Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning

Abstract

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.

CQDFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation

“…Gradient-based methods include explicit gradient descent (Simultaneous Perturbation Stochastic Approximation, SPSA [40][41][42][43][44][45]) and implicit gradient propagation (Neural Networks, NNs [3,12,19,[46][47][48]). As the name suggests, SPSA simultaneously optimizes both the target variable (OD flows) and the observations (traffic count) by gradient descent.…”

“…Wu et al proposed a multi-layered Hierarchical Flow Network to integrate multiple data sources and estimate travel demand [12]. Afandizadeh et al compared five machine learning methods on OD estimation problems: K-Nearest Neighbor, Random Forest, LightGBM, MLP, CNN [48]. However, as aforementioned, the distribution shift between data of the training set and test set makes the application of neural networks in the field of OD estimation still limited.…”

“…Multi-Layer Perceptron (MLP), a widely used neural network block, was applied to the OD estimation in [19,48]. Here, we use the encoder-decoder framework including six linear transformation layers with dimensions of 1024, 512, 256, 256, 512, 1024, respectively, and the LeakyReLU is used as the activation function between layers.…”