Signal Priority Research Articles

Reinforcement Learning for Transit Signal Priority with Priority Factor

Public transportation has been identified as a viable solution to mitigate traffic congestion. Transit signal priority (TSP) control, which is widely used at signalized intersections, has been recognized as a practical strategy to improve the efficiency and reliability of bus operations. However, traditional TSP control may fall short of efficiency and is facing several challenges of negative externalities for non-transit users and the need to handle conflicting priority requests. Recent studies have proposed the use of reinforcement learning (RL) methods to identify efficient traffic signal control (TSC). Some of these studies on RL-based TSC have incorporated the concept of max-pressure (MP), which is a maximal weight-matching algorithm to minimize queue sizes. Nevertheless, the existing RL-based TSC methods focus on private vehicles and cannot adequately distinguish between buses and private vehicles. In prior research, RL-based control has been implemented within the context of bus rapid transit (BRT) systems. This study proposes a novel RL-based TSC strategy that leverages the MP concept and extends it to incorporate TSP control. This is the first implementation of RL-based TSP control within the mixed-traffic road network. A significant innovation of this research is the introduction of the priority factor (PF), which is designed to prioritize bus movements at signalized intersections. The proposed RL-based TSP with PF control seeks to balance the competing objectives of enhancing bus operations while mitigating adverse impacts on non-transit users. To evaluate the performance of the proposed TSP method with the PF mechanism, simulations were conducted on an arterial and a grid network under dynamic traffic conditions. The simulation results demonstrated that the proposed TSP with PF not only reduces bus travel times and resolves conflicts between priority requests but also does not make a significant negative impact on passenger car operations. Furthermore, the PF can be dynamically assigned according to the number of passengers on each bus, suggesting the potential for the proposed approach to be applied in various traffic management scenarios.

The temporal dynamics of visual attention.

Researchers have long debated how humans select relevant objects amid physically salient distractions. An increasingly popular view holds that the key to avoiding distractions lies in suppressing the attentional priority of a salient distractor. However, the precise mechanisms of distractor suppression remain elusive. Because the computation of attentional priority is a time-dependent process, distractor suppression must be understood within these temporal dynamics. In four experiments, we tracked the temporal dynamics of visual attention using a novel forced-response method, by which participants were required to express their latent attentional priority at varying processing times via saccades. We show that attention could be biased either toward or away from a salient distractor depending on the timing of observation, with these temporal dynamics varying substantially across experiments. These dynamics were explained by a computational model assuming the distractor and target priority signals arrive asynchronously in time and with different influences on saccadic behavior. The model suggests that distractor signal suppression can be achieved via a "slow" mechanism in which the distractor priority signal dictates saccadic behavior until a late-arriving priority signal overrides it, or a "fast" mechanism which directly suppresses the distractor priority signal's behavioral expression. The two mechanisms are temporally dissociable and can work collaboratively, resulting in time-dependent patterns of attentional allocation. The current work underscores the importance of considering the temporal dynamics of visual attention and provides a computational architecture for understanding the mechanisms of distractor suppression. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Evaluating the performance of implementing regionally coordinating bus priority signals under different control schemes

Bus Rapid Transit (BRT) proves its effectiveness in alleviating traffic congestion, especially in urban areas. The implementation of Transit Signal Priority (TSP) for BRT has shown a significant reduction in delays. However, in densely populated urban areas, this priority can inadvertently cause additional delays for other modes of transportation. In this paper, we propose a control strategy for Regionally Coordinating Bus Priority Signals Control (RCBPSC) at urban intersections. The aim is not only to reduce bus delays but also to consider minimizing delays for pedestrians and other vehicles. To achieve this, we modeled two consecutive intersections along the Amman BRT. Essentially, we evaluated three different control scenarios in addition to the current base scenario. These scenarios include adaptive traffic signal control, RCBPSC with no signal timing optimization, and RCBPSC with signal optimization. Simulation results indicate that the adaptive traffic signal timing has the worst operational performance in terms of average delay and Level of Service (LOS) compared to the base scenario. Additionally, the results show that BRT delays significantly decrease at both intersections when we implement RCBPSC scenarios. When implementing RCBPSC with optimization scenarios, the results show an average reduction of more than 60% in intersection delay, a decrease in emissions of more than 50%, and an improved LOS for system users compared to the base scenario. The findings of this work can help agencies improve the current operational condition of BRT when implementing RCBPSC.

Cardiac adverse events associated with lacosamide: a disproportionality analysis of the FAERS database

Lacosamide was the first approved third-generation antiepileptic drug. However, real-world data regarding its adverse cardiac reactions in large samples still need to be completed. We evaluated the cardiac safety profile of lacosamide using the Food and Drug Administration Adverse Event Reporting System (FAERS). We performed disproportionality analysis computing reporting odds ratio (ROR) as a quantitative metric to assess the signal of lacosamide-related cardiac adverse events (AEs) from 2013 Q1 to 2022 Q4. The signal was considered significant when the lower limit of the 95% confidence interval (CI) of the ROR exceeded 1, and ≥ 5 AEs were reported. Serious and nonserious cases were compared by statistical analysis, and signals were further prioritized using a rating scale. A total of 812 cardiac AEs associated with lacosamide were identified, and 92 signals were detected, of which 17 AEs were significantly associated signals. The median time-to-onset (TTO) for moderate priority signals was 10 days, whereas for weak priority signals, it was 54 days. Notably, all cardiac AEs exhibited an early failing pattern, indicating the risk gradually decreasing. Based on the comprehensive analysis of the FAERS database and prioritization of cardiac AE signals, our research enhances the awareness among healthcare professionals regarding cardiac AEs associated with lacosamide.

Traffic signal priority control for public transport rapid transit based on a step-by-step prediction algorithm

Traffic signal priority control for public transport rapid transit based on a step-by-step prediction algorithm

Mining of neurological adverse events associated with valbenazine: A post-marketing analysis based on FDA adverse event reporting system

PurposeValbenazine is commonly used to treat tardive dyskinesia, and we conducted a pharmacovigilance analysis using the Food and Drug Administration Adverse Event Reporting System (FAERS) to evaluate neurological safety signals associated with valbenazine. MethodsData was collected in FAERS from the second quarter of 2017 to the fourth quarter of 2023 for data cleaning. Neurological adverse event (AE) signals of valbenazine were mined by calculating reporting odds ratios (ROR), information component (IC) and empirical Bayesian geometric mean (EBGM). The serious and non-serious cases and signals were prioritized using a rating scale. ResultsThe number of neurological AE reports where the primary suspect (PS) drug was 8981 for valbenazine. Significant AE signals were identified by the preferred term (PT) analysis for valbenazine, including somnolence (ROR 19.69), tremor (ROR 15.17), and tardive dyskinesia (ROR 236.91), among which 18 AEs were identified as new signals. Patient age (p < 0.009) and sex (p = 0.197) might be associated with an increased risk of neurological AE severity. Notably, the association between valbenazine and neurological disorders remained when stratified by sex, age, and reporter type. AE timing analysis was performed for the drug and four moderate clinical priority signals [i.e., somnolence, balance disorder, parkinsonism, and akathisia (priorities 7)], showing the same early failure type profiles. ConclusionsThe increase in neurological safety signals is identified in the post-marketing research of valbenazine. Clinicians need to pay attention to not only common AEs but also be alert to new neurological AE signals when using valbenazine.

Wavy-attention network for real-time cyber-attack detection in a small modular pressurized water reactor digital control system

Global interest in advanced reactors has been reignited by recent investments in small modular reactors and micro-reactor design. The use of digital devices is essential for meeting the size and modularity requirements of small modular reactor controls. By fully digitizing the small modular reactor control systems, critical information can be obtained to optimize control, reduce costs, and extend the reactor’s lifetime. However, the potential for cyber-attacks on digital devices leaves digital control systems vulnerable. To address this risk, this study presents a novel wavy-attention network for sensor attack detection in nuclear plants. The wavy-attention network comprises stacks of batch-normalized, dilated, one-dimensional convolution neural networks, and sequential self-attention modules, superior to conventional single-layer networks on sequence classification tasks. To evaluate the proposed wavy-attention network architecture, the International Atomic Energy Agency’s Asherah Nuclear Simulator and a false data injection toolbox found in the literature, both implemented in MATLAB/SIMULINK, are utilized. This approach leverages changes in process measurements to identify and classify cyber-attacks on priority signals using the proposed wavy-attention network. Three false data injection attacks are simulated on the simulator’s pressure, temperature, and level sensors to obtain representative process measurements. The wavy-attention network is trained and validated with normal and compromised process variables obtained from the simulator. The performance of the wavy-attention network to discriminate between the reactor states using the test set shows 99% accuracy, as opposed to other baseline models such as vanilla convolution neural networks, long short-term memory networks, and bi-directional long short-term memory networks with 90%, 77%, and 91% accuracy, respectively. An ablation study is also conducted to test the contribution of each component of the proposed architecture. The theoretical framework of the proposed wavy-attention network and its implementation for nuclear reactor digital sensor attack detection are discussed in this paper.

Real-time scheduling and routing of shared autonomous vehicles considering platooning in intermittent segregated lanes and priority at intersections in urban corridors

Anticipating the forthcoming integration of shared autonomous vehicles (SAVs) into urban networks, the imperative of devising an efficient real-time scheduling and routing strategy for these vehicles becomes evident if one is to maximize their potential in enhancing travel efficiency. In this study, we address the problem of jointly scheduling and routing SAVs across an urban network with the possibility of platooning the vehicles at intersections to reduce their travel time. We argue that this is especially useful in large urban areas. We introduce a novel vehicle scheduling and routing method that allows a specific number of SAVs to converge at the intersections of urban corridors within designated time intervals, facilitating the formation of SAV platoons. Dedicated lanes and signal priority control are activated to ensure that these platoons go through the corridors efficiently. Based on the above concept, we propose a linear integer programming model to minimize the total travel time of SAVs and the delays experienced by the conventional vehicles due to SAV priority, thereby optimizing the overall performance of the road network. For large instances, we develop a two-stage heuristic algorithm to solve it faster. In the first stage, leveraging an evaluation index that manifests the compatibility of each vehicle-to-request combination, we allocate passenger requests to a fleet of SAVs. In the second stage, a customized genetic algorithm is designed to coordinate the paths of various SAVs, thus achieving the desired vehicle platooning effect. The optimization method is tested on a real-world road network in Shanghai, China. The results display a remarkable reduction of 15.76 % in the total travel time of the SAVs that formed platoons. The overall performance of the road network could be improved with the total travel time increase of conventional vehicles significantly smaller than the reduction observed in SAVs’ total travel time.

Research Progress and Prospects of Transit Priority Signal Intersection Control Considering Carbon Emissions in a Connected Vehicle Environment

Transit priority control is not only an important means for improving the operating speed and reliability of public transport systems, but it is also a key measure for promoting green and sustainable urban transportation development. A review of signal intersection transit priority control strategy in a connected vehicle environment is conducive to discovering important research results on transit priority control at home and abroad and will promote further developments in urban public transport. This study analyzed and reviewed signal intersection transit priority control at four levels: traffic control sub-area divisions, transit signal priority (TSP) strategy, speed guidance strategy, and the impacts of intersection signal control on carbon emissions. In summary, the findings were the following: (1) In traffic control sub-area divisions, the existing methods were mainly based on the similarity of traffic characteristics and used clustering or search methods to divide the intersections with high similarity into the same control sub-areas. (2) The existing studies on the TSP control strategy have mainly focused on transit priority control based on fixed phase sequences or phase combinations under the condition of exclusive bus lanes. (3) Studies on speed guidance strategy were mainly based on using constant bus speeds to predict bus arrival times at intersection stop lines, and it was common to guide only based on bus speed. (4) The carbon emissions model for vehicles within the intersection mainly considered two types of vehicles, namely, fuel vehicles and pure electric vehicles. Finally, by analyzing deficiencies in the existing studies, future development directions for transit priority control are proposed.

Adaptive Sustainable Mobility Solutions by Using Vehicle-Integrated Photovoltaic in Kigali City

In a fast-paced urbanized culture, sustainable transportation remains a significant challenge. Quick and smooth mobility of people and goods with the possibility of regulating traffic congestion and preventing climate change is essential for every public transportation user. Urban population in developing nations like Rwanda is expected to double by the year 2050, and balancing the supply and demand of the urban transport system will be a significant issue. Information technology enables elements within the transportation system, including vehicles, roads, traffic lights, and message signs, to become intelligent by embedding them with sensors, processors, algorithms, and actuators, thus empowering them to communicate through various technologies. These technologies improve transportation system performance by reducing congestion, increasing safety and traveler convenience, and protecting the climate against some greenhouse gases. Different mobility solutions are stated in this document. The use of electric vehicles, Passenger Information Systems, Car-sharing and ride-sharing services, electronic payment, Bus Signal Priority, and Vehicle-Integrated Photovoltaics. Using photovoltaics in Rwanda is adequate because of the electricity problem. This is the best solution for fighting against climate change due to fuel from motorized traffic.

Passenger‐based adaptive transit signal priority for BRT systems with multiple loading areas

AbstractAn adaptive transit signal priority strategy is presented in this paper with the objective of passenger delay minimization at isolated intersections serving conflicting bus rapid transit (BRT) routes. The proposed passenger‐based adaptive signal priority for BRT systems (PASPB) is designed to optimize both green times and phase sequences at the start of each cycle and for a prespecified decision horizon. Since a public transportation (PT) vehicle travel time model capable of estimating the dwell time at stops with multiple loading areas has not yet been developed, PT vehicle dwell time is modeled in this study by analyzing the cases of passenger service by single or double PT vehicles. The problem is formulated as a mixed‐integer nonlinear program (MINLP) and at each execution, the optimization is conducted by genetic algorithm. The model is deployed to a real‐field intersection with conflicting BRT routes under the SUMO microsimulation environment. The results show that PASPB outperforms the SYNCHRO optimal solution and phase insertion strategy regarding PT passenger delay. Besides, the sensitivity analysis proves that at high demand levels of the PT system or general traffic, PASPB presents the best performance in terms of general traffic, PT, and total passenger delay compared to other models.

Capped Robust Optimal Control Method to Improve Tram Operation Reliability Considering Random Number of Passengers at Station Serving Multiple Lines

Tram signal priority control is a crucial approach for enhancing the reliability of tram operations and has been implemented in various cities. Nevertheless, unpredictable tram operations influenced by tram dwell time during station stops can cause signal priority control failure at intersections. It is challenging to precisely predict tram dwell time at stations that offer multiple lines. To address this issue, the proposed research presents a capped robust optimal control (CRC) technique for tram signal priority. This method entails considering the stochastic number of passengers boarding and alighting at stations with multiple lines. Furthermore, tram delay calculation models at intersections are established and integrated into an objective function. The main objective of this strategy is to enhance tram operation reliability and maximise tram operation efficiency while reducing the adverse impact of tram priority on other vehicles at the intersection. A case study was conducted to evaluate the effectiveness of the CRC method. The results indicate that the CRC technique significantly improves tram operation reliability and efficiency.

EXAMINING THE SAFETY IMPACTS OF TRANSIT PRIORITY SIGNAL SYSTEMS USING SIMULATION TECHNIQUES

Transit Priority Signal (TPS) systems are increasingly used to improve traffic efficiency and reduce passenger waiting times. However, such systems may carry potential safety risks. This study aims to investigate the safety effects of TPS at intersections. Our study utilized the SUMO traffic simulation program to create a road network model containing nine signalized intersections. Subsequently, the TPS system was applied to selected bus routes within the road network, and the cases with and without TPS implementation were compared in terms of safety and performance. In safety-oriented comparisons, surrogate safety measures were employed, including number of conflict and Time to Collision (TTC). Signalized intersection performances were measured and compared in terms of the number and duration of stops. The analysis results indicate that TPS enhances safety and transportation performance for buses, but adversely impacts safety and transportation performance for passenger cars. This study underscores the importance of considering safety aspects in the implementation of TPS aimed at improving passenger transportation efficiency. These findings may contribute to the enhancement of public transportation infrastructure and the implementation of appropriate safety measures.

Transit Signal Priority under Connected Vehicle Environment: Deep Reinforcement Learning Approach

Transit Signal Priority (TSP) is a traffic signal control strategy that can provide priority to transit vehicles and thus improve transit service and enhance transportation equity. Conventional TSP strategies often ignore the fluctuation of passenger occupancy in transit vehicles, leading to sub-optimal solutions for the entire system. The use of Connected Vehicle (CV) technology enables the adoption of a more fine-grained objective in optimizing traffic signals, such as person delay, by allowing real-time information on passenger occupancy to be obtained. In this study, a deep reinforcement learning algorithm, deep Q-network (DQN), is applied to develop a traffic signal controller that minimizes the average person delay. The proposed DQN controller is tested in a simulation environment modeled after a real-world intersection and compared with pretimed and actuated controllers. Results show that the proposed DQN controller has the best performance in terms of average person delay. Compared to the baseline, it reduces the average person delay by 18.77% in peak hours and 23.37% in off-peak hours. Furthermore, it also results in decreased average delays for both buses and cars. The sensitivity analysis results indicate that the proposed controller has the potential for practical applications, as it can effectively handle some dynamic changes.

The Impact of Priority in Coordinated Traffic Lights on Tram Energy Consumption

Traffic signal priority issues have been a research subject for several decades in Poland and worldwide. Traffic control algorithms have evolved considerably during this period and have become increasingly advanced. Most of them operate within coordinated street sequences, which adds to their complexity. Tramway priority affects traffic conditions for other road users, so many aspects must be taken into account when choosing a priority solution. Typically, one of the main criteria for evaluating the effectiveness of priority is reducing travel time for the priority vehicle while ensuring that the travel times of other traffic participants through the intersection are maintained or slightly deteriorated. However, the energy aspects are often overlooked. This publication aims to investigate how local priority for tramways in traffic signals of coordinated streets affects energy consumption for tramway traction needs. The study was conducted using a microscopic modeling method with PTV Vissim software (ver. 2021). The models were built for coordinated sequences with different levels of priority. Real traffic control algorithms with priority were implemented into the model on the sequence of Marymoncka Street and Grochowska Street in Warsaw. Then, by introducing changes to the parameters of the algorithms, their effect on traffic characteristics, including estimated power consumption, was studied. The results obtained from the computer simulation were statistically processed using R software (ver. 4.3.2). The analysis results prove the effectiveness of tramway priority operation, show its impact on electricity consumption, and allow us to determine the limits of its effective application. Thus, they complement the knowledge of the impact of tramway priority on traffic. The research results also have practical value, as they help us to make rational decisions in the process of designing traffic control algorithms at intersections with a multi-criteria approach.

Evaluating Performance and Satisfaction of Bus Raid Transit Using the Hybrid T2NN-APPRESAL Method

There are many factors that affect the environment in general, the most important of which is climate change as well as population growth. To preserve the environment, researchers seek to find the best ways and solutions to preserve the quality of the environment through the use of renewable energy to reduce emissions resulting from other energies as well as provide a good life for people in light of population density. The high One of these solutions is bus rapid transit (BRT). It's a public transportation system that uses dedicated lanes to provide fast, efficient bus service. BRT systems often feature stations with elevated platforms for easy boarding, prepayment systems to speed up boarding, and traffic signal priority to keep buses moving smoothly. BRT is designed to offer many of the benefits of light rail or subway systems at a lower cost and with more flexibility. It's commonly implemented in cities around the world as a way to improve public transportation and reduce congestion. The term "BRT" is mainly used in the Americas and China, while in India, it is called "BRTS," and in Europe, it is often referred to as a "busway" or "BHLS." This paper seeks to analyze the BRT system based on its features according to the user’s satisfaction using the Approach for Preference, Performance and Ranking Evaluation with SAtisfaction Level (APPRESAL) method to overcome the problems of multi-criteria decision-making (MCDM) methods that lack in dealing with user satisfaction as well as the influence of factors on the analysis under the type-2 neutrosophic numbers (T2NN) environment to overcome ambiguity and uncertainty resulting from the collected data.

Coordinated Transit Signal Priority and Vehicle Guidance for Automated Buses

Coordinated Transit Signal Priority and Vehicle Guidance for Automated Buses

Analysing the Environmental and Social Impacts of a Novel User‐Based Transit Signal Priority Strategy in a Connected Vehicle Environment

Transit signal priority (TSP) is a traffic control strategy aiming at prioritising public transit vehicles at signalised intersections. The emergence of connected vehicles (CVs) provides the opportunity to enhance TSP operation, mitigating challenges such as the negative impact on nontransit users and the management of conflicting priority requests. Furthermore, traffic control policies produce environmental impacts, whilst TSP strategies are typically evaluated based on common traffic flow indicators, such as average vehicle speed, delay and/or the number of stops. In light of the recent progress made in CV technology, we propose and assess two user‐based TSP strategies. The first approach aims to minimise total user delay at a signalised intersection, whilst the second considers both reducing bus schedule delay and total user delay. We also measure the environmental effects of these TSP strategies. A microscopic simulation environment is used to compare the proposed methods’ performance against a conventional TSP ring‐and‐barrier controller in a case study involving two adjacent signalised intersections in Helsinki, Finland. The findings indicate that implementing the proposed strategies effectively enhances TSP performance whilst also lowering adverse environmental impacts.

Transit Signal Priority Strategy With Heterogeneous Graph-Based Deep Reinforcement Learning for Autonomous Public Transit Vehicles

Transit Signal Priority Strategy With Heterogeneous Graph-Based Deep Reinforcement Learning for Autonomous Public Transit Vehicles

Stochastic-priority-integrated signal coordination considering connected bus operation uncertainties

Multimodal arterial signal coordination for buses and passenger vehicles can improve arterial travel smoothness and efficiency. However, uncertainty in bus operations requires signal priority at intersections, which impacts coordination and increases stop times for other traffic types. Therefore, this study proposes a stochastic priority-integrated signal coordination (SPIC) method. It includes an offline stochastic programme to determine the arterial signal coordination, i.e. cycle length and offsets, considering the stochastic signal priority, and an online mixed-integer nonlinear programme to determine the signal priority together with the bus arrival and departure times at and from stops and intersections in a connected vehicle environment. A scenario-based heuristic algorithm is proposed to solve the SPIC efficiently. Numerical studies have validated that SPIC can improve the efficiency of buses and passenger vehicles. Sensitivity analyses show that the SPIC effectively reduces delays with fluctuations in the bus travel time, dwell time, and passenger vehicle demands.