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Integrating public transit signal priority into max-pressure signal control: Methodology and simulation study on a downtown network
Abstract
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.
... Signal-timing schemes with BSP/TSP can not only improve traffic efficiency [18,19] but also improve the reliability of bus services [20,21]. For better reliability and punctuality of bus services, a series of Conditional Signal Priority (CSP) models were built to serve the bus priority requests distinctively [7,[22][23][24][25][26]. The impacts of CSP on service reliability were investigated using a simulation platform, and the findings showed that the improvement of the CSP in bus service reliability was 3.2% when compared with a non-priority scenario [24]. ...
Bus bunching is a severe problem that affects the service levels of public transport systems. Most of the previous studies in the field of Bus Signal Priority (BSP) and Transit Signal Priority (TSP) focus on reducing a bus delay at signalised intersections and ignore the importance of balancing the bus headways. However, since general BSP methods allocate uneven priorities for individual buses, the headways of bus sequences are prioritised or delayed randomly, increasing the likelihood of bus bunching. To address this problem and to improve the reliability of bus services, we propose an online optimisation model to determine the signal duration and splits for each traffic intersection and each signal cycle for bus priority. The proposed model is able to induce the signal timing back to a baseline when the BSP request frequency is low. Using the proposed model, a statistically significant reduction of 10.0% was achieved for bus headway deviation and 6.4% for passenger waiting times. The simulation-based evaluation results also indicate that the proposed model does not affect the efficiency of bus services and other vehicles significantly.
... Some cities have begun to take a series of governance measures to make urban traffic more environmentally friendly and sustainable, such as controlling the total number of motor vehicles, requiring higher emission standards, promoting new energy vehicles, improving public transit service, and optimizing non-motorized transportation systems [2][3][4]. ...
In recent years, vehicle emissions have become one of the important pollutant sources of the urban atmosphere. Scholars and decision-makers are constantly expected to accurately grasp the dispersion of vehicle pollutants to formulate a series of policies and strategies which can facilitate a friendly and sustainable urban environment, such as controlling the total number of vehicles, requiring higher emission standards, promoting new energy vehicles, improving public transit service, and optimizing non-motorized transportation systems. This paper provides a review of the mechanism research methods and mathematical modeling approaches for urban vehicle pollutant dispersion. The mechanism research methods reviewed include field measurements, wind tunnel experiments, and numerical simulations. The modeling approaches involve two kinds of popular models: Box models (STREET, CPBM, AURORA, PBM) and Gaussian models (CALINE, HIWAY, OSPM, CALPUFF, R-LINE, ADMS series, EPISODE, CityChem, SIRANE, MUNICH). Moreover, this paper clarifies the basic assumption, fundamental principle, related research, applicable conditions, and limitations of these mechanism research methods and modeling approaches.
... Deep reinforcement learning is another efficient method used by Long et al. in passenger-based strategies to minimize passenger delay [33]. As an alternative, the maximization of passenger throughput proved superior performance compared to the delay-based strategies, especially at high congestion levels [34][35][36]. ...
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.
... Xu et al. (2021) calculated the priority value for each bus by a fuzzy controller to handle the multiple conflicting TSP requests. Xu et al. (2022) integrated TSP into max-pressure control policy and determined the serving sequence of conflicting requests by the optimal solution of a mixed-integer linear program that maximizes the total pressure; however, their method only works for bus rapid transit with exclusive bus lanes. 4) Traffic signal cycle. ...
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.
This paper proposes a novel decentralized signal control algorithm that seeks to improve traffic delay equity, measured as the variation of delay experienced by individual vehicles. The proposed method extends the recently developed delay-based max pressure (MP) algorithm by using the sum of cumulative delay experienced by all vehicles that joined a given link as the metric for weight calculation. Doing so ensures the movements with lower traffic loads have a higher chance of being served as their delay increases. Three existing MP models are used as baseline models with which to compare the proposed algorithm in microscopic simulations of both a single intersection and a grid network. The results indicate that the proposed algorithm can improve the delay equity for various traffic conditions, especially for highly unbalanced traffic flows. Moreover, this improvement in delay equity does not come with a significant increase to average delay experienced by all vehicles. In fact, the average delay from the proposed algorithm is close to—and sometimes even lower than—the baseline models. Therefore, the proposed algorithm can maintain both objectives at the same time. In addition, the performance of the proposed control strategy was tested in a connected vehicle environment. The results show that the proposed algorithm outperforms the other baseline models in both reducing traffic delay and increasing delay equity when the penetration rate is less or equal to 60%, which would not be exceeded in reality in the near future.
Backpressure (BP) control was originally used for packet routing in communications networks. Since its first application to network traffic control, it has undergone different modifications to tailor it to traffic problems with promising results. Most of these BP variants are based on an assumption of perfect knowledge of traffic conditions throughout the network at all times, specifically the queue lengths (more accurately, the traffic volumes). However, it has been well established that accurate queue length information at signalized intersections is never available except in fully connected environments. Although connected vehicle technologies are developing quickly, a fully connected environment in the real world is still far. This paper tests the effectiveness of BP control when incomplete or imperfect knowledge about traffic conditions is available. BP control is combined with a speed/density field estimation module suitable for a partially connected environment. The proposed system is referred to as a BP with estimated queue lengths (BP-EQ). The robust-ness of BP-EQ is tested to varying levels of connected vehicle penetration, and BP-EQ is compared with the original BP (i.e. assuming accurate knowledge of traffic conditions), areal-world adaptive signal controller, and optimized fixed timing control using microscopic traffic simulation with field calibrated data. These results show that with a connected vehicle penetration rate as little as 10%, BP-EQ can outperform the adaptive controller and the fixed timing controller in terms of average delay, throughput, and maximum stopped queue lengths under high demand scenarios.
Transit signal priority (TSP) can be used to improve bus operations at intersections. However, implementing TSP can often increase the delay of non-transit modes. Therefore, it is necessary to evaluate the effects of TSP both on car and bus operations to determine optimal locations to equip with TSP to improve network operations. To do so, the link transmission model is used to evaluate the travel times of both cars and buses on the network while accounting for dynamic queuing and queue spillover. This method is then used to evaluate different combinations of locations for TSP implementation and to determine the optimal configuration that can minimize the total travel time of network users, including bus and car passengers. The sensitivity of the proposed algorithm to demand level, changes in transit network, implementation strategy, and solution method are also evaluated. For all tested scenarios, the TSP configurations found to be optimum achieve a significant reduction of total bus passenger travel time while creating minimal effect on total car travel time. The results reveal that in general, not all intersections should be equipped with TSP, and intersections that carry high demand within a network are promising locations for TSP implementation to reduce the total travel time of network users. Additionally, it is found that the total travel time of network users can be further decreased by only activating TSP for buses with more than a certain number of on-board passengers.
The traffic control of an arbitrary network of signalized intersections is considered. This work presents a new version of the recently proposed max-pressure controller, also known as back-pressure. The most remarkable features of the max-pressure algorithm for traffic signal control are: scalability, stability, and distribution. The modified version presented in this paper improves the practical applicability of the max-pressure controller by considering as input travel times instead of queue lengths. The two main practical advantages of this new version are: (i) travel times are easier to estimate than queue lengths, and (ii) max-pressure controller based on travel times has an inherent capacity-aware property, i.e., it takes into account the finite capacity of each link. Travel time tends to diverge when the queue length is close to its capacity. It should be noted that previous max-pressure algorithms rely exclusively on queue length measurements, which may be difficult to accomplish in practice. Moreover, these previous algorithms generally assume queues with unbounded capacity. This may be problematic because a model with unbounded capacity links is not able to reproduce spillbacks, which are one of the most critical phenomena that a traffic signal controller should avoid. After presenting the new version of the max-pressure controller, it is analyzed and compared with existing control policies in a microscopic traffic simulator. Moreover, results of a real implementation of the developed algorithm to a signalized intersection, located at an urban arterial in Jerusalem, are shown and analyzed. To the best of the authors’ knowledge, this experiment is the first real implementation of a max-pressure controller at a signalized intersection.
The innovative concept of intermittent bus lanes may lead to an important increase of bus system performance while limiting the reduction of the capacity devoted to general traffic. The main idea is that a general traffic lane can be intermittently converted to an exclusive bus lane. Frequently studied by analytical papers, practical demonstrations of the intermittent bus lane strategy are not numerous. Especially, the results of the two previous field tests are very specific to the test sites and are hardly transposable. This paper tries to fill this gap by proposing the results and the lessons learned of a new real-field demonstration in Lyon, France. After a detailed presentation of the 350-m case study, effects of an intermittent bus lane strategy on traffic conditions are evaluated. Then, analyses of the impacts on the bus systems performance are carefully performed and also compared to more classical bus operations: a transit signal priority strategy. The results show that an intermittent bus lane can be a promising strategy especially when it is combined with transit signal priority. The median travel time of the buses is significantly reduced whereas the regularity of the line increases.
In this paper, a decentralized traffic signal control strategy named max pressure control is reviewed and examined. This control strategy aims at optimizing overall network throughputs, but applies a distributed approach that only requires local information to generate timing plans for each intersection. A Vissim simulation study is conducted to compare existing max pressure schemes. The results show that a recently proposed cyclic-based approach performs more poorly than the original non-cyclic approach. Further, to address two issues that hinder existing schemes: frequent changes of phase and queue spillover/blockage, two modifications are suggested. The simulation reveals that network performance can be improved after modifications.
With the attractive feature of guaranteeing maximum network throughput, backpressure routing has been widely used in wireless communication networks. Motivated by the backpressure routing, in this paper, we propose a delay-based traffic signal control algorithm in transportation networks. We prove that this delay-based control achieves optimal throughput performance, same to the queue-based traffic signal control in literature. However, a vehicle at a lane whose queue length remains very small may be excessively delayed under queue- based signal control. Our delay-based backpressure control can deal with the excessive delays, and achieve better fairness with respect to delay, while still guaranteeing throughput optimality. Moreover, a general weighted control scheme combining the queue-based and delay-based schemes is also investigated, to provide a more flexible control according to the quality of service requirements. Numerical results explore their performance under both homogeneous and heterogeneous traffic scenarios.
With the increasing of transit vehicles in urban networks, traditional active transit signal priority (TSP) control may fall short of efficiency and bring negative impacts to non-priority approaches due to overusing priority grants. To reduce the frequency of activating TSP under high bus volumes, this study presents a new model, named INTEBAND, to facilitate the platooning of both passenger-cars and buses along arterials. The model integrates the conventional signal progression model with bus progression model to balance the travel delay of car-users and bus-passengers. Taking Jingshi road in Jinan for example, this study employs VISSIM for model evaluations. Further sensitivity analysis reveals that INTEBAND could clearly outperform the two conventional models in reducing network person delay when the ratio of bus-passengers and car-users exceeds a threshold of 1.5. Moreover, a multi-classifier model based on simulation results is to conveniently answer when INTEBAND can yield more benefit than conventional progression models.
Transit Signal Priority (TSP) control widely used at signalized intersections has been recognized as a practical strategy to improve the efficiency and reliability of bus operations. A proper TSP control can effectively reduce bus travel time at local arterials without bringing negative impacts to the network. This paper provides a comprehensive review of TSP controls by presenting two primary sections: priority control methods and system evaluations. Based on the different types of priority strategies, the existing control methods are sub-divided into two categories: passive control and active control. Particularly, the active control methods are further categorized as two groups: rule-based approach and model-based one. In the review section of system evaluations, this paper presents several effective ways found in the literature, including analytical evaluation, simulation test and field test. Also to further demonstrate the effectiveness of TSP in real-world applications, this study analyzes its field benefits in 24 cities around the world.
The development of Autonomous Vehicle or Self-Driving car integrates with the wireless communication technology which would be a big step for road transportation today. The autonomous crossing of an intersection with the autonomous vehicle will play a major role in the future of Intelligent Transportation System (ITS). The fundamental objective of this work is to manage autonomous vehicles crossing an intersection with no collisions; as well as maintaining that the car drives continuously, or to decrease the waiting time at the intersection. In this paper, a discrete model of an intersection is designed. The Vehicle to Infrastructure (V2I) communication is implemented to exchange information between a car and intersection manager. The safe trajectory of autonomous vehicles for the Autonomous Intersection Management is determined and presented using discrete mathematics.
The control of a network of signalized intersections is considered. Vehicles arrive in iid (independent, identically distributed) streams at entry links, independently make turns at intersections with fixed probabilities or turn ratios, and leave the network upon reaching an exit link. There is a separate queue for each turn movement at each intersection. These are point queues with no limit on storage capacity. At each time the control selects a ‘stage’, which actuates a set of simultaneous vehicle movements at given iid saturation flow rates. Network evolution is modeled as a controlled store-and-forward (SF) queuing network. The control can be a function of the state, which is the vector of all the queue lengths. A set of demands is said to be feasible if there is a control that stabilizes the queues, that is the time-average of every mean queue length is bounded. The set of feasible demands D is a convex set defined by a collection of linear inequalities involving only the mean values of the demands, turn ratios and saturation rates. If the demands are in the interior Do of D, there is a fixed-time control that stabilizes the queues. The max pressure (MP) control is introduced. At each intersection, MP selects a stage that depends only on the queues adjacent to the intersection. The MP control does not require knowledge of the mean demands. MP stabilizes the network if the demand is in Do. Thus MP maximizes network throughput. MP does require knowledge of mean turn ratios and saturation rates, but an adaptive version of MP will have the same performance, if turn movements and saturation rates can be measured. The advantage of MP over other SF network control formulations is that it (1) only requires local information at each intersection and (2) provably maximizes throughput. Examples show that other local controllers, including priority service and fully actuated control, may not be stabilizing. Several modifications of MP are offered including one that guarantees minimum green for each approach and another that considers weighted queues; also discussed is the effect of finite storage capacity.
This study is focused on capacity and travel times in a signalized corridor and bus lanes with intermittent priority (BLIPs). These strategies consist of opening the bus lane to general traffic intermittently when a bus is not using it. Although the benefits of such strategies have been pointed out in the literature, the activation phase has received little attention. In an attempt to fill this gap, the activation of BLIP strategies was studied analytically. To this end, the extended kinematic wave model with bounded acceleration was chosen. BLIP activation reduced capacity and increased the travel time of buses. However, even if this strategy seems to be counterproductive at first, it clearly increases the performance of transit buses on a larger scale.
The control of a network of signalized intersections is considered. A queuing
network with exogenous arrivals is used for modelling. Previous works
demonstrate that the so-called back-pressure control provides stability
guarantees under infinite queues capacities. In this paper, we highlight the
failing of state-of-the-art back-pressure control under finite capacities by
identifying sources of non work-conservation and congestion propagation. We
propose the use of a normalized pressure which guarantees work-conservation and
mitigates congestion propagation while ensuring fairness at low traffic density
and recovering original back-pressure as capacities grow to infinity. This
capacity-aware back-pressure control enables to handle higher arrival rates, as
indicated by simulation results, and keeps the key benefits of back-pressure:
ability to be distributed over intersections and O(1) complexity.
Bus rapid transit (BRT), characterized by modern vehicles, dedicated busway and applications of intelligent transportation systems (ITS) technologies, is increasingly considered as a cost‐effective approach of providing a high‐quality transport service. Many cities across the world have recently launched ambitious programmes of BRT system implementation with varying success. This paper intends to provide an overview of the recent developments of BRT across the globe, and discusses the current issues and debates relating to the land development impact of BRT. It considers in turn the impact of BRT examining technical performance, cost issues and land development impact. The paper concludes that appropriately designed and operated BRT systems offer an innovative approach to providing a high‐quality transport service, comparable to a rail service but at a relatively low cost and short implementation time. In common with other forms of mass transit, a full‐featured BRT has the potential to offer significant effects on land development; the literature review also indicates that more work is needed to investigate this.
Although streetcar systems benefit from a strong identity, they face considerable challenges as a result of mixed traffic operations. These problems have been compounded by growing urban auto traffic, which has increased competition for limited road space and time. Active traffic signal priority (TSP) has been identified as a cost-effective way to improve the management of traffic systems to make on-street public transport more reliable, faster, and more cost-effective. Although the implementation of TSP is growing throughout the world, relatively few studies have examined its application to streetcar-based systems. This paper reviews the experience with TSP in Melbourne, Australia, and Toronto, Canada. These cities run some of the world’s oldest and largest streetcar-based TSP systems. This paper describes the TSP systems adopted in the two cities, including key experiences. TSP performance is reviewed, and identified problems and issues are assessed. The review established that the TSP systems in the two cities have many similarities including the configuration of approach and request loop and stop line and cancel loop detection, the degree of priority provided, and the targeting of clearance phases for turning traffic at intersections. There are some slight differences in the handling of bunching trams and opposing tram movements, which are better handled in the Toronto case. The two systems see rather different futures for TSP development. Toronto is focused on full systemwide TSP implementation and advancement of TSP algorithms, whereas Melbourne aims to make priority more conditional on the degree of lateness of trams and on the degree of traffic congestion experienced.
This article presents a new method for explicitly computing solutions to a Hamilton-Jacobi partial differential equation for which initial, boundary and internal conditions are prescribed as piecewise affine functions. Based on viability theory, a Lax-Hopf formula is used to construct analytical solutions for the individual contribution of each affine condition to the solution of the problem. The results are assembled into a Lax-Hopf algorithm which can be used to compute the solution to the partial differential equation at any arbitrary time at no other cost than evaluating a semi-analytical expression numerically. The method being semi-analytical, it performs at machine accuracy (compared to the discretization error inherent to finite difference schemes). The performance of the method is assessed with benchmark analytical examples. The running time of the algorithm is compared with the running time of a Godunov scheme.
This article proposes a new approach for computing a semi-explicit form of the solution to a class of Hamilton-Jacobi (HJ) partial differential equations (PDEs), using control techniques based on viability theory. We characterize the epigraph of the value function solving the HJ PDE as a capture basin of a target through an auxiliary dynamical system, called ??characteristic system??. The properties of capture basins enable us to define components as building blocks of the solution to the HJ PDE in the Barron/Jensen-Frankowska sense. These components can encode initial conditions, boundary conditions, and internal ??boundary?? conditions, which are the topic of this article. A generalized Lax-Hopf formula is derived, and enables us to formulate the necessary and sufficient conditions for a mixed initial and boundary conditions problem with multiple internal boundary conditions to be well posed. We illustrate the capabilities of the method with a data assimilation problem for reconstruction of highway traffic flow using Lagrangian measurements generated from Next Generation Simulation (NGSIM) traffic data.
Bus operations on arterials are often hindered by traffic signals and car queues. Transit signal priority (TSP) strategies can be used to improve bus operations on arterials. Analytically quantifying the impacts of TSP in mixed traffic, where cars and buses share lanes, is challenging since car queues can slow down buses, while slow-moving buses can create bottlenecks for cars. Furthermore, computational costs increase significantly when considering an arterial with multiple intersections. To tackle these challenges, this paper first develops a dynamic programming framework to model and evaluate TSP along an arterial. Next, the algorithm is utilized to determine the changes to car and bus delays as a result of TSP implementation along an arterial, and the sensitivity of the algorithm to the signal timing plan, bus stop locations and dwell durations, and the bus headway is evaluated. Next, a bi-level optimization framework is proposed to determine the optimal location of TSP implementation along arterials. Finally, the results of integrating TSP with dedicated bus lanes is evaluated. The results suggest that the benefits of TSP largely depend on the signal setting, and bus stop location, and that as the bus headway decreases, the marginal benefit of providing TSP also decreases. Additionally, the results suggest that the specific intersection at which TSP is implemented can play a large role on its operational impacts. Finally, it is found that in some scenarios, the benefits of implementing TSP alone can be larger than implementing dedicated bus lanes alone.
Max-pressure traffic signal control has many desirable properties. It is analytically proven to maximize network throughput if demand could be served by any signal control. Despite its network-level stability properties, the control itself is decentralized and therefore easily computed by individual intersection controllers. Discussions with city engineers have suggested that a major barrier to implementation in practice is the non-cyclical phase actuation of max-pressure control, which can actuate any phase, in arbitrary order, to serve the queue(s) with highest pressure. This arbitrary phase selection may be confusing to travelers expecting a signal cycle, and is therefore unacceptable to some city traffic engineers. This paper revises the original max-pressure control to include a signal cycle constraint. The max-pressure control must actuate an exogenous set of phases in order, with each phase actuated at least one time step per cycle. Each cycle has a maximum length, but the length can be reduced if desired. Within those constraints, we define a modified max-pressure control and prove its maximum stability property. The revised max-pressure control takes the form of a model predictive control with a one cycle lookahead, but we prove that the optimal solution can be easily found by enumerating over phases. The policy is still decentralized. Numerical results show that as expected, the cyclical max-pressure control performs slightly worse than the original max-pressure control due to the additional constraints, but with the advantage of greater palatability for implementation in practice.
With the development of vehicle-to-infrastructure and vehicle-to-vehicle technologies, vehicles will be able to communicate with the controller at the intersection. Autonomous driving technology enables vehicles to follow the instructions sent from the controller precisely. Autonomous intersection management considers each vehicle as an agent and coordinates vehicle trajectories to resolve vehicle conflicts inside an intersection. This study proposes an autonomous intersection management algorithm called AIM-ped considering both vehicles and pedestrians which is able to produce the total optimal throughput when combined with max pressure control. This study analyzes the stability properties of the algorithm based on a simpler version of AIM-ped, which is a conflict region model of the autonomous intersection management. To implement the proposed algorithm in simulation, this study combines the max-pressure control with an existing trajectory optimization algorithm to calculate optimal vehicle trajectories. Simulations are conducted to test the effects of pedestrian demand on intersection efficiency. The simulation results show that delays of pedestrians and vehicles are negatively correlated and the proposed algorithm can adapt to the change in the pedestrian demand and activate vehicle movements with conflicting trajectories.
Autonomous intersection management (AIM) (which coordinates intersection movements to avoid signal phases) and dynamic lane reversal (DLR) (which frequently changes lane directions in response to time-varying demand) have previously been proposed for connected autonomous vehicles. A major open question for both is finding the optimal control policy. This paper develops a decentralized max-pressure policy that controls both AIM and DLR based on queue lengths on adjacent links. Using a stochastic queueing model, we prove that the max-pressure policy is also throughput-optimal; any demand that can be stabilized (queue lengths remain bounded) will be stabilized by the max-pressure policy. We show numerically that DLR significantly increases the stability region, particularly for asymmetric demand. Since the stochastic queueing model excludes some realistic aspects of traffic flow, we adapt the max-pressure control for simulation-based dynamic traffic assignment. Results on a city network show significant improvements from max-pressure AIM with and without DLR.
With the forecasted emergence of autonomous vehicles in urban traffic networks, new control policies are needed to leverage their potential for reducing congestion. While several effort s have studied the fully autonomous traffic control problem, there is a lack of models addressing the more imminent transitional stage wherein legacy and autonomous vehicles share the urban infrastructure. We address this gap by introducing a new policy for stochastic network traffic control involving both classes of vehicles. We conjecture that network links will have dedicated lanes for autonomous vehicles which provide access to traffic intersections and combine traditional green signal phases with autonomous vehicle-restricted signal phases named blue phases. We propose a new pressure-based, decentralized , hybrid network control policy that activates selected movements at intersections based on the solution of mixed-integer linear programs. We prove that the proposed policy is stable, i.e. maximizes network throughput, under conventional travel demand conditions. We conduct numerical experiments to test the proposed policy under varying proportions of autonomous vehicles. Our experiments reveal that considerable trade-offs exist in terms of vehicle-class travel time based on the level of market penetration of autonomous vehicles. Further, we find that the proposed hybrid network control policy improves on traditional green phase traffic signal control for high levels of congestion, thus helping in quantifying the potential benefits of autonomous vehicles in urban networks.
As every user knows buses tend to bunch. To alleviate this problem, transit agencies introduce slack into their schedules and then hold buses back to schedule at pre-established control points along their routes. Unfortunately, this practice retards buses and only works with low frequency systems; i.e., when the headways are long. For higher frequency systems, which effectively operate without a schedule, headway-based control strategies show promise but unfortunately, they also retard buses. To alleviate bus retardation in all its forms, transit signal priority (TSP) is commonly used. Curiously however, the potential of TSP to enhance bus control practices has not been explored in detail.
This paper evaluates a form of TSP designed for this purpose. In this version of TSP, which we call conditional signal priority (CSP), buses send priority requests only when the requests improve reliability. The evaluation includes both scheduled (low frequency) systems, and unscheduled (high frequency) systems operated by headways. A mathematical model based on Brownian motion is proposed for the former. This model allows us to optimize the CSP strategy and to develop formulas for the expectation and variance of the steady state deviations from the schedule–with CSP, with conventional TSP and with neither. Simulations confirm the accuracy of these findings. It is found that CSP improves reliability considerably and that it is better for traffic than TSP because it reduces the number of priority requests. For high frequency systems operated with a fixed number of buses CSP is shown to not just improve reliability and reduce the number of priority requests of TSP (by about 50%) but also to reduce the average headway by speeding up the buses.
Microscopic traffic simulation is an invaluable tool for traffic research. In recent years, both the scope of research and the capabilities of the tools have been extended considerably. This article presents the latest developments concerning intermodal traffic solutions, simulator coupling and model development and validation on the example of the open source traffic simulator SUMO.
Queue jumper lanes are a special type of bus preferential treatment that allows buses to bypass a waiting queue through a right-turn bay and then cut out in front of the queue by getting an early green signal. The performance of queue jumper lanes is evaluated under different transit signal priority (TSP) strategies, traffic volumes, bus volumes, dwell times, and bus stop and detector locations. Four TSP strategies are considered: green extension, red truncation, phase skip, and phase insertion. It was found that queue jumper lanes without TSP were ineffective in reducing bus delay. Queue jumper lanes with TSP strategies that include a phase insertion were found to be more effective in reducing bus delay while also improving general vehicle operations than those strategies that do not include this treatment. Nearside bus stops upstream of check-in detectors were preferred for jumper TSP over farside bus stops and nearside bus stops downstream of check-in detectors. Through vehicles on the bus approach were found to have only a slight impact on bus delay when the volume-to-capacity (v/c) ratio was below 0.9. However, when v/c exceeded 0.9, bus delay increased quickly. Right-turn volumes were found to have an insignificant impact on average bus delay, and an optimal detector location that minimizes bus delay under local conditions was shown to exist.
Connected vehicles give more precise and detailed information on vehicle movements, thus can be beneficial to provide priority to public transportation. This paper proposes a transit signal priority algorithm using connected vehicle information for multimodal traffic control. The algorithm can also be adapted to scenarios with near-side or far-side bus stops. Moreover, it can minimize either signal delay or schedule delay for buses while minimizing additional car delays. Simulation is conducted for different volume to capacity ratios, bus arrivals, bus occupancies, and penetration rates. Results show that this algorithm successfully reduces the total passenger delay. It is also shown that this algorithm is not sensitive to the assumed bus passenger occupancy, nor the estimation of bus dwell time, hence does not require accurate information on these parameters. Overall, this algorithm seems rather promising as it significantly reduces the delay of buses with minimal increase to the delay of cars in the conflicting approach.
The city of Portland, Oregon, the Tri-County Metropolitan Transportation District of Oregon, and the Transportation Research Institute at Oregon State University have been involved in the Powell Boulevard Pilot Project to evaluate bus priority at traffic signals. Two priority techniques were tested in the pilot project. Green extension-early green return was tested at far-side stop locations, and queue jump was tested at a near-side stop location. In addition, two bus detection technologies were tested, which used different methods of bus detection. The pilot project involved four intersections along a 2-mi section of Southeast Powell Boulevard. Extensive traffic-impact studies were carried out before and after implementation of the bus priority technology. The project results include a summary of the equipment evaluation and the results of the traffic survey.
Transit signal priority (TSP) is a vital aspect of the improvement of transit service. However, the effect of bus dwell time on TSP is often neglected, and few researchers have proposed a TSP strategy that predicts the bus dwell time and then implements bus priority. This study focused on the prediction of bus dwell time, which defined the bus arrival time at the intersection, and subsequently established a multiobjective TSP strategy that uses that prediction. The data extracted from the Changzhou, China, bus rapid transit (BRT) Line 2 were used to propose a hybrid model based on the autoregressive integrated moving average and the support vector machine to predict the dwell time. Next, the multiobjective TSP, with the real-time average passenger delay, the maximum queue length, and the exhaust emissions as its optimization objectives, was solved through the use of the fuzzy compromise approach. Finally, the strategy was evaluated with the microscopic simulation software VISSIM. The findings demonstrated that the prediction model produced satisfactory results, and the simulation results suggested that the proposed strategy could significantly reduce the intersection delay, the stop rate, and the exhaust emissions of BRT. Moreover, higher traffic flows corresponded to better benefits being achieved through this strategy. In addition, the delay, the queue length, and the exhaust emissions of general vehicle traffic would be effectively controlled. The findings of this study could be helpful to traffic managers in the development of appropriate signal timing strategies and the enhancement of operating efficiency and environmental quality at intersections.
Bus Rapid Transit (BRT) is growing in popularity throughout the world. The reasons for this phenomenon include its passenger and developer attractiveness, its high performance and quality, and its ability to be built quickly, incrementally, and economically. BRT also provides sufficient transport capacity to meet demands in many corridors, even in the largest metropolitan regions. In the United States, the development of BRT projects has been spurred by the Federal Transit Administration’s (FTA) BRT initiative. These projects have been undertaken, in part, because of the imbalance between the demand for “New Starts” funds and available resources. Decisions to make BRT investments should be the result of a planning process that stresses problem solving, addressing needs, and the objective examination of a full range of potential solutions, of which BRT is only one. Good planning practice means matching potential market characteristics with available rights-of-way. BRT involves an integrated system of facilities, services, amenities, operations, and Intelligent Transportation Systems (ITS) improvements that are designed to improve performance, attractiveness to passengers, image, and identity. Because they can be steered as well as guided, BRT vehicles can operate in a wide range of environments without forcing transfers or requiring expensive running way construction over the entire range of their operation. Through this flexibility, BRT can provide one-seat, high-quality transit performance over a geographic range beyond that of dedicated guideways. To the maximum extent practical, the system should transfer the service attributes of rail transit to BRT. Even where implementation of a comprehensive, integrated BRT system is not possible, many of its components can be adapted for use in conventional bus systems with attendant benefits in speed, reliability, and transit image/attractiveness. In summary, BRT is growing in popularity because it can be cost-effective and it works. This article describes BRT concepts and components, traces BRT’s evolution, gives its current status, and outlines some of the findings to date of the Transportation Research Board’s (TRB) Transit Cooperative Research Program (TCRP) A-23 project, “Implementation Guidelines for Bus Rapid Transit.”
This paper deals with traffic signal control with finite queue capacities in a discrete-time and stochastic setting. A so-called “pressure releasing policy” (PRP) is introduced to optimally release traffic pressure at every time slot, where the traffic pressure at each intersection incorporates knowledge of turning ratios and information of neighboring and ingress queues. PRP does not require knowledge of arrival rates. Moreover, it employs a set of weights satisfying a given condition to handle downstream queue spillover, and an algorithm is provided to generate one possible set of weights. Define the throughput region as the closure of the set of all arrival rate vectors that can be stably supported over the network under the assumption on infinite queue capacities. It is shown that PRP under finite queue capacities can still achieve the closed-loop stability with a reduction on the throughput region. The reduction is a function of weights and internal queue capacities, and PRP with finite but sufficiently large internal queue capacities can be arbitrarily close to recovering the throughput region.
Transit signal priority (TSP) is a control strategy that has been used extensively to improve transit operations in urban networks. However, several issues related to TSP deployment-including the effect of TSP on auto traffic and the provision of priority to transit vehicles traveling in conflicting directions at traffic signals-have not yet been addressed satisfactorily by existing control systems. This paper presents a real-time, traffic-responsive signal control system for signal priority on conflicting transit routes that also minimizes the negative effects on auto traffic. The proposed system determines the signal settings that minimize the total person delay in the network while assigning priority to the transit vehicles on the basis of their passenger occupancy. The system was tested through simulation at a complex signalized intersection located in Athens, Greece, that had heavy traffic demands and multiple bus lines traveling in conflicting directions. Results showed that the proposed system led to significant reductions in transit users' delay and the total person delay at the intersection.
This paper presents a person-based traffic responsive signal control system for transit signal priority (TSP) on conflicting transit routes. A mixed-integer nonlinear program (MINLP) is formulated, which minimizes the total person delay at an intersection while assigning priority to the transit vehicles based on their passenger occupancy. The mathematical formulation marks an improvement to previous formulations by ensuring global optimality for undersaturated traffic conditions and intersection design and traffic characteristics that lead to convex objective functions in reasonable computation time for real-time applications. The system has been tested for a complex signalized intersection located in Athens, Greece, which is characterized by multiple bus lines traveling in conflicting directions. Testing includes cases with deterministic vehicle arrivals at the intersection and emulation-in-the-loop simulation (EILS) tests that incorporate stochasticity in the vehicle arrivals. The results show that the proposed person-based traffic responsive signal control system reduces the total person delay at the intersection and effectively provides priority to transit vehicles, even when perfect information about the auto and transit arrivals at the intersection is not available.
In this paper, a person-capacity-based optimization method for the integrated design of lane markings, exclusive bus lanes, and passive bus priority signal settings for isolated intersections is developed. Two traffic modes, passenger cars and buses, have been considered in a unified framework. Person capacity maximization has been used as an objective for the integrated optimization method. This problem has been formulated as a Binary Mixed Integer Linear Program (BMILP) that can be solved by a standard branch-and-bound routine. Variables including, allocation of lanes for different passenger car movements (e.g., left turn lanes or right turn lanes), exclusive bus lanes, and passive bus priority signal timings can be optimized simultaneously by the proposed model. A set of constraints have been set up to ensure feasibility and safety of the resulting optimal lane markings and signal settings. Numerical examples and simulation results have been provided to demonstrate the effectiveness of the proposed person-capacity-based optimization method. The results of extensive sensitivity analyses of the bus ratio, bus occupancy, and maximum degree of saturation of exclusive bus lanes have been presented to show the performance and applicable domain of the proposed model under different composition of inputs.
Segregated transit lanes are an efficient means of improving transit reliability and speed on shared urban roads. A major limitation of these lanes, however, is their impact on road capacity and traffic congestion. Intermittent bus lanes (IBL) are an innovative concept for addressing this limitation. IBLs include variable message signs and flashing lights embedded in the pavement that warn motorists to avoid transit lanes only when buses are coming. This approach prioritizes transit while limiting impacts on other road users. If feasible, this approach may substantially increase the scope of transit priority in cities. However, feasibility of IBL is a major concern. A trial was undertaken in Lisbon, Portugal, in 2005 and 2006. Although the results were promising, more practical experience with IBL is required to justify its widespread implementation. This paper reviews the performance of a variation on the IBL concept, the dynamic fairway (DF) adopted for trams in Melbourne, Australia. The system was initiated in 2001 and is still operational. The paper documents the world’s first practical, ongoing experience with IBL-DF operation. Future plans for a Melbourne bus-based IBL called the “moving bus lane” are also presented. Overall, the performance of the Lisbon IBL trial appears to be better than that of the Melbourne DF. However, the circumstances of the two examples were different, including the road configuration, transit mode, levels of congestion, and the newness of the technologies involved. Significantly, both applications found good driver compliance with transit lanes, suggesting the IBL-DF concept has practical performance benefits.
This research developed and tested the concept of advanced detection and cycle length adaptation as a strategy for providing priority for transit vehicles. In a departure from control strategies that rely on detection only a few seconds in advance of the stopline, a control algorithm was developed in which transit vehicles are detected two to three cycles in advance of their arrival at an intersection stopline, and phase lengths were then constrained so that the transit-serving phase was green for a 40-s predicted arrival window. Methods were developed for selecting whether to extend or compress phase lengths to shift a green period to cover the arrival window. Adaptive control was combined with actuated control using traffic density and queue length estimation, transit stopline actuation, and peer-to-peer communication for coordination in the peak travel direction. The method was applied by simulation to Boston, Massachusetts' Huntington Avenue corridor, which is served by a light-rail line running partly in mixed traffic and partly in a median reservation. The prediction/adaptation algorithm resulted in 82% of the trains arriving during the green phase. This control strategy resulted in substantial improvements to transit travel time and regularity with negligible impacts on private traffic and pedestrians, and was found to be more effective than simple preemption.
Recent progress in technology has facilitated the design, testing, and deployment of traffic signal priority strategies for transit buses. However, a clear consensus has not emerged about the evaluation of these strategies. Each agency implementing these strategies can have differing goals, and there are often conflicting issues, needs, and concerns among the various stakeholders. To assist in the evaluation of such strategies an evaluation framework and plan was developed that provides a systematic method to assess potential impacts. The use of this framework and plan is illustrated on the Columbia Pike corridor in Arlington, Virginia, with the use of the INTEGRATION simulation package. In building on previous efforts on this corridor, the work presents a method of simulating conditional priority to late buses to investigate the impacts of priority on service reliability. By using the measures developed in this research, a conditional priority strategy designed to increase bus service reliability without resulting in severe traffic-related impacts was tested. Simulation results indicated statistically significant improvements of 3.2% in bus service reliability and 0.9% for bus efficiency, whereas negative traffic-related impacts were found in the form of increased overall delay to the corridor of 1.0% on a vehicle basis or 0.6% on a person basis. These results are also comparable and consistent with the results of other research.
This paper presents the findings of a study evaluating the potential benefits of implementing transit signal priority along the Columbia Pike arterial corridor, in Arlington, Va. The study uses the INTEGRATION microscopic traffic simulation model to evaluate the impact of a number of alternative priority strategies on both the prioritized buses and general traffic during the morning peak and midday traffic periods. The transit priority strategies considered include providing priority to express buses traveling along Columbia Pike, to both express and regular buses along the arterial, and to all buses within the study corridor. The priority logic that is considered in the study provides simple green extensions and green recalls within a fixed-time traffic signal control environment. The simulation results indicate that the buses provided with priority would typically benefit from transit priority, but that these benefits may be obtained at the expense of the overall traffic, particularly when traffic demand is high. However, it is also found that in periods of lesser demand, the overall negative impacts could be negligible due to the availability of spare capacity at the signalized intersections.
This paper evaluates strategies for operating buses on signal-controlled arterials using special lanes that are made intermittently available to general traffic. The advantage of special bus lanes, intermittent or dedicated, is that they free buses from traffic interference; the disadvantage is that they disrupt traffic.We find that bus lanes with intermittent priority (BLIPs), unlike dedicated ones, do not significantly reduce street capacity. Intermittence, however, increases the average traffic density at which the demand is served, and as a result increases traffic delay. These delays are more than offset by the benefits to bus passengers as long as traffic demand does not exceed by much the maximum flow possible on the non-special lanes; the smaller the excess the better. BLIPs are not intended for roadways nearing or in excess of capacity.The main factors determining whether an intermittent system saves time are: the traffic saturation level; the bus frequency; the improvement in bus travel time achieved by the special lane; and the ratio of bus and car occupant flows. In some scenarios where a dedicated bus lane could not be operated, a BLIP can save to bus and car occupants together as much as 20 persons-min of travel per bus-km. The required conditions for this to happen are quite particular. Typical savings are smaller. Formulae are given.
Genetic algorithms have been shown to be effective tools for optimizations of traffic signal timings. However, only one tool that combines Genetic Algorithms and traffic microsimulation has matured to a commercial deployment: Direct CORSIM optimization, a feature of TRANSYT-7F. This paper presents a genetic algorithm formulation that builds on the best of the recorded methods, by extending their capabilities. It optimizes four basic signal timing parameters and transit priority settings using VISSIM microsimulation as the evaluation environment. The program is the first optimization tool that optimizes traffic control transit priority settings on roads with both private and transit traffic. These settings are optimized either simultaneously with the basic signal timings or separately, thus improving overall traffic operations without changing existing basic signal timings. The optimization has been tested on two VISSIM models: a suburban network of 12 signalized intersections in Park City, UT, and an urban corridor with transit operations in Albany, NY. The results show that timing plans optimized by the genetic algorithm outperformed the timing plans from the field and SYNCHRO. Optimization of the transit priority settings shows that adjustment of these settings has significant impact on travelers delay on the corridors with mixed traffic and transit operations.
This article describes the development and implementation of adaptive transit signal priority (TSP) on an actuated dual-ring traffic signal control system. After providing an overview of architecture design of the adaptive TSP system, the article presents an adaptive TSP optimization model that optimizes green splits for three consecutive cycles to minimize the weighted sum of transit vehicle delay and other traffic delay, considering the safety and other operational constraints under the dual-ring structure of signal control. The model is illustrated using a numerical example under medium and heavily congested situations. The findings from a field operational test are also reported to validate and demonstrate the developed TSP system. At a congested intersection, it is found that the average bus delay and average traffic delay along the bus movement direction were reduced by approximately 43% and 16%, respectively. Moreover, the average delay of cross-street traffic was increased by about 12%.
The stability of a queueing network with interdependent servers is
considered. The dependency among the servers is described by the
definition of their subsets that can be activated simultaneously.
Multihop radio networks provide a motivation for the consideration of
this system. The problem of scheduling the server activation under the
constraints imposed by the dependency among servers is studied. The
performance criterion of a scheduling policy is its throughput that is
characterized by its stability region, that is, the set of vectors of
arrival and service rates for which the system is stable. A policy is
obtained which is optimal in the sense that its stability region is a
superset of the stability region of every other scheduling policy, and
this stability region is characterized. The behavior of the network is
studied for arrival rates that lie outside the stability region.
Implications of the results in certain types of concurrent database and
parallel processing systems are discussed