This study develops an integrated dynamic routing and off-block (IDRO) optimization framework for real-time airport surface operations. Instead of relying on complex taxi time prediction models, the proposed approach employs adynamic route impedanceformulation as a practical heuristic to capture real-time congestion conditions on the airport surface. The impedance integrates domain-specific operational factors, including free flow taxi time, turning penalties, and potential aircraft conflicts, enabling real-time route evaluation and decision-making without extensive simulation. Building upon this impedance representation, the IDRO framework coordinates both taxi routing and off-block release timing under a real-time First-Come-First-Served (FCFS) principle. The approach was implemented and tested using a high-fidelity cellular automata simulator calibrated with operational data from Beijing Capital International Airport. Compared with real-world baseline operations, the proposed framework reduced average taxi time, flight delay, and taxi conflicts by 10.43%, 24.27%, and 31.82%, respectively. A comparative study with another similar approach in the literature also reveals the advantage of the proposed strategy. These results demonstrate the framework’s computational efficiency, consistent performance across scenarios, and strong potential for real-world deployment in large-scale airport environments.

Effectively leveraging data and domain knowledge remains a significant challenge in controlling the Internet of Unmanned Agent (IUA). This article proposes a novel multiagent deep reinforcement learning-based cooperative control model called MARL-CTV to efficiently control two key IUA agents: 1) connected and automated vehicle (CAV) and 2) controllable traffic signal light (TSL). The CAV agents are controlled by the deep deterministic policy gradient (DDPG) algorithm, and the TSL agent is controlled by a dueling double deep Q-network (D3QN). To reduce the control burden and ensure the cumulative reward converges, the actor and critic networks are pretrained by the expert dataset, and the expert dataset initializes the experience replay buffer of DDPG. This dataset is generated by multiple velocity profiles derived from a genetic algorithm (GA) based on various random initial states of CAVs. Numerical experiments conducted using a joint simulation platform composed of SUMO and CARLA and real-world data from CitySim demonstrate the effectiveness of MARL-CTV. Specifically, when the market penetration rate (MPR) of CAV is 35%, MARL-CTV enables most CAVs to pass through the signalized intersection without stop-and-go behavior, reducing average travel time by 24.2%, fuel consumption by 22.7%, and the traffic conflicts by 68.3%.

Non-line of sight (NLOS) and multipath are known to cause pseudorange measurement errors, leading to excessive positioning errors in challenging urban environments. Although GNSS and inertial measurement units (IMU) integrated system can enhance the positioning performance, the positioning accuracy is still constrained by the filter performance. The existing pseudorange correction algorithm employs simple measurement noise covariance adjusting strategy, which is unable to adapt the varying measurement noise in the complex urban environment. To solve this issue, a 3-D grid-based resilient pseudorange error prediction algorithm is proposed to adjust the measurement noise covariance R in Kalman filter (3D-RKF) in urban areas. The urban area is divided by the proposed 3-D grid layout and the pseudorange errors are predicted by ensemble bagged regression tree (EBRT) grid by grid to achieve fine-scale pseudorange prediction. R is then updated by the proposed model reliability indicator (MRI)-based adaptive fusion strategy. Experimental results in complex urban areas prove the proposed algorithm can reach a 3-D accuracy of 10.16 m, with an improvement of 52% compared to the EKF-based fusion, 33% compared to 2-D grid-based adaptive Kalman filter algorithm without MRI-based adaptive fusion strategy (2D-AKF) and 22% compared to 3-D grid-based adaptive Kalman filter algorithm without MRI-based adaptive fusion strategy (3D-AKF), respectively.

Global navigation satellite systems (GNSS), integrated with an inertial measurement unit (IMU) and visual sensors, are widely used for vehicular navigation. With the advancement of emerging vehicular technologies, the performance requirements for positioning, navigation, and timing (PNT) have become critical, emphasizing not only positioning accuracy but also high reliability. However, GNSS signals are susceptible to reflection and diffraction in urban environments, leading to multipath effects, such as non-line-of-sight (NLOS) reception and multipath interference. The GNSS positioning errors will increase significantly, causing the integrated navigation system to fail to meet the high-performance navigation requirements. To address this issue, we have proposed a robust GNSS/IMU/visual odometry (VO) fusion algorithm with a new GNSS weighting model and an adaptive VO velocity measurement update algorithm for urban navigation. In particular, a multipath inflation factor, based on real-time IMU and VO data, is proposed for the GNSS weighting model to mitigate multipath effects. A VO variance attenuation factor based on zero velocity detection in a robust extended Kalman filter (REKF) is also designed to adaptively adjust the covariance of VO measurements, enhancing the overall robustness of the integrated system. A field test was conducted in urban environments. The results show that the proposed algorithm achieves horizontal and 3-D positioning accuracy of 3.38 m and 5.00 m, respectively, outperforming the conventional GNSS/IMU/VO integration using C/N0-based weighting model. The improvements in horizontal and 3-D positioning accuracy are 63.4% and 56.1%, respectively.

The efficiency of urban traffic management and congestion alleviation relies heavily on accurate forecasting of Origin-Destination (O-D) demand matrices. Existing models primarily focus on estimating O-D demand for various travel purposes throughout the day, which is characterised by its pulsating nature. However, these models often compromise the precision of peak-hour forecasts, leading to unreliable dynamic traffic control and challenges in effectively reducing peak-hour congestion. To tackle this challenge, this paper proposes a novel method for predicting commuting O-D demand matrices. Our method employs community detection algorithms on road networks to precisely partition commute O-D regions, incorporating Points of Interest (POIs). We also present a spatio-temporal dynamic weighted hypergraph model that leverages these partitioned regions, time characteristics from observed O-D trips, and meteorological data to improve forecasting. Comparative analyses with contemporary models and ablation studies indicate our method significantly enhances prediction accuracy, by approximately 5%. These findings imply that the proposed method more effectively encompasses the varied characteristics of commuting during peak hours, thereby providing more accurate demand matrices for urban traffic management.

To date immunity to disruptions of multi-scale urban road networks (URNs) has not been effectively quantified. This study uses robustness as a meaningful - if partial - representation of immunity. We propose a novel Relative Area Index (RAI) based on traffic assignment theory to quantitatively measure the robustness of URNs under global capacity degradation due to three different types of disruptions, which takes into account many realistic characteristics. We also compare the RAI with weighted betweenness centrality, a traditional topological metric of robustness. We employ six realistic URNs as case studies for this comparison. Our analysis shows that RAI is a more effective measure of the robustness of URNs when multi-scale URNs suffer from global disruptions. This improved effectiveness is achieved because of RAI’s ability to capture the effects of realistic network characteristics such as network topology, flow patterns, link capacity, and travel demand. Also, the results highlight the importance of central management when URNs suffer from disruptions. Our novel method may provide a benchmark tool for comparing robustness of multi-scale URNs, which facilitates the understanding and improvement of network robustness for the planning and management of URNs.

The fast advancement of urban transportation systems in the recent decades has on one hand improved efficiency in traffic control and management, yet on the other hand brought new obstacles and interferences in audio related services in transportation systems, which is one of the dominating components in urban transportation systems, such as end-to-end Voice over Internet Protocol (VoIP) communications, risk alerting, and personalised recommendation services. The movement of vehicles/trains and the growing complexity of transportation infrastructures has become a big threat to the audio related services. Hence it is crucial to evaluate the Quality of Experience (QoE) of audio related services. Different from traditional algorithms which use digital signal processing to evaluate the QoE of mobile users, in this paper, we propose a two-stage brain-alike neural network aided graph learning algorithm to evaluate the QoE of audio signals with the aid of EEG feature extraction. The results are evaluated by newly-collected on-site data in public transportation environments and are examined by a branch of human experts to show that our algorithm outperforms other benchmark algorithms in term of human perception and accuracy of classification.

A self-designed AMR detector named Smart Road Stud (SRS) is proposed. SRS, installed along lane markings, is not only a detector but also a lane markings enhancement device. Because a single SRS needs to detect vehicles on two lanes, the vehicle detection method for SRS is different from the AMR detectors installed in the middle of the lane or at the roadside. Based on SRS, an innovative mathematical model based on the magnetic dipole is developed to simulate vehicle magnetic field. According to the model, lane information can be inferred to achieve traffic volume detection. Results show that a single SRS delivers traffic volume detection accuracy of 97%, providing a strong foundation for future research to achieve higher detection performance based on multi-SRS linkage. The significance of this paper is to meet the detection requirements of warning equipment installed on lane markings.

The Covid-19 pandemic has hit hard on the highly-organised yet risk-vulnerable modern societies, and has introduced new characteristics to large-scale emergencies, which feature long persistence in duration, high frequency in occurrence, large sensitivity to individual behaviours, and extreme high hazard propagation rate owing to the highly-efficient transport networks. This has raised new challenges on long-term emergency preparedness of urban transportation systems in terms of safety, efficiency, robustness and sustainability. Non-cooperative behaviours of transport participants could result in severe performance degradation in emergency preparedness, and mandatory restrictions in human activities can be economical costly and difficult in operation. In addition, current arrangement models for the disaster financial assistance have not been elaborately designed for civilian behaviour optimisation although with great potential as an economical instrument. Hence, in this paper, we propose a reward based traffic control mechanism to generate cooperative behaviours and optimise resource allocation in an urban transportation system for emergency preparedness via distributing credit coins, which can also be treated as a financial assistance approach during long-term disasters. A queueing theory based analytic model is employed to mimic the behaviours of civilians in the transportation system under the emergency preparedness state and a probability choice model is utilised to optimise the emergency preparedness strategies of the system. The experimental results show that the introduction of the incentive based traffic control mechanism can significantly reduce hazard response time, travel delay as well as the energy usage of the urban transportation system at the expense of monetary rewards.

The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. These effects cause range errors that degrade GPS positioning accuracy. Enhancements in the design of antennae and receivers deliver a level of reduction of multipath. However, NLOS signal reception and residual effects of multipath are still to be mitigated sufficiently for improvements in range errors and positioning accuracy. Recent machine learning-based methods have shown promise in improving pseudorange-based position solutions by considering multiple variables from raw GPS measurements. However, positioning accuracy is limited by low accuracy signal reception classification. Unlike the existing methods, which use machine learning to directly predict the signal reception classification, we use a gradient boosting decision tree (GBDT)-based method to predict the pseudorange errors by considering the signal strength, satellite elevation angle and pseudorange residuals. With the predicted pseudorange errors, two variations of the algorithm are proposed to improve positioning accuracy. The first corrects pseudorange errors and the other either corrects or excludes the signals determined to contain the effects of multipath and NLOS signals. The results for a challenging urban environment characterized by high-rise buildings on one side, show that the 3-D positioning accuracy of the pseudorange error correction-based positioning measured in terms of the root mean square error is 23.3 m, an improvement of more than 70% over the conventional methods.

The detection of driving events could be useful for reducing accidents, fleet management and insurance premiums etc. Currently, top of the range vehicles and large fleets employ expensive driver monitoring systems. However, most drivers do not have access to such systems. The required monitoring platform would have to deliver the required performance while also being affordable and accessible. A candidate with considerable promise is the smartphone with sensors built-in that could be exploited for the detection of driving events. However, to date it has not been possible to achieve the required correct, missed and false detection rates in addition to the computational efficiency for real-time operations. This paper proposes a novel bagging tree and dynamic time warping (DTW) integrated algorithm for the detection of driving events employing acceleration and orientation data from a smartphone's low cost three-axis accelerometers and gyroscopes. The bagging tree-based machine learning algorithm provides the initial maneuver detection results, as well as the location of the event start and end points. Event detection is then achieved by calculating the similarity of the results predicted through the bagging tree algorithm with the corresponding templates extracted from the experience datasets, while also applying a number of constraints to verify the calculated results. Field test results show that the proposed integrated algorithm is superior to the state-of-the-art, achieving a high correct detection accuracy of 97.5%, a low missed detection of 2.5% and a false detection rate of 2.9%. The corresponding results for the best alternative candidate method are 90.2%, 9.8% and 11.7%. Furthermore, the improvement in computational efficiency offered by our proposed approach is three to more than ten times greater than that of the other state-of-the-art algorithms.

This paper summarizes the main results obtained during the development of an Enhanced Precise Point Positioning (EPPP) Global Navigation Satellite Systems multifrequency user algorithm. The main innovations include the application of precise ionospheric corrections to facilitate the resolution of undifferenced carrier phase ambiguities, ambiguity validation, and integrity monitoring. The performance of the EPPP algorithm in terms of accuracy, convergence time, and integrity is demonstrated with actual GPS and simulated Galileo data. This can be achieved with very limited bandwidth requirements for EPPP users (less than 300 b/s for dual-frequency GPS data).

Recently, network localization systems that are based on inter-node ranges have received significant attention. Geographic routing has been considered an application which can utilize the location information from these localization systems. In this paper, we firstly recognize that sensor network localization algorithms generate positioning data with different error patterns compared to those networks where node positions are determined directly from GNSS measurements. Secondly, by simulating practical sensor network scenarios using data from our localization algorithm, we observe that existing geographic routing algorithms in wireless sensor networks (WSNs) adopt very simplistic methods in the treatment of position error, without due consideration of error distribution. Additionally, an insight is given into localization algorithms for WSNs with inhomogeneous error environments. Our observations represent an initial step toward a detailed understanding and design of efficient geographic routing algorithms in location aware WSNs.