Signal Priority Research Articles

A two-stage stochastic optimization model for integrated tram timetable and speed control with uncertain dwell times

Modern trams usually own passive transit signal priority (TSP) to avoid interruption from traffic signals along the route. The key to TSP depends on the stick to the recommended travel time between intersections strictly. However, the effectiveness of the TSP can be weakened by dwell time fluctuation due to uncertain passenger demand at the stations. This paper proposes a two-stage stochastic optimization model for timetable and tram control to improve the TSP reliability considering uncertain dwell times. The first stage of the model focuses on designing timetable alternatives, and the second stage evaluates the timetables through expected travel time and energy consumption under different dwell time disturbance scenarios. The Brute force algorithm is developed to attain the optimal tram control, while the non-dominated sorting genetic algorithm II (NSGA-II) and GUROBI solver are both adopted to optimize the timetables. A case study of Nanjing Tram Line 1 in China is performed to demonstrate the effectiveness of the proposed approach. The results show that compared to the existing method, the proposed method reduces energy consumption by 16.0% and the number of stops at intersections decreases by 73.7% with the same travel time.

Bus bunching simulation based on the lattice Boltzmann model of traffic flow

Bus bunching is a typical phenomenon observed in daily public transit operations and that significantly reduces the reliability and attractiveness of transit service. Although various control strategies, such as vehicle holding, speed guidance, and signal priority, have been proposed to eliminate or alleviate bus bunching, the impact of road traffic flow on bus bunching has not been well studied. To bridge this gap, this work proposes a new mesoscopic simulation model by incorporating the lattice Boltzmann model to simulate bus bunching considering the impacts of road traffic flow. Simulation results show that by utilizing the new simulation model, different phase diagrams can be observed. Under different conditions of traffic flow-density evolution, i.e., no oscillation, single oscillation, global oscillation, and irregular oscillation, the resulting bus trajectories are significantly different. In addition, an investigation of a real-world bus line in Beijing with global positioning system (GPS)-based vehicle location data shows that some results of the model are qualitatively consistent with the actual characteristics of the bus route. It shows that the proposed new simulation model has great potential for enhancing our understanding of the impacts of road traffic flow on bus bunching.

Signalized intersection delay analysis using transit signal priority (TSP) and dedicated short-range communication (DSRC) system for bus rapid transit (BRT)

Signalized intersection delay analysis using transit signal priority (TSP) and dedicated short-range communication (DSRC) system for bus rapid transit (BRT)

Deep reinforcement learning for transit signal priority in a connected environment

Transit signal priority (TSP) is an effective measure to reduce traffic congestion and improve bus efficiency in metropolises. The connected vehicle technology and reinforcement learning (RL) algorithms can respectively provide more detailed and accurate information and more robust algorithms to traffic signal control systems, to develop smarter TSP strategies. This paper proposes an extended Dueling Double Deep Q-learning with invalid action masking (eD3QNI) algorithm for TSP strategy in a connected environment. The algorithm considers multiple conflicting bus priority requests and the constraints on the traffic light and phase skipping rule, aiming to improve the person delay of buses. Its performance is evaluated by simulation for a single intersection with two traffic demands and random arrivals, schedule deviations, occupancies of buses. Results demonstrate that eD3QNI produces lower average person delay and schedule delay than fixed-time signal, active TSP strategies, and other common RL methods. It also shows that the invalid action masking (IAM) method is superior to the usual variable decision points (VDP) method in terms of high convergence speed, effective performance improvement, and application of domain knowledge on the RL algorithm. The penetration rates of connected buses do not affect the converging speed of the proposed method, and an environment with a higher penetration rate will show better performance. Moreover, under the proposed method, different specific reward functions can be incorporated as desired to realize different operational goals for the TSP strategies.

Transit Signal Priority Enabling Connected and Automated Buses to Cut Through Traffic

This research proposes a TSPcut controller that enables connected and automated buses to cut through traffic to make TSP green light. The proposed controller overcomes the shortcomings of conventional TSP strategies and is able to: 1) overtake slowing moving vehicles in order to catch TSP green time; 2) decide the best time to pass the intersection; 3) considering the stochasticity of surrounding traffic; and 4) functional under partially connected and automated environment. It takes full advantage of connected vehicle technology by taking in real-time vehicle and infrastructure information as optimization input. The problem is formulated as an SMPC problem and is solved by a high-efficient dynamic programming algorithm. The nonlinear bicycle model is adopted as the system dynamics to realize CAV bus’s lane-changing and overtaking function. The stochasticity of surrounding traffic is considered as a probability distribution which is transformed into a linear chance constraint. Simulation evaluation is conduct to compare the TSPcut against NTSP, CTSP and BocTSP. Sensitive analysis is conducted for congestion levels. The evaluation results demonstrate that the TSPcut improves the bus delay reduction by 17.9%–49.1%, and the benefits are 3.5% to 16.1% greater than that of other TSP systems. The range is caused by different congestion levels. In addition. Further tests are conducted to analyze how CAV bus’s arrival time and the speed of background traffic influence the performance of the TSPcut.

A Value Proposition of Cooperative Bus-Holding Transit Signal Priority Strategy in Connected and Automated Vehicles Environment

In this study, we assess a cooperative bus-holding transit signal priority strategy (C-BHTSP) in a connected and automated vehicles (CAV) environment. The research objectives of this study are to quantify the benefits of C-BHTSP to transit, its impact on opposing non-transit traffic, and the CAV market penetration rate required to neutralize that impact. A simulation model was developed in VISSIM using detailed, high-quality, up-to-date data for transit, non-transit traffic, and signal control parameters. Our assessment shows that the proposed strategy results in about a 60% reduction in transit delay compared to the base scenario. The results also show that a CAV MPR of 32% was found sufficient to overcome the negative impact of C-BHTSP on the opposing non-transit traffic while maintaining transit benefits. Higher MPRs produce substantial improvements in the overall travel time, delay, fuel consumption, and greenhouse gas emissions. The framework and findings of this study provide a blueprint for the decision-making process of implementing transit priority under varying CAV MPRs.

Study on the Proposed Expansion of the Fire Engine Priority Signal System to Ensure Golden Time

The golden period is a concept in disaster response target-time management. It refers to the centralized response time required to maximize lives saved in an emergency. The National Fire Agency estimates that the golden period arrival time of fire engines is seven minutes. However, the arrival rate has dropped by about 68% in the last five years. Concessions and special provisions for the violation of laws are permitted to enable the swift response of fire engines. Despite this, the vehicles still cannot reach their destinations timeously. In this study, the effectiveness of the priority signal system was investigated using construction status and dispatch data. VISSIM, a traffic flow simulation software, was used to simulate the effectiveness of the priority signal system introduced to Cheong-ju city. This study can be used as the basis for the systematic construction of priority signal systems and further investigation on the topic.

A Study of Applying Eco-Driving Speed Advisory System on Transit Signal Priority

Transit Signal Priority (TSP) has long been seen as a cost-effective way to reduce bus delays at intersections. With Connected Vehicle (CV) technology, a speed advisory system guides buses to pass intersections in an energy-saving way. The integration of TSP and speed advisory may reduce bus delays and enhance energy consumption performances. This study proposed a system of integrating eco-driving speed advisory on TSP under CV environment. A TSP strategy based on intersections passing probability is designed. In addition to signal priority, this study designed and implemented an eco-driving speed advisory algorithm. A real electric bus route in Tainan City, Taiwan is used for the case study. Intersection layout and traffic related parameters are established in microscopic traffic simulation software SUMO (Simulation of Urban Mobility) to verify the effectiveness of the proposed model. The results provide an insight into how cooperation between signals and vehicles can enhance performances of energy consumption and signal-incurred traffic delays.

An evaluation of emergency vehicle preferential treatment strategies by traffic simulation

Emergency vehicles (EVs) must reach their destination within the shortest amount of time considering the importance of their service in saving lives and reducing damage to property. The aim of this research was to reduce the delay experienced by these vehicles through the alteration of traffic control signals to give preferential treatment to EVs upon detection. Specifically, this study compared three algorithms with concepts of EV pre-emption, longest queue first and emergency vehicle signal priority (EVSP) based on average general and EV delays through a traffic simulation approach using the Vissim software program and its component object model interface. The performance evaluation was applied at Tuk Chai intersection in Bangkok, Thailand as a case study. The second algorithm of longest queue first with pre-emption was found to be the best approach for both peak hour traffic conditions and 0.8 (peak) traffic conditions (i.e. peak hour traffic volume reduced by 20%), performing better than the do-nothing condition by 69.70% for stochastic EV input and 79.14% for fixed EV input. The findings also show that the algorithm of preferential treatment using EVSP strategies produced a competitive and robust performance with EV delay values better than the do-nothing condition by 75.96, 77.27 and 45.67% in 0.8 (peak), peak hour and 1.2 (peak) traffic conditions, respectively (the last of these three being peak hour traffic volume increased by 20%).

Priority coding in the visual system.

Although we are continuously bombarded with visual input, only a fraction of incoming visual events is perceived, remembered or acted on. The neural underpinnings of various forms of visual priority coding, including perceptual expertise, goal-directed attention, visual salience, image memorability and preferential looking, have been studied. Here, we synthesize information from these different examples to review recent developments in our understanding of visual priority coding and its neural correlates, with a focus on the role of behaviour to evaluate candidate correlates. We propose that the brain combines different types of priority into a unified priority signal while also retaining the ability to differentiate between them, and that this happens by leveraging partially overlapping low-dimensional neural subspaces for each type of priority that are shared with the downstream neural populations involved in decision-making. Finally, we describe the gulfs in understanding that have resulted from different research approaches, and we point towards future directions that will lead to fundamental insights about neural coding and how prioritization influences visually guided behaviours.

A Person-Based Adaptive Traffic Signal Control Method with Cooperative Transit Signal Priority

Real-time traffic signal control has long been a critical way to improve traffic congestion. Transit Signal Priority (TSP) is seen as a cost-effective way to reduce travel time variability. Most of the previous studies develop real-time signal control systems on a vehicle basis, which is unable to efficiently provide preferential treatment on transit vehicles. Person-based signal control systems, which transform traffic delay computation units from vehicle to passenger, have been proposed to try to address this limitation. However, their models, optimizing signal plan cycle-by-cycle, cannot rapidly respond to traffic variations. This study proposes a Person-based Adaptive traffic signal control method with Cooperative Transit signal priority (PACT). In PACT, not only do Road-Side Units (RSUs) perform signal optimization, but also On-Board Units (OBUs) provide in-vehicle speed advisory to reduce delays. The interaction between RSU and OBU is conducted second-by-second, which has high adaptability to traffic variations. Experiments are performed based on real traffic data via traffic simulation platform SUMO. The results indicate that PACT can efficiently reduce delays of both bus passengers and auto passengers at a signalized intersection. Compared to preoptimized signal plans, the results show that each passenger on transit vehicles experiences 33%–70% decreases in delays, and each auto passenger experiences 3%–29% decreases in delays. PACT can reduce 80%–98% in delays when the occupancy weight factor is relatively large, showing the potential of extending PACT on performing signal preemption.

Integrating public transit signal priority into max-pressure signal control: Methodology and simulation study on a downtown network

Max-pressure signal control has been analytically proven to maximize the network throughput and stabilize queue lengths whenever possible. Since there are many transit lines operating in the metropolis, the max-pressure signal control should be extended to multi-modal transportation systems to achieve more widespread usage. The standard max-pressure controller is more likely to actuate phases during high-demand approaches, which may end up ignoring the arrival of buses, especially in bus rapid transit. In this paper, we propose a novel max-pressure signal control that considers transit signal priority of bus rapid transit systems to achieve both maximum stability for private vehicles and reliable transit service. This study revises the original max-pressure control to include constraints that provide priority for buses. Furthermore, this policy is decentralized which means it only relies on it relies only on the local conditions of each intersection. We set the simulation on the real-world road network with bus rapid transit systems. Numerical results show that the max-pressure signal control which considers transit signal priority can still achieve maximum stability compared with other signal control integrated with transit signal priority. Furthermore, the max-pressure control reduces private vehicle travel time and bus travel time compared to the current signal control.

Modeling Modified Intermittent Bus Lane Integrated with Transit Signal Priority Under Mixed Traffic Condition

Modeling Modified Intermittent Bus Lane Integrated with Transit Signal Priority Under Mixed Traffic Condition

Analysis of risky driving behaviors among bus drivers in China: The role of enterprise management, external environment and attitudes towards traffic safety

Several studies have shown that enterprise management (e.g. culture, salary) and external environment (e.g. traffic congestion) predict risky driving behaviors and accident involvement. However, this process has not been systematically investigated in bus drivers. The present study uses structural equation model to assess whether enterprise management and external environment are associated with risky self-reported driving behaviors both directly and indirectly, through the effects of attitudes towards traffic safety in a large sample of bus drivers.Three hundred and thirty-one bus drivers (mean age = 39.5, SD = 5.6 years) completed a structured and anonymous questionnaire measuring enterprise management, external environment, attitudes toward traffic safety, and self-reported risky driving behaviors (i.e., speeding, fatigue driving, running the light) in the last 6 months. Structural equation modeling analysis revealed that enterprise management, and external environment were associated with risky driving behaviors both directly and indirectly. In particular both of them were directly correlated with bus drivers’ attitudes toward traffic safety which, in turn, were related to the five types of self-reported risky driving behaviors. The present findings suggest that measures related to the impact factors could be carried out to reduce the probabilities of the risky driving behaviors among bus drivers, such as improving the salary level of bus drivers, setting up bus lanes and priority signals to alleviate road congestion, optimizing shift schedules, implementing effective safety education, etc. These findings can provide the empirical basis for evidence-based road safety interventions in the context of public transport.

Competence of Bus Rapid Transit Systems Coupled with Transit Signal Priority at Signalized Junctions

Abstract One of the primary causes of poor public transport performance is delays at intersections. Among the efficient and sustainable solutions to boost mass transportation performance, Bus Rapid Transit (BRT) consists of infrastructures integrating dedicated bus lanes and smart operational service with different ITS technologies like Transit Signal Priority (TSP). This research studies the competence of buses operating on junctions of the BRT corridor where they have Signal Priority on the dedicated lane. The studied intersection is located around the center of the Addis Ababa BRT-B2 line, which is relatively gentle grade and characterized by the high traffic and pedestrian volume. Microscopic models were created for the chosen intersection, along with possible calibration and validation; moreover, a statistical comparison was performed to evaluate different scenarios with the goal of displaying the deployment benefits. To assess the performance of BRT buses and their overall influence on general traffic, scenarios with and without TSP were evaluated. PTV VISSIM and the VisVAP add-on simulation program were used to examine TSP alternatives. Incorporating TSP reduced the travel time by up to 4.78% in the priority direction, the average travel speed increased by 7.25%, and the queue length also reduced by a maximum of 6%, whereas in the non-priority direction, the queue length increased by a maximum of 2.5%. Moreover, the overall average passenger delay has reduced by an average amount of 15%. One of the simplest ways to improve transit performance could be signal priority strategies, which has a minor influence on the general traffic.

Endogenous Electric-Field-Coupled Electrospun Short Fiber via Collecting Wound Exudation.

Endogenous electric fields (EF) are the basis of bioelectric signal conduction and the priority signal for damaged tissue regeneration. Tissue exudation directly affects the characteristics of endogenous EF. However, current biomaterials lead to passive repair of defect tissue due to limited management of early wound exudates and inability to actively respond to coupled endogenous EF. Herein, the 3D bionic short-fiber scaffold with the functions of early biofluid collection, response to coupled endogenous EF, is constructed by guiding the short fibers into a 3D network structure and subsequent multifunctional modification. The scaffold exhibits rapid reversible water absorption, reaching maximum after only 30 s. The stable and uniform distribution of polydopamine-reduced graphene oxide endows the scaffold with stable electrical and mechanical performances even after long-term immersion. Due to its unique - bionic structure and tissue affinity, the scaffold further acts as an "electronic skin," which transmits endogenous bioelectricity via absorbing wound exudates, promoting the treatment of diabetic wounds. Furthermore, under the endogenous EF, the cascade release of vascular endothelial growth factor accelerates the healing process. Thus, the versatile scaffold is expected to be an ideal candidate for repairing different defect tissues, especially electrosensitive tissues.

New MDP Model and Learning Algorithm for Bus Scheduling Problem with Conditional Signal Priority

Buses are expected to be punctual, but deviation from the schedule is a common occurrence. Transit signal priority (TSP) and conditional signal priority (CSP) are methods to help the bus perform the schedule better by giving traffic signal priority to the bus. Markov decision process (MDP) is suitable for modeling such sequential decision process. In this article, we point out the Markov property of the system based on the analysis of the bus driving process. We model the process of bus driving with CSP as a Markov decision process. Then, the Deep Q Network (DQN) algorithm is applied to solve this MDP model. To the best of our knowledge, this is the first time that the bus scheduling problem with CSP has been modeled as an MDP model and solved by a learning algorithm. Numerical experiments verify the applicability of the DQN algorithm to solve this MDP model.

Progression Control Model to Enhance Performance of Transit Signal Priority

Transit signal priority (TSP) plays an essential role in reducing traffic jams in a multimodal road traffic environment. Traffic experts expect that the demand for cars will be transferred to buses as the bus-service quality improves. This study proposes a multimodal environment TSP model with dedicated median bus lanes to simultaneously increase bus travel speed at signalized intersections and reduce car delay times. The proposed TSP model includes three optimization stages. The TSP is first optimized using a MAXBAND-based bandwidth model after a partitioned TSP control group is defined relative to a median bus stop to support non-stop bus movement. The optimal combination between these groups is selected using the delay-difference-of-offset technique. A kinematic wave-based offset optimization model is proposed to reduce the car delay time. We analyze the performance of this model in improving two-way bus speeds and reducing the car delay time in Seoul’s bus rapid transit system. The proposed TSP model decreases the stop ratio of buses by analyzing the bus trajectory from the micro-simulation. This model can be used as a center-based control to improve the mobility of buses and cars in a multimodal environment.

Quantifying the Safety Benefits of Transit Signal Priority Using Full Bayes Before–After Study

AbstractTransit signal priority (TSP) is a strategy that prioritizes the movement of transit vehicles through a signalized intersection to minimize transit delay and improve travel time reliability...

Conditional Transit Signal Priority Optimization at Stop-to-Stop Segments to Improve BRT On-Time Performance

Bus rapid transit (BRT) as a valid means of public transportation for overpopulated country of China is an especial mode of travel to relieve traffic congestion. Despite utilizing exclusive bus line, BRT constantly experiences delay at intersections when encountering a red signal. Transit signal priority (TSP) considered as a promising control strategy to improve the operation efficiency of BRT has been widely used at isolated intersections or arterials to reduce travel delay. However, as a transport with schedule, the unexpected arrival time of transit vehicle at stop lines of consecutive intersections inevitably affects the operation reliability of the BRT system. To solve the problem, this study develops an optimization model of conditional TSP for bus rapid transit with the objective of improving the on-time performance of the BRT system while mitigating adverse impacts on private vehicles. A stop-to-stop segment is established with both road sections and intersections for the consideration of the transit operation reliability. The constraints on the degree of saturation and queue overflow were considered for mitigating adverse impacts of TSP. A mixed-integer non-linear programming procedure is adopted to formulate the optimization model. The mathematical model is linearized and solved by the branch-and-bound method. Extensive numerical analyses are conducted, and the proposed conditional TSP strategy is compared with unconditional TSP and no-TSP control to evaluate its performance under various traffic and signal conditions. A case study of a BRT line in Shanghai, China, was selected to illustrate the effectiveness of the proposed model. For the tested scenarios, the transit on-time performance was improved by 21%, with an incurred cost of 3.4% increase in the delay of private vehicles. This indicates that the proposed model performs well in maintaining the reliability of the transit system with the least impact on general traffic.