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Date
2021-09Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Understanding complicated city traffic patterns has been recognized as a key goal by twenty- first century urban planners and traffic management systems, resulting in a significant rise in the quantity and variety of traffic data gathered. For example, taxi firms in a growing number of large cities have begun to collect metadata for each individual vehicle trip, such as origin, destination, and travel duration. Taxi data offer information on traffic patterns, allowing the study of urban flow – what will traffic look like between two sites at a certain day and time in the future? In this paper, we propose a method based on sparse GPS probe data that focuses on how to allocate travel time data to the different links traveled between GPS observations. This model incorporates the spatial correlations between the links in a network. The main goal of this work is to show, how with a simple adjustment in previously known parametric methods we can consider spatial correlations and improve our results in a more realistic way. For estimation of arterial travel time, the methodology is applied to a case study for the partial network of New York City based on the data, collected from the taxicabs in New York City, providing the locations of origins, destinations and travel times. The model estimates quarter hourly averages of urban link travel times using OD trip data. This study proposes a more accurate approach for estimating link travel times that fully utilizes the partial information received from taxis data in cities. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000504154Publication status
publishedPublisher
STRCEvent
Subject
Travel time estimation; Spatial correlations; Urban road networks; GPS probe dataOrganisational unit
08686 - Gruppe Strassenverkehrstechnik
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG
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ETH Bibliography
yes
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