Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation

Metro origin-destination prediction is a crucial yet challenging time-series analysis task in intelligent transportation systems, which aims to accurately forecast two specific types of cross-station ridership, i.e., Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete OD matrices of previous time intervals can not be obtained immediately in online metro systems, and conventional methods only used limited information to forecast the future OD and DO ridership separately. In this work, we proposed a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM), which fully exploits heterogeneous information of historical data (e.g., incomplete OD matrices, unfinished order vectors, and DO matrices) to jointly learn the evolutionary patterns of OD and DO ridership. Specifically, an OD modeling branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices, while a DO modeling branch takes DO matrices as input to capture the spatial-temporal distribution of DO ridership. Moreover, a Dual Information Transformer is introduced to propagate the mutual information among OD features and DO features for modeling the OD-DO causality and correlation. Based on the proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD and DO ridership simultaneously. Extensive experiments conducted on two large-scale benchmarks demonstrate the effectiveness of our method for online metro origin-destination prediction

Enhanced Methods for Utilization of Data to Support Multi-Scenario Analysis and Multi-Resolution Modeling

The success of analysis and simulation in transportation systems depends on the availability, quality, reliability, and consistency of real-world data and the methods for utilizing the data. Additional data and data requirements are needed to support advanced analysis and simulation strategies such as multi-resolution modeling (MRM) and multi-scenario analysis. This study has developed, demonstrated, and assessed a systematic approach for the use of data to support MRM and multi-scenario analysis. First, the study developed and examined approaches for selecting one or more representative days for the analysis, considering the variability in travel conditions throughout the year based on cluster analysis. Second, this study developed and analyzed methods for using crowdsourced data vii to estimate origin-destination demands and link-level volumes for use as part of an MRM with consideration of the modeling scenario(s). The assessment of the methods to select the representative day(s) utilizes statistical measures, in addition to measures and visualization techniques that are specific to traffic operations. The results of the assessment indicate that the utilization of the K-means clustering algorithm with four clusters and spatio-temporal segregation of the variables demonstrated superior performance over other tested approaches, such as the use of the Gaussian Mixture clustering algorithm and the use of different segregation levels. The study assessed methods for the use of third-party crowdsourced data from StreetLight (SL) as part of the Origin-Destination Matrix Estimation (ODME), which identifies the method resulting in the closest origin-destination demands to the original seed matrices and real-world link counts. The results of the study indicate that Method 3(b) produced the best performance, which utilized combined data from demand forecasting models, crowdsourced data, and traffic counts. Additionally, this study examined regression models between crowdsourced data and count station data developed for link-level estimation of the volumes. This study also examined the accuracy and transferability of the link-level estimation of the volumes to determine if the crowdsourced data combined with available volume data at several locations can be used to predict missing or unavailable volumes in different locations on different days and times within the network. Regression models produced low errors than the default SL estimates when hourly or daily traffic volumes were taken into account. For similar traffic conditions, the models predicted directional traffic volume close to the real-world value