Transit Signal Priority Control Research Articles

New Fuzzy Bus Signal Priority Control System Design Based on Wireless Sensor Networks

Bus rapid transit (BRT) is one of most effective methods to optimize urban traffic. Transit signal priority (TSP) is very important to increase the efficiency of BRT. TSP provides priority signals for public transport vehicles based on the advanced inspection and communication system. In this paper, TSP control system is designed with wireless sensor networks, which can support real-time information exchanging and bus positioning function through critical time syn- chronization. Taking into consideration that TSP maybe interfere other vehicles when giving the priority to public transport vehicles, this paper puts forward a fuzzy control algorithm which decides what kinds of traffic signals support based on the bus information. And this algorithm can reduce average vehicle delay at intersection, and realize the optimal control balance between bus priority and other vehicles.

Transit signal priority control at signalized intersections: a comprehensive review

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 significantly 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 is further categorized as two groups: rule-based approach and model-based one. In the review section of system evaluations, this paper presents three effective ways found in the literature: analytical evaluation, simulation test, and field test. Also to further demonstrate the effectiveness of TSP in practice, this study analyzes its field benefits in 24 cities around the world.

Method of Transit Signal Priority Control Based on the Prediction of Bus Dwell Time at Bus Station

A method is put forward to overcome the disadvantages of traditional transit priority control which lack the information of residence time on bus station to achieve the better target of transit priority. Under the background that the SCM and RFID have been wildly used, and it has been a trend for intelligent traffic system to combine these two technologies organically. We establish a new combined forecasting model to predict the bus dwell time on bus station by collecting the bus station information, analyzing the time series features of bus dwell time and studying the mechanism of ES and RBF models. According to the prediction the signal is adjusted correspondingly. Thus we achieve the goal of enhancing traffic efficiency and simultaneously decrease the negative effect to the social vehicles.

A Real-Time Transit Signal Priority Control Model Considering Stochastic Bus Arrival Time

Transit signal priority (TSP) strategy gives transit vehicles preferential treatments to move through an intersection with minimum delay. To produce a good TSP timing, advance planning with enough look-ahead time is the key. This,however, means added uncertainty about bus arrival time at stop bar. In this paper, we proposed a stochastic mixed-integer nonlinear program (SMINP) model as the core component of a real-time TSP control system. The model adopts a novel approach to capture the impacts of the priority operation to other traffic by using the deviations of the phase split times from the optimal background split times. In addition, the model explicitly accounts for the randomness of the bus' arrival time by considering the bus stop dwell time and the delay caused by standing vehicle queues. The SMINP is implemented in a simulation evaluation platform developed using a combination of a microscopic traffic simulator and a commercial optimization solver. Comparison analyses were performed to compare the proposed control model with the state-of-the-practice TSP system [i.e., ring-barrier controller (RBC)-TSP]. The results showed the SMINP has yielded asmuch as 30% improvement of bus delay compared with RBC-TSP in a single-bus case. In a multiple-bus case, SMINP handles the bus priority request much more effectively under congested traffic conditions.

A Study of an Adaptive Two-Phrase Signal Control Strategy for Resolving Conflicting Transit Signal Priority Calls

This paper presents the development and evaluation of an adaptive two-phrase signal control strategy to resolve conflicting Transit Signal Priority (TSP) requests. The strategy was designed to provide adaptive transit signal priority control, using vehicle systems and existing traffic control devices. In this paper, the strategys efficiency was tested using a micro-simulation software VISSIM and build one arterial road contains five intersections and serves more than twenty conflicting bus lines. The VAP module was used to control TSP of conflicting requests. In the simulation, actual data was used. Finally, control efficiency about adaptive signal control strategy is discussed. The results show that the presented strategy can improve the operation efficiency of bus corporations. This signal control strategy reduced the travel delay time by 33 % to 55% of transit, while has little impact on private traffic. The strategy shows promising results. In addition, with minor upgrades, it can be applied to any type of intersection.

Development and Evaluation of Adaptive Transit Signal Priority Control with Updated Transit Delay Model

Transit signal priority (TSP) strategies are widely used to reduce bus travel delay and to increase bus service reliability. State-of-the-art strategies enable dynamic (and optimal), rather than predetermined, TSP plans to reflect real-time traffic conditions. These dynamic plans are called adaptive TSP. Existing adaptive TSP strategies normally use a performance index (PI), which is a weighted summation of all types of delays, to evaluate each candidate TSP plan and the weights to reflect the corresponding priority. The performance of an adaptive TSP depends on three factors: delay estimation, weights determination, and optimization formulation. In this context, there are three key academic contributions: (a) an enhanced bus delay estimation model based on advance detection, (b) a mechanism to adjust the PI weights dynamically to reflect the changing necessity of TSP under different conditions, and (c) TSP optimization formulated into a quadratic programming problem with an enhanced delay-based PI to obtain global optimization with the use of MATLAB solvers. In addition, an adaptive TSP simulation platform using a full-scale signal simulator, ASC/3, in VISSIM was developed. The optimal TSP plans were granted or were rejected on the basis of TSP events, such as check-in, check-out, and multiple TSP requests. Through a case study in VISSIM, this research found that, compared with conventional active TSP strategies, the new adaptive TSP strategy could further reduce bus travel time while maintaining a better balance of service on non-TSP approaches along a 7.4-km bus corridor in Edmonton, Alberta, Canada.

Development of Model-based Transit Signal Priority Control for local Arterials

This paper presents a transit signal priority (TSP) model designed to benefit both bus riders and passenger-car users. Most of conventional priority methods are applied at the isolated intersection. However, this kind of control strategies may failed to reduce the travel time since the prioritized buses have to stop at the downstream intersections. Therefore, along the line of headway-based research, this study intends to develop a new TSP control approach with the concerns of bus passenger delay on the entire arterial. Moreover, a basic method for queue length estimation is presented to evaluate the impacts of TSP control on passenger cars. The control objective is to minimize bus passenger waiting time at the downstream bus stop, simultaneously ensuring the total person delay of entire intersection is not increased. Using the microscopic simulation, the proposed strategy has shown its benefits in reducing bus passenger waiting time and total intersection delay.

Bus detection based on sparse representation for transit signal priority

Transit signal priority (TSP), which is one of the most important issues in intelligent transportation systems, aims to provide priority signals with an advanced inspection system to public transport vehicles. In this paper, by introducing the advanced object detection technique into intelligent transport systems, we propose an automatic bus detection algorithm and apply it to the transit signal priority (TSP) system. The contributions of this paper fall into two folds: (1) we propose a bus detection algorithm. In this algorithm, an illumination-independent color feature is used for bus detection, which is useful in practical illumination environments. In addition, the widely-used sparse representation technique is extended to cost-sensitive kernel sparse representation, that can effectively combine different features for bus detection. (2) A transit signal priority control scheme is proposed based on the bus detection results. This control scheme optimizes the traffic lights signal according to whether a bus is coming or not. Experimental and simulation results show that the proposed intelligent TSP system based on bus detection is effective.

Rule-based transit signal priority control method using a real-time transit travel time prediction model

An advanced rule-based transit signal priority (TSP) control method is presented in this paper. An online transit travel time prediction model is the key component of the proposed method, which enables the selection of the most appropriate TSP plans for the prevailing traffic and transit condition. The new method also adopts a priority plan redevelopment feature that enables modifying or even switching the already implemented priority plan to accommodate changes in the traffic conditions. The proposed method utilizes conventional green extension and red truncation strategies and also two new strategies including green truncation and queue clearance. The new method is evaluated against a typical active TSP strategy and also the base case scenario assuming no TSP control in microsimulation. The evaluation results indicate that the proposed method can produce significant benefits in reducing the bus delay time and improving the service regularity with negligible adverse impacts on the non-transit street traffic.

Development and Evaluation of Algorithm for Resolution of Conflicting Transit Signal Priority Requests

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.

Predictive Strategy for Transit Signal Priority at Fixed-Time Signalized Intersections: Case Study in Nanjing, China

It is essential to give priority to transit vehicles at signalized intersections in the megacities of China such as Nanjing. This study developed a transit signal priority (TSP) strategy for fixed-time signals with the use of techniques for advanced and continuous vehicle detection and prediction of arrival time. In the proposed model, transit vehicles were traced once they were positioned at one intersection and were advancing to the subject intersection. For instance, the location and the speed of a transit vehicle were measured every few seconds, and the prediction was made simultaneously by a model integrating historic and real-time data. With the predicted arrival time, the length of the transit signal phase would be adjusted to give the green light to the transit vehicle. The proposed strategy was evaluated with VISSIM simulation and compared with conventional TSP strategies (green extension and early green) in a typical roadway and traffic setting of Nanjing. Results indicated that the conventional TSP strategy could hardly result in significant improvement in transit delay and that the proposed strategy significantly reduced the transit travel time. Despite these findings, the adverse impacts on the delay of other road traffic by TSP could not be ignored. Results of the simulation model indicated that the proposed predictive TSP strategy was less disruptive to other traffic and that its performance in nonpeak periods was superior to that in peak periods. The results also suggest that a well-designed algorithm for the prediction of arrival time is the key to success in implementing predictive TSP control.

Development of Dynamic Transit Signal Priority Strategy

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 effect of queuing representations on modeling transit signal priority systems in mixed mode operation

AbstractThis paper examines quantitatively the effect of modeling assumptions on transit signal priority (TSP) control in mixed mode operation. The primary sources of delays to buses at intersections are queues formed at intersections (queuing delay) and traffic signals (signal delay). We show that models based on point queues and models based on spatial queues would lead to different control strategies and yield different results for the benefit to buses in terms of delay reduction. Situations in which the spatial content of queues is crucial to modeling TSP systems are identified in the paper. We conclude that given that models with the spatial queue representation are more realistic in capturing the queuing dynamics at intersections they should be incorporated in the decision‐making process for initiating priority calls in a TSP system.

Advanced Transit Signal Priority Control with Online Microsimulation-Based Transit Prediction Model

An advanced transit signal priority (TSP) control method is presented: it provides priority operation in response to real-time traffic and transit conditions. A high-performance online microscopic simulation model was developed for the purpose of predicting transit travel time along an intersection approach. The proposed method was evaluated through application to a hypothetical intersection with a nearside bus stop. The performance of the proposed method was compared with that of normal signal operation without TSP and a conventional signal priority method. The experimental results indicated that the developed method provided efficient and effective priority operation for both transit vehicles and automobiles. The proposed method significantly reduced transit vehicle delays as well as side-street traffic delay compared with conventional active priority control.

Advanced Transit Signal Priority Control with Online Microsimulation-Based Transit Prediction Model

An advanced transit signal priority (TSP) control method is presented: it provides priority operation in response to real-time traffic and transit conditions. A high-performance online microscopic simulation model was developed for the purpose of predicting transit travel time along an intersection approach. The proposed method was evaluated through application to a hypothetical intersection with a nearside bus stop. The performance of the proposed method was compared with that of normal signal operation without TSP and a conventional signal priority method. The experimental results indicated that the developed method provided efficient and effective priority operation for both transit vehicles and automobiles. The proposed method significantly reduced transit vehicle delays as well as side-street traffic delay compared with conventional active priority control.