Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. LEARNING DRIVEN TECHNIQUES <s> Providing travel time information to travelers on available route alternatives in traffic networks is widely believed to yield positive effects on individual drive behavior and (route/departure time) choice behavior, as well as on collective traffic operations in terms of, for example, overall time savings and-if nothing else-on the reliability of travel times. As such, there is an increasing need for fast and reliable online travel time prediction models. Previous research showed that data-driven approaches such as the state-space neural network (SSNN) are reliable and accurate travel time predictors for freeway routes, which can be used to provide predictive travel time information on, for example, variable message sign panels. In an operational context, the adaptivity of such models is a crucial property. Since travel times are available (and, hence, can be measured) for realized trips only, adapting the parameters (weights) of a data-driven travel time prediction model such as the SSNN is particularly challenging. This paper proposes a new extended Kalman filter (EKF) based online-learning approach, i.e., the online-censored EKF method, which can be applied online and offers improvements over a delayed approach in which learning takes place only as realized travel times are available. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. LEARNING DRIVEN TECHNIQUES <s> Route planning in uncertain and dynamic networks has recently emerged as an active and intense area of research, both due to industry needs and technological advances. This paper investigates methods to predict travel times along the arcs and estimate arrival times at the nodes of a stochastic and dynamic network in real time. It is shown that, under fairly mild conditions, the developed travel and arrival time estimators are unbiased and that the error variance of the arrival time estimator is bounded. Simulation results are used to demonstrate the efficiency of the proposed algorithm. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. LEARNING DRIVEN 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> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. LEARNING DRIVEN TECHNIQUES <s> Modeling and predicting human and vehicle motion is an active research do- main. Due to the difﬁculty of modeling the various factors that determine motion (eg in- ternal state, perception, etc.) this is often tackled by applying machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (eg camera, laser scanner), and then using that model to predict further motion. Un- fortunately, most current techniques use off-line learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has ﬁnished. In this paper, we present an approach which is able to learn new motion patterns incrementally, and in parallel with prediction. Our work is based on a novel extension to Hidden Markov Models called Growing Hidden Markov models. <s> BIB004 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. LEARNING DRIVEN TECHNIQUES <s> A key research issue in activity recognition in real-world applications, such as in intelligent transportation systems (ITS), is to automatically learn robust models of activities that require minimal human training. In this paper, we contribute a novel approach for learning sequenced spatiotemporal activities in outdoor traffic intersections. Concretely, by representing the activities as sequences of actions, we contribute a semisupervised learning algorithm that learns activities as complete stochastic context-free grammars (SCFGs), namely, the grammar structure and the parameters. Our approach has been implemented and tested on real-world scenes, and we present experimental results of the grammar learning and activity recognition applied to data collection and traffic monitoring applications using video data. <s> BIB005 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. LEARNING DRIVEN TECHNIQUES <s> Due to modern pervasive wireless technologies and high-performance monitoring systems, spatio-temporal information plays an important role in areas such as intelligent transportation systems (ITS), surveillance, scheduling, planning, or industrial automation. Security or criminal/terrorist threat prevention in modern ITS is one of today's most relevant concerns. This paper presents an algorithm for online spatio-temporal risk assessment in urban environments. In its first phase, the algorithm uses the online nearest neighbor clustering (NNC) algorithm to identify a set of significant places. In the second phase, a fuzzy inference engine is employed to quantify the level of risk that each significant place poses to the place of interest (e.g., vehicle, person, building, or an object of high assets). The contributions of the presented algorithm are given as follows: 1) recognition and extraction of the set of the most significant places; 2) dynamic adaptation of the solution to time-dependent traffic distributions; 3) parametric control by adjusting geographical proximity threshold, significance threshold, and discount factor; and 4) online risk assessment. The performance of the algorithm was demonstrated on a problem of traffic density estimation and risk assessment in a virtual urban environment. <s> BIB006 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. LEARNING DRIVEN 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> BIB007 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. LEARNING DRIVEN TECHNIQUES <s> Eco-routing is a vehicle navigation method that aims to minimize fuel or energy consumption for a given trip. It is based on a hypothesis that we can trade extra travel time for lower consumption. While the hypothesis was experimentally verified the design of a method that would fully exploit its potential proves challenging. Current solutions hinge on assumption that energy spent on any given road does not change in time. We challenge validity of this assumption by studying performance of such methods in detailed second-by-second simulation that pronounces the time-dependencies. This allows us to quantify the real savings attainable with current eco-routing. <s> BIB008 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. LEARNING DRIVEN TECHNIQUES <s> We consider a cellular network where mobile transceiver devices that are owned by self-interested users are incentivized to cooperate with each other using tokens, which they exchange electronically to “buy” and “sell” downlink relay services, thereby increasing the network's capacity compared to a network that only supports base station-to-device (B2D) communications. We investigate how an individual device in the network can learn its optimal cooperation policy online , which it uses to decide whether or not to provide downlink relay services for other devices in exchange for tokens. We propose a supervised learning algorithm that devices can deploy to learn their optimal cooperation strategies online given their experienced network environment. We then systematically evaluate the learning algorithm in various deployment scenarios. Our simulation results suggest that devices have the greatest incentive to cooperate when the network contains (i) many devices with high energy budgets for relaying, (ii) many highly mobile users (e.g., users in motor vehicles), and (iii) neither too few nor too many tokens. Additionally, within the token system, self-interested devices can effectively learn to cooperate online, and achieve up to 20 percent throughput gains on average compared to B2D communications alone, all while selfishly maximizing their own utilities. <s> BIB009 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. LEARNING DRIVEN TECHNIQUES <s> This paper proposes a novel routing protocol for OppNets called MLProph, which uses machine learning (ML) algorithms, namely decision tree and neural networks, to determine the probability of successful deliveries. The ML model is trained by using various factors such as the predictability value inherited from the PROPHET routing scheme, node popularity, node's power consumption, speed, and location. Simulation results show that MLProph outperforms PROPHET+, a probabilistic-based routing protocol for OppNets, in terms of number of successful deliveries, dropped messages, overhead, and hop count, at the cost of small increases in buffer time and buffer occupancy values. <s> BIB010
The existing learning-based mechanisms utilize different metrics which serve as an input for the analysis component BIB001 . Although, these metrics (such as trip duration) might be useful, however the challenge is that they depend on the traffic conditions, which are highly unpredictable and change over time. Therefore, these shallow traffic prediction models are not reliable for accurate modeling of the problem and real-world applications BIB008 . To solve the problem, van Lint BIB001 propose a new traffic prediction model which utilizes the temporal and spatial correlations. This learning model uses generic traffic flow features in a layered fashion. The area of concern for this model is high computational cost and limited scalability. In BIB002 , a predictor-corrector Kalman filter for travel and arrival time predictions is proposed. The authors use mathematical model with the node-arc network in which arcs represent the roadways while nodes represent the junctions. However, the focus of this contribution was more on mathematical modeling instead of actual traffic flows and condition in the network. Linda and Manic BIB006 use the active nearest neighbor for the identification of important places. In the second phase of the proposed algorithm, fuzzy logic is employed to compute the risk-level for the places identified in the first step. For the purpose of autonomous navigation in cities, it is extremely important to analyze the trajectory pattern of the vehicles. One of the hurdles in such analysis is the inefficiency of the algorithms that rely on offline trajectory data BIB003 . This means after completing the learning stage, these algorithms are unable to learn diverse patterns. The solution to this problem is provided in BIB004 which focuses on incremental learning of trajectory patterns along with the predictions. The authors propose a growing hidden Markov model in which the constraints are derived through online learning for efficient identification of new trajectories. Angkititrakul et al. BIB007 predict the trajectory of the vehicle using Gaussian model by considering the parameters such as lanecrossing and driver correction events as inputs for learning. Several automatic learning models are discussed in literature considering the mobility in vehicular and D2D networks BIB005 , BIB009 . Veeraraghavan and Papanikolopoulos BIB005 propose a semi-supervised learning algorithm that represents the activities as sequence of actions and learns those activities as stochastic grammar. Similarly Mastronarde et al. BIB009 propose a supervised learning algorithm in which devices can learn the optimal cooperation strategy for providing the relaying services such as audio/video data. Another application of learning algorithms is routing decision in opportunistic networks BIB010 which uses decision trees and neural networks for efficient routing decisions. The parameters used for learning include routing scheme, device popularity, speed, location, and energy consumption.