Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Crowd flow prediction <s> Bike-sharing systems are widely deployed in many major cities, providing a convenient transportation mode for citizens' commutes. As the rents/returns of bikes at different stations in different periods are unbalanced, the bikes in a system need to be rebalanced frequently. Real-time monitoring cannot tackle this problem well as it takes too much time to reallocate the bikes after an imbalance has occurred. In this paper, we propose a hierarchical prediction model to predict the number of bikes that will be rent from/returned to each station cluster in a future period so that reallocation can be executed in advance. We first propose a bipartite clustering algorithm to cluster bike stations into groups, formulating a two-level hierarchy of stations. The total number of bikes that will be rent in a city is predicted by a Gradient Boosting Regression Tree (GBRT). Then a multi-similarity-based inference model is proposed to predict the rent proportion across clusters and the inter-cluster transition, based on which the number of bikes rent from/ returned to each cluster can be easily inferred. We evaluate our model on two bike-sharing systems in New York City (NYC) and Washington D.C. (D.C.) respectively, confirming our model's advantage beyond baseline approaches (0.03 reduction of error rate), especially for anomalous periods (0.18/0.23 reduction of error rate). <s> BIB001 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Crowd flow prediction <s> Predicting the movement of crowds in a city is strategically important for traffic management, risk assessment, and public safety. In this paper, we propose predicting two types of flows of crowds in every region of a city based on big data, including human mobility data, weather conditions, and road network data. To develop a practical solution for citywide traffic prediction, we first partition the map of a city into regions using both its road network and historical records of human mobility. Our problem is different than the predictions of each individual's movements and each road segment's traffic conditions, which are computationally costly and not necessary from the perspective of public safety on a citywide scale. To model the multiple complex factors affecting crowd flows, we decompose flows into three components: seasonal (periodic patterns), trend (changes in periodic patterns), and residual flows (instantaneous changes). The seasonal and trend models are built as intrinsic Gaussian Markov random fields which can cope with noisy and missing data, whereas a residual model exploits the spatio-temporal dependence among different flows and regions, as well as the effect of weather. Experiment results on three real-world datasets show that our method is scalable and outperforms all baselines significantly in terms of accuracy. <s> BIB002 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Crowd flow prediction <s> Advances in location-acquisition and wireless communication technologies have led to wider availability of spatio-temporal (ST) data, which has unique spatial properties (i.e. geographical hierarchy and distance) and temporal properties (i.e. closeness, period and trend). In this paper, we propose a Deep -learning-based prediction model for S patio- T emporal data (DeepST). We leverage ST domain knowledge to design the architecture of DeepST, which is comprised of two components: spatio-temporal and global. The spatio-temporal component employs the framework of convolutional neural networks to simultaneously model spatial near and distant dependencies, and temporal closeness, period and trend. The global component is used to capture global factors, such as day of the week, weekday or weekend. Using DeepST, we build a real-time crowd flow forecasting system called UrbanFlow1. Experiment results on diverse ST datasets verify DeepST's ability to capture ST data's spatio-temporal properties, showing the advantages of DeepST beyond four baseline methods. <s> BIB003 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Crowd flow prediction <s> Forecasting the flow of crowds is of great importance to traffic management and public safety, yet a very challenging task affected by many complex factors, such as inter-region traffic, events and weather. In this paper, we propose a deep-learning-based approach, called ST-ResNet, to collectively forecast the in-flow and out-flow of crowds in each and every region through a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the framework of the residual neural networks to model the temporal closeness, period, and trend properties of the crowd traffic, respectively. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of the crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. We evaluate ST-ResNet based on two types of crowd flows in Beijing and NYC, finding that its performance exceeds six well-know methods. <s> BIB004 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Crowd flow prediction <s> Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation. <s> BIB005 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Crowd flow prediction <s> Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dependencies (nearby and distant), temporal dependencies (closeness, period, trend), and external conditions (e.g., weather and events). We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast two types of crowd flows (i.e. inflow and outflow) in each and every region of a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the residual neural network framework to model the temporal closeness, period, and trend properties of crowd traffic. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. We have developed a real-time system based on Microsoft Azure Cloud, called UrbanFlow, providing the crowd flow monitoring and forecasting in Guiyang City of China. In addition, we present an extensive experimental evaluation using two types of crowd flows in Beijing and New York City (NYC), where ST-ResNet outperforms nine well-known baselines. <s> BIB006 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Crowd flow prediction <s> With the rapid development of urban traffic, forecasting the flows of crowd plays an increasingly important role in traffic management and public safety. However, it is very challenging as it is affected by many complex factors, including spatio-temporal dependencies of regions and other external factors such as weather and holiday. In this paper, we proposed a deep-learning-based approach, named STRCNs, to forecast both inflow and outflow of crowds in every region of a city. STRCNs combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network structures to capture spatio-temporal dependencies, simultaneously. More particularly, our model can be decomposed into four components: Closeness captures the changes of instantaneous flows; Daily influence detects the changes of daily influence flows regularly; Weekly influence reacts weekly patterns of influence flows and External influence gets the influence of external factors. For the first three properties (Closeness, Daily influence and Weekly influence), we give a branch of recurrent convolutional network units to learn both spatial and temporal dependencies in crowd flows. External factors are fed into a two-layers fully connected neural network. STRCNs assigns different weights to different branches, and then merges the outputs of the four parts together. Experimental results on two data sets (MobileBJ and TaxiBJ) demonstrate that STRCNs outperforms classical time series and other deep-learning-based prediction methods. <s> BIB007
In recent years, there are many researchers focus on citywide-level traffic flow prediction BIB002 BIB001 , to forecast the citywide-level crowd flow. In the next part, before starting touch deep learning models, we will follow the related definitions of the crowd flow prediction problem in advance BIB003 . Region BIB003 . There are many definitions of Region in terms of different scales and semantic meanings. In most studies, they often partition a city into an I*J grid map based on longitude and latitude and a grid cell called a region, as shown in Figure 1 . Inflow/Outflow BIB003 . Let P be a collection of trajectories at the t th time interval. A grid cell (i, j) means the i th row and the j th column, the inflow and outflow of the crowds at the time interval t are defined respectively as where T r : g 1 → g 2 → ... → g |T r | is a trajectory in P, and g k is the geospatial coordinate, g k ∈ (i, j) means the point g k lies within grid (i, j) , and vice versa, |·| denotes the cardinality of a set. At the tth time interval, inflow and outflow in all I*J regions denote as a tensor X t ∈ R 2 * I * J where (X t ) 0,i, j = x in,i, j t , (X t ) 1,i, j = x out,i, j t . Prediction Target BIB003 . Given the historical observation {X t |t = 0, ..., n − 1}, predict X n . In 2016, a deep neural network-based prediction model called DeepST was proposed, which can capture spatial and temporal properties to predict citywide crowd flows BIB003 . The architecture of DeepST contains two parts: time sequence part and external factors part. From the history observation, the time serials contains temporal closeness, period and seasonal trend properties. The external factors have some related information with crowd flows prediction, e.g., dayofweek, weekday/weekend and meteorological condition. Then convolution layers are employed to capture the temporal closeness, period and seasonal trend properties, and the convolution layers output is fused followed by three sequential convolutional layers. At last, this result is fused with the output of the external factors captured by fully-connected layers and the prediction target X n is obtained. And they built a real-time flow forecasting system (called as UrbanFlow) based on the DeepST model. In 2017 and 2018, a novel deep learning-based model (ST-ResNet) is presented shown in Figure 6 and the city is partitioned by using a grid-based method for forecasting the crowd flows in each and every region of a city BIB006 BIB004 . where • is Hadamard product (i.e., element-wise multiplication), W c , W p and W q are the learnable parameters that adjust the degrees affected by closeness, period and trend, respectively. They concatenate the output of the three components after fusion with the external component. To model citywide dependencies, they employ residual learning in this ST-ResNet model, which has been demonstrated to be very effective for training super deep neural networks of over 1000 layers BIB006 BIB005 . The residual unit used in the ST-ResNet is shown in Figure 7 . But in short-term crowd flows prediction problem, the residual network structure of ST-ResNet can be removed to get much more better performance, because it's not necessary to use the residual network structure to capture the distant spatial dependencies far away from the target region. And the ST-ResNet also needs too many data to train the model, so it has not good performance if we can't get much available data BIB007 . Some researchers choose the ST-ResNet model as baseline to do further urban crowd flows prediction tasks BIB006 BIB007 , because it outperforms other deep learning-based methods before.