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Multi-modal traffic signal control with priority, signal actuation and coordination
Abstract
Both coordinated-actuated signal control systems and signal priority control systems have been widely deployed for the last few decades. However, these two control systems are often conflicting with each due to different control objectives. This paper aims to address the conflicting issues between actuated-coordination and multi-modal priority control. Enabled by vehicle-to-infrastructure (v2i) communication in Connected Vehicle Systems, priority eligible vehicles, such as emergency vehicles, transit buses, commercial trucks, and pedestrians are able to send request for priority messages to a traffic signal controller when approaching a signalized intersection. It is likely that multiple vehicles and pedestrians will send requests such that there may be multiple active requests at the same time. A request-based mixed-integer linear program (MILP) is formulated that explicitly accommodate multiple priority requests from different modes of vehicles and pedestrians while simultaneously considering coordination and vehicle actuation. Signal coordination is achieved by integrating virtual coordination requests for priority in the formulation. A penalty is added to the objective function when the signal coordination is not fulfilled. This “soft” signal coordination allows the signal plan to adjust itself to serve multiple priority requests that may be from different modes. The priority-optimal signal timing is responsive to real-time actuations of non-priority demand by allowing phases to extend and gap out using traditional vehicle actuation logic. The proposed control method is compared with state-of-practice transit signal priority (TSP) both under the optimized signal timing plans using microscopic traffic simulation. The simulation experiments show that the proposed control model is able to reduce average bus delay, average pedestrian delay, and average passenger car delay, especially for highly congested condition with a high frequency of transit vehicle priority requests.
... Other similar studies include Ye and Xu (2017), Song et al. (2018), and Long et al. (2020). Different from the aforementioned studies, He et al. (2012He et al. ( , 2014 and Hu et al. (2016) accommodated multiple priority requests and simultaneously considered coordination among consecutive signals. This guaranteed the mobility benefit from TSP logic at an upstream intersection was not wasted at downstream intersections. ...
... where t s i;j and t a i;j are measured in seconds; and t a i;j is calculated based on He et al. (2014). It is important that a holding policy be applied for the buses running earlier than scheduled. ...
... As to the links with bus routes, bus stops were located 200 m downstream of the intersections. Bus arrival detectors were set 200 m before the stop line of intersections, similar to He et al. (2014), and they detected the arrival of buses and bus basic information. Queue counters were located at the stop line, and they collected the number of queuing vehicles and other vehicles' basic information. ...
In this paper, a multi-agent-based control method is proposed to design the transit signal priority (TSP) scheme at urban traffic networks. One agent controls an intersection. The coordination among different intersection agents is deployed to guarantee the benefits from TSP at upstream intersections. At one intersection there exists usually many bus routes and thus multiple TSP requests and coordination requests can occur at one phase simultaneously. Therefore, the proposed method also aims at resolving conflicting TSP requests and/or coordination requests. A multi-level fuzzy controller consisting of transit signal priority controller, green time adjustment controller 1, fuzzy negotiation controller, and green time adjustment controller 2 is then introduced to realize these objectives. Following that, fuzzy inference decisions and algorithms are provided for describing the control process of the proposed multi-agent method. An urban traffic network with 25 intersections is selected to conduct a case study to verify the performance of the proposed method by comparison and sensitivity analysis. Simulation results demonstrate that the proposed method outperforms the other three methods in terms of the average delay of buses and other vehicles under different traffic demands and bus departure intervals.
... More sophisticated objective functions have been investigated by some authors to comprehensively incorporate various aspects of general traffic and operating conditions of PT mode. Deviation from the background signal timing [12,13], control delay of the critical approach [14], platoon coordination delay [15], waiting time at the stop [16] and energy consumption and emissions of PT vehicles [17] are among the proposed MOEs to be considered in the objective function of ATSP strategies. Multiobjective optimization is another favourite area in the development of ATSP strategies. ...
... However, the related studies indicated that the ATSP strategies developed based on kinematic wave theory faced some intrinsic limitations due to the non-linear terms in the delay functions. Since the non-linear terms increase the complexity of the problem, some simplifying techniques were utilized in the literature such as focusing merely on PT vehicle trajectory [12][13][14], using linear relaxations for quadratic terms of the delay function [8,15,26,27], or simplifying assumption of equal speeds for queue dissipation and formation shockwaves [22]. Another subject of interest in the ATSP studies would be the possibility of altering phase sequence. ...
This paper proposes a kinematic wave‐based adaptive transit signal priority control (KATC) aiming to minimize average passenger delay at intersection level. The passenger delay minimization problem is formulated as a mixed‐integer non‐linear program (MINLP) with two decision variables of green time and phase sequence. The adoption of the phase sequence in the optimization and not involving the common simplifying assumptions in the delay models are the main contributions of the current study. General traffic and public transportation (PT) vehicle delay are estimated using kinematic wave theory. Genetic algorithm is utilized to solve the MINLP problem at 1‐cycle intervals and for a decision horizon of 3 consecutive cycles. The performance of KATC is evaluated against SYNCHRO and the KATC model without phase sequence optimization, using VISSIM. The results of the experiments indicate superior performance of KATC over the two other models in terms of both average passenger delay and PT passenger delay, especially at low congestion levels. Furthermore, increasing PT passenger occupancy can effectively contribute to higher passenger delay improvements. The adverse impacts on passenger vehicles are restricted to a 3.4% and 2.7% increase in general traffic delay compared to SYNCHRO and the KATC model without phase sequence optimization, respectively.
... The set of network critical links and their traffic volumes were essential factors in determining the appropriate control strategy. In a study by He et al., multiple priority requests from various modes of transportation (e.g., emergency vehicles, buses, and pedestrians) were accommodated in a coordinated and actuated signal control system (14). Considering an isolated semi-actuated intersection, Lin et al. investigated the effect of sidestreet traffic volumes on the major-street green time ratio. ...
The semi-actuated coordinated operation mode is a type of signal control where minor approaches are placed with detectors to develop actuated phasing while major movements are coordinated without using detection systems. The objective of this study is to propose a cost-effective approach for reducing delay in the semi-actuated coordinated signal operation without incurring any extra costs in terms of installing new detectors or developing adaptive controller systems. We propose a simple approach for further enhancing a pre-optimized timing plan. In this method, the green splits of non-coordinated phases are multiplied by a factor greater than one. In the meantime, the amount of green time added to the non-coordinated phases is subtracted from the coordinated phases to keep the cycle length constant. Thus, if the traffic demand on the side streets exceeds the expected traffic flow, the added time in the non-coordinated phase enables the non-coordinated phases to accommodate the additional traffic demand. A regression analysis is implemented so as to identify the optimal value of the mentioned factor, called Actuated Factor (ActF). The response variable is the average delay reduction (seconds/vehicle) of the simulation runs under the proposed signal timing plan compared to the simulation runs under the pre-optimized timing plan, obtained through the macroscopic signal optimization tools. External traffic movements, left-turn percentage, and ActF are the explanatory variables in the model. Results reveal that the ActF is the only significant variable with the optimal value of 1.15 that is applicable for a wide range of traffic volumes.
... Traffic signal coordination synchronizes signals along major corridors, minimizing stops and maintaining continuous traffic flow (Bazzan, 2005;Putha et al., 2012;Li and Ban, 2020). Transit signal priority (TSP) systems further enhance efficiency by adjusting signals to reduce delays for public transit vehicles (Smith et al., 2005;He et al., 2014;Yu et al., 2020). ...
Leveraging recent advances in generative AI, multi-agent systems are increasingly being developed to enhance the functionality and efficiency of smart city applications. This paper explores the transformative potential of large language models (LLMs) and emerging Retrieval-Augmented Generation (RAG) technologies in Intelligent Transportation Systems (ITS), paving the way for innovative solutions to address critical challenges in urban mobility. We begin by providing a comprehensive overview of the current state-of-the-art in mobility data, ITS, and Connected Vehicles (CV) applications. Building on this review, we discuss the rationale behind RAG and examine the opportunities for integrating these Generative AI (GenAI) technologies into the smart mobility sector. We propose a conceptual framework aimed at developing multi-agent systems capable of intelligently and conversationally delivering smart mobility services to urban commuters, transportation operators, and decision-makers. Our approach seeks to foster an autonomous and intelligent approach that (a) promotes science-based advisory to reduce traffic congestion, accidents, and carbon emissions at multiple scales, (b) facilitates public education and engagement in participatory mobility management, and (c) automates specialized transportation management tasks and the development of critical ITS platforms, such as data analytics and interpretation, knowledge representation, and traffic simulations. By integrating LLM and RAG, our approach seeks to overcome the limitations of traditional rule-based multi-agent systems, which rely on fixed knowledge bases and limited reasoning capabilities. This integration paves the way for a more scalable, intuitive, and automated multi-agent paradigm, driving advancements in ITS and urban mobility.
... Han et al. [21] and Yazdani et al. [24] presented adaptive traffic signal models that minimized the total waiting times of both vehicles and pedestrians. He et al. [25] investigated the conflicts between coordinated-actuated signals and multimodal priority control (pedestrians, emergency vehicles, and transit buses). They proposed a formulation that minimized the total weighted delay for priority requests and passenger vehicles, and a penalty in the case of dissatisfactory signal coordination. ...
Efficient control of traffic signals for vehicles and pedestrians at intersections is critical for relieving traffic congestion. Considering the unique characteristics of intersections, such as the number of roads, the presence or absence of crosswalks, road geometric shapes, and traffic demand patterns, an appropriate phase sequence and duration for traffic signals must be established at each intersection. This paper proposes a simulation‐based two‐stage algorithm comprising integer‐constrained Adam (ICA) and tabu search (TS) to optimize the phase sequence and duration for arbitrary intersections with arbitrary traffic‐demand patterns. The ICA promptly identifies a promising region in which a global optimal solution is likely to be obtained, whereas TS determines the best solution near the region. The performance of the proposed algorithm that optimizes phase durations with fixed phase sequence is evaluated against several baseline methods using 24 instances across six actual intersections. Experimental results show that the proposed algorithm reduces the average travel time by 20.2% compared with existing traffic signals within a computation time of 4 min, thus providing a near‐optimal solution eight times faster than commonly used population‐based metaheuristics. Furthermore, the algorithm demonstrates robust performance across heterogeneous vehicles and recommends the best phase sequence that effectively alleviates congestion in current traffic signal systems. The optimized phase sequence with best phase durations further reduces the average travel time by approximately 11.3% compared with the existing phase sequence with best phase durations at an actual intersection. To facilitate its widespread use, a free, open web‐service system named “Smart Intersection for Traffic Efficiency” is developed, which enables users to optimize traffic signal systems without requiring optimization background or simulation knowledge.
... Moreover, future research can incorporate appropriate transition algorithms [93] and smaller rescue vehicles [106,112] in their models, which can help reduce the negative impacts and transition times [79,92]. The negative impact on pedestrians and non-motorized vehicles also needs to be considered [111]. ...
Emergency vehicles (EMVs) play an important role in saving human lives and mitigating property losses in urban traffic systems. Due to traffic congestion and improper priority control strategies along the rescue route, EMVs may not be able to arrive at rescue spots on time, which also increases traffic risk and has a negative impact on social vehicles (SVs). The greater the negative impact on SVs, such as increased delay times and queue length, the more profound the negative impacts on urban environmental sustainability. Proper rescue route selection and priority control strategies are essential for addressing this problem. Consequently, this paper systematically reviews the studies on EMV routing and priority control. First, a general bibliometric analysis is conducted using VOSviewer. This study also classifies the existing studies into three parts: EMV travel time prediction (EMV-TTP), EMV routing optimization (EMV-RO), and EMV traffic priority control (EMV-TPC). Finally, this study provides future research suggestions on five aspects: 1. uncovering authentic demand characteristics through EMV data mining, 2. incorporating the distinct characteristics of EMV in EMV-RO models, 3. implementing active EMV-TPC strategies, 4. concentrating more on the negative impacts on SVs, and 5. embracing the emerging technologies in the future urban traffic environment.
... Several methods have been adopted in ATSP strategies to take into consideration the priority of PT vehicles in the mathematical program formulation. Using constant weighting factors for PT vehicles was investigated in some studies as a measure of priority assignment [6][7][8]. Dynamic weighting factors were also suggested to better capture the real-time operating condition of the traffic network [9,10]. These time-varying weighting factors involve general traffic congestion level and PT vehicle demand in particular. ...
An 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.
... Signal Group-Based Traffic Control: This approach, based on manufacturer-specific concepts, facilitates more direct manipulation of signals for a group of movements (signal groups) in traffic signal control algorithms. Representative examples include the signal timing interval-based control frameworks (e.g., VS-PLUS), where the signal timing plan is defined by specifying time points and intervals for traffic signal activation by the control algorithm, and the ring-barrier control (RBC) framework (the National Electrical Manufacturers Association (NEMA) standard controllers based on the concept of ring-and-barrier design [8]). This category aims to overcome the limitations associated with signal stages by increasing flexibility in signal manipulation for various movements. ...
Developing appropriate signal timing strategies is a primary concern in traffic signal control; however, professionals are constrained by predefined controller options. Existing signal controllers in North America adhere to National Electrical Manufacturers Association (NEMA) standards with ring-barrier control (RBC) design. Due to unique traffic conditions and objectives for each traffic mode, conflicts arise, making it challenging to devise suitable traffic control strategies within current controllers. The limitations include inflexibility in defining pretimed signal diagrams, modifying them, and defining algorithms for modification. This paper introduces the event-based controller (EBC) framework, implemented in DUMKA_E software, capable of replicating NEMA-based RBC functionalities while providing additional flexibility beyond conventional RBC controllers. The EBC eliminates the asymmetry of flexibilities in existing traffic controller frameworks, addressing the challenges mentioned. Overcoming critical limitations of RBC, such as barrier constraints and restricted functionalities to built-in algorithms, the EBC empowers professionals to craft custom traffic signal control strategies. Unlike approaches enhancing RBC with complex computation, the EBC offers flexible functionalities without sacrificing simplicity. The study shows that the EBC’s fundamental concepts are no more intricate than RBC’s, positioning the EBC as a potential traffic controller framework. The paper establishes the EBC’s capability to reproduce RBC controllers’ core logic, showcasing advanced abilities for more efficient control logic than RBC. The EBC’s functionalities enable context-sensitive traffic signal control strategies, catering to special conditions like multi-modal activities and diverse priority requests, highlighting its full potential.
... Recently, many researchers have performed additional research on intersection vehicle traffic control methods in intelligent transportation systems considering an environment of vehicle road cooperation or autonomous driving, including more intelligent signal control [8][9][10][11][12][13][14][15] as well as non-signal centralized and distributed control [16][17][18][19][20][21][22][23][24][25]. However, most of these studies pay little attention to the number of lanes and do not discuss the impact of the number of lanes on intersection control and countermeasures. ...
Non-signalized intersections have only ever been suitable for low traffic flow; however, with the development of autonomous driving technology and new control methods, the operation efficiency of this kind of intersection may be improved. In view of the shortcomings of existing non-signalized intersection control methods in multilane situations and inspired by railway trains, an interlocking-block intersection control model is proposed. In this study, vehicles between parallel lanes are combined into a few combos, and the combo shape can be determined according to a pairing model and the interlocking angle range, and the gaps between the front and rear vehicles are simulated as blocks in a railway system, which are added into the intersection control model as virtual blocked cars (VBCs) for optimization. In setting the optimization objectives, the connotation and realization of fairness are discussed. Experimental results show that compared with signalized intersections, roundabouts, and non-signalized intersections without control, the interlocking-block intersection control model greatly reduces vehicle delay. Compared with an existing model, the calculation speed in a multilane situation has been greatly improved, while the vehicle delay is similar.
... Previous studies on active TSP at arterial intersections predominantly focused on achieving maximum "green wave" and minimizing delay [6]. Conflict between the arterial coordination signal control system and the TSP control system is common [7]. The arterial coordination TSP cannot ensure the continuous normal operation of the original green wave belt, leading to potential delays and TSP failures. ...
Transit Signal Priority (TSP) is a system designed to grant right-of-way to buses, yet it can lead to delays for private vehicles. With the rapid advancement of network technology, self-driving buses have the capability to efficiently acquire road information and optimize the coordination between vehicle arrival and signal timing. However, the complexity of arterial intersections poses challenges for conventional algorithms and models in adapting to real-time signal priority. In this paper, a novel real-time signal-priority optimization method is proposed for self-driving buses based on the CACC model and the powerful deep Q-network (DQN) algorithm. The proposed method leverages the DQN algorithm to facilitate rapid data collection, analysis, and feedback in self-driving scenarios. Based on the arrival states of both the bus and private vehicles, appropriate actions are chosen to adjust the current-phase green time or switch to the next phase while calculating the duration of the green light. In order to optimize traffic balance, the reward function incorporates an equalization reward term. Through simulation analysis using the SUMO framework with self-driving buses in Zhengzhou, the results demonstrate that the DQN-controlled self-driving TSP optimization method reduces intersection delay by 27.77% and 30.55% compared to scenarios without TSP and with traditional active transit signal priority (ATSP), respectively. Furthermore, the queue length is reduced by 33.41% and 38.21% compared to scenarios without TSP and with traditional ATSP, respectively. These findings highlight the superior control effectiveness of the proposed method, particularly during peak hours and in high-traffic volume scenarios.
... Cesme et al. proposed a new adaptive control model based on single intersection actuated signal control and added additional rules to realize arterial signal coordination [19]. He et al. realized the arterial coordinated actuated signal by adding virtual requests to the actuated signal control model [20]. ...
The arterial signal coordination is an effective method to improve traffic operational efficiency and reduce vehicle delay. In this paper, a two-stage arterial signal coordination model under dynamic traffic demands is established, and the signal timing and offset are adjusted according to the dynamic traffic demands. The objective is to minimize the expected intersection delay and the overflow of the coordinated direction. In the first stage, a calculation model for intersection signal timing based on phase clearing reliability is proposed by the reverse causal-effect modeling approach, which can calculate the signal timing of each intersection in real time. In the second stage, an offset calculation model is established to achieve the goal of minimizing delay in the coordinated direction, which can calculate the offset of trunk coordination in real time. The concept of phase clearance reliability is introduced in the model, which can dynamically adjust the balance between the coordinated phase and the non-coordinated phase, thus taking the overall control efficiency of intersections into account. We then develop an algorithm to solve the problem and then apply the model and the solution algorithm to an arterial road with three intersections to investigate and compare its performance with the Allsop’s method and the Webster’s method. A comparison between the proposed coordinated two-stage logic and a coordinated actuated logic is also conducted in the case study to show the advantages and disadvantages.
... To overcome the aforementioned disadvantages of conventional strategies, researchers have been utilising optimisation or deep reinforcement learning (DRL) technologies to propose new solutions. Several different person-delay-based scheduling technologies to manage the multiple conflicting priority requests at an isolated intersection are designed by Christofa et al. [5], Hu et al. [6] and He et al. [7]. These optimisation procedures are mixed-integer linear or nonlinear programs, whose final objectives minimise the person-based delay. ...
... In connected environments, vehicle real-time location data can be used to predict the trajectories and size of approaching platoons in crossing directions to optimize signal control setting (He et al., 2012;Goodall et al., 2014;Li et al, 2014;Al Islam et al., 2020;Qi et al., 2020). Research shows that queuing delay can be reduced at signalized intersections by prioritizing the movements of vehicles (Goodall et al., 2013;He et al., 2014;Liu et al., 2019), minimizing predicted queue lengths and platoon splitting (Priemer and Friedrich, 2009;Feng et al., 2015;Tiaprasert et al., 2015;Comert, 2016), optimizing signal offsets (Day and Bullock, 2016;Li and Ban, 2018), and metering the traffic at the network perimeter (Mohebifard et al., 2019;Mohajerpoor et al., 2020). The effectiveness of the actuated signal control strategies, however, largely depends on a high penetration rate and the availability of vehicle realtime location data for accurate prediction of the trajectories in crossing directions (Smith et al., 2010;He et al., 2012;Goodall et al., 2013;Zhong et al., 2020). ...
Integration of artificial intelligence and wireless communication technologies in Connected Automated Vehicles (CAVs) enables coordinating the movement of the platoons of CAVs at signal-free intersections. The efficiency of the platoon coordination process can be improved by reducing the spacing between successive platoons to increase capacity; however, such improvement in efficiency can have adverse impacts on the robustness of the coordination process. In this research, we balance the trade-off between the efficiency and robustness of traffic operations in signal-free networks at a macroscopic scale. To this end, we use a rule-based approach to express the process of coordinating CAV platoons at intersections as a set of governing equations that provide an analytical basis to develop a stochastic model for traffic operations. We derive the platoon synchronization success probability for a general distribution of the error in synchronizing the movement of platoons in crossing directions and formulate the expected capacity as a function of the synchronization success probability. We then balance the trade-off between efficiency and robustness at a macroscopic scale by adjusting the average spacing set between successive platoons. In urban networks, adjusting the spacing between successive platoons also changes the vehicular density and consequently the traffic speed. We account for the interrelationship between the traffic speed and inter-platoon spacing in balancing the trade-off between the efficiency and robustness of traffic operations using the concept of the Macroscopic Fundamental Diagram (MFD) and extend the stochastic traffic model to the network level. We evaluate the analytical results of the research using a simulation model. The numerical results of the research show that optimizing the system by adjusting the platoon spacing can improve robustness by 13% at the cost of a 4% reduction from the maximum capacity at the network level.
... Therefore, more advanced logic is necessary to resolve such conflicts (e.g., Xu et al., 2016). Recent studies also investigated balancing priority requests from other road users, like emergency vehicles, commercial trucks and pedestrians (e.g., He et al., 2014). ...
... In this respect, by focusing on intersections as the critical bottlenecks of traffic and (particularly) using information of Connected Vehicles (CVs) 1 as the new source of information, traffic control methods have been continuously improved (Guo, Li, and Ban 2019;Moradi et al. 2022). However, despite the importance of the Saturation Flow Rate (SFR) in many of such emerging control methods (see, for example, the models proposed in (He, Larry Head, and Ding 2014;Yang, Zheng, and Menendez 2017;Moradi et al. 2021)), not much research has been done to enhance the quality 2 of SFR estimation models by (accordingly) incorporating information of CVs. The aim of this paper is to incorporate such information, which is expected to lead to better dynamic estimations of the SFR for use in emerging traffic control methods. ...
Connected Vehicles (CVs) could enhance traffic management systems by providing detailed and real-time information. Theoretically, such information can be exploited for the provision of efficient movement of traffic, especially at intersections identified as the bottlenecks of traffic systems. Aimed at the same purpose, this paper uses information of CVs to estimate the Saturation Flow Rate (SFR), particularly in the transition period during which CVs and conventional vehicles will coexist. To this end, we retain the advantages of data-driven techniques to capture the underlying dynamics of the SFR by considering information of CVs as the only input. In this regard, we correlate the dynamic variations of the SFR to the mutual interactions among the contributing parameters extracted from the limited pieces of CVs’ information using a neural network. Comprehensive simulations under precisely designed settings in VISSIM show a hoped-for SFR estimation accuracy level, which can further augment intelligent intersection controller initiatives.
... Again, the data link layer is divided into two layers relevant to their function : MAC (Medium Access control), LLC (Logical Link Layer). This simple protocol architecture is reason for its real-time features due to its low overhead in protocols OS which reduces the delays [5]. Since he development in urban areas, number of buildings and intersections are increased. ...
DSRC stands for Dedicated Short-Range Communications which is a wireless technology that integrates with Intelligent Transportation System to communicate between vehicle and roadside infrastructure units. DSRC has more advanced features compared to wireless technologies such as Wi-Fi, GSM, WiMAX, and etc. This paper summarizes the features, technical mechanism, and the applications of Dedicated Short-Range Communication in Intelligent Transportation Systems (ITS).
... To distinguish priority level, some scholars proposed an optimization model to measure the priority level association with transit modes, occupancy, bus routes, and schedule [19][20][21]. A request-based mixed-integer linear programming model is formulated that explicitly accommodates multiple priority requests from diferent modes of vehicles, provides signal timings that minimize the total person delay of an intersection, and allocates weights to vehicles only based on their occupancy [22]. Another mixed-integer linear programming model is formulated with subjecting to the realworld constraints for bus rapid transit schedule optimization by minimizing the travel time; in this model, the schedule at each bus stop and the signal priority control are simultaneously optimized at intersections along the bus rapid line [23]. ...
Multiaccess edge computing (MEC) and connected vehicle (CV) technologies have shown great potential and strength for traffic perception and real-time computing, which can be applied to enhance the efficiency of connected transit bus operations under their lower penetration conditions. Moreover, for the transit signal priority system, how to establish a model to measure traffic demand for conflicting priority request resolution and improve system response time has been widely researched for the last few decades. This paper proposes a dynamic priority weight (DPW) model for connected transit buses and a traffic signal control approach to coordinate multidirectional conflicting priority requests at a signalized intersection. The proposed model takes advantage of vehicle location, speed, and signal timing data to build time to change (TTOC) correlation functions to measure priority weights of both single-vehicle and directionality accumulation with consideration of vehicles arriving during the current green phase and conflict phase conditions; then, the aggregated priority weight value of each movement can be calculated in real-time. Once the maximum aggregated priority weight value among all movements is determined, the corresponding phase switch strategy is presented for the conflicting request resolution control problem. Homologous algorithm software for distributed deployment can be subsequently used for swift response. Simulation results show that the proposed DPW model-based traffic signal control method shows significant performance advancement, where the queueing vehicle number decrease exceeds 1 pcu/s and the throughput rate of major movements increases by approximately 2% without sacrificing the performance of minor movements in a large amount. What is more, it shows better delay optimization for social vehicles than the algorithm with delay as the objective while declining bus delay appreciable quantity with 43.4 s in average. Field test results also show that this method has excellent abilities to improve intersectional traffic capacity, for which queueing vehicle number and throughput rate indicators of all phases dramatically improved with 1.92 pcu/s and 6.68% on average, except for a slight degradation of individual minor traffic movements with 0.99 pcu/s and 0.11%.
... Up to now, a quite limited number of scholars have studied coordination control depending on CV technology. Some research studied how to provide progression to buses requesting transit signal priority (TSP) by collecting CV data, enabling the served TSP to be utilized at the downstream intersections (Hu et al. 2015;He et al. 2014). Simulation results indicated that the provided progression could improve that the performance of buses (e.g., bus delay). ...
The aim of this study was to apply signal coordination for eight signalized intersections in three different locations and compare the performance before and after the adjustments. To achieve the above objective, we collected traffic data from the sites and from the ministry of works. VISSIM software was used for evaluation and analysis of intersections. SYNCHRO software was used for finding the best timing plan alongside manual solution (trial-and-error). We got a high delay and bad level of service for all of the selected intersections before applying the signal coordination. Then the improvement strategy of optimizing signal timing and coordination was applied to the traffic flow in the study areas. Finally, this improvement was found to have good effect on the level of service and delays, where we reduced the delay in all the locations by approximately 34% and improved the level of surface from F to E on Estiqlal highway and 16th December Highway and from D to C on Tubli highway. The strategies and design from this research can be implemented on the selected locations and serves as a benchmark for other similar studies in the region.
This paper proposes a new fuzzy architecture for the management of an isolated intersection. The challenge of this study is to reduce the waiting time of vehicles in the lanes compared to what exists in the state of the art. The proposed approach is based on five fuzzy logic-based controllers, four for phase management and one for phase selection. The priority of a phase is managed dynamically and intelligently through a flexible cycle. A significant gain in terms of average and cumulative vehicle waiting time is proven in the simulation results compared to other architectures presented in the literature. The performance of the developed system is proven in three different scenarios, the first one containing a low vehicle arrival rate, the second one with a medium arrival rate, and the last one in a critical case with a high arrival rate. Our findings highlight the potential of fuzzy logic-based approaches for the development of intelligent transportation systems that can help alleviate traffic congestion and improve overall urban mobility.
In modern urban areas, inefficiency traffic management is one of the main causes of road congestion, leading to reduced fuel efficiency and increased traffic safety hazards. Traditional researches typically focus only on enhancing the throughput of intersections by optimizing traffic signals or individual vehicle trajectories. However, these methods often overlook the dynamic nature of the traffic system and the potential benefits of vehicle platooning, limiting their effectiveness in complex traffic environments. Addressing this challenge, this paper presents MARP, a Cooperative Multi-Agent Deep Reinforcement Learning (DRL) System for Connected Autonomous Vehicle (CAV) Platooning. Utilizing vehicle to vehicle (V2I) and vehicles to infrastructure (V2V) technologies, MARP integrates sensing, computing, and communication to collect and process real-time data on traffic conditions, thereby achieving dynamic synchronization between traffic signal controllers and CAV platoons. By constructing platoons that collaborates with the infrastructure through a multi-agent DRL collaboration model, MARP adapts to real-time traffic flow changes, significantly optimizing the fluidity and efficiency of the entire traffic network. Detailed experiments show that MARP effectively reduces traffic congestion, shortens intersection travel times, and cuts fuel consumption and emissions, surpassing the state-of-the-art approach.
Connected and autonomous vehicles offer the possibility to carry out control strategies, thus having great potential to improve traffic efficiency and road safety. The efficient passage of an emergency vehicle calls for the collaborative driving decision-making among multiple vehicles in a dynamically changing local area. However, existing work fails to efficiently adapt to dynamic and complex traffic conditions, thus cannot well solve the task. For better solution, we propose a flexible cooperative multi-agent reinforcement learning approach based on value function factorization, called Q-LSTM. Since the traffic environment is partially observable, the centralized training and decentralized execution paradigm is adopted to learn effective cooperative strategies for individual agents. To flexibly adapt to the changing neighborhood condition around the emergency vehicle, we introduce a long short-term memory network to decompose the learned global value function into local value function of each agent within the neighborhood, whose quantity and entities vary over time. To address the credit assignment problem and realize different roles of the emergency and regular vehicles, reward mechanism and the way agent-wise Q-networks update are well-designed. Extensive experiments are conducted on the Simulation of Urban MObility platform. Results show that our Q-LSTM outperforms state-of-the-art value-based MARL methods. Moreover, the robustness and adaptability of the Q-LSTM are verified in the cases of increased traffic density.
With the rapid development of artificial intelligence (AI) and connected vehicle (CV) technology, researchers are actively exploring the utilization of deep reinforcement learning (DRL) algorithms combined with real-time traffic information from CVs to optimize traffic signal control. These controllers have showcased better performance than traditional controllers. However, a major drawback is their heavy reliance on pure CV environments, which has not been adequately addressed. This study proposes a novel traffic signal controller based on proximal policy optimization (PPO), integrating a multi-discrete action space and a combined state space, to enhance robustness in mixed traffic environments where CVs and non-connected vehicles coexist. Evaluations through simulation experiments on a real-world-based intersection testbed demonstrate superior performance in terms of both effectiveness and robustness compared to some popular controllers, including the deep Q-network (DQN) based controller, pretimed controller, and actuated controller. The results indicate that the proposed controller significantly reduces the average delay. Furthermore, its performance remains reliable even in environments with a CV market penetration rate as low as 20%. The findings highlight that the utilization of PPO with multi-discrete actions and combined state space effectively addresses the challenges posed by mixed traffic environments, making it a promising solution for real-world traffic signal control.
Conflicting objectives of traffic signal settings for arterials with multimodal traffic often trouble practitioners, particularly when one needs to consider various modes and competing flows en masse. Although many sophisticated real-time adaptive signal control systems have been developed over the past decades, how to offer transit-friendly signals while responding to the fluctuating demand of general traffic remains an ongoing issue that has yet to be fully addressed. As such, this study has proposed an adaptive traffic control system adjusting cycle length, offsets, and splits featuring the capability of 1) generating progression bands for both through and turning vehicles in real-time; 2) offering transit-friendly signal for high-frequency service without disrupting the general traffic; and 3) a decision process to select the signal plans among a pool of candidates that concurrently satisfy multiple objectives while offering a stable plan when altering the signal plan in real-time. The signal plan adjustment is on a cycle-by-cycle basis, and its short computation time allows the plan to be ready prior to the beginning of the next cycle. The promising results can be seen in the case study: the proposed model has shown improvements in the networkwide delay, ranging from 4.7% to 5.7%, compared to state-of-the-practice coordination-actuation. By integrating the objective of reducing bus delays, the system has further shown a significant reduction in bus delays, ranging from 6.6% to 15.0%.
Traffic congestion is commonly found in mega-cities globally. Traffic congestion is not only harmful to efficiency, but also to equity and sustainability of urban development. Intelligent transportation system (ITS) can reduce traffic congestion at least possible cost with a range of technologies. Recent development in connected vehicle (CV) technologies is expected to enable more efficient traffic sensing and management. For traffic sensing, this chapter explores the potential of CV-based mobile sensing in improvement of spatial coverage and temporal frequency. For traffic management, along with CV-based traffic state sensing, real-time traffic control or route guidance could be more adaptive to change in traffic, hence, the traffic system efficiency could be improved. However, the impact of CV penetration rate and robustness of CV-based ITS solutions remain to be studied. With the comprehensive reviews on studies of CV-based ITS traffic management, this chapter can help guide future traffic management policy design with incorporation of data-based solutions.
Roads are becoming increasingly congested due to urbanization and the increased use of private vehicles. As a result, emergency vehicles like ambulances, police cars, and fire trucks get stuck in traffic and take longer to get where they're going, which may cause risk, damage to property, and losing valuable lives. The acoustic-based pre-emption technology uses the EV's siren to alert oncoming cars. Therefore, acoustic reflection from a building or other large container vehicle might cause a fall. This paper implements a vehicle-to-infrastructure (V2I) communication system that anticipates traffic signals using Global Positioning System (GPS) and Dedicated Short Range Communication (DSRC). Line of sight (LOS) is not required when choosing the desired range of DSRC communication, and the traffic signal unit receives pre-emption messages anytime an emergency vehicle is nearby. Thus, these messages cause the traffic light to turn green for the emergency vehicle instead of operating normally. As a result, this type of system necessitates two hardware modules, one at each vehicle On-Board-Unit (OBU) and one at each intersection Road-Side-Unit (RSU), as well as the Decision Support System i.e., Traffic Management Controller (TMC). In the hardware OBU and RSU, the IMX-6 Processor, GPS Module, and DSRC transmitter and receivers are used. The Emergency Vehicle (EV) equipped with the OBU transmits requests to the intersection unit, which is fully autonomous and can be employed as a component of the Intelligent Transportation System (ITS), negating the need for a driver in this method.
During both daily operation and emergency evacuation, the corners of walking facilities in subway stations play an important role in efficient circulation. However, the effectiveness of the corner is difficult to assess. In this paper, a method of passenger gathering and scattering analysis based on queueing models was proposed to investigate the corner performance in subway stations. Firstly, we constructed a set of state spaces of passenger flow according to passenger density and proposed the state transition model of passenger flow. Moreover, the model of passenger flow blocking and unblocking probability were also presented. Then, to illustrate the validity of the method and model, several passenger gathering-scattering scenarios and were simulated to verify the influence of passenger distribution and facility width on passenger walking, and the blocking probability, throughput, and expected time were also analyzed under various widths of the target corridor and arrival rates. Results showed that the proposed model can reproduce the trend of walking parameters changing and the self-organizing phenomenon of 'faster is lower'. With the increase of arrival rates of passengers, walking speeds of passengers decrease and the expected walking time is prolonged, and the blocking probability sharply increased when the arrival rate exceeded 7 peds/s. In addition, with change of width of the target facility, efficiency of capacity of walking circulation facility fluctuated. With the width of the target corridor enlarged by 10%, the steady state of passenger flow was less crowded. Therefore, corridor width is critical to the circulation efficiency of passengers in subway stations. The conclusions will help to develop reasonable passenger flow control plans to ease the jam and keep passengers walking safely.
Existing signal control systems for urban traffic are usually based on traffic flow data from fixed location detectors. Because of rapid advances in emerging vehicular communication, connected vehicle (CV)-based signal control demonstrates significant improvements over existing conventional signal control systems. Though various CV-based signal control systems have been investigated in the past decades, these approaches still have many issues and drawbacks to overcome. We summarize typical components and structures of these existing CV-based urban traffic signal control systems and digest several important issues from the summarized vital concepts. Last, future research directions are discussed with some suggestions. We hope this survey can facilitate the connected and automated vehicle and transportation research community to efficiently approach next-generation urban traffic signal control methods and systems.
The advancement of the Intelligent Transport Systems (ITS) and the emerging Connected and Automated Vehicles (CAVs) technology are acknowledged to hold a great potential to mitigate challenging problems in the current transportation networks. Particularly, a proper traffic control strategy with a precise vehicular movement control scheme can alleviate the congestion and improve the safety and efficiency of the traffic. This paper proposes a novel bi-level control framework that combines a design of traffic signal timings at a network level, and a detailed trajectory control policy for individual vehicles at a link-level within a network of CAVs. We develop a group-based longitudinal trajectory planning scheme to coordinate vehicular movements at the lower level of our framework while abiding by the signal operations along with end-to-end vehicle routing decisions from the upper network level optimization. This joint and mutual interaction between the two different control strategies in the urban signalized corridors is complex and can significantly affect the overall network’s performance, nevertheless has not been explored previously in the literature. The proposed framework enables such studies where we derive an efficient algorithm that iteratively solves the mixed-integer linear programming (MILP) and linear programming models in each link at the lower and over the network at the upper levels, respectively. Numerical results show the effectiveness of the proposed joint control framework in network performance regarding the average travel time, queue formation and dissipation across the network.
This paper proposes a reinforcement learning-based collaborative multi-agent actor and critic scheme (RL-CMAS) under edge computing architecture for emergency vehicle preemption. The RL-CMAS deployed a parallel training process at the cloud side for building knowledge and well accelerating learning. Priority of message and model of message offloading strategy have been developed. The simulation results show that the proposed RL-CMAS is efficient in detecting even complex data. Finally, a comparison was made with other benchmark methods, namely, Regular scheduling algorithm, Alameddine’s DTOS algorithm, and independent multi-agent actor-critic. The result showed the proposed method outperforming the other three bench marking methods. The proposed RL-CMAS provides reduction in message processing delay, total delay, and an increase of message delivery success ratio of 14.22%, 18.21%, and 8.86% respectively.
This paper presents a priority-based coordination system that provides preferential treatment to vehicles traveling along a coordinated route. A mixed-integer linear program model is enhanced to consider coordination as a form of priority along with the multi-modal priority for eligible emergency, transit, and freight connected vehicles and provides dilemma zone protection to freight vehicles in a connected vehicle environment. The optimization model generates an optimal signal timing schedule that minimizes the total weighted delay of the coordination requests, priority requests, and dilemma zone requests, and maximizes the flexible implementation of the optimal signal timing schedule. The optimal signal timing schedule allows real-time vehicle actuation using traditional vehicle detection. The simulation experiments and statistical analysis show that priority-based coordination can achieve performance equivalent to a traditional coordination system. The priority-based coordination method is integrated into the priority control Multi-modal Intelligent Traffic Signal System and is implemented in the Maricopa County SMARTDrive Program SM test bed in Anthem, Arizona, and in Portland, Oregon.
The goal of this study was the development and evaluation of an algorithm for resolving conflicting requests for transit signal priority (TSP). This algorithm was designed to work with actual traffic controllers without the need for new hardware or software installations. The algorithm was tested in VISSIM microsimulation and ASC/3 software-in-the-loop controllers on an intersection that will be upgraded to serve two conflicting bus rapid transit (BRT) lines. The ASC/3 logic processor was used to control built-in TSPs in the case of conflicting requests and to develop custom-TSP strategies that would not rely on built-in TSP. Custom TSP provides a much higher level of TSP for transit vehicles than built-in TSP, and it creates opportunities for more adaptable TSP control. The results showed that the widely used first-come, first-served policy for resolution of conflicting TSP requests was not the best solution. Such a policy could perform worse than a policy that provided no priority. For the analyzed intersection, the first-come, first-served option even increased BRT delays by 13% more than did the no-TSP option. The presented algorithm can help resolve the problem of the conflicting TSP requests. The algorithm worked best when combined with several TSP strategies. For the custom-TSP strategies, the application of the algorithm reduced BRT delays by more than 30%, with minimal impact on vehicular traffic. The algorithm shows promising results, and with small upgrades, it can be applied to any type of TSP.
A unified platoon-based mathematical formulation called PAMSCOD is presented to perform arterial (network) traffic signal control while considering multiple travel modes in a vehicle-to-infrastructure communications environment. First, a headway-based platoon recognition algorithm is developed to identify pseudo-platoons given probe vehicles’ online information. It is assumed that passenger vehicles constitute a significant majority of the vehicles in the network. This algorithm identifies existing queues and significant platoons approaching each intersection. Second, a mixed-integer linear program (MILP) is solved to determine future optimal signal plans based on the current traffic controller status, online platoon data and priority requests from special vehicles, such as transit buses. Deviating from the traditional common network cycle length, PAMSCOD aims to provide multi-modal dynamical progression (MDP) on the arterial based on the probe information. Microscopic simulation using VISSIM shows that PAMSCOD can easily handle two common traffic modes, transit buses and automobiles, and significantly reduce delays for both modes under both non-saturated and oversaturated traffic conditions as compared to traditional state-of-practice coordinated-actuated signal control with timings optimized by SYNCHRO.
Providing signal priority for buses has been proposed as an inexpensive way to improve transit efficiency and productivity and to reduce operation costs. Bus signal priority has been implemented in several U.S. cities to improve schedule adherence, reduce transit operation costs, and improve customer ride quality. Current signal priority strategies primarily utilize sensors to detect buses at a fixed or preset distance away from an intersection. Traditional presence detection systems, ideally designed for emergency vehicles, usually send a signal priority request after a preprogrammed time offset as soon as transit vehicles are detected without the consideration of bus readiness. The objective of this study is to take advantage of the already equipped Global Positioning System and automated vehicle location system on the buses in Minneapolis, Minnesota, and to develop an adaptive signal priority strategy that could consider bus schedule adherence, number of passengers, location, and speed. Buses can communicate with intersection signal controllers by using wireless technology to request signal priority. Communication with the roadside unit (e.g., traffic controller) for signal priority may be established by using the readily available wireless local area network (WLAN) 802.11× or the dedicated short-range communication (DSRC) 802.11p protocol currently under development for wireless access to and from the vehicular environment. This paper describes the proposed priority logic and its evaluation with the use of microscopic traffic simulation. Simulation results indicate that a 12% to 15% reduction in bus travel time during a.m. peak hours (7 to 9 a.m.) and a 4% to 11% reduction in p.m. peak hours (4 to 6 p.m.) could be achieved by providing signal priority for buses. Average bus delay time was reduced in the range of 16% to 20% and 5% to 14% during a.m. and p.m. peak periods, respectively.
Transit signal priority (TSP) is a popular strategy used to enhance the performance of transit systems by modifying the signal control logic to give transit vehicles priority at signalized intersections. Conventional TSP strategies used in most cities have been shown to offer significant benefits by reducing delay of transit vehicles. However, concerns about shortcomings of conventional TSP strategies have limited their application. The main concern is a potential negative impact on cross street traffic. Another concern is the static nature of conventional TSP strategies and the lack of responsiveness to real-time traffic and transit conditions. A dynamic TSP control system has been developed that can provide signal priority in response to real-time traffic and transit conditions. The dynamic TSP system consists of three main components: a virtual detection system, a dynamic arrival prediction model, and a dynamic TSP algorithm. Two case studies are presented to test and compare the dynamic and the conventional TSP systems. A hypothetical intersection is simulated in the first case study, and a proposed light rail transit line is simulated in the second. For both case studies, a virtual detection system was developed in VISSIM, along with a linear travel time arrival prediction model. Finally, a dynamic TSP algorithm was developed to determine what TSP strategy to use and when to apply it. The results show that the dynamic TSP system reduced the total delay of transit vehicles and outperformed the conventional TSP system for reducing transit trip travel time.
The adaptive traffic signal control system, as well as the Transit Signal Priority (TSP) concept has both been viewed as important opportunities for reducing overall traffic congestions and improving bus transit services. However, few studies were conducted on combining them together. A dynamic signal timing optimization model was developed in this research, which aims at reallocating green times among the phases with considering the real-time traffic flow condition and the bus priority request. In the model, both arrival and departure flows are described by time-dependent functions, the arrival of a bus with priority request is represented by giving a weight factor to the traffic demand of the associated approach. The weight factor is also defined as a time-dependent function of three variables: present traffic demand and queuing conditions of the bus arriving approach vs. that of the intersection and the lateness of the target bus. As the bus is converted to a relevant number of arrivals of passenger vehicles by the weight factor, the objective of the dynamic model is to minimize the average delay at the intersection. KEYWORDS Adaptive traffic signal control, Adaptive Transit signal priority, Dynamic optimization model TRB 2003 Annual Meeting CD-ROM Paper revised from original submittal.
Representatives of traffic control agencies (TCAs)-including police officers, firefighters, and other traffic law enforcement officers who override automatic traffic signal controls-are crucial to the mitigation of nonrecurrent traffic congestion caused by planned and unplanned events. An unanswered question is how well TCA officers perform compared with state-of-practice automatic traffic signal controls. This study assessed the performance of TCA manual multimodal traffic signal control during special events. First, an interview was designed to promote understanding of the control rules of TCAs and the practice of manual traffic signal control. Next, a simulation experiment was conducted to record control actions during multimodal traffic flows containing buses, pedestrians, and passenger cars. Third, a TCA performance index was developed through a comparison of manual control with actuated signal control and optimal control solutions from an online optimization model, which assumed the availability of rich vehicle information, to determine the best control strategies. The results show that manual traffic control can significantly improve control performance, even to the point of approaching the performance of the optimized timing plan. Large variations, however, were observed during the study.
A heuristic algorithm is presented for traffic signal control with simultaneous multiple priority requests at isolated intersections in the context of vehicle-to-infrastructure communications being available on priority vehicles, such as emergency vehicles and transit buses. This heuristic algorithm can achieve near-optimal signal timing when all simultaneous requests are considered and can be visualized in a phase time diagram. First, the problem with the control of multiple priority traffic signals is transformed into a network cut problem that is polynomial solvable under some reasonable assumptions. Second, a phase time diagram is presented to visualize and evaluate priority delay given a signal plan and a collection of priority request arrival times. Microscopic traffic simulation is used to compare the heuristic with the state-of-the-practice algorithms for transit signal priority. The proposed heuristic algorithm could reduce average bus delay in congested conditions by about 50%, especially with a high frequency of conflicting priority requests.
This article presents a priority signal control model for multiple bus requests. The proposed model aims to generate the optimal priority serving sequence to maximize the utilization of available green times by buses, but not to incur excessive congestion for other vehicular traffic. This study first depicts the serving sequence for multiple priority requests as a multistage decision process and explicitly models three bus priority strategies under the constraints of minimum green time, acceptable degree of saturation, and length of priority window. Further, it formulates a dynamic programming model to optimize the serving sequence for multiple priority requests as well as the corresponding signal timing plans under various levels of bus occupancy, schedule deviation, and traffic demand. A rolling time horizon approach is employed to solve the proposed model in real time. Comparative analysis results have shown that the proposed dynamic programming model outperforms the first-come-first-serve policy in terms of reducing bus delays, improving schedule adherence, and minimizing the impacts on other vehicular traffic. Computational performance analysis has further demonstrated the potential of the proposed model and algorithm to be applied in real-time bus priority control system.
Research on using high-resolution event-based data for traffic modeling and control is still at early stage. In this paper, we provide a comprehensive overview on what has been achieved and also think ahead on what can be achieved in the future. It is our opinion that using high-resolution event data, instead of conventional aggregate data, could bring significant improvements to current research and practices in traffic engineering. Event data records the times when a vehicle arrives at and departs from a vehicle detector. From that, individual vehicle’s on-detector-time and time gap between two consecutive vehicles can be derived. Such detailed information is of great importance for traffic modeling and control. As reviewed in this paper, current research has demonstrated that event data are extremely helpful in the fields of detector error diagnosis, vehicle classification, freeway travel time estimation, arterial performance measure, signal control optimization, traffic safety, traffic flow theory, and environmental studies. In addition, the cost of event data collection is low compared to other data collection techniques since event data can be directly collected from existing controller cabinet without any changes on the infrastructure, and can be continuously collected in 24/7 mode. This brings many research opportunities as suggested in the paper.
A real-time, on-line control algorithm is proposed that aims to maintain the adaptive functionality of actuated controllers while improving the performance of traffic-actuated signal control system. To be consistent with the operation logic of existing signal control devices, only those four basic control parameters that can be found in modern actuated controllers are considered: phase sequence, minimum green, unit extension and maximum green. Microscopic simulation is used to test and evaluate the proposed control algorithm comparing with free-mode actuated, actuated-coordinated and volume–density control in a calibrated signalized network. Simulation results indicate that the proposed algorithm has the potential to improve the performance of the network at different traffic demand levels.
Traffic volumes are naturally variable and fluctuate from day to day. Robust optimization approaches have been utilized to address the uncertainty in traffic signal timing optimization. However, due to complicated nonlinear programming models, obtaining a global optimal solution is difficult. Instead of working with nonlinear programming models, we propose a discretization modeling approach, where the cycle, green time, and traffic volume are divided into a finite number of discrete values. The robust signal timing problem is formulated as a binary integer program. Two dynamic programming algorithms are then developed. We obtain optimal solutions for all of the instances with respect to the inputs generated from the discretization.
One problem in existing bus priority strategies is that while a decision is being made to grant priority at an intersection, the bus arrival time at the downstream intersections is not considered. Moreover, only strategies for late buses are discussed; the strategies for early buses are seldom studied. This research tests a different bus priority approach, coordinated and conditional bus priority (CCBP). Coordinated, signalized intersection groups are adopted as control objects. Buses are detected one or more cycles before their arrival at the first intersection of the control object. A CCBP, with two kinds of priority strategies (increasing and decreasing bus delay strategies), is proposed. A model was built to generate the optimal combination of priority strategies for intersection groups so that the real delay of buses would be close to the permitted delay defined by the bus operation system. In the field application, the CCBP approach is compared with other two options: no priority and unconditional priority. Significant reductions on bus delay deviation and bus headway deviation were achieved with the use of the CCBP approach. Application of the CCBP approach resulted in only minor increases in total average delay of motor vehicles. The results of the field application studies performed as part of this study suggested that the CCBP approach could be used to decrease bus delay deviation and enhance the reliability of bus service without significantly affecting the delay of other motor vehicles.
This paper presents a model of the core logic of a traffic signal controller. The model is formulated on the basis of the traditional North American ring, phase, and barrier construct and includes phase intervals such as minimum and maximum times, pedestrian service, alternative minimum times, and a priority service extension. The mathematical model is based on precedence graphs that are familiar to engineers involved with project management techniques such as Gantt charts, the critical path method, and the program evaluation and review technique. The model presents an analytical framework for the analysis of complex controller behaviors and is demonstrated for the case of multiple priority requests. An example shows that a first-come, first-served policy for serving priority requests can result in more delay than will a multiple-priority-request policy generated by the model developed in this paper. Additional controller behaviors, such as preemption, coordination, and offset transition, can be analyzed with this model.
Conditional priority for buses at signalized intersections means that late buses are given priority and early buses are not. This scheme is a method of operational control that improves service quality by keeping buses on schedule. A conditional bus priority implementation in Eindhoven, the Netherlands, is described. Results show the strong improvement in schedule adherence compared with a no-priority situation. Traffic impacts at an intersection were studied for three scenarios--no priority, absolute priority, and conditional priority. Compared with no priority, absolute priority increased delays significantly while conditional priority had almost no impact.
Vehicle actuated controls are designed to adapt green and red times automatically, according to the actual dynamics of the arrival, departure and queuing processes. In turn, drivers experience variable delays and waiting times at these signals. However, in practice, delays and waiting times are computed at these systems with models that assume stationariety in the arrival process, and that are capable of computing simply expectation values, while no information is given on the uncertainty around this expectation. The growing interest on measures like travel time reliability, or network robustness motivates the development of models able to quantify the variability of traffic at these systems.This paper presents a new modeling approach for estimating queues and signal phase times, based on probabilistic theory. This model overcomes the limitations of existing models in that it does not assume stationary arrival rates, but it assumes any temporal distribution as input, and allows one to compute the temporal evolution of queue length and signal sequence probabilities. By doing so, one can also quantify the uncertainty in the estimation of delays and waiting times as time-dependent processes. The results of the probabilistic approach have been compared to the results of repeated microscopic simulations, showing good agreement. The smaller number of parameters and shorter computing times required in the probabilistic approach makes the model suitable for, e.g., planning and design problems, as well as model-based travel time estimation.
This study reports on a case study examining the impact of emergency vehicle preemption on closely spaced arterial traffic signals. The study was conducted on SR 26 a principal arterial and main thoroughfare connecting Interstate 65 with US 52 on the East side of Lafayette, Indiana. This study examined four coordinated intersections along SR 26 using seven preemption paths and three different transition algorithms (smooth, add, and dwell). The number of preempts in the simulation period varied from one to three preempts for equal simulation periods. The findings generally show that a single preemption call had minimal effect on the overall travel time and delay through the network. The results also indicate that the smooth transitioning algorithm performed the best with most scenarios and paths, for both the arterial and side streets. When multiple emergency vehicles preempt at closely spaced time intervals, the impact of preemption was more severe. For the network studied, the most severe impact observed on arterial travel time was an increase in the average arterial travel time on the order of 20-30 seconds. This study focused on emergency vehicle preemption but the general procedures described here could also be applied to railroad preemption or transit priority.
An analytical model for real-time estimation of travel times along signalized arterials was developed. The application of the model on two arterial sites and comparisons of the estimated travel times with simulated and field data show that the model accurately predicts travel times on the selected sites. In this article, we present several important extensions and refinements to the model including treatment of long queues and queue spillovers, and algorithms for signal priority to transit vehicles. We also describe the integration of the model into an archival data management system for continuous measurement and monitoring of traffic performance along arterials.
The performance of signal timings obtained by using conventional approaches for pre-timed control systems is often unstable under fluctuating traffic conditions. This paper presents three models to determine robust optimal signal timings that are less sensitive to fluctuations of traffic flows or perform better against the worst-case scenario without losing much optimality. Computational experiments are conducted to validate the model formulations and solution algorithms.
How to estimate queue length in real-time at signalized intersection is a long-standing problem. The problem gets even more difficult when signal links are congested. The traditional input–output approach for queue length estimation can only handle queues that are shorter than the distance between vehicle detector and intersection stop line, because cumulative vehicle count for arrival traffic is not available once the detector is occupied by the queue. In this paper, instead of counting arrival traffic flow in the current signal cycle, we solve the problem of measuring intersection queue length by exploiting the queue discharge process in the immediate past cycle. Using high-resolution “event-based” traffic signal data, and applying Lighthill–Whitham–Richards (LWR) shockwave theory, we are able to identify traffic state changes that distinguish queue discharge flow from upstream arrival traffic. Therefore, our approach can estimate time-dependent queue length even when the signal links are congested with long queues. Variations of the queue length estimation model are also presented when “event-based” data is not available. Our models are evaluated by comparing the estimated maximum queue length with the ground truth data observed from the field. Evaluation results demonstrate that the proposed models can estimate long queues with satisfactory accuracy. Limitations of the proposed model are also discussed in the paper.
Control strategies for transit priority have long been recognized as having the potential to improve traffic performance for transit vehicles, which could also lead to improved schedule reliability, reduced operating costs, and greater ridership. However, there have been relatively few successful implementations of transit priority measures on urban networks with signalized intersections in coordinated signal systems. Existing control strategies are reviewed, the major factors affecting transit priority are identified, and the formulation of both passive and active transit priority strategies for arterials with coordinated traffic signals are described. The proposed strategies were evaluated on a real-life arterial corridor. The proposed passive and active priority strategies placed major emphasis on the systemwide improvements to the transit movements and on minimization of the adverse impacts to the rest of the traffic stream. The criteria used to grant priority include the availability of spare green time in the system cycle length, progression at the downstream intersection(s), and schedule adherence. An evaluation technique was also developed to assist in the design of the signal priority strategies and to predict the impacts of the transit priority measures.
A rule-based procedure for determining real-time signals timings at a signalized intersection is described. It incorporates the effects of the traffic interference caused by on-line loading/ unloading of transit vehicles at the intersection. This procedure generates a number of short-term alternative real-time phase sequences for various levels of transit priority, based on a number of decision rules. It then evaluates these signal sequences and selects the one with the least overall cost to all traffic. The procedure is illustrated in terms of a simulated application to a critical intersection in Toronto's Queen Street corridor using real data. The preliminary simulation tests indicate the potential reduction in total compared to fixed-time operation, which results largely from selectively ushering transit vehicles to their loading positions at strategic times and serving cross-street traffic while the transit vehicles are loading.
To explore the advantage of integrating bus-preemption with adaptive signal control, this study has produced two integrated models for adaptive bus-preemption control in the absence and presence of Automatic Vehicle Location systems. Instead of using prespecified strategies, such as phase extension, phase early start, and/or special bus phase, the proposed models make a preemption decision based on a performance index which includes vehicle delay, bus schedule delay, and passenger delay. This study also contains an extensive simulation evaluation with respect to the integration of adaptive control with bus-preemption. Under both with and without Automatic Vehicle Location environments, both developed models yield quite promising results.
To explore the advantage of integrating bus-preemption with
adaptive signal control, this study has produced two integrated models
for adaptive bus-preemption control in the absence and presence of
automatic vehicle location (AVL) systems. Instead of using prespecified
strategies (such as phase extension, phase early start and/or special
bus phase) the proposed models make a preemption decision based on a
performance index which includes vehicle delay, bus schedule delay, and
passenger delay. This study also contains an extensive simulation
evaluation with respect to the integration of adaptive control with
bus-preemption. Under both with and without AVL environments, both
developed models yield quite promising results
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