## 1 Introduction  
University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), Computer Science Department Signal, Image and Speech Laboratory (SIMPA) Laboratory, El Mnaouar, BP 1505, Bir El Dju 31000, Oran, Algeria  
Continued growth in road traffic demand generates numerous challenges, such as traffic congestion, pollution, and road traffic accidents, which could cause severe injuries and even deaths [1, 2, 3]. In recent years, growing attention has been paid to the correlation between health and cities to mitigate obesity, pollution, climate change, and road traffic injuries. Hence, governments are more engaged in creating safer, more comfortable, and more connected bicycling and walking environments. While walking and bicycling are beneficial to both the city environment and its citizens' health, there is not much research focusing on modeling and forecasting pedestrian and bike traffic flow compared to motorized vehicle-based traffic flow [4]. For cities attempting to encourage walking and bicycling activities (i.e., nonmotorized travel), it is essential to quantify the need for facilities supporting active transportation [5]. Importantly, pedestrian and bicycle traffic flows are characterized by their sensitivity to environmental conditions (e.g., weather situations and topography) and are more dynamic. A timely and accurate forecast of nonmotorized traffic flow (walking and bicycling) is essential in developing walkable cities [4]. Due to the high dynamic behavior of motorized-based traffic flow, modeling and predicting pedestrian and bicycle traffic flows become a challenging task. Deep recurrent neural networks have recently gained great success in modeling the time-dependence in time series data. Thus, this work attempted to develop an innovative deep learning-driven approach to forecasting bicycle and pedestrian traffic flows. Short and long-term forecasting techniques represent helpful tools for efficiently managing road traffic flow. In the last decades, much effort has been made to develop and improve traffic flow forecasting [6; 7; 8]. Time-series methods, such as autoregressive integrated moving average (ARIMA) and its extensions, are widely exploited in modeling and forecasting traffic flow [7; 9; 10]. Crucially, parametric models provide generally reasonable performance in the case of traffic flow with regular variations, but the forecasting quality can be degraded when the traffic flow exhibits irregular variations [11]. Several non-parametric techniques have been designed in the literature to mitigate this challenge. As data-driven methods, machine learning methods, such as support vector machine [12] and neural network [13], have been widely used to enhance forecasting of traffic flow. The central feature of data-based methods is their capacity to model complex data without an analytical model formulation. For example, [PERSON] et al. in [14] introduced an approach for forecasting road traffic speed by coupling a radial basis function neural network and the aid of the Fuzzy system. They showed that the coupled model reduced the mean absolute percentage error to 6.4% and provided performance superior than the time series and simplex prediction methods. In [15], [PERSON] et al. considered a hybrid learning-based approach by merging the benefit of support vector regression (SVR) and the gravitational search algorithm (GSA). They applied GSA to determine the optimal values of the SVR parameters and showed that the SVR-GSA provides better results than SVR-particle swarm optimization (PSO). In [16], [PERSON] et al. presented an innovative approach by constructing multiple base forecasting models, each with different time lag and performance. More specifically, they employed the least-squares SVR (LSSVR) and investigated the influence of time lag on forecasting quality. In [17], [PERSON] et al. introduced an approach to forecast lane-level traffic flow by integrating extreme gradient boosting and complete ensemble empirical mode decomposition.