The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Dynamic Travel Time Prediction with Real-Time and Historic Data
Abstract
Travel time prediction has been an interesting research area for decades during which various prediction models have been developed. This paper discusses the results and accuracy generated by different prediction models developed in this study. The employed real-time and historic data are provided by the Transportation Operations Coordinating Committee, which collected them using road side terminals (RST) installed on the New York State Thruway. All the tagged vehicles equipped with EZ pass are scanned by RSTs, while dynamic information (e.g., vehicle entry times and associated RST numbers) are recorded. The emphasis of this study is focused on modeling real-time and historic data for travel time prediction. Factors that would affect the prediction results are explored. The Kalman filtering algorithm is applied for travel time prediction because of its significance in continuously updating the state variable as new observations. Results reveal that during peak hours, the historic path-based data used for travel-time prediction are better than link-based data due to smaller travel-time variance and larger sample size.
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Citations
Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Cited by
Travel time prediction with LSTM neural network
Duan, Y., L.V., Y., & Wang, F.-Y. (2016) · 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795686 · Cited by 336
A predictive-control framework to address bus bunching
Andres, M., & Nair, R. (2017) · Transportation Research Part B: Methodological · doi:10.1016/j.trb.2017.06.013 · Cited by 95
Headway-based bus bunching prediction using transit smart card data
Yu, H., Chen, D., Wu, Z., Ma, X., & Wang, Y. (2016) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2016.09.007 · Cited by 80
Prediction of Bus Motion and Continuous Query Processing for Traveler Information Services
Predic, B., Stojanovic, D., Djordjevic-Kajan, S., Milosavljevic, A., & Rancic, D. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-75185-4_18 · Cited by 10
Metadata
- Authors
- Chien, S. I.-J., & Kuchipudi, C. M.
- Venue
- Journal of Transportation Engineering
- Published
- 2003-11
- Volume
- 129
- Issue
- 6
- Pages
- 608-616
- ISSN
- 0733-947X (ISSN-L)
- 1943-5436
- DOI
- 10.1061/(asce)0733-947x(2003)129:6(608
- OpenAlex
- W1991694886
- Cited by
- 294
- References
- 1
- FWCI
- 18.37
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chien, S. I.-J., & Kuchipudi, C. M. (2003). Dynamic Travel Time Prediction with Real-Time and Historic Data. Journal of Transportation Engineering. https://doi.org/10.1061/(asce)0733-947x(2003)129%3A6(608
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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