Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms

Abstract

A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collected by neighbor detectors can make traffic missing data imputation more accurate. This review analyzes traffic temporal data imputation methods. Research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets are reviewed. Then, five representative methods are tested under different missing patterns and missing ratios. California performance measurement system (PeMS) data including traffic volume and speed are selected to conduct the test. Probabilistic principal component analysis performs the best under the most conditions.

Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges

“…Looking into reviews done more specifically in the field of traffic, [2] provided a summary of the methods of traffic data collection, splitting them into fixed and mobile types, as well as explained the classification of various missing data types along with traffic imputation methods. Meanwhile, [5] reviewed temporal data imputation methods specifically, providing a more in-depth analysis of the state-of-the-art data imputation methods that utilized only the temporal aspect of traffic, covering their application conditions and limitations, as well as providing a list of popular public datasets. [4] focused on traffic state estimation in urban road networks, of which there are missing data for segments due to the unavailability of traffic detectors due to installation costs as well as faulty detectors, with a focus on methods that fuses multiple sources of data into their models.…”

“…In these existing works, it is noted several times that while random missing data has been tested quite often, research that simulates non-random missing data due to situations such as faulty detectors is significantly less. Also, the authors would like to note that many public datasets, such as PeMS [12], are freeway traffic datasets, which do not equate to an urban traffic environment, as also mentioned by [5] and [4]. Certain studies may make use of road segments in an urban environment [11], but a few individual roads are not representative of urban traffic as a whole.…”

“…Only by utilizing these data can the ITS process, learn, predict, and resolve the traffic issues it oversees. While some literature review includes reviews on public datasets [5], they focus on looking at the effect these datasets would have on the models rather than cover the different methods of traffic data retrieval. The aim of this section is to provide insight into the different methods of traffic data collection, as well as provide a definition of the type of missing data, as well as possible data pre-processing methods that could be used to augment a limited dataset.…”

“…There has also been literature that compiled other available public datasets, such as [5], and has shown that these datasets tend towards freeways, highways, expressways, or limited signalized intersections. As shown here, there is a lack of public datasets with regard to urban traffic networks on a wider scale, as well as fewer public datasets outside of America and China, with few specific datasets in countries like Spain and England.…”

Attention-Based Gated Recurrent Graph Convolutional Network for Short-Term Traffic Flow Forecasting

“…Initially, grid-based graphs were applied to represent spatial dependencies in trafc fows [11][12][13] and then these graphs were fed to CNN to extract temporal dependencies. However, these CNN-based methods are not capable of handling non-Euclidean distance structure data and have some limitations in capturing spatial dependencies [14].…”