Robust real-time route inference from sparse vehicle position data
The ability to correctly infer the route traveled by vehicles in real time from infrequent, noisy observations of their position is useful for several traffic management applications. This task, known as map matching, is efficiently performed through probabilistic inference on a Hidden Markov Model...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
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2015
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| Online Access: | https://hdl.handle.net/10356/79437 http://hdl.handle.net/10220/25599 |
| _version_ | 1826110376445476864 |
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| author | Jagadeesh, George Rosario Srikanthan, Thambipillai |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Jagadeesh, George Rosario Srikanthan, Thambipillai |
| author_sort | Jagadeesh, George Rosario |
| collection | NTU |
| description | The ability to correctly infer the route traveled by vehicles in real time from infrequent, noisy observations of their position is useful for several traffic management applications. This task, known as map matching, is efficiently performed through probabilistic inference on a Hidden Markov Model that represents the candidate vehicle states and the transitions between them. In this paper, we present new methods for improving the accuracy and timeliness of existing solutions. We propose assigning the transition probability between a pair of candidate vehicle states by considering the alternative paths present in the context. A discrete route choice model is used to estimate the probability that a driver would choose the path under consideration over the best alternative available. In order to facilitate real-time operation, we present a simple yet effective heuristic to reduce the output latency of the route-inference algorithm with negligible loss of accuracy. Tests conducted with ground truth GPS data from a dense urban region in Singapore show that the proposed techniques outperform the conventional baseline approach. |
| first_indexed | 2024-10-01T02:33:23Z |
| format | Conference Paper |
| id | ntu-10356/79437 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:33:23Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/794372020-05-28T07:18:55Z Robust real-time route inference from sparse vehicle position data Jagadeesh, George Rosario Srikanthan, Thambipillai School of Computer Engineering 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC) Computer Science Engineering The ability to correctly infer the route traveled by vehicles in real time from infrequent, noisy observations of their position is useful for several traffic management applications. This task, known as map matching, is efficiently performed through probabilistic inference on a Hidden Markov Model that represents the candidate vehicle states and the transitions between them. In this paper, we present new methods for improving the accuracy and timeliness of existing solutions. We propose assigning the transition probability between a pair of candidate vehicle states by considering the alternative paths present in the context. A discrete route choice model is used to estimate the probability that a driver would choose the path under consideration over the best alternative available. In order to facilitate real-time operation, we present a simple yet effective heuristic to reduce the output latency of the route-inference algorithm with negligible loss of accuracy. Tests conducted with ground truth GPS data from a dense urban region in Singapore show that the proposed techniques outperform the conventional baseline approach. Accepted version 2015-05-19T02:29:13Z 2019-12-06T13:25:15Z 2015-05-19T02:29:13Z 2019-12-06T13:25:15Z 2014 2014 Conference Paper Jagadeesh, G. R., & Srikanthan, T. (2014). 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC). Robust real-time route inference from sparse vehicle position data, 296-301. https://hdl.handle.net/10356/79437 http://hdl.handle.net/10220/25599 10.1109/ITSC.2014.6957707 186125 en © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ITSC.2014.6957707]. application/pdf |
| spellingShingle | Computer Science Engineering Jagadeesh, George Rosario Srikanthan, Thambipillai Robust real-time route inference from sparse vehicle position data |
| title | Robust real-time route inference from sparse vehicle position data |
| title_full | Robust real-time route inference from sparse vehicle position data |
| title_fullStr | Robust real-time route inference from sparse vehicle position data |
| title_full_unstemmed | Robust real-time route inference from sparse vehicle position data |
| title_short | Robust real-time route inference from sparse vehicle position data |
| title_sort | robust real time route inference from sparse vehicle position data |
| topic | Computer Science Engineering |
| url | https://hdl.handle.net/10356/79437 http://hdl.handle.net/10220/25599 |
| work_keys_str_mv | AT jagadeeshgeorgerosario robustrealtimerouteinferencefromsparsevehiclepositiondata AT srikanthanthambipillai robustrealtimerouteinferencefromsparsevehiclepositiondata |