Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. DATA FUSION BASED TECHNIQUES <s> The potential impact of advances in data mining, data fusion and information management on the efficient exploitation of the transport infrastructure are addressed. It is argued that the energy currently consumed in transport is poorly exploited for a number of reasons that the application of intelligent infrastructure analysis can significantly address: traffic congestion implies reduced fuel efficiency; the average number of people travelling per car is very low; and public/private transport combination options are difficult to plan. Relevant advances in intelligent information systems are reviewed, and a number of scenarios illustrating how these weaknesses might be addressed are presented. For each, the feasibility of the required technology and the timescales for deployment are discussed. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. DATA FUSION BASED TECHNIQUES <s> Path prediction is the only way that an active safety system can predict a driver's intention. In this paper, a model-based description of the traffic environment is presented - both vehicles and infrastructure - in order to provide, in real time, sufficient information for an accurate prediction of the ego-vehicle's path. The proposed approach is a hierarchical-structured algorithm that fuses traffic environment data with car dynamics in order to accurately predict the trajectory of the ego-vehicle, allowing the active safety system to inform, warn the driver, or intervene when critical situations occur. The algorithms are tested with real data, under normal conditions, for collision warning (CW) and vision-enhancement applications. The results clearly show that this approach allows a dynamic situation and threat assessment and can enhance the capabilities of adaptive cruise control and CW functions by reducing the false alarm rate. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. DATA FUSION BASED TECHNIQUES <s> Traffic flow forecasting is an important issue for the application of Intelligent Transportation Systems. Due to practical limitations, traffic flow data may be incomplete (partially missing or substantially contaminated by noises), which will aggravate the difficulties for traffic flow forecasting. In this paper, a new approach, termed the selective random subspace predictor (SRSP), is developed, which is capable of implementing traffic flow forecasting effectively whether incomplete data exist or not. It integrates the entire spatial and temporal traffic flow information in a transportation network to carry out traffic flow forecasting. To forecast the traffic flow at an object road link, the Pearson correlation coefficient is adopted to select some candidate input variables that compose the selective input space. Then, a number of subsets of the input variables in the selective input space are randomly selected to, respectively, serve as specific inputs for prediction. The multiple outputs are combined through a fusion methodology to make final decisions. Both theoretical analysis and experimental results demonstrate the effectiveness and robustness of the SRSP for traffic flow forecasting, whether for complete data or for incomplete data <s> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. DATA FUSION BASED TECHNIQUES <s> This paper presents a new approach to data fusion for automatic recognition, surveillance, and tracking in intelligent transportation systems. Robust data alignment (RDA), i.e., finding relational maps among a sequence of invariant feature data sets, is one of the key requirements for successful data fusion. To achieve RDA for correspondenceless data fusion, we construct a cost criterion based on the information theory and solve an optimization problem with a mixed search strategy that combines the Nelder-Mead simplex and random search methods. We evaluate the cost criterion and search strategy by a numerical stability test and suggest an outlier rejection technique for refining the previous feature data and, at the same time, extracting moving vehicles that are contained in the collected outliers. Experimental results on a video sequence that is collected from an unmanned aerial vehicle indicate the potential of aerial monitoring and tracking systems built on our information-theoretic RDA. <s> BIB004 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. DATA FUSION BASED TECHNIQUES <s> In this paper, an aggregation approach is proposed for traffic flow prediction that is based on the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and neural network (NN) models. The aggregation approach assembles information from relevant time series. The source time series is the traffic flow volume that is collected 24 h/day over several years. The three relevant time series are a weekly similarity time series, a daily similarity time series, and an hourly time series, which can be directly generated from the source time series. The MA, ES, and ARIMA models are selected to give predictions of the three relevant time series. The predictions that result from the different models are used as the basis of the NN in the aggregation stage. The output of the trained NN serves as the final prediction. To assess the performance of the different models, the naive, ARIMA, nonparametric regression, NN, and data aggregation (DA) models are applied to the prediction of a real vehicle traffic flow, from which data have been collected at a data-collection point that is located on National Highway 107, Guangzhou, Guangdong, China. The outcome suggests that the DA model obtains a more accurate forecast than any individual model alone. The aggregation strategy can offer substantial benefits in terms of improving operational forecasting. <s> BIB005 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. DATA FUSION BASED TECHNIQUES <s> The prediction of lane changes has been proven to be useful for collision avoidance support in road vehicles. This paper proposes an interactive multiple model (IMM)-based method for predicting lane changes in highways. The sensor unit consists of a set of low-cost Global Positioning System/inertial measurement unit (GPS/IMU) sensors and an odometry captor for collecting velocity measurements. Extended Kalman filters (EKFs) running in parallel and integrated by an IMM-based algorithm provide positioning and maneuver predictions to the user. The maneuver states Change Lane (CL) and Keep Lane (KL) are defined by two models that describe different dynamics. Different model sets have been studied to meet the needs of the IMM-based algorithm. Real trials in highway scenarios show the capability of the system to predict lane changes in straight and curved road stretches with very short latency times. <s> BIB006 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. DATA FUSION BASED TECHNIQUES <s> On current railway systems, it is becoming ever more necessary to install safety elements to avoid accidents. One of the causes that can provoke serious accidents is the existence of obstacles on the tracks, either fixed or mobile. In this paper, a multisensory system that can inform the monitoring system about the existence of obstacles is proposed. The system for obstacle detection consists of two emitting and receiving barriers, which are placed on opposing sides of the railway, respectively, and use infrared and ultrasonic sensors, thus establishing different optical and acoustic links between them. The interruption of one or several links should produce an alarm. However, even without the existence of objects, degradation of links could occur due to atmospheric attenuation, solar radiation, etc., also producing an activation of the alarm system. Since detection is based on the lack of radiation in the detectors, the use of complementary sensors for the same task is justified. Since the minimum size of an object for which an alarm is required to be generated is 50 × 50 × 50 cm, in some situations, several links are interrupted; however, alarms should not be generated. Typical cases are the flight of leaves or the movement of small animals in the scanned area. To avoid alarm activation in such situations, this paper proposes the combined use of diverse techniques of data fusion, based on fuzzy logic and the Dempster-Shafer theory of evidence, to validate the existence of objects, providing a highly reliable detection system. <s> BIB007 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. DATA FUSION BASED TECHNIQUES <s> In this paper, we propose a new framework for discriminating the initial maneuver of a lane-crossing event from a driver correction event, which is the primary reason for false warnings of lane departure prediction systems (LDPSs). The proposed algorithm validates the beginning episode of the trajectory of driving signals, i.e., whether it will cause a lane-crossing event, by employing driver behavior models of the directional sequence of piecewise lateral slopes (DSPLS) representing lane-crossing and driver correction events. The framework utilizes only common driving signals and allows the adaptation scheme of driver behavior models to better represent individual driving characteristics. The experimental evaluation shows that the proposed DSPLS framework has a detection error with as low as a 17% equal error rate. Furthermore, the proposed algorithm reduces the false-warning rate of the original lane departure prediction system with less tradeoff for the correct prediction. <s> BIB008 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> B. DATA FUSION BASED TECHNIQUES <s> Short-term traffic volume forecasting is widely recognized as an important element of intelligent transportation systems, because the accuracy of predictive methods determines the performance of real-time traffic control and management to some extent. The goal of this article is to propose a two-dimensional prediction method using the Kalman filtering theory based on historical data. In the first dimension, using Kalman filtering, we predict the values of traffic flows based on data from the current day and historical data separately. The two predicted values are fused using an equation with weight coefficients where the weight coefficients can be generated in real time in the process of prediction. Accordingly, in the second dimension, using Kalman filtering again, we obtain the predicted value of weight coefficients. In addition, some extreme cases during the process of weight coefficient prediction are discussed, and solutions are proposed as well. The accuracy of the two-dimensional forecasting method... <s> BIB009
Another approach for achieving high performance in BD networks is to use multiple models. For example the work presented in BIB005 utilizes moving average, autoregressive moving average, and exponential smoothing model for traffic flow predictions. The predictions from these models are utilized in aggregation stage by the neural networks providing the ultimate prediction. Several algorithms fuse the predictions from different models for short-term flow prediction and trajectory changes BIB009 , BIB006 . Toledo-Moreo and ZamoraIzquierdo BIB006 fuse the prediction from Kalman filtering in an equation of weighted coefficients. These weighted coefficients are generated in real-time during the prediction process. For efficient analysis and prediction of flow dynamics, Shawe et al. BIB001 suggest to utilize multi-source driven data fusion strategy. Such a strategy results in extremely broad and holistic information which increases the performance gain. Another data fusion approach is presented in BIB004 for surveillance and tracking of vehicles. The mechanism is based on robust data alignment that finds relational maps between the invariant feature datasets. These datasets consist of aligned images that are gathered from different unnamed aerial vehicles. Several solutions focus on utilizing multi-source information fusion that combines core evidence for highly dynamic, diverse, and conflicting data received through different sources BIB008 ,  . The ultimate objective is to extract a comprehensive estimate about an event by integrating information received from multiple sources. Angkititrakul et al. BIB008 analyze several approaches for fusing video streams and propose an algorithm for vehicle trajectory prediction considering the lane-crossing and driver correction events. One of the problems with the proposed approach is that it considers only the most common driving signals which limit the scope and application for adaptability. The approach used by Kong et al.  is based on multi-source information that includes fusing data from underground loop detector and GPS. The first part of the algorithm creates a credible and robust platform for the fusion of multi-source data, while, in the second part, wave theory is applied for estimating the mean speed. One of the issues with data collection using roadside sensors and GPS is that the collected data is sparse due to random distribution of vehicles in time and space. Mehta and Chana  discuss several techniques for flow estimation by considering parameters such as running time and accuracy of used data. It is observed that most of the data comes from probing (roadside sensors, GPS, etc.) at different sampling rates. The authors propose higher sampling interval which results in lower quantity and accuracy of data but provides efficiency in terms of processing. Polychronopoulos et al. BIB002 propose a hierarchical structure algorithm for predicting the trajectory of moving objects using the fusion of environmental and vehicle dynamic data. García et al. BIB007 present a multi-sensory system for the detection of obstacles by using a set of diverse techniques for data fusion. Sun and Zhang BIB003 suggest a new approach which integrates the spatial and temporal information for the flow forecast. For selecting the input variables, they use Pearson correlation coefficient. The resulting multiple outputs are then integrated using a fusion algorithm for final predictions.