Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Traffic flow prediction <s> We seek to extract and explore statistics that characterize New York City traffic flows based on 700 million taxi trips in the 2010–2013 New York City taxi data. This paper presents a two-part solution for intensive computation: space and time design considerations for estimating taxi trajectories with Dijkstra's algorithm, and job parallelization and scheduling with HTCondor. Our contribution is to present a solution that reduces execution time from 3,000 days to less than a day with detailed analysis of the necessary design decisions. <s> BIB001 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Traffic flow prediction <s> Traffic volume estimation at the city scale is an important problem useful to many transportation operations and urban applications. This paper proposes a hybrid framework that integrates both state-of-art machine learning techniques and well-established traffic flow theory to estimate citywide traffic volume. In addition to typical urban context features extracted from multiple sources, we extract a special set of features from GPS trajectories based on the implications of traffic flow theory, which provide extra information on the speed-flow relationship. Using the network-wide speed information estimated from a travel speed estimation model, a volume related high level feature is first learned using an unsupervised graphical model. A volume re-interpretation model is then introduced to map the volume related high level feature to the predicted volume using a small amount of ground truth data for training. The framework is evaluated using a GPS trajectory dataset from 33,000 Beijing taxis and volume ground truth data obtained from 4,980 video clips. The results demonstrate effectiveness and potential of the proposed framework in citywide traffic volume estimation. <s> BIB002 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Traffic flow prediction <s> Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance. <s> BIB003 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Traffic flow prediction <s> The urban traffic flow prediction is a significant issue in the intelligent transportation system. In consideration of nonlinear and spatial-temporal features of urban traffic data, we propose a deep hybrid neural network improved by greedy algorithm for urban traffic flow prediction with taxi GPS trace. The proposed deep neural network model first combines the convolutional neural network (CNN), which extracts the spatial features, with the long short term memory (LSTM), which captures the temporal information, to predict urban traffic flow. Then, the proposed model is trained by a greedy policy to short time consumption and improves accuracy when a network goes deeper. Experimental results with real taxis GPS trajectory data from ${Xi'an}$ city show that the improved deep hybrid CNN-LSTM model can achieve higher prediction accuracy and shorter time consumption compared with existing methods. <s> BIB004 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Traffic flow prediction <s> Abstract Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches. <s> BIB005 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Traffic flow prediction <s> Traffic forecasting has been an active research field in recent decades, and with the development of deep-learning technologies, researchers are trying to utilize deep learning to achieve tremendous improvements in traffic forecasting, as it has been seen in other research areas, such as speech recognition and image classification. In this study, we summarize recent works in which deep-learning methods were applied for geospatial data-based traffic forecasting problems. Based on the insights from previous works, we further propose a deep-learning framework, which transforms geospatial data to images, and then utilizes the state-of-the-art deep-learning methodologies such as Convolutional Neural Network (CNN) and residual networks. To demonstrate the simplicity and effectiveness of our framework, we present a formulation of the New York taxi pick-up/drop-off forecasting problem, and show that our framework significantly outperforms traditional methods, including Historical Average (HA) and AutoRegressive Integrated Moving Average (ARIMA). <s> BIB006 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Traffic flow prediction <s> Predicting flows (e.g., the traffic of vehicles, crowds, and bikes), consisting of the in-out traffic at a node and transitions between different nodes, in a spatio-temporal network plays an important role in transportation systems. However, this is a very challenging problem, affected by multiple complex factors, such as the spatial correlation between different locations, temporal correlation among different time intervals, and external factors (like events and weather). In addition, the flow at a node (called node flow) and transitions between nodes (edge flow) mutually influence each other. To address these issues, we propose a multitask deep-learning framework that simultaneously predicts the node flow and edge flow throughout a spatio-temporal network. Based on fully convolutional networks, our approach designs two sophisticated models for predicting node flow and edge flow, respectively. These two models are connected by coupling their latent representations of middle layers, and trained together. The external factor is also integrated into the framework through a gating fusion mechanism. In the edge flow prediction model, we employ an embedding component to deal with the sparse transitions between nodes. We evaluate our method based on the taxicab data in Beijing and New York City. Experimental results show the advantages of our method beyond 11 baselines, such as ConvLSTM, CNN, and Markov Random Field. <s> BIB007
Traffic flow prediction plays an important role and is a hot research field in urban flow forecasting BIB001 BIB002 BIB003 BIB004 , because the taxi GPS data can access easily relatively and it can represent the citizens's traffic behaviors without the limitation of fixed lines. Fox example, Jiang et al. BIB006 developed a deep learning framework which transforms geospatial data to images using Convolutional Neural Network(CNN) and residual networks for traffic prediction. Wu et al. BIB005 proposed a novel model, which combines the CNN and RNN to capture the spatial-temporal features as well as learn the importance of past traffic flow using attention mechanism. This method makes full use of the temporal and spatial characteristics of traffic flow to model and improve prediction performance. Since the forecast of traffic flow is affected by complex factors such as temporal relationship, spatial correlation, and other external factors (weather and events), it is more challenging to accurately predict traffic flow. Zhang et al. BIB007 studied a multitask deep learning framework to simultaneously forecast the node flow and edge flow in the spatial-temporal networks. The model outperforms 11 baselines and shows great prediction performance in traffic flow forecast.