Predicting the travel demand plays an indispensable role in urban transportation planning. Data collection methods for estimating the origin–destination (OD) demand matrix are being extensively shifted from traditional survey techniques to the pre-collected data from intelligent transportation systems (ITSs). This shift is partly due to the high cost of conducting traditional surveys and partly due to the diversity of scattered data produced by ITSs and the opportunity to derive extra benefits out of this big data. This study attempts to predict the OD matrix of Tehran metropolis using a set of ITS data, including the data extracted from automatic number plate recognition (ANPR) cameras, smart fare cards, loop detectors at intersections, global positioning systems (GPS) of navigation software, socio-economic and demographic characteristics as well as land-use features of zones. For this purpose, five models based on machine learning (ML) techniques are developed for training and test. In evaluating the performance of the models, the statistical methods show that the convolutional neural network (CNN) leads to the best performance in terms of accuracy in predicting the OD matrix and has the lowest error in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). Moreover, the predicted OD matrix was structurally compared with the ground truth matrix, and the CNN model also shows the highest structural similarity with the ground truth OD matrix in the presented case.

The origin-destination (OD) matrices express the number and the pattern of trips distributed between OD pairs. OD matrix structural comparison can be used to identify different mobility patterns in the cities. A comparison of two OD matrices could express their difference from both numerical and structural aspects. Limited methods, such as the mean structural similarity (MSSIM) index and geographical window-based structural similarity index (GSSI), have been developed to compare the structural similarity (SSIM) of two matrices. These methods calculate the structural similarities of two OD matrices by grouping the OD pairs into local windows. The obtained results from the MSSIM entirely depend on the dimensions of the chosen windows. Meanwhile, the GSSI method only focuses on the geographical adjacency and correlation of zones while selecting local windows. Accordingly, this paper developed a novel method named Socioeconomy, Land-use, and Population Structural Similarity Index (SLPSSI) in which local windows are selected according to socioeconomic, land-use, and population properties for SSIM comparison of OD matrices. The proposed method was tested on Tehran’s OD matrix extracted from cell phone Geographic Position System (GPS) data. The advantage of this method over two previous ones was observed in determining the new pattern of trips on local windows and more precise detection of SSIM of the weekdays. The SLPSSI approach is up to 10 percent more accurate than the MSSIM method and up to 5.5 percent more accurate than the GSSI method. The proposed method also had a better performance on sparse OD matrices. It is capable of better determining the SSIM of sparse OD matrices by up to 8% compared with the GSSI method. Finally, the sensitivity analysis results indicate that the suggested method is robust and reliable since it is sensitive to applying both constant and random coefficients.

Origin-destination (OD) matrices provide transportation experts with comprehensive information on the number and distribution of trips. For comparing two OD matrices, it is vital to consider not only the numerical but also the structural differences, including trip distribution priorities and travel patterns in the study region. The mean structural similarity (MSSIM) index, geographical window-based structural similarity index (GSSI), and socioeconomic, land-use, and population structural similarity index (SLPSSI) have been developed for the structural comparison of OD matrices. These measures have undeniable drawbacks that fail to correctly detect differences in travel patterns, therefore, a novel measure is developed in this paper in which geographical, socioeconomic, land-use, and population characteristics are simultaneously considered in a structural similarity index named GSLPSSI for comparison of OD matrices. The proposed measure was evaluated using OD matrices of smartphone Global Positioning System (GPS) data in Tehran metropolitan. Also, the robustness of the proposed measure was verified using sensitivity analysis. GSLPSSI was found to have up to 21%, 15%, and 9% higher accuracy than MSSIM, GSSI, and SLPSSI, respectively, regarding structural similarity calculation. Furthermore, the proposed measure showed 7% higher accuracy than SLPSSI in the structural similarity index of two sparse OD matrices.