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Imputing missing data is a critical task in data-driven intelligent transportation systems. During recent decades there has been a considerable investment in developing various types of sensors and smart systems, including stationary devices (e.g., loop detectors) and floating vehicles equipped with global positioning system (GPS) trackers to collect large-scale traffic data. However, collected data may not include observations from all road segments in a traffic network for different reasons, including sensor failure, transmission error, and because GPS-equipped vehicles may not always travel through all road segments. The first step toward developing real-time traffic monitoring and disruption prediction models is to estimate missing values through a systematic data imputation process. Many of the existing data imputation methods are based on matrix completion techniques that utilize the inherent spatiotemporal characteristics of traffic data. However, these methods may not fully capture the clustered structure of the data. This paper addresses this issue by developing a novel data imputation method using PARATUCK2 decomposition. The proposed method captures both spatial and temporal information of traffic data and constructs a low-dimensional and clustered representation of traffic patterns. The identified spatiotemporal clusters are used to recover network traffic profiles and estimate missing values. The proposed method is implemented using traffic data in the road network of Manhattan in New York City. The performance of the proposed method is evaluated in comparison with two state-of-the-art benchmark methods. The outcomes indicate that the proposed method outperforms the existing state-of-the-art imputation methods in complex and large-scale traffic networks.
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An Empirical Validation of Network Learning With Taxi GPS Data From Wuhan, China
In prior research, a statistically cheap method was developed to monitor transportation network performance by using only a few groups of agents without having to forecast the population flows. The current study validates this multiagent inverse optimization (MAIO) method using taxi GPS trajectory data from the city of Wuhan, China. Using a controlled 2,062-link network environment and different GPS data processing algorithms, an online monitoring environment was simulated using real data over a 4-h period. Results show that using samples from only one origin-destination (OD) pair, the MAIO method can learn network parameters such that forecasted travel times have a 0.23 correlation with the observed travel times. By increasing the monitoring from just two OD pairs, the correlation improved further, to 0.56.
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- Award ID(s):
- 1652735
- PAR ID:
- 10213708
- Editor(s):
- Vlacic, L.
- Date Published:
- Journal Name:
- IEEE intelligent transportation systems magazine
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 1939-1390
- Page Range / eLocation ID:
- 42-58
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/MITS.2020.3037324
