Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts
This paper presents two origin-destination flow estimation models using sampled GPS positions of probe vehicles and link flow counts. The first model, named as SPP model (scaled probe OD as prior OD), uses scaled probe vehicle OD matrix as prior OD matrix and applies conventional generalized least s...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/4341532 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850160851260014592 |
|---|---|
| author | Xianfeng Yang Yang Lu Wei Hao |
| author_facet | Xianfeng Yang Yang Lu Wei Hao |
| author_sort | Xianfeng Yang |
| collection | DOAJ |
| description | This paper presents two origin-destination flow estimation models using sampled GPS positions of probe vehicles and link flow counts. The first model, named as SPP model (scaled probe OD as prior OD), uses scaled probe vehicle OD matrix as prior OD matrix and applies conventional generalized least squares (GLS) framework to conduct OD correction using link counts; the second model, PRA model (probe ratio assignment), is an extension of SPP in which the observed link probe ratios are also included as additional information in the OD estimation process. For both models, the study explored a new way to construct assignment matrices directly from sampled probe trajectories to avoid sophisticated traffic assignment process. Then, for performance evaluation, a comprehensive numerical experiment was conducted using simulation dataset. The results showed that when the distribution of probe vehicle ratios is homogeneous among different OD pairs, both proposed models achieved similar degree of improvement compared with the prior OD pattern. However, under the case that the distribution of probe vehicle ratios is heterogeneous across different OD pairs, PRA model achieved more significant reduction on OD flow estimations compared with SPP model. Grounded on both theoretical derivations and empirical tests, the study provided in-depth discussions regarding the strengths and challenges of probe vehicle based OD estimation models. |
| format | Article |
| id | doaj-art-a4aeb34407f44af3b24c7288c618b28a |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-a4aeb34407f44af3b24c7288c618b28a2025-08-20T02:23:04ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/43415324341532Origin-Destination Estimation Using Probe Vehicle Trajectory and Link CountsXianfeng Yang0Yang Lu1Wei Hao2Department of Civil, Construction & Environmental Engineering, San Diego State University, San Diego, CA, USABaidu Online Network Technology Co., Ltd., Beijing, ChinaUniversity Transportation Research Center, City College of New York, New York, NY, USAThis paper presents two origin-destination flow estimation models using sampled GPS positions of probe vehicles and link flow counts. The first model, named as SPP model (scaled probe OD as prior OD), uses scaled probe vehicle OD matrix as prior OD matrix and applies conventional generalized least squares (GLS) framework to conduct OD correction using link counts; the second model, PRA model (probe ratio assignment), is an extension of SPP in which the observed link probe ratios are also included as additional information in the OD estimation process. For both models, the study explored a new way to construct assignment matrices directly from sampled probe trajectories to avoid sophisticated traffic assignment process. Then, for performance evaluation, a comprehensive numerical experiment was conducted using simulation dataset. The results showed that when the distribution of probe vehicle ratios is homogeneous among different OD pairs, both proposed models achieved similar degree of improvement compared with the prior OD pattern. However, under the case that the distribution of probe vehicle ratios is heterogeneous across different OD pairs, PRA model achieved more significant reduction on OD flow estimations compared with SPP model. Grounded on both theoretical derivations and empirical tests, the study provided in-depth discussions regarding the strengths and challenges of probe vehicle based OD estimation models.http://dx.doi.org/10.1155/2017/4341532 |
| spellingShingle | Xianfeng Yang Yang Lu Wei Hao Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts Journal of Advanced Transportation |
| title | Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts |
| title_full | Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts |
| title_fullStr | Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts |
| title_full_unstemmed | Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts |
| title_short | Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts |
| title_sort | origin destination estimation using probe vehicle trajectory and link counts |
| url | http://dx.doi.org/10.1155/2017/4341532 |
| work_keys_str_mv | AT xianfengyang origindestinationestimationusingprobevehicletrajectoryandlinkcounts AT yanglu origindestinationestimationusingprobevehicletrajectoryandlinkcounts AT weihao origindestinationestimationusingprobevehicletrajectoryandlinkcounts |