As urbanization accelerates, the rise in private car usage has become a double-edged sword, symbolizing economic growth while exacerbating urban air pollution due to increased carbon emissions. This paper studies the problem of carbon emission prediction of private cars in urban environments, enabling effective carbon emissions reduction and energy conservation guidance. Existing methods struggle with costly carbon emission collection and rely on precise emission factors, inaccuracies in modeling spatial similarities across urban regions, and complexities in modeling global temporal variations. To solve these issues, the Multi-View CollAboRative graph network (MVCAR) framework is proposed for private car carbon emission prediction. MVCAR employs a trajectory-based method to estimate carbon emissions from private car mobility to represent the spatial-temporal carbon emissions effectively. To capture the geo-spatial and semantic regional similarities of the carbon emissions, MVCAR constructs multi-view graphs and utilizes multi-view graph convolution networks. Furthermore, MVCAR integrates collaborative gated recurrent networks to model the spatial-temporal correlations of carbon emissions. The collaborative gated recurrent networks include a multi-view gated recurrent unit (GRU) and a sequential GRU. The multi-view GRU models the multiple spatial-temporal correlations of carbon emissions. A learnable temporal module embeds various temporal features, and further feeds these features into sequential GRU to capture the temporal variations of carbon emissions. Finally, a collaborative strategy that synergistically combines multi-view and sequential GRUs through stacked training. Extensive experiments on real datasets demonstrate the superiority of the proposed MVCAR.

Vehicle trajectory collection usually faces challenges such as inaccurate and incomplete trajectory data, mainly due to missing trajectories caused by Global Navigation Satellite System (GNSS) outages. In this paper, a novel ensemble transfer regression framework is proposed for urban environments with transfer learning as the primary solution for constructing a fine-grained trajectory dataset during GNSS outages. First, GNSS and motion information are fused for the training process. Then, a regression-to-classification (R2C) process is employed to implement incremental training to adapt to dynamically changing environments. Third, to account for GNSS outages, transfer learning is integrated to construct a data filtering strategy that minimizes negative sample weights during the current scenario. Finally, a more accurate classification-type loss function for ensemble learning is designed to obtain the ensemble transfer regression model. We utilize real-world datasets to verify the accuracy of the comparative methods and the proposed framework in trajectory interpolation prediction. The experimental results show that our framework is significantly superior to the comparative methods.

Long-term evolution advanced (LTE-A) heterogeneous networks have been observed to offer reliable and service-differentiated communication, thereby enabling numerous mobile applications such as smart meters, remote sensors, and vehicular applications. This fact envisions the trend of Internet of Things (IoT) underlaying heterogeneous small cell networks. On this basis, this paper proposes an energy-efficient framework for such a scenario, where multitier heterogeneous small cell networks provide wireless connection and seamless coverage for mobile users and IoT nodes. In our proposed framework, an elastic cell-zooming algorithm based on the quality of service and traffic loads of end-users is performed by adaptively adjusting the transmission power of small cells in order to reduce energy consumption. In addition, aiming at the high energy efficiency of IoT underlaying small cell networks, a clustering-based IoT structure is used, where a SWIPT-CH selection algorithm is proposed to maximize the average residual energy of IoT nodes and to mitigate resource competition between IoT nodes and mobile users. Extensive simulations demonstrate that our proposed framework can significantly enhance the energy efficiency for IoT underlaying small cell networks with guaranteed outage probability.

Mobile-edge computing (MEC) has been witnessed as a promising solution for the vehicular task offloading. Due to the limited computing resource of individual MEC servers, it faces challenges when higher requirements are put forward for timely task processing of a large amount of computations in the emerging vehicular applications. In this article, we strive to realize the efficient vehicular task offloading via heat-aware MEC cooperation from the game theory perspective. Here, the heat indicates the vehicle density and is tightly related to the requests of vehicle users when they drive through the hot zones. Specifically, a deep learning-based prediction method is proposed, capturing the dynamic time-varying heat value of the hot zones based on the analysis of the real-world private car trajectory data. To identify the role of MEC in the cooperation, we take the time-delay constraint into consideration for the task offloading. To realize MEC grouping for task offloading in MEC cooperation, we formulate the MEC grouping as a utility maximization problem via designing a noncooperative game-theoretic strategy selection based on regret-matching. Furthermore, we derive the correlated equilibrium and prove that the fast convergence can be achieved. Extensive simulation results validate the effectiveness of the proposed vehicular task offloading approach under various system parameters, such as computation workload, time slots, and MEC servers number. The proposed method outperforms the existing methods, which is able to significantly reduce the task complete delay, and in the meantime enhance the MEC energy efficiency with end users' quality-of-experience guaranteed.

Understanding the travel behavior of private cars will generate promising solutions on addressing urban problems such as alleviating traffic congestion and improving transport services. In this paper, we focus on investigating the individual travel patterns of private car users based on a large-scale private car trajectory dataset. To achieve this goal, we first analyze the stop-and-wait information from the private car trajectory data and utilize DBSCAN method to implement clustering with the aim at identifying the frequently-visit places (FVPs). After that, we leverage Markov chain to study the spatial-temporal transition characteristics when private cars travel among their FVPs. Finally yet importantly, we design the concept of spatial-temporal entropy rate and conduct a quantitative study for measuring the regularity of each individual private car's mobility. We validate the proposed methodology based on a real-world dataset including 25,564 private cars driving during one month in China. Extensive experiments demonstrate that the proposed method outperforms the existing methods in terms of the accuracy on measuring the mobility behavior. Moreover, we observe that, on one side, the travel pattern is easier to mine from the private car users with fewer FVPs, on the other side, there are also a small number of users whose FVPs are large, while their mobility are relatively regular. Our work is the first effort to explore individual travel patterns of private car users via studying private car trajectory big data, thereby being able to provide new insight into the research of human travel activities, traffic management and urban planning.

Short-term traffic flow prediction plays a crucial component in transportation management and deployment. In this paper, a novel regression framework for short-term traffic flow prediction with automatic parameter tuning is proposed, with the SVR being the primary regression model for traffic flow prediction and the Bayesian Optimization being the major method for parameters selection. First, the preprocessing of raw traffic flow is carried out by seasonal difference to eliminate the non-stationary of the data. Then, Support Vector Regression model is trained by the pre-processed data. In order to optimize the model parameters, the generalization performance of SVR is modeled as a sample from a Gaussian process (GP). Bayesian optimization determines the parameters configuration of the regression model by optimizing the acquisition function over the GP. Finally, the optimal short-term traffic flow regression model is constructed through repeated GP update and iteratively multiple training of the model. Experiment results show that the accuracy of proposed method is superior to methods of classical SARIMA, MLP-NN, ERT and Adaboost.

Vehicle state including location and motion information plays an important role in various applications such as Internet of Vehicles (IoV), autonomous cars, and driving safety monitoring. Achieving accurate vehicle state is a challenging task in those applications due to the noise disturbances. Recent studies suggest that noise is not generally Gaussian distributed and many physical environments can be handled more accurately as non-Gaussian rather than Gaussian model. Inspired by this observation, we strive to improve the vehicle state estimation by investigating the effects of that assumption when process and measurement noises are non-Gaussian distributed. Here, process noise represents the noise during the state information processing. To that end, we exploit the generalized error distribution (GED) to compute the non-Gaussian probability density during the vehicle state estimation. We then derive extensive theoretical analysis targeting to estimate the parameters such as the mean and the variance (or covariance matrix) related to both process and measurement noises and reduce the computational burden of the distribution. Further, we propose a non-Gaussian particle filter for vehicle state estimation (nGPF-VSE) algorithm wherein we utilize the genetic operator resampling (GOR) technique to enhance the efficiency of particle filter (PF) relying on the selection of the importance sampling distribution. To evaluate the performance of the proposed approach, we conduct numerical simulations on the popular system of state-space equations and a real experiment for estimating the vehicle state. The results from the numerical simulations, experimental data and the statistical evaluation confirm that nGPF-VSE outperforms existing methods in terms of vehicle state accuracy.

Private cars, a class of small motor vehicles usually registered by an individual for personal use, constitute the vast majority of city automobiles and hence significantly affect urban traffic. In particular, private cars tend to stop-and-wait (SAW) in specific regions during daily driving. This SAW behavior produces a spatiotemporal aggregation effect, which facilitates the formation of urban hot zones. In this paper, we investigate the SAW behavior and aggregation effect based on large-scale private car trajectory data. Specifically, motivated by the first law of geography, we leverage the kernel density estimation (KDE) method and extend it to three dimensions to capture the density distribution of the SAW data. Furthermore, according to the inherent relationship between the present SAW density and future SAW aggregation, we propose a 3D-KDE-based prediction model to characterize the dynamic spatiotemporal aggregation effect. In addition, we design a modified inertia weight particle swarm optimization (MIW-PSO) algorithm to determine the optimal weight coefficients and to avoid local optima during SAW prediction. Extensive experiments based on real-world private car SAW data validate the effectiveness of our method for discovering dynamic aggregation effects, therein outperforming the current methods in terms of the Kullback-Leibler (KL) divergence, mean absolute error (MAE), and root mean square error (RMSE). To the best of the authors’ knowledge, our work is the first to utilize private car trajectory data to study the aggregation effect in urban environments, thereby being able to provide new insight into the study of traffic management and the evolution of urban traffic.

A road network is a critical aspect of both urban planning and route recommendation. This article proposes an efficient approach to build a fine-grained road network based on sparsely sampled private car trajectory data under complex urban environment. In order to resolve difficulties introduced by low sampling rate trajectory data, we concentrate sample points around intersections by utilizing the turning characteristics from the large-scale trajectory data to ensure the accuracy of the detection of intersections and road segments. In front of complex road networks including many complex intersections, such as the overpasses and underpasses, we first layer intersections into major and minor one, and then propose a simplified representation of intersections and corresponding computable model based on the features of roads, which can significantly improve the accuracy of detected road networks, especially for the complex intersections. In order to construct fine-grained road networks, we distinguish various types of intersections using direction information and detected turning limit. To the best of our knowledge, our road network building method is the first time to give fine-grained road networks based on low-sampling rate private car trajectory data, especially able to infer the location of complex intersections and its connections to other intersections. Last but not the least, we propose an effective parameter selection process for the Density-Based Spatial Clustering of Applications with Noise based clustering algorithm, which is used to implement the reliable intersection detection. Extensive evaluations are conducted based on a real-world trajectory dataset from 1,345 private cars in Futian district, Shenzhen city of China. The results demonstrate the effectiveness of the proposed method. The constructed road network matches close to the one from a public editing map OpenStreetMap, especially the location of the road intersections and road segments, which achieves 92.2% intersections within 20m and 91.6% road segments within 8m.

Mobile Edge Cloud Computing (MECC) has becoming an attractive solution for augmenting the computing and storage capacity of Mobile Devices (MDs) by exploiting the available resources at the network edge. In this work, we consider computation offloading at the mobile edge cloud that is composed of a set of Wireless Devices (WDs), and each WD has an energy harvesting equipment to collect renewable energy from the environment. Moreover, multiple MDs intend to offload their tasks to the mobile edge cloud simultaneously. We first formulate the multi-user multi-task computation offloading problem for green MECC, and use Lyaponuv Optimization Approach to determine the energy harvesting policy: how much energy to be harvested at each WD; and the task offloading schedule: the set of computation offloading requests to be admitted into the mobile edge cloud, the set of WDs assigned to each admitted offloading request, and how much workload to be processed at the assigned WDs. We then prove that the task offloading scheduling problem is NP-hard, and introduce centralized and distributed Greedy Maximal Scheduling algorithms to resolve the problem efficiently. Performance bounds of the proposed schemes are also discussed. Extensive evaluations are conducted to test the performance of the proposed algorithms.

The advances in mobile sensing and vehicle cloud service techniques have generated massive spatial-temporal trajectory data, which has caused the crises of storage and communication. In this paper, we propose a novel Opportunistic Compression and Transmission approach, namely OCT, with aims of reducing trajectory transmission overhead and storage cost. We first present the design of trajectory collection terminal based on GPS & OBD wherein the main process of OCT can be implemented rather than the cloud server. Within the proposed OCT, we devise a map-matching method based on MIV-matching and calibrating trajectory, which significantly reduces sampling errors of raw trajectories. Then we divide trajectory data into two parts, i.e. spatial and temporal parts, and realize compression operation separately. By using a prediction model based on historical trajectory velocity, we make use of opportunistic transmission of trajectory data from the GPS & OBD terminal to vehicle cloud server and thus dramatically decrease the transmission overhead. The proposed OCT not only realizes real-time trajectory preprocessing and compressing, but also ensures high trajectory compression ratio. To validate the performance of the OCT, we collect a large-scale private car trajectory data from real urban environments. Extensive experiments verify the effectiveness and superiority of the proposed method.

Heterogeneous small cell networks (HSCN), as a promising paradigm to increase end-user data rates and improve the overall capacity, is expected to be a key cellular architecture in 5G wireless networks. However, energy consumed in HSCN is considerable due to the massive use of small cells. In this paper, we investigate the energy consumption issue which stems from the enormous number of running small cell base stations (SBSs) deploying in the HSCN. We first propose power consumption models so as to characterize the active state and the idle state of SBSs, respectively. Then two sleep modes for SBSs tier, i.e. random sleep mode and load-awareness dynamic sleep mode, are proposed. The random sleep is designed based on a binomial distribution of the SBS operation probability. Through the analysis on activeness of SBSs, we define the operation probability for the SBS applying the proposed dynamic sleep mode is associated to its traffic load level. The closed-form expressions of success probability for coverage, which is used to decide whether an active user can connect to a SBS successfully, are derived for the proposed sleep modes. Energy consumption minimizations are presented for the two proposed sleep modes under the success probability constraint. Simulation results prove the effectiveness of the proposed two sleep modes. Different energy saving gains can be achieved via using of the energy saving strategy. The superior of the dynamic sleep mode by comparing the random sleep is also verified in terms of energy consumption, success probability and power efficiency.

Mobility management and handover, in the presence of the macro-small wireless cellular networks, are of the critical challenging issues, owing to the large- scale small cells deploying and frequent mobility of user equipment (UE). Existing literatures include variously studied the inbound handover, namely the UE's movement from a macrocell to a small cell. The outbound handover (OHO), which refers to the UE's movement from a small cell to a macrocell, is also of importance. In this paper, instead of the concentric circles based handover regions, we propose a novel model for the OHO regions by utilizing the principle of the circle of Apollonius. Based on the mathematical analysis for the boundaries of OHO regions, we derive the closed-form expression to characterize the relationship between the OHO hysteresis margin and UE's mobility in terms of the velocity and the moving direction. Then, we develop the UE's movement based regulatory algorithm to adaptively adjust hysteresis for achieving the proper OHO. Simulation results show the effectiveness of the proposed OHO model. The proposed hysteresis adjusting algorithm outperforms the existing schemes in terms of the decrease for radio link failure and pingpong effect during OHO process.

In this paper, we investigate the hierarchical clustering for dense small cells and devise non-cooperative game-theoretic scheme, with aim at increasing the network throughput and minimizing both cross- and co-tier interference. By studying the distances between the small cells and the requirements of cluster formation, we propose a novel hierarchical clustering scheme for the densely deployed small cells, which consists of two phases, i) an absorption mechanism is designed to form the small cells into clusters; ii) to balance cluster populations, a segmentation algorithm is proposed for the clusters that contains excessive small cells. Within each small cell cluster, the spectrum is split into central frequency and marginal frequency which can be reused in a FFR manner. Then, we formulate the spectrum allocation and interference mitigation issues as a non-cooperative game, in which a game theoretical strategy optimization algorithm based on regret-matching is proposed to reach the correlated equilibrium. Numerical results reveal that our approach can achieve the correlated equilibrium with fast convergence and it is effective in offloading traffic and increasing the system throughput in dense small cell networks.

Land vehicle localization and navigation mainly relies on the Global Positioning System (GPS)/Inertial Navigation System (INS) integration. In this paper, we propose a unified incremental regression framework that enables vehicle localization with high accuracy in urban environment. Within the framework, we propose a nonlinear Gauss Process Regression (GPR) approach to perform vehicle position prediction during GPS outages. By mapping nonlinear data into high-dimensional space by kernel function, the proposed GPR based approach is able to deal with the nonlinearity issue in GPS denied environment. We design a Particle Swarm Optimization (PSO) based algorithm to optimize GPR hyper-parameters, which are tuned with high time efficiency for vehicular position prediction. By real-world road experiments, the proposed method is evaluated against Artificial Neural Network (ANN), Support Vector Machine Regression (SVR) and Partial Least Squares Regression (PLSR). The results reveal that the proposed outperforms the others by 22.8%-65.2% improvement in the positional accuracy.

Short-term traffic flow forecasting has been a crucial component in the area of intelligent transportation systems (ITS), which plays a significant role in operating traffic management systems and dynamic traffic assignment effectively as well as proactively. In this paper, a novel short-term traffic flow prediction method called Ensemble Real-time Sequential Extreme Learning Machine (ERS-ELM) with simplified single layer feed- forward networks (SLFN) structure under freeway peak traffic condition and non-stationary condition is proposed. By quickly training historical data and incrementally updating model with new arrived data, ERE-ELM has the characteristics of less training time consumption and high prediction accuracy. Ensemble mechanism is also used to improve stability and robustness. Experiment results show that average mean absolute percentage error (MAPE), test root mean square error (RMSE) as well as training time consumption of proposed method is superior to classical Wave-NN, MLP-NN and ELM methods.

Abstract In this paper, we investigate the inbound handover confusion in the two‐tier macrocell‐small cell networks with help of mobility prediction. Instead of studying the mobile user's (MU) movement, we propose an analytical model for the activity status of small cells, which is to exploit the statistical property of inbound handover events that would happen in small cells. We design the cell status prediction algorithm to obtain the optimal prediction outcome of the next status for the small cells. On avoiding the handover confusion, we develop a dynamic allocation approach of physical cell identifier according to the prediction results. We design (i) the cell status prediction‐based strategy, by which the dedicated PCIs will be assigned to the small cells with busy activity status while the other small cells share the public PCIs, and (ii) an integrated strategy in order to fully exploit the usage of PCI. We formulate the preference relation for small cells via reference signal received quality relation integrated with status prediction information using Bayesian average method. Simulation results reveal that the proposed algorithms yield higher accuracy than the conventional methods; in the meantime, handover confusions can be reduced significantly during the inbound handover. Copyright © 2016 John Wiley & Sons, Ltd.

In severe non-line-of-sight environment, the three-dimensional localization methods face a major challenge due to the undetectable directed path and the unavailable arrival angles. In this paper, we proposed a novel three-dimensional localization scheme by exploiting the distance and departure angle of single-bounce path, which is capable in non-line-of-sight localization even with lacking information of spatial arrival angles. With the help of height information of the scattering plane, we study the three-dimensional spatial location relationship among the reference station, unknown node and scatterers and devise a new optimization algorithm which can apply in severe non-line-of-sight environment. Furthermore, the performance of the algorithm is studied by using root mean-square error analysis and Cramer-Rao lower bound. The simulation results show that the derived analytical results closely match the simulated results. The numerical results of cumulative distribution function in location error demonstrate that utilizing sufficient single-bounce path signals can yield to acceptable three-dimensional positioning accuracy in severe non-line-of-sight multipath environments.

Vehicle position prediction has become more and more critical for most applications in intelligent transportation systems (ITS). Prediction based INS/GPS integration provides continuous and reliable navigation solution when compared to standalone Inertial Navigation System (INS) or Global Positioning System (GPS). Although there have been several research works for fusing INS and GPS data to bridge navigation during GPS outages, most of them are offline methods and do not consider sensors data fluctuation due to traffic incident, inclement weather conditions or rush hour. This paper proposes a supervised statistical learning technique called Online Support Vector Machine for Regression (OL-SVR) for the prediction of vehicle position. During GPS availability, the OL-SVR models INS errors by fusing the INS and GPS data; meanwhile during outages, the trained OL-SVR method is utilized to predict accurate vehicle position. The proposed method is compared with two well-known prediction techniques including Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN). Experiments conducted at rush hour on real urban roads and simulation results prove that OL-SVR is more efficient and accurate in position prediction than PLSR and ANN, achieving an accuracy improvement of 20.3%-64.8%.

In this paper, we investigate the energy consumption issue which stems from the enormous number of running small cells deploying in the heterogeneous networks. We first propose two power consumption models so as to characterize the active state and the idle state of small cells respectively. Then two sleep modes for small cells tier, random sleep mode and load-awareness dynamic sleep mode, are proposed. The random sleep is designed based on a binomial distribution of the small cell operation probability. Through the analysis on activeness of small cell base stations (SBSs), we define the operation probability for the small cell applying the proposed dynamic sleep mode is associated to its traffic load level. The closed-form expressions of success probability, which is used to decide whether an active user can connect to a small cell successfully, are derived for the proposed two sleep modes. Energy consumption minimizations are presented for each of the proposed sleep modes with condition on success probability constraint. Simulation results prove the effectiveness of the proposed two sleep modes. Different energy saving gains can be achieved via using of the concrete sleep mode. The superior of the dynamic sleep mode by comparing the random sleep is also verified in terms of energy consumption, success probability and power efficiency.

In this study, the authors develop a novel solution for hybrid global navigation satellite systems, differential navigation satellite systems and time of arrival cooperative positioning (CP) based on iterative finite difference particle filter (PF) in GNSS‐terrestrial navigation and challenging environments. A variant of finite difference filters called divided difference filter (DDF) was used as an importance density for particle generation. Various proposal distributions have been proposed to improve the performance of PF, but practical situations have encouraged the researchers to design better candidate for proposal distributions in order to gain better performance especially for hybrid CP system. The author's proposed method named hybrid cooperative particle‐based DDF solves the problem of linearisation of non‐linear functions that are based on Jacobian matrices which often cannot be applied in practical applications of non‐linear estimation techniques. An iterative reweighted information filter based on the extended Kalman filter (KF) was integrated during the measurement update phase to smooth the output of the DDF used for particles update. Simulation results based on a realistic outdoor scenario show that the proposed solution outperforms some well‐known state‐of‐the‐art in hybrid CP systems, such as hybrid cooperative unscented KF in terms of accuracy and availability and provides good performance even in challenging conditions.