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Developing travel time estimation methods using sparse GPS data
Existing methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high frequency data sources from specialist data providers which can be...
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| Format: | Default Article |
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2016
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| Online Access: | https://hdl.handle.net/2134/20491 |
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| _version_ | 1818172284982001664 |
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| author | Irum Sanaullah Mohammed Quddus Marcus Enoch |
| author_facet | Irum Sanaullah Mohammed Quddus Marcus Enoch |
| author_sort | Irum Sanaullah (7178816) |
| collection | Figshare |
| description | Existing methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high frequency data sources from specialist data providers which can be expensive and are not always readily available. The study reported here therefore sought to better estimate travel time using ‘readily available’ vehicle trajectory data from moving sensors such as buses, taxis and logistical vehicles equipped with GPS in ‘near’ real-time. To do this, accurate locations of vehicles on a link were first map-matched to reduce the positioning errors associated with GPS and digital road maps. Two mathematical methods were then developed to estimate link travel times from map-matched GPS fixes, vehicle speeds and network connectivity information with a special focus on sampling frequencies, vehicle penetration rates and time window lengths. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-2 Berkeley’s Mobile Century Project, and these were used to evaluate several travel time estimation methods, the results of which were then validated against reference travel time data collected from high resolution video cameras. The results indicate that vehicle penetration rates, data sampling frequencies, vehicle coverage on the links and time window lengths all influence the accuracy of link travel time estimation. The performance was found to be best in the 5 minute time window length and for a GPS sampling frequency of 60 seconds. |
| format | Default Article |
| id | rr-article-9451385 |
| institution | Loughborough University |
| publishDate | 2016 |
| record_format | Figshare |
| spelling | rr-article-94513852016-04-27T00:00:00Z Developing travel time estimation methods using sparse GPS data Irum Sanaullah (7178816) Mohammed Quddus (1258701) Marcus Enoch (1171632) Other built environment and design not elsewhere classified Travel time estimation GPS data Map-matching Vehicle penetration rate Sampling frequency Built Environment and Design not elsewhere classified Existing methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high frequency data sources from specialist data providers which can be expensive and are not always readily available. The study reported here therefore sought to better estimate travel time using ‘readily available’ vehicle trajectory data from moving sensors such as buses, taxis and logistical vehicles equipped with GPS in ‘near’ real-time. To do this, accurate locations of vehicles on a link were first map-matched to reduce the positioning errors associated with GPS and digital road maps. Two mathematical methods were then developed to estimate link travel times from map-matched GPS fixes, vehicle speeds and network connectivity information with a special focus on sampling frequencies, vehicle penetration rates and time window lengths. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-2 Berkeley’s Mobile Century Project, and these were used to evaluate several travel time estimation methods, the results of which were then validated against reference travel time data collected from high resolution video cameras. The results indicate that vehicle penetration rates, data sampling frequencies, vehicle coverage on the links and time window lengths all influence the accuracy of link travel time estimation. The performance was found to be best in the 5 minute time window length and for a GPS sampling frequency of 60 seconds. 2016-04-27T00:00:00Z Text Journal contribution 2134/20491 https://figshare.com/articles/journal_contribution/Developing_travel_time_estimation_methods_using_sparse_GPS_data/9451385 CC BY-NC-ND 4.0 |
| spellingShingle | Other built environment and design not elsewhere classified Travel time estimation GPS data Map-matching Vehicle penetration rate Sampling frequency Built Environment and Design not elsewhere classified Irum Sanaullah Mohammed Quddus Marcus Enoch Developing travel time estimation methods using sparse GPS data |
| title | Developing travel time estimation methods using sparse GPS data |
| title_full | Developing travel time estimation methods using sparse GPS data |
| title_fullStr | Developing travel time estimation methods using sparse GPS data |
| title_full_unstemmed | Developing travel time estimation methods using sparse GPS data |
| title_short | Developing travel time estimation methods using sparse GPS data |
| title_sort | developing travel time estimation methods using sparse gps data |
| topic | Other built environment and design not elsewhere classified Travel time estimation GPS data Map-matching Vehicle penetration rate Sampling frequency Built Environment and Design not elsewhere classified |
| url | https://hdl.handle.net/2134/20491 |