacities, compression ratios, arrival rate patterns, and data capture rates are simulated for our experiments. As a result, our approach provides more accurate estimation than conventional data collection methods by achieving up to 65% relative reduction in travel time estimation error. With a low compression ratio, our approach can still provide accurate estimation, therefore reducing OBU hardware costs. Lastly, our approach can improve travel time estimation accuracy when CVs are in traffic congestion as it provides a broader spatial–temporal coverage of traffic conditions and can accurately and efficiently recover the original CV data. 2021-09-20T17:17:03Z 2021-09-20T17:17:03Z 2019-10-18 2020-09-24T20:44:47Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131430 en https://doi.org/10.1007/s42421-019-00009-5 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Springer Nature Singapore Pte Ltd. application/pdf Springer Singapore Springer Singapore |
| spellingShingle | Lin, Lei Li, Weizi Peeta, Srinivas Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing |
| title | Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing |
| title_full | Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing |
| title_fullStr | Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing |
| title_full_unstemmed | Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing |
| title_short | Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing |
| title_sort | efficient data collection and accurate travel time estimation in a connected vehicle environment via real time compressive sensing |
| url | https://hdl.handle.net/1721.1/131430 |
| work_keys_str_mv | AT linlei efficientdatacollectionandaccuratetraveltimeestimationinaconnectedvehicleenvironmentviarealtimecompressivesensing AT liweizi efficientdatacollectionandaccuratetraveltimeestimationinaconnectedvehicleenvironmentviarealtimecompressivesensing AT peetasrinivas efficientdatacollectionandaccuratetraveltimeestimationinaconnectedvehicleenvironmentviarealtimecompressivesensing |