A Digital Twin-Based Platform towards Intelligent Automation with Virtual Counterparts of Flight and Air Traffic Control Operations

Automation technologies have been deployed widely to boost the efficiency of production and operations, to trim the complicated process, and to reduce the human error involved. Nevertheless, aviation remains human-centred and requires collaboration between different parties. Given the lack of a collaborative decision-making training platform for air traffic operations in the industry, this study utilises the concept of cyber-physical systems (CPS) to formulate a system architecture for pilots and air traffic control officers training in collaborative decision making by linking and integrating the virtual counterparts of flights and air traffic control operations. Collaborative decision-making training and the corresponding intelligent automation aids could be realised and supported. A performance analysis via a flight task undertaken with different computational load settings was prepared to evaluate the platform’s latency and integrity. The latency is presented using its 95% confidence interval, and integrity is presented using the percentage of data loss during wireless transmission. The results demonstrated convincing performance and a promising system robustness in both domains.

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Intelligent traffic control at signalized intersections in urban areas is vital for mitigating congestion and ensuring sustainable traffic operations. Poor traffic management at road intersections may lead to numerous issues such as increased fuel consumption, high emissions, low travel speeds, excessive delays, and vehicular stops. The methods employed for traffic signal control play a crucial role in evaluating the quality of traffic operations. Existing literature is abundant, with studies focusing on applying regression and probability-based methods for traffic light control. However, these methods have several shortcomings and can not be relied on for heterogeneous traffic conditions in complex urban networks. With rapid advances in communication and information technologies in recent years, various metaheuristics-based techniques have emerged on the horizon of signal control optimization for real-time intelligent traffic management. This study critically reviews the latest advancements in swarm intelligence and evolutionary techniques applied to traffic control and optimization in urban networks. The surveyed literature is classified according to the nature of the metaheuristic used, considered optimization objectives, and signal control parameters. The pros and cons of each method are also highlighted. The study provides current challenges, prospects, and outlook for future research based on gaps identified through a comprehensive literature review.

Coordinated Perimeter Flow and Variable Speed Limit Control for Mixed Freeway and Urban Networks

Recent studies have leveraged the existence of network macroscopic fundamental diagrams (MFD) to develop regional control strategies for urban traffic networks. Existing MFD-based control strategies focus on vehicle movement within and across regions of an urban network and do not consider how freeway traffic can be controlled to improve overall traffic operations in mixed freeway and urban networks. The purpose of this study is to develop a coordinated traffic management scheme that simultaneously implements perimeter flow control on an urban network and variable speed limits (VSL) on a freeway to reduce total travel time in such a mixed network. By slowing down vehicles traveling along the freeway, VSL can effectively meter traffic exiting the freeway into the urban network. This can be particularly useful since freeways often have large storage capacities and vehicles accumulating on freeways might be less disruptive to overall system operations than on urban streets. VSL can also be used to change where freeway vehicles enter the urban network to benefit the entire system. The combined control strategy is implemented in a model predictive control framework with several realistic constraints, such as gradual reductions in freeway speed limit. Numerical tests suggest that the combined implementation of VSL and perimeter metering control can improve traffic operations compared with perimeter metering alone.

A Method for Traffic Flow Forecasting in a Large-Scale Road Network Using Multifeatures

Accurate traffic prediction on a large-scale road network is significant for traffic operations and management. In this study, we propose an equation for achieving a comprehensive and accurate prediction that effectively combines traffic data and non-traffic data. Based on that, we developed a novel prediction model, called the adaptive deep neural network (ADNN). In the ADNN, we use two long short-term memory (LSTM) networks to extract spatial-temporal characteristics and temporal characteristics, respectively. A backpropagation neural network (BPNN) is also employed to represent situations from contextual factors such as station index, forecast horizon, and weather. The experimental results show that the prediction of ADNN for different stations and different forecast horizons has high accuracy; even for one hour ahead, its performance is also satisfactory. The comparison of ADNN and several benchmark prediction models also indicates the robustness of the ADNN.

Multi-Level Conditional Transit Signal Priority in Connected Vehicle Environments

Connected Vehicles (CV) are an emerging technology with a large potential to improve traffic operations and safety. This paper develops and tests advanced CV-based multi-level conditional Transit Signal Priority (TSP). The algorithms are using the latitude/longitude (lat/lon) coordinates of CV vehicles and intersections to establish communication, share information and request priority. The TSP strategies are implemented through controllers’ built-in features and logic processor, using Econolite ASC/3 as a representative traffic signal controller. The tests were performed in VISSIM microsimulation with the ASC/3 Software-in-the-Loop (SIL) controller emulator. State Street in Salt Lake City, UT, is selected as a test-case corridor. The paper shows that the developed signal control priority (SCP) algorithms are successful in reducing delays for target vehicles in excess of 6%, without significant impacts on other traffic. The information obtained from CV vehicles can be used to further enhance control algorithms and create adaptive SCP programs.

Using microsimulation to evaluate safety and operational implications of newer roundabout layouts for European road networks

“Standard” roundabouts, for example those designed in some European countries, can often be characterized by low levels of safety or capacity and a high degree of sustainability. Given the proliferation of newer layouts, it is of interest to explore whether design practices could be improved by capitalizing on the experience gained internationally. Operational aspects of some of these designs have been explored previously, but there is a need to compare both the operational and safety performance of new designs to that of standard roundabouts. The objective of this paper is to evaluate the safety and operational implications of various potential alternatives to the standard roundabouts that proliferate in Europe and elsewhere. Microsimulation is used to simulate traffic operations at roundabout layout alternatives at the same levels of volume to capacity (V/C) ratio and also with the same traffic flow. Operational performance measures include the common level of service parameters, while measures of safety are based initially on time to collision (TTC) values. Threshold values of TTC were then applied in defining conflicts that are used for crash-based safety evaluation by applying crash-conflict models estimated in published research. Interesting insights were revealed, suggesting that the newer layouts should be considered where warranted by cost-benefit considerations. Keywords: road safety; traffic simulation; roundabout design; sustainable-transport indicators

Using microsimulation to evaluate safety and operational implications of newer roundabout layouts for European road networks

“Standard” roundabouts, for example those designed in some European countries, can often be characterized by low levels of safety or capacity and a high degree of sustainability. Given the proliferation of newer layouts, it is of interest to explore whether design practices could be improved by capitalizing on the experience gained internationally. Operational aspects of some of these designs have been explored previously, but there is a need to compare both the operational and safety performance of new designs to that of standard roundabouts. The objective of this paper is to evaluate the safety and operational implications of various potential alternatives to the standard roundabouts that proliferate in Europe and elsewhere. Microsimulation is used to simulate traffic operations at roundabout layout alternatives at the same levels of volume to capacity (V/C) ratio and also with the same traffic flow. Operational performance measures include the common level of service parameters, while measures of safety are based initially on time to collision (TTC) values. Threshold values of TTC were then applied in defining conflicts that are used for crash-based safety evaluation by applying crash-conflict models estimated in published research. Interesting insights were revealed, suggesting that the newer layouts should be considered where warranted by cost-benefit considerations. Keywords: road safety; traffic simulation; roundabout design; sustainable-transport indicators