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A new method is proposed for selecting Access Points (APs) with propagation direction combination based on Eight-Diagram for indoor localization in indoor environments, which can not only improve positioning accuracy, but also reduce computational complexity by using fewer APs. With propagation direction combination based on eight basic directions, for instance, east, south, west, north, southeast, northeast, southwest and northwest, provided by the Eight-Diagram, the proposed AP selection algorithm can be applied to indoor scenarios where Wi-Fi RSSI is corrupted by multipath interference. Experiments were conducted and the results demonstrate that the proposed AP selection algorithm achieves an accuracy considerably better than the WKNN, MaxMean, InfoGain and PCA methods. Due to the use of fewer APs, the proposed algorithm has lower computational complexity than the MaxMean and InfoGain algorithms, and equivalent with the PCA algorithm. [ABSTRACT FROM AUTHOR]

As one of the main threats to people’s health, especially for the elderly, falls have caused a large number of accidents. Detecting falls in time can minimize the severity of the injury and save lives. Therefore, designing fall detection algorithms to assist people’s daily life has been a hot research topic. In the last decade, different fall detection approaches based on wearable sensors have been proposed. However, since the hardware resources of wearable sensors are very limited, designing accurate but energy-efficient fall detection algorithms remains an open challenge. To deal with this, an accurate but low-cost fall detection algorithm is proposed in this paper. Particularly, a novel cascade and parallel method that efficiently employs the characteristics of human falls and the advanced modeling ability of the neural network is proposed. Also, a novel design of a lightweight convolutional neural network with self-attention is proposed to achieve the best recognition/numerical complexity tradeoff. The proposed method, namely CMFALL, is evaluated together with a multitude of state-of-the-art models on a large dataset, where it performs the best with an F1-score exceeding 99%. Meanwhile, its computational cost and model size are only a few thousandths of other models. Such low computational cost and small size not only enable to embed it in a wearable sensor but also make the system power requirements quite low, which can enhance the autonomy of the wearable fall detector. [ABSTRACT FROM AUTHOR]

Lane-level road network updating is crucial for urban traffic applications that use geographic information systems contributing to, for example, intelligent driving, route planning and traffic control. Researchers have developed various algorithms to update road networks using sensor data, such as high-definition images or GPS data; however, approaches that involve change detection for road networks at lane level using GPS data are less common. This paper presents a novel method for automatic change detection of lane-level road networks based on GPS trajectories of vehicles. The proposed method includes two steps: map matching at lane level and lane-level change recognition. To integrate the most up-to-date GPS data with a lane-level road network, this research uses a fuzzy logic road network matching method. The proposed map-matching method starts with a confirmation of candidate lane-level road segments that use error ellipses derived from the GPS data, and then computes the membership degree between GPS data and candidate lane-level segments. The GPS trajectory data is classified into successful or unsuccessful matches using a set of defuzzification rules. Any topological and geometrical changes to road networks are detected by analysing the two kinds of matching results and comparing their relationships with the original road network. Change detection results for road networks in Wuhan, China using collected GPS trajectories show that these methods can be successfully applied to detect lane-level road changes including added lanes, closed lanes and lane-changing and turning rules, while achieving a robust detection precision of above 80%. [ABSTRACT FROM PUBLISHER]

Coherence loss is a critical issue in interferometric synthetic aperture radar geodesy, particularly when short-wavelength radar images are used to monitor earthquake deformation, and it may result in isolated fringes in an interferogram. The conventional unwrapping algorithms may incompletely unwrap or wrongly estimate the integer jumps between isolated fringes. In this paper, we propose a novel method to connect the isolated fringes in earthquake slip inversion. We use multiple starting points to unwrap the interferogram and then solve the integer ambiguities among the starting points by a dislocation-model-based integer least squares estimator. This estimator allows us to provide a quantitative evaluation of the reliability of the integer solutions in terms of two indicators (the success rate and residual ratio). The algorithm is robust to a certain degree of data noise and fault geometry error, as tested. Simulated experiments and case studies demonstrate that the proposed method can give better unwrapping results than the conventional approaches such as the minimum-cost flow (MCF), statistical-cost network-flow algorithm for phase unwrapping (SNAPHU), and iterative forms of MCF and SNAPHU with the assistance of a slip model. The earthquake slip inversion therefore benefits from the more accurate unwrapping results. [ABSTRACT FROM AUTHOR]

Turn restrictions, such as 'no left turn' or 'no U-turn', are commonly encountered in real road networks. These turn restrictions must be explicitly considered in the shortest path problem and ignoring them may lead to infeasible paths. In the present study, a hybrid link-node Dijkstra's (HLND) algorithm is proposed to exactly solve the shortest path problem in road networks with turn restrictions. A new hybrid link-node labelling approach is devised by using a link-based labelling strategy at restricted nodes with turn restrictions, and a node-based labelling strategy at unrestricted nodes without turn restrictions. Computational results for several real road networks show that the proposed HLND algorithm obtains the same optimal results as the link-based Dijkstra's algorithm, while having a similar computational performance to the classical node-based Dijkstra's algorithm. [ABSTRACT FROM AUTHOR]

Due to the low-cost and lightweight units, single-wavelength LiDAR bathymetric systems are an ideal option for shallow-water (<12 m) bathymetry. However, one disadvantage of such systems is the lack of near-infrared and Raman channels, which results in difficulties in extracting the water surface. Therefore, the choice of a suitable waveform processing method is extremely important to guarantee the accuracy of the bathymetric retrieval. In this paper, we test six algorithms for single-wavelength bathymetric waveform processing, i.e. peak detection (PD), the average square difference function (ASDF), Gaussian decomposition (GD), quadrilateral fitting (QF), Richardson–Lucy deconvolution (RLD), and Wiener filter deconvolution (WD). To date, most of these algorithms have previously only been applied in topographic LiDAR waveforms captured over land. A simulated dataset and an Optech Aquarius dataset were used to assess the algorithms, with the focus being on their capability of extracting the depth and the bottom response. The influences of a number of water and equipment parameters were also investigated by the use of a Monte Carlo method. The results showed that the RLD method had a superior performance in terms of a high detection rate and low errors in the retrieved depth and magnitude. The attenuation coefficient, noise level, water depth, and bottom reflectance had significant influences on the measurement error of the retrieved depth, while the effects of scan angle and water surface roughness were not so obvious. [ABSTRACT FROM AUTHOR]

This study investigates the time-dependent reliable shortest path problem (TD-RSPP), which is commonly encountered in congested urban road networks. Two variants of TD-RSPP are considered in this study. The first variant is to determine the earliest arrival time and associated reliable shortest path for a given departure time, referred to as the “forward” TD-RSPP. The second problem is to determine the latest departure time and associated reliable shortest path for a given preferred arrival time, referred as the “backward” TD-RSPP. It is shown in this article that TD-RSPP is not reversible. The backward TD-RSPP cannot be solved by the algorithms designed for the forward problem using the reverse search from destination to origin. In this study, two efficient solution algorithms are proposed to solve the forward and backward TD-RSPP exactly and the optimality of proposed algorithms is rigorously proved. The proposed solution algorithms have potential applications in both advanced traveler information systems and stochastic dynamic traffic assignment models. [ABSTRACT FROM AUTHOR]

Autonomous vehicle navigation is challenging since various types of road scenarios in real urban environments have to be considered, particularly when only perception sensors are used, without position information. This paper presents a novel real-time optimal-drivable-region and lane detection system for autonomous driving based on the fusion of light detection and ranging (LIDAR) and vision data. Our system uses a multisensory scheme to cover the most drivable areas in front of a vehicle. We propose a feature-level fusion method for the LIDAR and vision data and an optimal selection strategy for detecting the best drivable region. Then, a conditional lane detection algorithm is selectively executed depending on the automatic classification of the optimal drivable region. Our system successfully handles both structured and unstructured roads. The results of several experiments are provided to demonstrate the reliability, effectiveness, and robustness of the system. [ABSTRACT FROM PUBLISHER]

Large-scale global positioning system (GPS) positioning information of floating cars has been recognised as a major data source for many transportation applications. Mapping large-scale low-frequency floating car data (FCD) onto the road network is very challenging for traditional map-matching (MM) algorithms developed for in-vehicle navigation. In this paper, a multi-criteria dynamic programming map-matching (MDP-MM) algorithm is proposed for online matching FCD. In the proposed MDP-MM algorithm, the MDP technique is used to minimise the number of candidate routes maintained at each GPS point, while guaranteeing to determine the best matching route. In addition, several useful techniques are developed to improve running time of the shortest path calculation in the MM process. Case studies based on real FCD demonstrate the accuracy and computational performance of the MDP-MM algorithm. Results indicated that the MDP-MM algorithm is competitive with existing algorithms in both accuracy and computational performance. [ABSTRACT FROM PUBLISHER]

The aim of this study is to investigate the solution algorithm for solving the problem of determining reliable shortest paths in road networks with stochastic travel times. The availability of reliable shortest paths enables travelers, in the face of travel time uncertainty, to plan their trips with a pre-specified on-time arrival probability. In this study, the reliable shortest path between origin and destination nodes is determined using a multiple-criteria shortest path approach when link travel times follow normal distributions. The dominance conditions involved in such problems are established, thereby reducing the number of generated non-dominated paths during the search processes. Two solution algorithms, multi-criteria label-setting and A* algorithms, are proposed and their complexities analyzed. Computational results using large scale networks are presented. Numerical examples using data from a real-world advanced traveller information system is also given to illustrate the applicability of the solution algorithms in practice. [ABSTRACT FROM AUTHOR]

Local search heuristics for very large-scale vehicle routing problems (VRPs) have made remarkable advances in recent years. However, few local search heuristics have focused on the use of the spatial neighborhood in Voronoi diagrams to improve local searches. Based on the concept of a k-ring shaped Voronoi neighbor, we propose a Voronoi spatial neighborhood-based search heuristic and algorithm to solve very large-scale VRPs. In this algorithm, k-ring Voronoi neighbors of a customer are limited to building and updating local routings, and rearranging local routings with improper links. This algorithm was evaluated using four sets of benchmark tests for 200–8683 customers. Solutions were compared with specific examples in the literature, such as the one-depot VRP. This algorithm produced better solutions than some of the best-known benchmark VRP solutions and requires less computational time. The algorithm outperformed previous methods used to solve very large-scale, real-world distance constrained capacitated VRP. [ABSTRACT FROM PUBLISHER]

Abstract: Most existing approaches for pavement crack line detection implicitly assume that pavement cracks in images are with high contrast and good continuity. This assumption does not hold in pavement distress detection practice, where pavement cracks are often blurry and discontinuous due to particle materials of road surface, crack degradation, and unreliable crack shadows. To this end, we propose in this paper FoSA — F* Seed-growing Approach for automatic crack-line detection, which extends the F* algorithm in two aspects. It exploits a seed-growing strategy to remove the requirement that the start and end points should be set in advance. Moreover, it narrows the global searching space to the interested local space to improve its efficiency. Empirical study demonstrates the correctness, completeness and efficiency of FoSA. [Copyright &y& Elsevier]

Abstract: Optimal paths computed by conventional path-planning algorithms are usually not “optimal” since realistic traffic information and local road network characteristics are not considered. We present a new experiential approach that computes optimal paths based on the experience of taxi drivers by mining a huge number of floating car trajectories. The approach consists of three steps. First, routes are recovered from original taxi trajectories. Second, an experiential road hierarchy is constructed using travel frequency and speed information for road segments. Third, experiential optimal paths are planned based on the experiential road hierarchy. Compared with conventional path-planning methods, the proposed method provides better experiential optimal path identification. Experiments demonstrate that the travel time is less for these experiential paths than for paths planned by conventional methods. Results obtained for a case study in the city of Wuhan, China, demonstrate that experiential optimal paths can be flexibly obtained in different time intervals, particularly during peak hours. [Copyright &y& Elsevier]

Floating car data (FCD) is an important source of traffic data for the design and management of an intelligent transportation system, and the efficiency and accuracy of FCD map-matching influences the application of FCD. Most map-matching algorithms are very time-consuming because many links need to be evaluated as to whether they are the correct link for the travelling floating car, especially in urban areas where most FCD are collected. In fact, the highest degree of map-matching a link for an FCD is not always the correct link, so the use of candidate links is proposed here. In this study, candidate links are defined as those that fall within the FCD error. We propose a novel algorithm for quickly identifying accurate candidate links on the basis of a vector to raster road map conversion approach. First, a road buffer is constructed based on the size of the confidence region by taking into account the position error of the FCD and the width of the link. Second, all link buffers are converted to a raster map with resolution in metres, each pixel position of the raster map corresponding to a geographical coordinate. A pixel can have one or more values, which represent the link ID whether the link buffer covers the pixel; so a spatial index map between geographical coordinate and link ID is built in this way. Then, candidate links are calculated rapidly in the next three steps: (1) Selecting a link ID from the spatial index map that, according to the FCD positioning fix coordinate, must fall within the confidence region of the FCD. The number is very small and this step is very fast because it does not include a traverse process. (2) Getting data for selected link from the road networks vector map by the link ID. This step uses a binary search and is very fast. (3) Calculating the degree of map-matching of these links to identify candidate links of the FCD. This algorithm is far less time-consuming than earlier approaches. The algorithm was tested using more than 13 million real-world FCD collected in Wuhan City, China, and the performance was shown to be satisfactory. [ABSTRACT FROM AUTHOR]

A long tunnel is a typical linear man-made infrastructure. The shape, especially the cross-sectional shape, and geometric parameters of the tunnel are very important for the safety of transportation. A multi-sensor integration approach is presented to survey the shape of the tunnel dynamically. With efficient collection of high-precision spatial points of structure surface, a laser scanner is widely used for measuring the structure's three-dimensional geometric and deformation. In this article, two laser scanners and other position and orientation sensors, such as DGPS, IMU, and DMI, are integrated to form a MSI measuring system. With position and orientation sensors, the position and orientation of the laser scanners could be acquired dynamically, even in the tunnel where a GPS signal is out of reach. For fusing data collected from different sensors, data are generalized into a unified coordinate system. In our study, data from the laser scanner are first converted from the laser scanner coordinate system to the vehicle coordinate system. Then, vehicle coordinates are converted into geodetic coordinates by referencing the IMU coordinate. Calibrations are carried out in advance to obtain the parameters of conversion between coordinate systems. A time-domain tunnel deformation evaluation model is created on the basis of cross-sectional area change detection, where matching-based deformation detection (MDD) algorithm has been proposed for calculating the deformation area of two cross-sectional polygons. This article also reports a test to use MSI measuring system for Wuhan Yangtze River highway tunnel measurement. Results of the precision analysis verify the reliability of the proposed tunnel deformation evaluation model and the validity of the MDD algorithm. [ABSTRACT FROM AUTHOR]

Abstract: It is essential to generate multi-resolution triangulated irregular network (TIN) models dynamically and efficiently in three-dimensional (3D) visualization, virtual reality (VR), and geographic information systems (GIS), because the data processed is multiple in scale and large in volume. This paper proposes a new approach, which extends the iterative edge collapses and vertex splits algorithm, to dynamically generate multi-resolution TIN models. Moreover, a new method was proposed to improve the efficiency of vertex topological relationships storing in multi-resolution TIN models. And a set of rules were defined to improve the validity judgment of vertex splits and edge collapses. To evaluate the performance of the proposed approach, the root mean square error (RMSE) of the elevation of vertex and the quality of the shapes of triangles were measured to evaluate the quality of a generated multi-resolution TIN model. The experimental results demonstrate that the proposed approach can generate multi-resolution TIN models with a higher accuracy and better time performance. [Copyright &y& Elsevier]

Accurate atmospheric correction is decisive for ocean color remote sensing applications. Near infrared (NIR)-based algorithm performs well for clear waters; while shortwave infrared (SWIR)-based algorithm can obtain good results for turbid waters, however, it tends to produce noisy patterns for clear waters. A practical strategy is to apply NIR- and SWIR-based algorithm for clear and turbid waters, respectively, which is called NIR-SWIR combined atmospheric correction algorithm. However, the currently applied switching scheme for the NIR-SWIR algorithm undermines the atmospheric correction performance. This study aimed to find an applicable switching scheme for NIR-SWIR algorithm. Four MODIS land bands were used to switch the NIR- and SWIR-based algorithms. A simulated dataset was used to evaluate atmospheric performance of NIR- and SWIR-based algorithm. The switching threshold for each MODIS land band was determined as an R rs value at which SWIR-based algorithm performed better than NIR-based algorithm. The switching scheme was evaluated using matchups of simultaneous MODIS Aqua images and AERONET-OC data, and then tested with a MODIS Aqua image over the western Pacific Ocean. Results showed that the switching threshold for R rs (469), R rs (555), R rs (645) and R rs (859) were 0.009, 0.016, 0.009 and 0.0006 sr−1, respectively; R rs (645) with a threshold of 0.009 sr−1 and R rs (555) with a threshold of 0.016 sr−1 worked well for NIR-SWIR algorithm, while R rs (469) and R rs (859) produced worse performance. Therefore, R rs (555) > 0.016 sr−1 or R rs (645) > 0.009 sr−1 was recommended as the switching scheme for NIR-SWIR algorithm. Considering contrasted estuarine, coastal and some inland waters, combining NIR- and SWIR-based atmospheric correction algorithm with the proposed switching scheme should be useful for remote sensing monitoring over these waters. [ABSTRACT FROM AUTHOR]

Regularization is a routine approach used in earthquake slip distribution inversion to avoid numerically abnormal solutions. To date, most slip inversion studies have imposed uniform regularization on all the fault patches. However, adaptive regularization, where each retrieved parameter is regularized differently, has exhibited better performances in other research fields such as image restoration. In this paper, we implement an investigation into adaptive regularization for earthquake slip distribution inversion. It is found that adaptive regularization can achieve a significantly smaller mean square error (MSE) than uniform regularization, if it is set properly. We propose an adaptive regularization method based on weighted total least squares (WTLS). This approach assumes that errors exist in both the regularization matrix and observation, and an iterative algorithm is used to solve the solution. A weight coefficient is used to balance the regularization matrix residual and the observation residual. An experiment using four slip patterns was carried out to validate the proposed method. The results show that the proposed regularization method can derive a smaller MSE than uniform regularization and resolution-based adaptive regularization, and the improvement in MSE is more significant for slip patterns with low-resolution slip patches. In this paper, we apply the proposed regularization method to study the slip distribution of the 2011 Mw 9.0 Tohoku earthquake. The retrieved slip distribution is less smooth and more detailed than the one retrieved with the uniform regularization method, and is closer to the existing slip model from joint inversion of the geodetic and seismic data. [ABSTRACT FROM AUTHOR]

Abstract: Activity scheduling supports activity-based analysis in travel demand management and promotes a potentially popular traveler assistance service. A multi-objective approach is proposed to schedule joint participation of multiple individuals, in which the candidate space–time opportunities for joint participation are identified by a concept of time-varying network-based prisms, and optimal opportunities for joint participation are determined by the non-dominated sorting genetic algorithm-II (NSGA-II) with four objectives (i) minimizing cost for congestion charges, (ii) maximizing participation desirability of time-of-day, (iii) minimizing total travel distance and (iv) time in the trips of multiple individuals. A scenario of joint participation among four people is designed and implemented to demonstrate the feasibility of this approach. The results suggest that this approach has the ability to schedule activities within real situations. [Copyright &y& Elsevier]

Lane-based road information plays a critical role in transportation systems, a lane-based intersection map is the most important component in a detailed road map of the transportation infrastructure. Researchers have developed various algorithms to detect the spatial layout of intersections based on sensor data such as high-definition images/videos, laser point cloud data, and GPS traces, which can recognize intersections and road segments; however, most approaches do not automatically generate Lane-based Intersection Maps (LIMs). The objective of our study is to generate LIMs automatically from crowdsourced big trace data using a multi-hierarchy feature extraction strategy. The LIM automatic generation method proposed in this paper consists of the initial recognition of road intersections, intersection layout detection, and lane-based intersection map-generation. The initial recognition process identifies intersection and non-intersection areas using spatial clustering algorithms based on the similarity of angle and distance. The intersection layout is composed of exit and entry points, obtained by combining trajectory integration algorithms and turn rules at road intersections. The LIM generation step is finally derived from the intersection layout detection results and lane-based road information, based on geometric matching algorithms. The effectiveness of our proposed LIM generation method is demonstrated using crowdsourced vehicle traces. Additional comparisons and analysis are also conducted to confirm recognition results. Experiments show that the proposed method saves time and facilitates LIM refinement from crowdsourced traces more efficiently than methods based on other types of sensor data. [ABSTRACT FROM AUTHOR]