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  • 期刊名称：

    Intelligent Transportation Systems, IEEE Transactions on

  • 中文名称： 智能交通系统，IEEE事务
  • 刊频：
  • ISSN： 1524-9050
  • 出版社： -
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  • 1.

    【24hr】Railway System Energy Management Optimization Demonstrated at Offline and Online Case Studies

    包量

    机译 离线和在线案例研究演示了铁路系统能源管理优化
    摘要：This paper presents the two level optimization algorithms: a centralized day-ahead and decentralized minute-ahead algorithm for energy management in an integrated mainline railway system. All energy players, such as trains, infrastructure facilities, wayside storages, and distributed energy resources, are considered in the simulation. The algorithms are developed to demonstrate the railway energy management system architecture. This paper demonstrates the validity of the algorithms and analyzes the simulation results in offline and online real case studies. In the online case study, the developed system for minute ahead optimization and real-time operation was tested on the Malaga-Fuengirola line (Spanish railway) for a few hours. The optimization is done regarding three different objectives: cost optimization or energy consumption optimization or power demand optimization.
  • 2.

    【24hr】Capability of Current Car-Following Models to Reproduce Vehicle Free-Flow Acceleration Dynamics

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    机译 当前的跟车模型能够再现车辆自由流加速动力学
    摘要：Microscopic traffic simulation models are widely used to assess the impact of measures and technologies on the road transportation system. The assessment usually involves several measures of performance, such as overall traffic conditions, travel time, energy demand/fuel consumption, emissions, and safety. In doing so, it is usually assumed that traffic models are able to capture not only traffic dynamics but also vehicle dynamics (especially to compute energy/fuel consumption, emissions, and safety). However, this is not necessarily the case with the possibility of achieving unreliable outcomes when extrapolating from traffic to measures of performance related to the vehicle dynamics. The objective of the present paper is to assess the capability of existing car-following models to reproduce observed vehicle acceleration dynamics. A set of experiments was carried out in the Vehicle Emissions Laboratories of the European Commission Joint Research Centre in order to generate relevant data sets. These experiments are used to test the performance of three well-known car-following models. Although all models have been largely tested against their capability to correctly reproduce traffic dynamics, the findings raise concerns about their capability (and thus of the traffic models using them) to predict the effect on the microscopic vehicle dynamics and thus on emissions and energy/fuel consumption. The results of the present work can be considered valid beyond the analyzed car-following models, as simple acceleration rules are usually assumed in the vast majority of the traffic simulation frameworks. Consequently, it can be concluded that there is a number.
  • 3.

    【24hr】Optimal Control for Reducing Congestion and Improving Safety in Freeway Systems

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    机译 减少高速公路系统拥堵并提高安全性的最佳控制
    摘要：The efficiency of freeway systems is often hindered by recurrent and non-recurrent congestion. The occurrence of these events is due not only to the high number of vehicles trying to use a shared infrastructure, but also by phenomena that temporarily deteriorate the capacity of the infrastructure itself. Among them, road accidents are considered as one of the primary causes of non-recurrent congestion. At the same time, several studies also identify traffic breakdowns as events leading to vehicle crashes. A specific goal of this research is to develop a global safety index that quantifies the expected number of crashes as a function of the current traffic state in the freeway system. Moreover, on the basis of this new index and the performance indicator generally adopted to evaluate the traffic delay, a coordinated ramp metering scheme is proposed jointly considering the reduction of travel times for the drivers and the improvement of safety in the freeway system. The control strategy is sought by defining a nonlinear optimal control problem with constrained control variables, solved by applying a specific gradient-based algorithm. The simulation analysis investigates the multi-objective nature of the problem assessing whether and to what extent the two components of the cost criterion are conflicting objectives.
  • 4.

    【24hr】Guest Editorial Special Issue on Knowledge Discovery From Mobility Data for Intelligent Transportation Systems

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    机译 客座社论特刊，关于从智能交通系统的移动性数据中发现知识
    摘要：The recent technological advances on telecommunications create a new reality on mobility sensing. Nowadays, we live in an era where ubiquitous digital devices are able to broadcast rich information about human mobility in real-time and at a high rate. Such fact exponentially increased the availability of large-scale mobility data which has been popularized in the media as the new currency, fueling the future vision of our smart cities that will transform our lives. The reality is that we just began to recognize significant research challenges across a spectrum of topics. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders on building knowledge discovery pipelines over such data sources. However, such availability also raises privacy issues that must be considered by both industrial and academic stakeholders on using these resources.
  • 5.

    【24hr】Efficient Transport Simulation With Restricted Batch-Mode Active Learning

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    机译 受限批处理模式主动学习的高效运输仿真
    摘要：Simulation modeling is a well-known and recurrent approach to study the performance of urban systems. Taking into account the recent and continuous transformations within increasingly complex and multidimensional cities, the use of simulation tools is, in many cases, the only feasible and reliable approach to analyze such dynamic systems. However, simulation models can become very time consuming when detailed input-space exploration is needed. To tackle this problem, simulation metamodels are often used to approximate the simulators' results. In this paper, we propose an active learning algorithm based on the Gaussian process (GP) framework that gathers the most informative simulation data points in batches, according to both their predictive variances and to the relative distance between them. This allows us to explore the simulators' input space with fewer data points and in parallel, and thus in a more efficient way, while avoiding computationally expensive simulation runs in the process. We take advantage of the closeness notion encoded into the GP to select batches of points in such a way that they do not belong to the same high-variance neighborhoods. In addition, we also suggest two simple and practical user-defined stopping criteria so that the iterative learning procedure can be fully automated. We illustrate this methodology using three experimental settings. The results show that the proposed methodology is able to improve the exploration efficiency of the simulation input space in comparison with non-restricted batch-mode active learning procedures.
  • 6.

    【24hr】Traffic Risk Mining From Heterogeneous Road Statistics

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    机译 异构道路统计中的交通风险挖掘
    摘要：At present, a large amount of traffic-related data is obtained manually and through sensors and social media, e.g., traffic statistics, accident statistics, road information, and users' comments. In this paper, we propose a novel framework for mining traffic risk from such heterogeneous data. Traffic risk refers to the possibility of occurrence of traffic accidents. Specifically, we focus on two issues: 1) predicting the number of accidents on any road or at intersection and 2) clustering roads to identify risk factors for risky road clusters. We present a unified approach for addressing these issues by means of feature-based non-negative matrix factorization (FNMF). In particular, we develop a new multiplicative update algorithm for the FNMF to handle big traffic data. Using real-traffic data in Tokyo, we demonstrate that the proposed algorithm can be used to predict traffic risk at any location more accurately and efficiently than existing methods, and that a number of clusters of risky roads can be identified and characterized by two risk factors. In summary, our work can be regarded as the first step to a new research area of traffic risk mining.
  • 7.

    【24hr】Taxi Demand Forecasting: A HEDGE-Based Tessellation Strategy for Improved Accuracy

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    机译 出租车需求预测：基于HEDGE的细分策略，可提高准确性
    摘要：A key problem in location-based modeling and forecasting lies in identifying suitable spatial and temporal resolutions. In particular, judicious spatial partitioning can play a significant role in enhancing the performance of location-based forecasting models. In this paper, we investigate two widely used tessellation strategies for partitioning city space, in the context of real-time taxi demand forecasting. Our study compares (1) the Geohash tessellation and (2) the Voronoi tessellation, using two distinct taxi demand data sets, over multiple time scales. For the purpose of comparison, we employ classical time-series tools to model the spatio-temporal demand. Our study finds that the performance of each tessellation strategy is highly dependent on the city geography, spatial distribution of the data, and the time of the day, and that neither strategy is found to perform optimally across the forecast horizon. We propose a combining algorithm that selects the best tessellation strategy at each time step, based on their recent performance. Our algorithm is a non-stationary variant of the well-known HEDGE algorithm for choosing the best advice from multiple experts. We show that the proposed strategy performs consistently better than either of the two tessellation strategies across the data sets considered, at multiple time scales, and with different performance metrics. We achieved an average accuracy of above 80% per km2for both data sets considered at 60 min aggregation levels.
  • 8.

    【24hr】Fast and Scalable Big Data Trajectory Clustering for Understanding Urban Mobility

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    机译 快速且可扩展的大数据轨迹聚类，以了解城市交通
    摘要：Clustering of large-scale vehicle trajectories is an important aspect for understanding urban traffic patterns, particularly for optimizing public transport routes and frequencies and improving the decisions made by authorities. Existing trajectory clustering schemes are not well suited to large numbers of trajectories in dense city road networks due to the difficulty in finding a representative distance measure between trajectories that can scale to very large datasets. In this paper, we propose a novel Dijkstra-based dynamic time warping distance measure, trajDTW between two trajectories, which is suitable for large numbers of overlapping trajectories in a dense road network as found in major cities around the world. We also propose a novel fast-clusiVAT algorithm that can suggest the number of clusters in a trajectory dataset and identify and visualize the trajectories belonging to each cluster. We conduct experiments on a large-scale taxi trajectory dataset consisting of 3.28 million trajectories obtained from the GPS traces of 15 061 taxis within Singapore over a period of one month. Our analysis finds 13 trajectory clusters spanning the major expressways of Singapore, each of which can be further divided into two sub-clusters based on the travel direction. For each cluster, we provide a time-based distribution of trajectories to yield insights into how urban mobility patterns change with the time of day. We compare the trajectory clusters obtained using our approach with those obtained using popular general and trajectory specific clustering frameworks: DBSCAN, OPTICS, NETSCAN, and NEAT. We demonstrate that the clusters obtained using our novel fast-clusiVAT framework are better than those obtained using other clustering schemes, evaluated based on two internal cluster validity measures: Dunn's and Silhouette indices. Moreover, our fast-clusiVAT algorithm achieves significant speedup over a comparable approach without loss of cluster quality.
  • 9.

    【24hr】On Evaluating Floating Car Data Quality for Knowledge Discovery

    包量

    机译 评估漂浮式汽车数据质量以进行知识发现
    摘要：Floating car data (FCD) denotes the type of data (location, speed, and destination) produced and broadcasted periodically by running vehicles. Increasingly, intelligent transportation systems take advantage of such data for prediction purposes as input to road and transit control and to discover useful mobility patterns with applications to transport service design and planning, to name just a few applications. However, there are considerable quality issues that affect the usefulness and efficacy of FCD in these many applications. In this paper, we propose a methodology to compute such quality indicators automatically for large FCD sets. It leverages on a set of statistical indicators (named Yuki-san) covering multiple dimensions of FCD such as spatio-temporal coverage, accuracy, and reliability. As such, the Yuki-san indicators provide a quick and intuitive means to assess the potential “value” and “veracity” characteristics of the data. Experimental results with two mobility-related data mining and supervised learning tasks on the basis of two real-world FCD sources show that the Yuki-san indicators are indeed consistent with how well the applications perform using the data. With a wider variety of FCD (e.g., from navigation systems and CAN buses) becoming available, further research and validation into the dimensions covered and the efficacy of the Yuki-San indicators is needed.
  • 10.

    【24hr】License Plate Recognition Data-Based Traffic Volume Estimation Using Collaborative Tensor Decomposition

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    机译 基于协同张量分解的车牌识别数据流量估计
    • 作者：Wei Shao;Ling Chen;
    • 刊名：Intelligent Transportation Systems, IEEE Transactions on
    • 2018年第11期
    摘要：The sparse problem of traffic volume data is unavoidable due to budget limits and device malfunctions in traffic systems. To address this problem, we propose a license plate recognition (LPR) data and collaborative tensor decomposition (CTD)-based method to estimate the sparse traffic volume data. The method works in two phases: first, a vehicle-time matrix is created based on LPR data, and non-negative matrix factorization is employed to analyze vehicle types; second, a road traffic volume tensor and the corresponding matrix of vehicle types are created, and people's check-in data and point of interest information are introduced to complement the sparse tensor with CTD. Experimental results show that our method outperforms traditional estimation methods, and it can estimate traffic volume data even when the missing rate is high.
  • 11.

    【24hr】MPTR: A Maximal-Marginal-Relevance-Based Personalized Trip Recommendation Method

    包量

    机译 MPTR：一种基于最大边际相关性的个性化旅行推荐方法
    摘要：Personalized trip recommendation has drawn much attention recently with the development of location-based services. How to utilize the data in the location-based social network to recommend a single Point of Interest (POI) or a sequence of POIs for users is an important question to answer. Recommending the latter is called trip recommendation that is a challenging study because of the diversity of trips and complexity of involved computation. This work proposes a maximal-marginal-relevance-based personalized trip recommendation method that considers both relevance and diversity of trips in trip planning. An ant-colony-optimization-based trip planning algorithm is developed to efficiently plan a trip. Finally, case studies and experiments illustrate the effectiveness of our method.
  • 12.

    【24hr】Game-Theoretic Electric Vehicle Charging Management Resilient to Non-Ideal User Behavior

    包量

    机译 对非理想用户行为具有适应性的博弈论电动汽车充电管理
    摘要：In this paper, an electric vehicle (EV) charging competition, among EV aggregators that perform coordinated EV charging, is explored while taking into consideration potential non-ideal actions of the aggregators. In the coordinated EV charging strategy presented in this paper, each aggregator determines EV charging start time and charging energy profiles to minimize overall EV charging energy cost by including consideration of the actions of the neighboring aggregators. The competitive interactions of the aggregators are modeled by developing a two-stage non-cooperative game among the aggregators. The game is then studied under prospect theory to examine the impacts of non-ideal actions of the aggregators in selecting EV charging start times according to subjectively evaluating their opponents' actions. It is shown that the noncooperative interactions among the aggregators lead to a subgame perfect E-Nash equilibrium when the game is played with either ideal, or non-ideal, actions of the aggregators. A case study presented demonstrates that the benefits of the coordinated EV charging strategy, in terms of energy cost savings and peakto-average ratio reductions, are significantly resilient to non-ideal actions of the aggregators.
  • 13.

    【24hr】A Fully Automated Approach to Extract and Assess Road Cross Sections From Mobile LiDAR Data

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    机译 从移动LiDAR数据提取和评估道路横截面的全自动方法
    摘要：Road cross sections are designed to ensure safe operation of highways. Tangent segments are typically designed with cross slopes to ensure efficient drainage of water off the road's surface, likewise, on horizontal curves the cross section is superelevated (tilted) to help vehicles counteract centrifugal forces. In both cases, ineffective slopes that do not meet design requirements, put vehicles at risk of overturning and skidding. Similarly, if deficiencies exist in side slopes, the chance of recovery for vehicles that run-of-the-road decreases substantially. Thus, transportation agencies must constantly assess elements of a road's cross section to ensure that they meet current design standards throughout their service life. The microscopic nature of cross sectional elements makes measuring such information time consuming, highly disruptive to traffic, and resource intensive. To facilitate more efficient assessments of such features, this paper proposes a novel algorithm to extract road cross sections from light detection and ranging data. The algorithm involves estimating vectors which intersect the road's axis, whereby points within proximity to the vectors are retained and extracted. Slope information is then measured off the retained points. The proposed algorithm is fully automated and employs multivariate adaptive regression splines to identify locations of change in slope. The algorithm was tested on two highway segments in Alberta. The high efficiency and precise manner in which the slope data was extracted, demonstrates the value of using the proposed algorithm in performing network-level assessment of road cross sections.
  • 14.

    【24hr】Simplifying the Formal Verification of Safety Requirements in Zone Controllers Through Problem Frames and Constraint-Based Projection

    包量

    机译 通过问题框架和基于约束的投影简化区域控制器中安全要求的形式验证
    摘要：Formal methods have been applied widely to verifying the safety requirements of communication-based train control (CBTC) systems, while the problem situations could be much simplified. In industrial practices of CBTC systems, however, huge complexity arises, which renders those methods nearly impossible to apply. In this paper, we aim to reduce the state space of formal verification problems in zone controller, a sub-system of a typical CBTC. We achieve the simplification goal by reducing the total number of device variables. To do this, two projection methods are proposed based on problem frames and constraints, respectively. The problem frame-based method decomposes the system according to sub-properties through functional decomposition, while the constraint-based projection method removes redundant variables. Our industrial case study demonstrates the feasibility through an evaluation, confirming that these two methods are effective in reducing the state spaces of complex verification problems in this application domain.
  • 15.

    【24hr】Signal Multiobjective Optimization for Urban Traffic Network

    包量

    机译 城市交通网络信号多目标优化
    • 作者：Xiang Li;Jian-Qiao Sun;
    • 刊名：Intelligent Transportation Systems, IEEE Transactions on
    • 2018年第11期
    摘要：This paper proposes a multiobjective optimization method for signal control design at intersections in urban traffic network. The cell transmission model is employed for macroscopic simulation of the traffic. Additional rules are introduced to model different route choices from origins to destinations. Vehicle turning, merging, and diverging behaviors at intersections are considered. A multiobjective optimization problem (MOP) is formulated considering four measures in network traffic performance, i.e., maximizing system throughputs, minimizing traveling delays, enhancing traffic safety, and avoiding spillovers. The design parameters for an intersection include turning signal type, cycle time, signal offset, and green time in each phase. The resulting high-dimensional MOP is solved with the genetic algorithm (GA). An algorithm is proposed to assist the user to select and implement the optimal designs from the Pareto optimal solution set. A case study in a grid network of nine intersections is carried out to test the optimization algorithm. It is observed that the proposed method is able to achieve the optimal network performance with different traffic demands. The convergence and coefficient selection of GA are discussed. The guidelines for network signal design and operation from the current studies are presented.
  • 16.

    【24hr】The Impact of Electric Mobility Scenarios in Large Urban Areas: The Rome Case Study

    包量

    机译 大城市地区电动出行方案的影响：罗马案例研究
    摘要：In this paper, we evaluate the changes in energy demand and resulting climate change and air pollutant emissions from the electrification of both the private vehicle fleet and the public transport fleet in the city of Rome, Italy. This paper provides a well-to-wheel analysis and considers two alternative hypotheses for the vehicles fleet renewal up to 2025. A data-driven approach is followed, where real traffic patterns from floating car data are adopted as well as geo-referenced open data published in the General Transit Feed Specification format by Rome's public transport agency. Specific energy consumption models for electric vehicles have been calibrated, based on real driving cycles. Moreover, the economic benefit resulting from the reduction of externalities has been assessed.
  • 17.

    【24hr】Analytical Method for the Precise and Fast Prediction of Railway Running Times and its Applications

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    机译 铁路运行时间精确快速预测的解析方法及其应用
    摘要：The precise and fast prediction of running times is extremely important in railway operations and management. This paper deals with the analytical calculation of railway running times. It contains a new method for the computation of the acceleration process, leading to a fast and compact simulation framework. The approach is compared to a common algorithm, the velocity micro-step Euler-method. Additionally, the accuracy of the new acceleration calculation method is also compared to empirical acceleration behavior of a standard commuter train in Germany. As a result, three suggestions for the application of the algorithm (the calculation of minimum headway times, energy consumption of a train, and energy-efficient driving in the context of track capacity) are shown.
  • 18.

    【24hr】A Novel Resection–Intersection Algorithm With Fast Triangulation Applied to Monocular Visual Odometry

    包量

    机译 一种新颖的具有快速三角剖分的后方交会算法应用于单眼视觉里程表
    摘要：This paper proposes a new technique for two-view camera pose estimation applied to vehicular monocular visual odometry. The proposed method is based on the resection-intersection concept using Gauss-Newton non-linear regression to minimize projection residuals. Different from other solutions, the proposed method does not suppose fixed positions of points during resection but rather takes into account the adjustment in the 3-D position of points when calculating the new camera pose increment. A simplified fast two-view triangulation method is used to mitigate the cost of intense 3-D point position recalculation. The method is integrated into a monocular visual odometry solution and tested on the KITTI public dataset. The final system does not use loop closing or key frame selection, and RANSAC is replaced by sequential backward selection. The resection-intersection method presents robustness to errors in initial camera pose and rapid convergence rate, reaching the final camera pose in up to three iterations in 65% of the cases. A single thread Python/OpenCV implementation of the monocular visual odometry system on an Intel Core i7 spent around 75 ms per frame, of which one third was used in the resection-intersection step. The achieved results presented better accuracy than all other published monocular odometry works in the KITTI benchmark, in a simple solution using only two consecutive frames for camera pose estimation.
  • 19.

    【24hr】A Kalman Filter for Quasi-Dynamic o-d Flow Estimation/Updating

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    机译 用于拟动态o-d流量估算/更新的卡尔曼滤波器
    摘要：This paper proposes an extended Kalman filter for quasi-dynamic estimation/updating of o-d flows from traffic counts. The quasi-dynamic assumption-that is considering constant o-d shares across a reference period, whilst total flows leaving each origin may vary for each sub-period within the reference period-has been proven already realistic and effective in off-line o-d flows estimation using generalized least squares estimators. The specification of the state variables and of the corresponding transition and measurement equations of a quasi-dynamic extended Kalman filter are illustrated, and a closed-form linearization is presented under the assumption of an uncongested network and error-free assignment matrix. Results show satisfactory performance and parsimonious computational burden on real-size networks.
  • 20.

    【24hr】Estimating Inefficiency in Bus Trip Choices From a User Perspective With Schedule, Positioning, and Ticketing Data

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    机译 从用户的角度使用时间表，位置和票务数据估计公交出行选择的效率低下
    摘要：The availability of historical data on the global positioning systems' trajectories of vehicles and passenger boarding information for public bus fleets of large municipalities has given researchers and practitioners the opportunity to explore new challenges regarding the analysis of public transportation systems. This paper performs one such analysis as a case study examining the margin of improvement that passengers of a 1.8M people Brazilian city have when choosing their daily bus trips. In doing so, we document a number of not readily apparent challenges that must be overcome to leverage public transportation big data to policymakers, transportation systems operators, and citizens. Solutions are devised to each of these challenges and demonstrated on the analysis of the aforementioned 1.8M people city.
  • 21.

    【24hr】Spatio-Temporal Profiling of Public Transport Delays Based on Large-Scale Vehicle Positioning Data From GPS in Wrocław

    包量

    机译 基于弗罗茨瓦夫GPS大规模车辆定位数据的公共交通延误的时空分析
    摘要：In recent years, many studies on urban mobility based on large data sets have been published: most of them are based on crowdsourced GPS data or smart-card data. We present, what is to the best of our knowledge, the first exploration of public transport delay data harvested from a large-scale, official public transport positioning system, provided by the Wrocław municipality. We introduce the methodology to analyze the distribution of delays in public transport, enabling the improvement of timetables by making them more realistic, and thus improve passenger comfort. We evaluate the method considering the characteristics of delays between stops in relation to the direction, time, and delay variance of 1648 stop pairs from 16-mln delay reports. We construct a normalized feature matrix of likelihood of a given delay change happening at a given hour on the edge between two stops. We then calculate the distances between such matrices using the earth mover's distance and cluster them using hierarchical agglomerative clustering with Vor Hees's linkage method. As a result, we obtained six profiles of delay changes in Wrocław: edges nearly not impacting the delay at all, these not impacting the delay significantly, likely to cause strong increase of delay, these causing increase of delay, edges likely to cause strong decrease of delay, and finally these likely to cause decrease of delay (i.e., when a public transport vehicle is speeding). We analyze the spatial and mode of transport properties of each cluster and provide insights into reasons of delay change patterns in each of the detected profiles. Such insights can be successfully utilized in traffic structure optimization and transport model split.
  • 22.

    【24hr】Mining Smart Card Data for Travellers’ Mini Activities

    包量

    机译 挖掘旅行者迷你活动的智能卡数据
    • 作者：Boris Chidlovskii;
    • 刊名：Intelligent Transportation Systems, IEEE Transactions on
    • 2018年第11期
    摘要：In the context of public transport modeling and simulation, we address the problem of mismatch between simulated transit trips and observed ones. We point to the weakness of the current travel demand modeling process; the trips it generates are overly optimistic and do not reflect the real passenger choices. To explain the deviation of simulated trips from the observed trips, we introduce the notion of mini-activities the travelers do during the trips. We propose to mine the smart card data and identify characteristics that help detect the mini activities. We develop a technique to integrate them in the generated trips and learn such an integration from two available sources, the trip history and trip planner recommendations. For an input travel demand, we build a Markov chain over the trip collection and apply the Monte Carlo Markov Chain algorithm to integrate mini activities in such a way that the trip characteristics converge to the target distributions. We test our method on the trip data set collected in Nancy, France. The evaluation results demonstrate a very important reduction of the trip generation error, and a good capacity to cope with new simulation scenarios.
  • 23.

    【24hr】On Learning From Inaccurate and Incomplete Traffic Flow Data

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    机译 从不准确和不完整的交通流量数据中学习
    摘要：Today, we live in an era where pervasive sensor networks both collect and broadcast rich digital footprints about the human mobility. However, most of this data often comes in an incomplete and/or inaccurate fashion. In this paper, we propose a knowledge discovery framework to handle such issues in the context of automatic incident detection systems fed with traffic flow data. This framework operates in three steps: 1) it clusters sensors with a novel multi-criteria distance metric tailored for this purpose, followed by a heuristic rule that labels the abnormal groups; 2) then, a spatial cross-correlation framework identifies seasonal and individual abnormal readings to perform a more fine-grained filtering; and 3) finally, we propose a novel fundamental diagram that discovers the critical density of a given road section/spot on a data-driven fashion that is resistant to both outliers and noise within the input data. Large-scale experiments were conducted over traffic flow data provided by a major Asian highway operator. The obtained results illustrate well the contributions of this framework: it drastically reduces the noise within the raw data, and it also allows determining reliable definitions of traffic states (congestiono congestion) on a completely automated way.
  • 24.

    【24hr】Learning Low-Dimensional Representation of Bivariate Histogram Data

    包量

    机译 学习二元直方图数据的低维表示
    摘要：With an increasing amount of data in intelligent transportation systems, methods are needed to automatically extract general representations that accurately predict not only known tasks but also similar tasks that can emerge in the future. Creation of low-dimensional representations can be unsupervised or can exploit various labels in multi-task learning (when goal tasks are known) or transfer learning (when they are not) settings. Finding a general, low-dimensional representation suitable for multiple tasks is an important step toward knowledge discovery in aware intelligent transportation systems. This paper evaluates several approaches mapping high-dimensional sensor data from Volvo trucks into a low-dimensional representation that is useful for prediction. Original data are bivariate histograms, with two types-turbocharger and engine-considered. Low-dimensional representations were evaluated in a supervised fashion by mean equal error rate (EER) using a random forest classifier on a set of 27 1-vs-Rest detection tasks. Results from unsupervised learning experiments indicate that using an autoencoder to create an intermediate representation, followed by t -distributed stochastic neighbor embedding, is the most effective way to create low-dimensional representation of the original bivariate histogram. Individually, t -distributed stochastic neighbor embedding offered best results for 2-D or 3-D and classical autoencoder for 6-D or 10-D representations. Using multi-task learning, combining unsupervised and supervised objectives on all 27 available tasks, resulted in 10-D representations with a significantly lower EER compared to the original 400-D data. In transfer learning setting, with topmost diverse tasks used for representation learning, 10-D representations achieved EER comparable to the original representation
  • 25.

    【24hr】Scale-Free Properties of Human Mobility and Applications to Intelligent Transportation Systems

    包量

    机译 流动性的无标度特性及其在智能交通系统中的应用
    摘要：Characterizing and modeling node mobility is of critical importance in building intelligent transportation systems and their applications. In this paper, we discuss the scale-free properties of some important human mobility characteristics, namely spatial node density and mobility degree, and show that they exhibit behavior that can be described by a power-law. Based on their power-law characteristics, we derive analytical models for the spatial node density and mobility degree and show that the data generated by the proposed analytical models closely approach the empirical data extracted from the real mobility traces. Another contribution of our work is to use the proposed analytical models to build a synthetic mobility regime that is suitable for simulations of intelligent transportation systems. Finally, through network simulations, we show that the ad-hoc network routing behavior under our mobility regime closely approximates routing behavior when the corresponding real trace is used.
  • 26.

    【24hr】Applying a WNN-HMM Based Driver Model in Human Driver Simulation: Method and Test

    包量

    机译 基于WNN-HMM的驾驶员模型在人体驾驶员仿真中的应用：方法和测试
    摘要：Modeling and evaluation of human driving behavior are the core to intelligent transportations and autonomous vehicles. This paper applies a human-like driver model based on vehicle test data and the neural network. This is accomplished by compromising the merits of error-based HMM-PID module and style-based neural network algorithm, both of which will work together to form a united driver model. In the simulation, the comparisons on driving performance, e.g., fuel economy and target following ability, are presented between PID-like driver and the proposed human-like driver. Several driving behavior criteria published by SAE, e.g., energy rating and energy economy rating, are borrowed in this paper to provide standardized metrics for evaluating the driver performance on fuel economy and emissions. Experimental results verified the effectiveness of the proposed scheme.
  • 27.

    【24hr】Two-Time-Scale Hybrid Traffic Models for Pedestrian Crowds

    包量

    机译 行人人群的两时间尺度混合交通模型
    摘要：This paper introduces new models to describe pedestrian crowd dynamics in a typical unidirectional environment, such as corridors, pathways, and railway platforms. Pedestrian movements are represented in a two-dimensional space that is further divided into narrow virtual lanes. Consequently, pedestrians either move in a lane following each other or change lanes, when it is desirable. Within this framework, the motions of pedestrians are modeled as a two-dimensional and two-time-scale hybrid system. A pedestrian's movement along the crowd direction is labeled as the x direction and modeled by a real-valued process, a solution of a differential equation in continuous time, the lane change is labeled as the y direction. In contrast to the x direction dynamics, the movements in the y direction only happen at some time epoch. Although the movements are still on the same time horizon as the x direction movements, with a slight abuse of notation and for simplicity and convenience, we use discrete time as the time indicator, and model the movements by a recursive equation taking values in a finite set. Under common assumptions of crowd movements, we prove that the crowd movements in the x direction will converge to a uniform distance distribution and the convergence rate is exponential. Furthermore, by using a velocity-distance function to represent the common crowd and traffic congestion scenarios, we show that all pedestrians will asymptotically move with a uniform group speed. In the y direction, when pedestrians naturally wish to change to faster lanes, we show that the numbers in each virtual lanes converge to a balanced distribution and hence achieves asymptotic consensus as shown typically in a crowd behavior. Stability and convergence analysis is carried out rigorously by using properties of circular matrices, stability of networked systems, and stochastic approximations. Simulation studies are used to demonstrate the main properties of our modeling approach and establish its usefulness in representing pedestrian dynamics.
  • 28.

    【24hr】Multi-Level Contextual RNNs With Attention Model for Scene Labeling

    包量

    机译 具有场景标记注意模型的多级上下文RNN
    摘要：Image context in image is crucial for improving scene labeling. While the existing methods only exploit local context generated from a small surrounding area of an image patch or a pixel, the long-range and global contextual information is often ignored. To handle this issue, we propose a novel approach for scene labeling by multi-level contextual recurrent neural networks (RNNs). We encode three kinds of contextual cues, viz., local context, global context, and image topic context in structural RNNs to model long-range local and global dependencies in an image. In this way, our method is able to “see” the image in terms of both long-range local and holistic views, and make a more reliable inference for image labeling. Besides, we integrate the proposed contextual RNNs into hierarchical convolutional neural networks, and exploit dependence relationships at multiple levels to provide rich spatial and semantic information. Moreover, we adopt an attention model to effectively merge multiple levels and show that it outperforms average- or max-pooling fusion strategies. Extensive experiments demonstrate that the proposed approach achieves improved results on the CamVid, KITTI, SiftFlow, Stanford Background, and Cityscapes data sets.
  • 29.

    【24hr】A New Spectrum Management Scheme for Road Safety in Smart Cities

    包量

    机译 智慧城市道路安全的新频谱管理方案
    摘要：Traffic management in roads is one of the major challenges faced in developing efficient intelligent transportation systems. Recently, wireless networks have received significant attention for tackling this challenge. However, wireless technologies face the well-known spectrum scarcity problem due to the explosive demand for radio resources. To overcome this challenge, this study presents a novel intelligent traffic control system, utilizing the unused spectrum. Unlike existing works, the spectrum owners in this study hire free spectrum to drivers. The hired spectrum is deployed to build a short-range cost-effective wireless communication for monitoring traffic and enabling drivers to exchange warning messages, and thus enhancing road safety. Our objectives include minimizing crash probability, utilizing unused spectrum, and enabling spectrum owners to generate extra revenue. Numerical analysis demonstrates the capability of our approach to minimize the crash probability among vehicles under different operating road conditions.
  • 30.

    【24hr】Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

    包量

    机译 通过城市事件检测进行城市应用人群分析的进展
    摘要：The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined.
  • 31.

    【24hr】CNN-Based Distributed Adaptive Control for Vehicle-Following Platoon With Input Saturation

    包量

    机译 基于CNN的带输入饱和跟随排的分布式自适应控制。
    摘要：A neural network-based distributed adaptive approach combined with sliding mode technique is proposed for vehicle-following platoons in the presence of input saturation, unknown unmodeled nonlinear dynamics, and external disturbances. A simple and straightforward strategy by adjusting only a single parameter is proposed to compensate for the effect of input saturation. Two spacing polices (i.e., traditional constant time headway policy and modified constant time headway policy) are used to guarantee string stability and maintain the desired spacing. Chebyshev neural networks (CNN) are used to approximate the unknown nonlinear functions in the followers online, and the implementation of the basic functions of CNN depends only on the leader's velocity and acceleration. Furthermore, unlike existing approaches, the nonlinearities of consecutive vehicles need not satisfy the matching condition. Finally, simulations are carried out to illustrate the effectiveness and the advantage of the proposed methods, first using a numerical example, followed by a practical example of a high speed train platoon.
  • 32.

    【24hr】Vehicle Tracking at Nighttime by Kernelized Experts With Channel-Wise and Temporal Reliability Estimation

    包量

    机译 专家化的夜间夜间通道跟踪和时域可靠性估计
    摘要：Despite the fact that in recent years, vision-based tracking approaches have made significant progress, the task of tracking vehicles at night still remains challenging. Visual information is strongly deteriorated or at least degraded due to poor illumination conditions. This reduces the perceptive ability of vision systems significantly and can even lead to target loss, resulting in false estimation and/or false prediction of object behavior. In this paper, we propose a novel online-learning method to track vehicles at night. Our method is based on the kernelized correlation filter and assembles different feature channels to kernelized experts. By estimating their reliabilities, we force the appearance model to focus on the most discriminative visual features to accomplish the classification. In addition, a temporal optimization step in conjunction with a memory model is used to remove outliers and keep the most reliable samples to train the tracker models. Experiments over various daytime and weather conditions show that our approach outperforms existing trackers at night and in case of bad weather while offering state-of-the-art performance in more favorable situations. As our tracker has only little computational cost, it is appropriate for use cases with real-time requirements like in automotive or industrial applications.
  • 33.

    【24hr】Predictor-Based Adaptive Cruise Control Design

    包量

    机译 基于预测器的自适应巡航控制设计
    摘要：We develop a predictor-based adaptive cruise control design with integral action (based on a nominal constant time-headway policy) for the compensation of large actuator and sensor delays in vehicular systems utilizing measurements of the relative spacing as well as of the speed and the short-term history of the desired acceleration of the ego vehicle. By employing an input-output approach, we show that the predictor-based adaptive cruise control law with integral action guarantees all of the four typical performance specifications of adaptive cruise control designs, namely, 1) stability, 2) zero steady-state spacing error, 3) string stability, and 4) non-negative impulse response, despite the large input delay. The effectiveness of the developed control design is shown in simulation considering various performance metrics.
  • 34.

    【24hr】Discovering Fine-Grained Spatial Pattern From Taxi Trips: Where Point Process Meets Matrix Decomposition and Factorization

    包量

    机译 从出租车旅行中发现细粒度的空间格局：点过程遇到矩阵分解和因式分解的地方
    摘要：As increasing volumes of urban data are being available, new opportunities arise for data-driven analysis that can lead to improvements in the lives of citizens through evidence-based policies. In particular, taxi trip is an important urban sensor that provides unprecedented insights into many aspects of a city, from economic activity, human mobility to land development. However, analyzing these data presents many challenges, e.g., sparse data for fine-grained patterns, and the regularity submerged by seemingly random data. Inspired by above challenges, we focus on Pick-Up (PU)/Drop-Off (DO) points from taxi trips, and propose a fine-grained approach to unveil a set of low spatio-temporal patterns from the regularity-discovered intensity. The proposed method is conceptually simple yet efficient, by leveraging point process to handle sparsity of points, and by decomposing point intensities into the low-rank regularity and the factorized basis patterns, our approach enables domain experts to discover patterns that are previously unattainable for them, from a case study motivated by traffic engineers.
  • 35.

    【24hr】Two-Layered Mechanism of Online Unmanned Aerial Vehicles Conflict Detection and Resolution

    包量

    机译 在线无人飞行器冲突检测与解决的两层机制
    摘要：This paper presents a study on short-term cooperative conflict detection and resolution (CDR) of unmanned aerial vehicles (UAVs). A two-layered CDR mechanism is proposed, which aims at guaranteeing safe separation, minimizing the overall cost of UAVs, and improving computational efficiency. In the first layer, the information from the environment is processed. In the second layer, conflicts among UAVs are resolved by applying the local centralized optimization method, with consideration given to the dynamic constraints of UAVs. This paper studies the safe separation constraints of pairwise conflicts in virtue of a geometry-based method. A heading change and speed change mixed conflict resolution approach is applied. To meet with the online planning requirements, the vectorized stochastic parallel gradient descent-based method is proposed to find the local optimal heading change solutions. The linear safe separation constraints on speeds are derived. A periodicity feature-based method is used to depart the feasible sub-regions for each pairwise conflict. A mixed integer linear programming model is established to find the optimal speed change solutions. The experiments results show that the proposed heading change algorithm could greatly reduce the summation of additional flight distances of UAVs, and influences on air traffic, compared with other short-term algorithms; the computational efficiency of this algorithm satisfies the online planning requirement. Comparing with the existing algorithm, our speed change algorithm reduces the number of feasible sub-regions to 2nctimes lower, where ncis the number of pairwise conflict, and therefore, it reduces computation time dramatically.
  • 36.

    【24hr】Allocation of Wireless Power Transfer System From Viewpoint of Optimal Control Problem for Autonomous Driving Electric Vehicles

    包量

    机译 从自动驾驶电动汽车的最优控制问题看无线功率传输系统的分配
    摘要：This paper proposes a new approach for optimal allocation of wireless power transfer system (WPTSys) from a viewpoint of optimal control problem (OCP) for autonomous driving electric vehicles (EVs). These EVs are assumed to accurately follow a pre-determined speed profile. By transformation of the nonlinear optimization problem for optimal allocation of WPTSys to an OCP, well-known methods guaranteeing the global optimality of solution for the OCP are applied, such as Pontryagin's maximum principle or dynamic programming. Therefore, the global optimal solution of the allocation problem of WPTSys can be obtained. In addition, we consider a practical situation of EV operation from a probability point of view, where many EVs are operated with different initial battery state-of-charge.
  • 37.

    【24hr】Understanding People Flow in Transportation Hubs

    包量

    机译 了解交通枢纽中的人员流向
    摘要：In this paper, we aim to monitor the flow of people in large public infrastructures. We propose an unsupervised methodology to cluster people flow patterns into the most typical and meaningful configurations. By processing 3-D images from a network of depth cameras, we build a descriptor for the flow pattern. We define a data-irregularity measure that assesses how well each descriptor fits a data model. This allows us to rank flow patterns from highly distinctive (outliers) to very common ones. By discarding outliers, we obtain more reliable key configurations (classes). Synthetic experiments show that the proposed method is superior to standard clustering methods. We applied it in an operational scenario during 14 days in the X-ray screening area of an international airport. Results show that our methodology is able to successfully summarize the representative patterns for such a long observation period, providing relevant information for airport management. Beyond regular flows, our method identifies a set of rare events corresponding to uncommon activities (cleaning, special security, and circulating staff).
  • 38.

    【24hr】A Joint Multi-Criteria Utility-Based Network Selection Approach for Vehicle-to-Infrastructure Networking

    包量

    机译 车辆到基础设施网络的基于多标准联合的网络选择方法
    摘要：The emerging technologies for connected vehicles have become hot topics. In addition, connected vehicle applications are generally found in heterogeneous wireless networks. In such a context, user terminals face the challenge of access network selection. The method of selecting the appropriate access network is quite important for connected vehicle applications. This paper jointly considers multiple decision factors to facilitate vehicle-to-infrastructure networking, where the energy efficiency of the networks is adopted as an important factor in the network selection process. To effectively characterize users' preference and network performance, we exploit energy efficiency, signal intensity, network cost, delay, and bandwidth to establish utility functions. Then, these utility functions and multi-criteria utility theory are used to construct an energy-efficient network selection approach. We propose design strategies to establish a joint multi-criteria utility function for network selection. Then, we model network selection in connected vehicle applications as a multi-constraint optimization problem. Finally, a multi-criteria access selection algorithm is presented to solve the built model. Simulation results show that the proposed access network selection approach is feasible and effective.
  • 39.

    【24hr】Evaluation of Detection Approaches for Road Anomalies Based on Accelerometer Readings—AddressingWho’s Who

    包量

    机译 基于加速度计读数的道路异常检测方法评估-解决谁是谁
    摘要：A wide range of new possibilities in the area of intelligent transportation systems (ITS) emerged when sensors, such as accelerometers, were introduced in practically every smartphone. A clear example is using a driver's smartphone to detect the vertical movement experienced by the vehicle when passing over a pothole or bump; in other words, sensing the quality of the road. To this end, several approaches have been proposed in the literature, most of them based on thresholds applied to accelerometer readings. Nonetheless, no fair comparison of these approaches had been done until now, mainly because of the lack of public datasets. In this paper, we propose a platform to create road data sets that could be used by the community to create their own roads with their own requirements. Using this platform, we assembled a data set of 30 roads plagued with potholes and bumps, which we used to evaluate the most popular heuristics previously reported. From our study, a heuristic, called STDEV(Z), based on standard deviation analysis proposed by Mednis et al. obtained the best results among the considered reference methodologies. This finding suggests that measures of dispersion, specifically standard deviation, are among the best indicators to identify disruptions on accelerometer readings. From this point, we fused features used by all these heuristics within our own feature vector, which we used with a support vector machine. We show that the proposed methodology clearly outperforms all other evaluated methods. To support these conclusions, results were statistically validated. We expect to lay the first steps to homogenize future comparisons as well as to provide stronger baselines to be considered in subsequent works.
  • 40.

    【24hr】An Automated Method for the Study of Human Reliability in Railway Supervision Systems

    包量

    机译 铁路监管系统人员可靠性研究的自动化方法
    摘要：This paper presents an original experimental protocol, which aims to study human reliability in railway systems by computing the human error probability (HEP) of human operators. The experiment is conducted on a railway traffic management system that places operators in simulated situations involving railway failures. The obtained experimental result is analyzed first by two classical human reliability analysis methods to estimate the HEP of each subject. Then, a model of human operators using valuation-based system is proposed. Finally, a methodology automatically populates the proposed model by allowing the verification of temporal properties on the simulation trace.
  • 41.

    【24hr】Robust and Long-Term Object Tracking With an Application to Vehicles

    包量

    机译 鲁棒性和长期目标跟踪在车辆中的应用
    摘要：Recently, intelligent vehicles catch much attention in both academia and industry. The vision-based moving object/vehicle detection and tracking are typically the core techniques for the event and activity analysis and the understanding of the dynamic driving environment in an intelligent vehicle. However, due to the complicated non-stationary environment, most existing vision-based motion tracking algorithms proposed for other simple conditions are not able to consistently track the objects. Therefore, in this paper, we propose a robust and long-term tracking method for intelligent vehicles, in which a set of classifiers are dynamically maintained and sampled for tackling varied challenges. In contrast to previous methods, to increase the diversity, a set of basic classifiers trained sequentially on different small data sets over time is dynamically maintained. The subsets of basic classifiers are independent with each other and can be specified to solve certain different sub-problems occurred in a non-stationary environment. Thus, for every challenge, an optimal classifier can be approximated in a subspace spanned by the selected competitive classifiers, which can address the current problem according to the distribution of the samples and recent performance. As a result, the tracker can efficiently address the various “concept drift” problems occurred together in a long video sequence. Due to the use of sparse weights for the competitive classifiers, the tracker can keep the balance between the efficiency and the performance. Experimental results show that the tracker yields competitive performance under various challenging environmental conditions and, especially, can overcome several challenges simultaneously.
  • 42.

    【24hr】An Evaluation of Pseudonym Changes for Vehicular Networks in Large-Scale, Realistic Traffic Scenarios

    包量

    机译 在大规模的现实交通场景中对车载网络的化名更改的评估
    摘要：Changing pseudonym certificates are the agreed-upon approach for privacy-friendly message authentication in upcoming vehicular ad hoc networks and are included in recent standards. This paper examines the performance of four different pseudonym change strategies and their parameters using simulations of realistic, large-scale traffic scenarios. The strategies are assessed by measuring their effectiveness and efficiency in protecting drivers from being tracked by an attacker with limited coverage. In an urban scenario, all strategies achieve satisfactory privacy protection, but the change frequency required is rather high. In a highway scenario, the attacker algorithm achieves a high-tracking success for all strategies, especially in low traffic, even for very short change intervals. This paper proposes concrete change intervals for urban scenarios, which are higher than currently foreseen, but concludes that privacy protection in uniform traffic conditions remains a challenge.
  • 43.

    【24hr】Optimizing the Link Directions of Personal Rapid Transit Network

    包量

    机译 优化个人快速公交网络的链接方向
    摘要：Personal rapid transit (PRT) is a kind of innovative public transport service operated by autonomous vehicles on a dedicated guideway network. In the situation, where PRT guideway network is composed of unidirectional links, the congestion degree of network and vehicle travel distance depend on the arrangement of link directions, given certain traffic demand and network configuration. The motivation of this paper is to propose a method to optimize the link direction of PRT network for enhancing system performance. The proposed optimization problem considers both the vehicle travel time and waiting time at merging nodes as the optimization objective and is formulated as a non-linear programming model. A linear approximation-based solution method is further developed to obtain solutions in an efficient way and the exact solution is achieved via this method. The result shows that the proposed method is effective to find an optimized arrangement of link directions in the network given certain travel demand and network configuration. The computational results of the proposed model in comparison to other models are presented and discussed, which show that the significant reduction of waiting time does not necessarily lead to the high increase of travel time, suggesting the importance of congestion control for merging modes.
  • 44.

    【24hr】Scalability of Vehicular M2M Communications in a 4G Cellular Network

    包量

    机译 4G蜂窝网络中车载M2M通信的可扩展性
    摘要：Automated machine-to-machine communication over cellular wireless networks is likely to become a major source of traffic in future communication networks, e.g., for automated monitoring of infrastructure. The long-term evolution wireless network has been designed to provide enhanced capacity; however, the information rate density that can be accommodated is limited. Here, we report limits to the scalability of automated automobile monitoring systems in terms of delay and blocking rates versus vehicle density. The results of numerical modeling were found to agree well with approximate analytical results.
  • 45.

    【24hr】Inertial Measurement Units-Based Probe Vehicles: Automatic Calibration, Trajectory Estimation, and Context Detection

    包量

    机译 基于惯性测量单元的探测车：自动校准，轨迹估计和上下文检测
    摘要：Most probe vehicle data is generated using satellite navigation systems, such as the Global Positioning System (GPS), Globalnaya navigatsionnaya sputnikovaya Sistema (GLONASS), or Galileo systems. However, because of their high cost, relatively high position uncertainty in cities, and low sampling rate, a large quantity of satellite positioning data is required to estimate traffic conditions accurately. To address this issue, we introduce a new type of traffic monitoring system based on inexpensive inertial measurement units (IMUs) as probe sensors. IMUs as traffic probes pose unique challenges in that they need to be precisely calibrated, do not generate absolute position measurements, and their position estimates are subject to accumulating errors. In this paper, we address each of these challenges and demonstrate that the IMUs can reliably be used as traffic probes. After discussing the sensing technique, we present an implementation of this system using a custom-designed hardware platform, and validate the system with experimental data.
  • 46.

    【24hr】Channel Measurement, Simulation, and Analysis for High-Speed Railway Communications in 5G Millimeter-Wave Band

    包量

    机译 5G毫米波频段的高速铁路通信的信道测量，仿真和分析
    摘要：More people prefer to using rail traffic for travel or for commuting due to its convenience and flexibility. As the record of the maximum speed of rail has been continuously broken and new applications are foreseen, the high-speed railway (HSR) communication system requires higher data rate with seamless connectivity, and therefore, the system design faces new challenges to support high mobility. Millimeter-wave (mmWave) technologies are considered as candidates to provide wideband communication. However, mmWave is rarely explored in HSR scenarios. In this paper, channel characteristics are studied in the 5G mmWave band for typical HSR scenarios, including urban, rural, and tunnel, with straight and curved route shapes. Based on the wideband measurements conducted in the tunnel scenario by using the “mobile hotspot network” system, a 3-D ray tracer (RT) is calibrated and validated to explore more channel characteristics in different HSR scenarios. Through extensive RT simulations with 500-MHz bandwidth centered at 25.25 GHz, the power contributions of the multipath components are studied, and the dominant reflection orders are determined for each scenario. Path loss is analyzed, and the breakpoint is observed. Other key parameters, such as Doppler shifts, coherence time, polarization ratios, and so on, are studied. Suggestions on symbol rate, sub-frame bandwidth, and polarization configuration are provided to guide the 5G mmWave communication system design in typical HSR scenarios.
  • 47.

    【24hr】Real-Time Prediction of Seasonal Heteroscedasticity in Vehicular Traffic Flow Series

    包量

    机译 车辆流量序列中季节异方差的实时预测
    摘要：Over the past decade, traffic heteroscedasticity has been investigated with the primary purpose of generating prediction intervals around point forecasts constructed usually by short-term traffic condition level forecasting models. However, despite considerable advancements, complete traffic patterns, in particular the seasonal effect, have not been adequately handled. Recently, an offline seasonal adjustment factor plus GARCH model was proposed in Shiet al.2014 to model the seasonal heteroscedasticity in traffic flow series. However, this offline model cannot meet the real-time processing requirement proposed by real-world transportation management and control applications. Therefore, an online seasonal adjustment factors plus adaptive Kalman filter (OSAF+AKF) approach is proposed in this paper to predict in real time the seasonal heteroscedasticity in traffic flow series. In this approach, OSAF and AKF are combined within a cascading framework, and four types of online seasonal adjustment factors are developed considering the seasonal patterns in traffic flow series. Empirical results using real-world station-by-station traffic flow series showed that the proposed approach can generate workable prediction intervals in real time, indicating the acceptability of the proposed approach. In addition, compared with the offline model, the proposed online approach showed improved adaptability when traffic is highly volatile. These findings are important for developing real-time intelligent transportation system applications.
  • 48.

    【24hr】A Simulation Study of Predicting Real-Time Conflict-Prone Traffic Conditions

    包量

    机译 实时实时预测冲突交通状况的仿真研究
    摘要：Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative of pre-collision traffic dynamics. In this paper, this is overcome through the use of highly disaggregated vehicle-based traffic data from a traffic micro-simulation (i.e., VISSIM) and the corresponding traffic conflicts data generated by the surrogate safety assessment model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety so that traffic collisions data are not needed. Three classifiers (i.e., support vector machines, k-nearest neighbours, and random forests) are then employed to examine the proposed idea. Substantial efforts are devoted to making the traffic simulation as representative of the real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e., 30 s, 1 min, 3 min, and 5 min) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of random forests with 5-min temporal aggregation in the classification results. However, attention should be given to the calibration and validation of the simulation software so as to acquire more realistic traffic data, resulting in more effective prediction of conflicts.
  • 49.

    【24hr】The Impact of Road Configuration in V2V-Based Cooperative Localization: Mathematical Analysis and Real-World Evaluation

    包量

    机译 道路配置对基于V2V的合作本地化的影响：数学分析和实际评估
    摘要：Cooperative map matching (CMM) uses the global navigation satellite system (GNSS) position information of a group of vehicles to improve the standalone localization accuracy. While increasing accuracy is expected by increasing the number of participating vehicles, fundamental questions on how the vehicle membership within CMM affects the performance of the CMM results need to be addressed to provide guidelines for design and optimization of the vehicle network. This paper presents a theoretical study that establishes a framework for quantitative evaluation of the impact of the road constraints on the CMM accuracy. More specifically, a closed-form expression of the CMM error in terms of the road constraints and GNSS error is derived based on a simple CMM rule. The asymptotic decay of the CMM error as the number of vehicles increases is established and justified through numerical simulations. Moreover, it is proved that the CMM error can be minimized if the directions of the roads on which the connected vehicles travel obey a uniform distribution. Finally, the localization accuracy of CMM is evaluated based on the real traffic data. The contributions of this paper include establishing a theoretical foundation for CMM as well as providing insight and motivation for applications of CMM.
  • 50.

    【24hr】Machine Vision to Alert Roadside Personnel of Night Traffic Threats

    包量

    机译 机器视觉提醒路边人员夜间交通威胁
    摘要：In the United States, every year, several people whose job takes them to the sides of roads, are injured or killed by roadside collisions. This could be avoided if a warning signal could be sent to them. In this paper, we describe a machine-vision based alerting system which detects and tracks headlamps of cars in night traffic. The system automatically computes a “normal traffic” region in the image. Unusual trajectories of cars are detected when the images of their headlamps move out of that region. The system promptly sends a warning signal once a risk has been identified. The system runs on the Android smart phones, which are mounted on cars or on roadside fixtures.
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