Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.09579 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911503120072704 |
|---|---|
| author | Oriel-Singer Ilai-Bistritz Giseung-Park Woohyeon-Byeon Youngchul-Sung Amir-Leshem |
| author_facet | Oriel-Singer Ilai-Bistritz Giseung-Park Woohyeon-Byeon Youngchul-Sung Amir-Leshem |
| contents | Dynamic shortest-path routing, using real-time traffic data, enables path selection responsive to evolving conditions. Nevertheless, transportation planning tasks such as adaptive congestion pricing, fleet routing, and long-term operational decisions rely on offline traffic estimators. To address this problem, we develop a spatiotemporal predictor based on a low-rank decomposition of the traffic matrix and the temporal subspace coefficients. Using a recent large-scale measurement campaign over the Seoul road network, we show that our proposed predictor incurs an average excess travel time of less than 1.5 minutes. Moreover, our predictor's tail of the excess travel time distribution matches that of a near-real-time predictor. Results based on one year of traffic data are also demonstrated in simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09579 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Low-Rank Cyclostationarity Predictive Routing Is Almost as Good as Real-Time Data-based Routing Oriel-Singer Ilai-Bistritz Giseung-Park Woohyeon-Byeon Youngchul-Sung Amir-Leshem Signal Processing Dynamic shortest-path routing, using real-time traffic data, enables path selection responsive to evolving conditions. Nevertheless, transportation planning tasks such as adaptive congestion pricing, fleet routing, and long-term operational decisions rely on offline traffic estimators. To address this problem, we develop a spatiotemporal predictor based on a low-rank decomposition of the traffic matrix and the temporal subspace coefficients. Using a recent large-scale measurement campaign over the Seoul road network, we show that our proposed predictor incurs an average excess travel time of less than 1.5 minutes. Moreover, our predictor's tail of the excess travel time distribution matches that of a near-real-time predictor. Results based on one year of traffic data are also demonstrated in simulations. |
| title | Low-Rank Cyclostationarity Predictive Routing Is Almost as Good as Real-Time Data-based Routing |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2603.09579 |