Search

The principle of system resilience is its ability to withstand disruptions and maintain an equilibrium state. In urban network systems, adaptive traffic signal control (ATSC) has been an effective countermeasure to mitigate traffic flow disturbance and improve resilience. This research has explored the usage of a decentralised advantage actor-critic (a2c) algorithm-based ATSC in mitigating disruptions, particularly nonrecurring congestion caused by car accidents. A reward function has also been proposed, combining deduced resilience metric, safety indicator time to collision (TTC) and system performance. A virtual simulation environment was created using simulation of urban mobility (SUMO) to facilitate the evaluation of the proposed approach. In the grid simulation environment, an overall 5.8% improvement is achieved, exceeding benchmark algorithms in three metrics, especially performance with a margin of over 5.2%. Robustness against different levels of car accidents are proven as well. Further evaluation is also implemented based on a real-world case study and demonstrates an improvement of 20.08%, highlighting the correlation of proposed method's efficiency on the traffic flow rate and road structure., Author(s): Xiangmin Yang (corresponding author) [1]; Yi Yu [2]; Yuxiang Feng [1]; Washington Yotto Ochieng [1] 1. Introduction Due to the rapid development of globalisation, the world has become more [...]

The global increase in the population of Visually Impaired People (VIPs) underscores the rapidly growing demand for a robust navigation system to provide safe navigation in diverse environments. State-of-the-art VIP navigation systems cannot achieve the required performance (accuracy, integrity, availability, and integrity) because of insufficient positioning capabilities and unreliable investigations of transition areas and complex environments (indoor, outdoor, and urban). The primary reason for these challenges lies in the segregation of Visual Impairment (VI) research within medical and engineering disciplines, impeding technology developers' access to comprehensive user requirements. To bridge this gap, this paper conducts a comprehensive review covering global classifications of VI, international and regional standards for VIP navigation, fundamental VIP requirements, experimentation on VIP behavior, an evaluation of state-of-the-art positioning systems for VIP navigation and wayfinding, and ways to overcome difficulties during exceptional times such as COVID-19. This review identifies current research gaps, offering insights into areas requiring advancements. Future work and recommendations are presented to enhance VIP mobility, enable daily activities, and promote societal integration. This paper addresses the urgent need for high-performance navigation systems for the growing population of VIPs, highlighting the limitations of current technologies in complex environments. Through a comprehensive review of VI classifications, VIPs' navigation standards, user requirements, and positioning systems, this paper identifies research gaps and offers recommendations to improve VIP mobility and societal integration. [ABSTRACT FROM AUTHOR]

Floating car data are beneficial in estimating traffic conditions in wide areas and are playing an increasing role in traffic surveillance. However, widespread application is limited by low-sample frequency which makes it hard to get a complete picture of a vehicle's motion. An accurate and reliable reconstruction of a vehicle's trajectory could effectively result in a higher sampling frequency enabling a more accurate estimation of road traffic parameters. Existing methods require additional information such as nearby vehicles, signal timing strategies, and queue patterns which are not always available. To address this problem, this paper presents a method used with low-sample frequency data to reconstruct vehicle trajectories through intersections, without the need for extra information. Furthermore, the additional parameters for the speed-time curve distributions for deceleration rate and acceleration rate are generated. A piecewise deceleration and acceleration model is developed to calculate the acceleration rate for different travel modes in the trajectory. The distribution parameters of the acceleration data for each travel mode are then estimated using a new Expectation Maximization (EM) algorithm. The acceleration statistics are then used to reconstruct the corresponding parts of the trajectory. Compared to the reference trajectories (truth), the test results show that the method developed in this paper achieves improvement in accuracy ranging from 16 to 67% over the commonly used linear interpolation method. In addition, the proposed method is not very sensitive to the sampling interval of the floating car data, unlike the linear interpolation method where the error grows rapidly with increasing sampling interval., 1. Introduction The potential for the contribution of floating car data to traffic state monitoring has attracted significant research in the past decade [1-3]. However, constrained by the penetration rate [...]

This paper develops the two-lane traffic surveillance function of a smart road stud (SRS), which has the capabilities of high-resolution traffic data collection and transmission, and a light-based traffic guidance. The SRS addresses the limitations of the current systems with the potential for high performance and usage for a variety of applications. SRS nodes are mounted on the road surface along the lane separation lines instead of the intrusive middle of the lane detection sensors. The key innovation in SRS design is its ability to detect vehicles in two adjacent lanes in real time by employing a three-axis geomagnetic sensor mounting on the lane line, which is the basis of advanced applications such as traffic surveillance, driving guidance, and congestion monitoring. Multiple vehicles are recognized based on the superposition of the disturbance of the geomagnetic fields. Magnetic field changes along the axes are simulated and analyzed with vehicles modeled as magnet dipoles to determine the criteria for the detection of vehicles in adjacent lanes, which are verified by actual waveforms. Extended dual-window and two-lane vehicle detection algorithms are developed, enabling to distinguish five detection scenarios and count vehicles in two lanes. Field test results show that one SRS can detect traffic in adjacent lanes with a correct vehicle detection rate of over 98%, the same level achievable by intrusive middle of the lane detectors. Based on the enhanced features, high performance, cost-effectiveness, and value-added services, the SRS is a significant improvement on the conventional studs for intelligent transport system applications. [ABSTRACT FROM AUTHOR]