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Title: A Kriging based spatiotemporal approach for traffic volume data imputation

Journal Article · · PLoS ONE
ORCiD logo [1];  [2];  [3];  [1];  [4];  [5];  [5];
  1. National United Engineering Laboratory of Integrated and Intelligent Transportation, School of Transportation and Logistics, Southwest Jiaotong University, Hi-Tech Industrial Development Zone, Chengdu, Sichuan, China
  2. Model Risk Management, Bank of America, Charlotte, NC United States)
  3. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Civil & Environmental Engineering
  4. School of Architecture and Design, Southwest Jiaotong University, Hi-Tech Industrial Development Zone, Chengdu, Sichuan, China
  5. Oak Ridge National Laboratory, Knoxville, TN (United States) Center for Transportation Analysis
+ Show Author Affiliations

Along with the rapid development of Intelligent Transportation Systems, traffic data collection technologies have progressed fast. The emergence of innovative data collection technologies such as remote traffic microwave sensor, Bluetooth sensor, GPS-based floating car method, and automated license plate recognition, has significantly increased the variety and volume of traffic data. Despite the development of these technologies, the missing data issue is still a problem that poses great challenge for data based applications such as traffic forecasting, real-time incident detection, dynamic route guidance, and massive evacuation optimization. A thorough literature review suggests most current imputation models either focus on the temporal nature of the traffic data and fail to consider the spatial information of neighboring locations or assume the data follow a certain distribution. These two issues reduce the imputation accuracy and limit the use of the corresponding imputation methods respectively. As a result, this paper presents a Kriging based data imputation approach that is able to fully utilize the spatiotemporal correlation in the traffic data and that does not assume the data follow any distribution. A set of scenarios with different missing rates are used to evaluate the performance of the proposed method. The performance of the proposed method was compared with that of two other widely used methods, historical average and K-nearest neighborhood. Comparison results indicate that the proposed method has the highest imputation accuracy and is more flexible compared to other methods.

Research Organization:
Oak Ridge National Lab (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1627861
Journal Information:
PLoS ONE, Vol. 13, Issue 4; ISSN 1932-6203
Publisher:
Public Library of ScienceCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (2)

Traffic data reconstruction based on compressive sensing with neighbor regularization journal February 2020
Traffic Estimation for Large Urban Road Network with High Missing Data Ratio journal June 2019

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