Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Public transit flow prediction <s> Forecasting the inflow and outflow of crowds at metro station, immediately controlling the number of people entering at some special times and places to avoid the occurrence of malignant events for public safety, is of great significance to subway stations management and very challenging as it is affected by many complex elements, such as inter region station flows, major events or activities, and weather. We propose a approach based on deep residual learning, called ST-DRN, to discern the pattern of spatial and temporal and integrally predict the inflow and outflow of crowds in each subway station of a city. We propose an end-to-end structure of ST-DRN based on distinct attributes of spatio-temporal data. More specifically, we apply the residual neural network framework to model the temporal nearby, day, and week properties of crowd in subway station. For each feature, we design a branch of residual convolutional units, each of which handles the spatial properties of subway crowd. ST-DRN learns to dynamically summation the output of the three residual neural networks, assigning different weights to each branch. The summation is also further combined with external elements, such as weather, holiday and workdays or weekends, to forecast the final traffic flow of crowds in each station. Evaluations on the automatic fare collection (AFC) system subway record data in Suzhou demonstrate that we proposed ST-DRN outperforms than three prominent baseline methods. <s> BIB001 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Public transit flow prediction <s> One fundamental issue in managing bike sharing systems is bike flow prediction. Due to the hardness of predicting flow for a single station, recent research often predicts flow at cluster-level. However, they cannot directly guide fine-grained system management issues at station-level. In this paper, we revisit the problem of the station-level bike flow prediction, aiming to boost the prediction accuracy using the breakthroughs of deep learning techniques. We propose a multi-graph convolutional neural network model to predict flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective. More specifically, we construct multiple graphs for a bike sharing system to reflect heterogeneous inter-station relationships. Afterward, we fuse multiple graphs and apply the convolutional layers to predict station-level future bike flow. The results on realistic bike flow datasets verify that our multi-graph model can outperform state-of-the-art prediction models by reducing up to 25.1% prediction error. <s> BIB002 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Public transit flow prediction <s> Abstract This study aims to combine the modeling skills of deep learning and the domain knowledge in transportation into prediction of metro passenger flow. We present an end-to-end deep learning architecture, termed as Deep Passenger Flow (DeepPF), to forecast the metro inbound/outbound passenger flow. The architecture of the model is highly flexible and extendable; thus, enabling the integration and modeling of external environmental factors, temporal dependencies, spatial characteristics, and metro operational properties in short-term metro passenger flow prediction. Furthermore, the proposed framework achieves a high prediction accuracy due to the ease of integrating multi-source data. Numerical experiments demonstrate that the proposed DeepPF model can be extended to general conditions to fit the diverse constraints that exist in the transportation domain. <s> BIB003 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Public transit flow prediction <s> Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and transferring from station to station. An increasing number of deep learning algorithms are being utilized to forecast metro ridership due to the development of computational intelligence. However, limited efforts have been exerted to consider spatiotemporal features, which are important in forecasting ridership through deep learning methods, in large-scale metro networks. To fill this gap, this paper proposes a parallel architecture comprising convolutional neural network (CNN) and bi-directional long short-term memory network (BLSTM) to extract spatial and temporal features, respectively. Metro ridership data are transformed into ridership images and time series. Spatial features can be learned from ridership image data by using CNN, which demonstrates favorable performance in video detection. Time series data are input into the BLSTM which considers the historical and future impacts of ridership in temporal feature extraction. The two networks are concatenated in parallel and prevented from interfering with each other. Joint spatiotemporal features are fed into a fully connected network for metro ridership prediction. The Beijing metro network is used to demonstrate the efficiency of the proposed algorithm. The proposed model outperforms traditional statistical models, deep learning architectures, and sequential structures, and is suitable for ridership prediction in large-scale metro networks. Metro authorities can thus effectively allocate limited resources to overcrowded areas for service improvement. <s> BIB004
As an important component of the urban public transportation system, the metro has been rapidly deployed in the city because of its large capacity, high speed and high reliability, and it has attracted a large number of passengers BIB003 BIB004 BIB001  . Therefore, doing a good job of metro passenger flow forecast will not only help the metro management department to manage passenger travel demand and optimize metro dispatch, but also help passengers choose travel time and travel mode. Liu et al. BIB003 proposed an end-to-end deep learning model, named as DeepPF, to forecast the metro inbound and outbound passenger flow. They combine all the influence factors, such as temporal dependencies, spatial characteristics, metro operation properties and external environment factors to predict shortterm metro passenger flow. The experiment shows that the model has good prediction performance and can apply to general conditions. We can find that it is feasible and effective to use the deep learning methods to capture the temporal and spatial characteristics of metro data and predict the metro passenger flow. Ma et al. BIB004 analyzed the metro data's spatial-temporal characteristics and then developed a parallel framework which comprises convolutional neural network (CNN) and bi-directional long short-term memory network (BLSTM) to forecast metro passenger flow. The model was evaluated by Beijing metro network data and it outperformed traditional statistics methods. In recent years, bike-sharing is becoming more popular in urban transportation because of providing flexible transport mode and reducing the production of greenhouse gas. Chai et al. BIB002 proposed a novel multi-graph CNN method to predict bike flow at station-level. This method give us a novel graph neural network perspective to study traffic prediction.