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Indoor positioning is a critical component for numerous applications and services. However, GNSS systems face challenges in delivering accurate positioning information in indoor environments. Current indoor positioning research primarily concentrates on enhancing the positioning performance of individual terminals through various techniques. As we transition into the Internet of Things (IoT) era, former indoor positioning methods need refinement. In this paper, we propose a novel indoor positioning method that leverages robots as mobile base stations to mitigate the problem of inadequate fixed base stations and aims to enhance positioning accuracy by incorporating pedestrian inertial navigation data. The process involves several steps. First, the mobile robots accurately determine their positions and performing coordinate transformations to ensure consistency with pedestrian coordinate systems. Then, pedestrians use the ranging information from these robots along with their smartphones' sensors for multi-source fusion positioning. Finally, an Extended Kalman Filter (EKF) is applied to fuse the multiple sources of data, considering various sources of errors, to provide enhanced positioning performance. Experimental results demonstrate the effectiveness of this approach in addressing indoor positioning challenges. This method could benefit numerous scenarios involving robots, enhancing pedestrian positioning accuracy and overall system robustness. The paper provides a comprehensive exploration of this proposed method, its implications, and potential directions for future advancements. [ABSTRACT FROM AUTHOR]

• A laser differential imaging measurement model for immersed tunnel alignment is proposed. • The calibration model is given,and the measurement system is designed. • An underwater laser spot center extraction method is proposed. • Precision and repeatability assessment experiments are carried out, and are far better than the requirements. The precision of underwater alignment for tunnel elements directly impacts the closure state of immersed tunnels. The current global navigation satellite system measurement tower method provides centimeter-level precision; however, this is insufficient due to the tower deformation at great water depths, which fails to meet alignment requirements. In this study, a novel immersed tunnel alignment measurement method using laser differential imaging is developed. A unique sensor layout is designed to measure the axial angle and distance between tunnel elements. The system design is implemented, and its laser data processing scheme is optimized to improve performance base on this sensor layout. The experimental results demonstrate that the accuracy of underwater laser spot extraction is better than 1.16 pixels, and the precision of axial angle and axial distance alignment measurements is better than 25″ and 1.6 mm, with repeatability precision exceeding 10.55″ and 0.53 mm. The laser-based alignment measurement method is not affected by the depth of water, satisfying the required precision of immersed tunnel alignment. These results can be applied to future immersed tunnel installations without measuring towers, providing a novel method for achieving automated and unmanned intelligent construction of immersed tunnels. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) currently face formidable challenges in promotion, i.e., short driving ranges, long charging times, and few charging stations, thereby limiting their acceptability to taxi drivers. Leveraging massive-scale taxi GPS trajectory data, we present a novel real-time route recommendation system for electric taxi (ET) drivers. Taxi travel knowledge, including the probability of picking up passengers and the distribution of destinations, is learned from the raw GPS trajectories. Considering the cascading effect of route decision making, consecutive ET actions are modeled with an action tree. The corresponding expected net revenue is estimated based on the learned knowledge. A prototype online system is developed for providing route recommendations, e.g., when to go to a charging station or cruise on certain roads. An experiment in Shenzhen demonstrates that the average daily net revenue of ET drivers is better than those of 76.2% of gasoline taxi drivers. The presented approach not only increases the revenue of ET drivers in the short term but also improves the viability of EVs in the long run. [ABSTRACT FROM AUTHOR]

Global positioning system (GPS) data generated from taxi trips is a valuable source of information that offers an insight into travel behaviours of urban populations with high spatio-temporal resolution. However, in its raw form, GPS taxi data does not offer information on the purpose (or intended activity) of travel. In this context, to enhance the utility of taxi GPS data sets, we propose a two-layer framework to identify the related activities of each taxi trip automatically and estimate the return trips and successive activities after the trip, by using geographic point-of-interest (POI) data and a combination of spatio-temporal clustering, Bayesian inference and Monte Carlo simulation. Two million taxi trips in New York, the United States of America, and ten million taxi trips in Shenzhen, China, are used as inputs for the two-layer framework. To validate each layer of the framework, we collect 6,003 trip diaries in New York and 712 questionnaire surveys in Shenzhen. The results show that the first layer of the framework performs better than comparable methods published in the literature, while the second layer has high accuracy when inferring return trips. [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]

Integer ambiguity resolution (IAR) is important for rapid initialization of precise point positioning (PPP). Whereas many studies have been limited to Global Positioning System (GPS) alone, there is a strong need to add Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) to the PPP-IAR solution. However, the frequency-division multiplexing of GLONASS signals causes inter-frequency code bias (IFCB) in the receiving equipment. The IFCB causes GLONASS wide-lane uncalibrated phase delay (UPD) estimation with heterogeneous receiver types to fail, so GLONASS ambiguity is therefore traditionally estimated as float values in PPP. A two-step method of calibrating GLONASS IFCB is proposed in this paper, such that GLONASS PPP-IAR can be performed with heterogeneous receivers. Experimental results demonstrate that with the proposed method, GLONASS PPP ambiguity resolution can be achieved across a variety of receiver types. For kinematic PPP with mixed receiver types, the fixing percentage within 10 min is only 33.5% for GPS-only. Upon adding GLONASS, the percentage improves substantially, to 84.9%. [ABSTRACT FROM AUTHOR]

Intersections are the critical parts where different traffic flows converge and change directions, forming “bottlenecks” and “clog points” in urban traffic. Intersection travel time is an important parameter for public route planning, traffic management, and engineering optimization. Based on low-frequency spatial-temporal Global Positioning System (GPS) trace data, this article presents a novel method for estimating intersection travel time. The proposed method first analyzes the different travel patterns of vehicles through an intersection, then determines the range of an intersection dynamically and reasonably, and obtains traffic flow speed and delay at the intersection under different travel patterns using a fuzzy fitting approach. Finally, the average intersection travel time is estimated from traffic flow speed and delay and intersection range in different travel patterns. Wuhan road network data and GPS trace data from taxicabs were tested in the experiments and the results show that the proposed method can improve the accuracy of travel time estimation at city intersections. [ABSTRACT FROM AUTHOR]

Lane-based road network information, such as the number and locations of traffic lanes on a road, has played an important role in intelligent transportation systems. In this paper, we propose a Collecting Lane-based Road Information via Crowdsourcing (CLRIC) method, which can automatically extract detailed lane structure of roads by using crowdsourcing data collected by vehicles. First, CLRIC filters the high-precision GPS data from the raw trajectories based on region growing clustering with prior knowledge. Second, CLRIC mines the number and locations of traffic lanes through optimized constrained Gaussian mixture model. Experiments are conducted with taxi GPS trajectories in Wuhan, China, and the results show that CLRIC is quantified and displays detailed road networks with the number and locations of traffic lanes comparing with the satellite image and human-interpreted situation. [ABSTRACT FROM PUBLISHER]

Vehicle electrification is a promising approach towards attaining green transportation. However, the absence of charging stations limits the penetration of electric vehicles. Current approaches for optimizing the locations of charging stations suffer from challenges associated with spatial–temporal dynamic travel demands and the lengthy period required for the charging process. The present article uses the electric taxi (ET) as an example to develop a spatial–temporal demand coverage approach for optimizing the placement of ET charging stations in the space–time context. To this end, public taxi demands with spatial and temporal attributes are extracted from massive taxi GPS data. The cyclical interactions between taxi demands, ETs, and charging stations are modeled with a spatial–temporal path tool. A location model is developed to maximize the level of ET service on the road network and the level of charging service at the stations under spatial and temporal constraints such as the ET range, the charging time, and the capacity of charging stations. The reduced carbon emission generated by used ETs with located charging stations is also evaluated. An experiment conducted in Shenzhen, China demonstrates that the proposed approach not only exhibits good performance in determining ET charging station locations by considering temporal attributes, but also achieves a high quality trade-off between the levels of ET service and charging service. The proposed approach and obtained results help the decision-making of urban ET charging station siting. [ABSTRACT FROM AUTHOR]

Map matching method is a fundamental preprocessing technique for massive probe vehicle data. Various transportation applications need map matching methods to provide highly accurate and stable results. However, most current map matching approaches employ elementary geometric or topological measures, which may not be sufficient to encode the characteristic of realistic driving paths, leading to inefficiency and inaccuracy, especially in complex road networks. To address these issues, this article presents a novel map matching method, based on the measure of curvedness of Global Positioning System (GPS) trajectories. The curvature integral, which measures the curvedness feature of GPS trajectories, is considered to be one of the major matching characteristics that constrain pairwise matching between the two adjacent GPS track points. In this article, we propose the definition of the curvature integral in the context of map matching, and develop a novel accurate map matching algorithm based on the curvedness feature. Using real-world probe vehicles data, we show that the curvedness feature (CURF) constrained map matching method outperforms two classical methods for accuracy and stability under complicated road environments. [ABSTRACT FROM PUBLISHER]

Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems play a very important role in modern photogrammetry and laser scanning by virtue of their capability of direct measurement of high-precision position and attitude data in the WGS 84 datum. In practice, as georeferencing is often conducted in national coordinates, there is a need to transform GPS/INS data to the required national map projection frame first. This letter presents an improved coordinate-transformation-based method for the GPS/INS attitude transformation by taking the datum scale distortion and the length distortion into account. Experimental results show that the transformation errors of our improved method are on the order of magnitude of 1 × 10-5°, which can be safely ignored in aerial photogrammetric processing, whereas the maximum error of the previous coordinate-transformation-based method can be up to several 0.001°. [ABSTRACT FROM PUBLISHER]

Downsampling is a routine step before applying interferometric synthetic aperture radar (InSAR) data to earthquake inversion because of the high computational burden. In this letter, we make use of the matrix perturbation theory to describe the downsampling process, which is considered as matrix perturbation on inversion equation. First, we derive a formula to quantitatively assess the perturbation on the inversion solution caused by data downsampling. Next, we propose an equation-based InSAR data downsampling algorithm to better reduce the perturbation. The experiment with simulated data demonstrates that our new algorithm preserves the most details from full data inversion comparing with previous algorithms. Finally, we use our method to study the slip distribution of the 2008 Mw 6.3 Dangxiong earthquake. [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]

In this paper, a bottom–up vehicle emission model is proposed to estimate real-time \CO2 emissions using intelligent transportation system (ITS) technologies. In the proposed model, traffic data that were collected by ITS are fully utilized to estimate detailed vehicle technology data (e.g., vehicle type) and driving pattern data (e.g., speed, acceleration, and road slope) in the road network. The road network is divided into a set of small road segments to consider the effects of heterogeneous speeds within a road link. A real-world case study in Beijing, China, is carried out to demonstrate the applicability of the proposed model. The spatiotemporal distributions of \CO2 emissions in Beijing are analyzed and discussed. The results of the case study indicate that ITS technologies can be a useful tool for real-time estimations of \CO2 emissions with a high spatiotemporal resolution. [ABSTRACT FROM PUBLISHER]

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]

GPS trajectories generated by moving objects provide researchers with an excellent resource for revealing patterns of human activities. Relevant research based on GPS trajectories includes the fields of location-based services, transportation science, and urban studies among others. Research relating to how to obtain GPS data (e.g., GPS data acquisition, GPS data processing) is receiving significant attention because of the availability of GPS data collecting platforms. One such problem is the GPS data classification based on transportation mode. The challenge of classifying trajectories by transportation mode has approached detecting different modes of movement through the application of several strategies. From a GPS data acquisition point of view, this paper macroscopically classifies the transportation mode of GPS data into single-mode and mixed-mode. That means GPS trajectories collected based on one type of transportation mode are regarded as single-mode data; otherwise it is considered as mixed-mode data. The one big difference of classification strategy between single-mode and mixed-mode GPS data is whether we need to recognize the transition points or activity episodes first. Based on this, we systematically review existing classification methods for single-mode and mixed-mode GPS data and introduce the contributions of these methods as well as discuss their unresolved issues to provide directions for future studies in this field. Based on this review and the transportation application at hand, researchers can select the most appropriate method and endeavor to improve them. [ABSTRACT FROM AUTHOR]

Road inspections ensure an adequate infrastructure operation and traffic safety, but the widely used positioning modes of road inspection have large deviations with road mileage. This paper describes an automatic method of road mileage positioning for road inspection. It integrates binocular stereo vision system, Global Navigation Satellite System (GNSS), objection detection algorithm, and Optical Character Recognition into road mileage positioning. A validation test result shows the average deviation of the mileage positioning is 4.06 m, which meets the requirement of road inspection. The significance of this paper consists of improving the automatic method of mileage positioning with the rapid disposal of abnormal targets on roads. In future research, the targets of road inspection, such as pavement potholes, patch, wearing lane markings, will be detected by objection detection algorithms and be positioned by the mileage positioning method. • Binocular cameras, IMU, and GNSS are integrated into Road Inspection System. • Mileage piles are detected and classified into kilo- and hecto- signs by YOLOv5. • The information of road number and road mileage is extracted by PaddleOCR. • The distance of mileage pile and capturing point is measured by binocular vision. • Road mileage of inspection trajectory is calculated based on the measurements. • A case study and a validation test are provided. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a GPS/INS kinematic levelling method for the linear surveying engineering projects. [•] We construct a least squares collocation (LSC) model to improve the height accuracy. [•] A sliding least squares collocation (SLSC) model is proposed which performs better than the LSC model. [•] The corresponding covariance functions for the SLSC model are also presented. [Copyright &y& Elsevier]

This paper deals with the optimal acoustic sensor configuration for positioning seafloor geodetic node. We use the determinant of Fisher Information Matrix (FIM) to measure the performance of different sensor configuration. This criteria is calculated in both 2D and 3D scenarios on a more practical assumption that range observations have different weights depending on their value, instead of identical weights. Then the uncertainty of initial node position is also taken into consideration for the calculation of determinant of FIM which was often neglected in the optimizing process. We present a kind of optimal configurations based on the maximum of determinant (FIM), which is circular and has different optimal radius at each corresponding scenario. Simulative experiments are carried out to demonstrate the optimal configuration. Practical experiments in South China Sea can further prove the relationship between positioning accuracy, configuration and determinant of FIM. [ABSTRACT FROM AUTHOR]