Traffic Signal Operations Research Articles

통행시간과 점유율 기반의 실시간 신호운영 알고리즘

본 연구에서는 통행시간과 점유율의 융합 정보를 이용하는 새로운 실시간 신호제어 알고리즘을 제시하였다. 교통정보시스템의 통행시간 정보를 신호운영에 적용하였으며, 통행시간으로 부터 산정한 포화도를 신호제어에 이용하기 위한 프로세스를 개발하였다. 결정적 지체모형을 이용해 통행시간으로부터 대기행렬 길이를 생성하고, 대기행렬 길이를 다시 포화도로 변환하는 과정이 적용되었다. 또한 통행시간 기반 포화도와 루프검지기 포화도를 융합해 신호시간이 산정되도록 하였다. 신호제어 알고리즘의 효과평가를 위해 미시적 시뮬레이션 분석을 시행하였으며, 과포화 상태에서 기존 루프검지기 기반 실시간 신호제어 대비 최대 27%의 지체 감소 효과를 확인하였다. 또한 과포화 및 검지기 고장상황에 대한 효과적이고, 유용한 대응이 가능함을 확인하였다. 본 연구에서는 교통신호제어시스템과 교통정보시스템의 교통정보 통합이용 방안을 제시하였다는데 의의가 있겠다. This research suggested a new real-time traffic signal control algorithm using fusion data of the travel time and the occupancy rate. This research applied the travel time data of traffic information system to traffic signal operation, and developed the signal control process using the degree of saturation that was estimated from the travel time data. This algorithm estimates a queue length from the travel time based on a deterministic delay model, and includes the process to change from the queue length to the degree of saturation. In addition, this model can calculate the traffic signal timings using fusion data of the travel time and the occupancy rate based on the saturation degree. The micro simulation analysis was conducted for effectiveness evaluation. We checked that the average delay decreased by up to 27 percent. In addition, we checked that this signal control algorithm could respond to a traffic condition of oversaturation and detector breakdown effectively and usefully. This research has important contribution to apply the traffic information system to traffic signal operation sectors.

Parsimonious shooting heuristic for trajectory design of connected automated traffic part II: Computational issues and optimization

Advanced connected and automated vehicle technologies enable us to modify driving behavior and control vehicle trajectories, which have been greatly constrained by human limits in existing manually-driven highway traffic. In order to maximize benefits from these technologies on highway traffic management, vehicle trajectories need to be not only controlled at the individual level but also coordinated collectively for a stream of traffic. As one of the pioneering attempts to highway traffic trajectory control, Part I of this study (Zhou et al., 2016) proposed a parsimonious shooting heuristic (SH) algorithm for constructing feasible trajectories for a stream of vehicles considering realistic constraints including vehicle kinematic limits, traffic arrival patterns, car-following safety, and signal operations. Based on the algorithmic and theoretical developments in the preceding paper, this paper proposes a holistic optimization framework for identifying a stream of vehicle trajectories that yield the optimum traffic performance measures on mobility, environment and safety. The computational complexity and mobility optimality of SH is theoretically analyzed, and verifies superior computational performance and high solution quality of SH. A numerical sub-gradient-based algorithm with SH as a subroutine (NG-SH) is proposed to simultaneously optimize travel time, a surrogate safety measure, and fuel consumption for a stream of vehicles on a signalized highway section. Numerical examples are conducted to illustrate computational and theoretical findings. They show that vehicle trajectories generated from NG-SH significantly outperform the benchmark case with all human drivers at all measures for all experimental scenarios. This study reveals a great potential of transformative trajectory optimization approaches in transportation engineering applications. It lays a solid foundation for developing holistic cooperative control strategies on a general transportation network with emerging technologies.

Detector-Free Signal Offset Optimization with Limited Connected Vehicle Market Penetration: Proof-of-Concept Study

Connected vehicle (CV) data have the potential to transform traffic signal operations, but the success of control methods that are based on CV data depends on the level of market penetration. Recent studies of real-time operational strategies in CV environments suggest that penetrations exceeding 20% is required. This study explored the feasibility of using CV data to generate arrival profiles for optimizing arterial progression. Applications to offline (3-h analysis period) and online (15-min analysis period) offset optimization were considered. Vehicle arrival profiles obtained from real-world measurement were used as a basis for comparison. Subsampled distributions were used to estimate the potential distributions that might be obtained from CVs, and these distributions were statistically analyzed to explore the effects of penetration rate, analysis period, and traffic volume. For selected penetration rates ranging from 0.1% to 50%, the subsampled distributions were used to optimize the corridor, and the results were evaluated in the complete data model. The results show that over a 3-h window, successful offline optimization can be achieved with a CV penetration rate as low as 1%. Layering multiple days of data might allow offline optimization with penetration rates as low as 0.1%. Online optimization with 15-min windows requires somewhat higher penetration rates of at least 5%. The results suggest that early applications of CV data may be possible at very low levels of market penetration. In corridors with a high penetration rate of connected mobile devices, some private-sector probe data services may be on the cusp of providing the necessary data to facilitate detector-free optimization.

An Effects of Signal Phase Plan on the Traffic Signal Operation of 4-legged Intersection

This study analysis traffic phase order alternatives to maximize throughput. According to theoretical analysis alternative2(EW: left turn after through, NS: through after left turn) and alternative5(EW: through after left turn, NS: left turn after through) can minimize the maximum delay. Both alternatives split the phase that have the same destination link under the whole cycle length. This shows that phase order alternative can effect to the fully saturated intersection. In side of simulation analysis by microscopic traffic simulator PTV VISSIM F 7.0, each phase order alternatives can`t effect throughput under the non saturated condition. However under the saturated condition, the average controlled delay of the intersection has been changed by phase order alternatives. The simulation analysis shows that alternative2 and alternative5 increase throughput 3.8% to 5.1% under the saturated condition.

A simulation-based approach in determining permitted left-turn capacities

A fundamental objective of traffic signal operations is the development of phasing plans that reduce delays while maintaining a high level of safety. One issue of concern is the treatment of left-turn phasing, which can operate as a protected movement, a permitted movement yielding to conflicting traffic, a combination protected–permitted movement or as a split-phase intersection. While protected-only movements can improve safety for the turning movement, they can also increase delays and congestion at the intersection. Most states maintain independent guidance for determining left-turn phasing; however, the most common identified guidance for protected left-turn phases is using a threshold based on the cross product of the left-turn volume and opposing through movements. The use of the cross product has been questioned recently as an indicator for determining phase selection. Based on simulation analysis within this research, the cross product is shown to be a poor indicator of left-turn capacity and congestion at the intersection.This research proposes a simplified single variable exponential model to determine left-turn capacity based on opposing volume and percent green time to determine left-turn capacity thresholds for protected left-turn phasing. The model is developed based on observed capacity from 450 VISSIM microsimulation scenarios which evaluated varying opposing volume, opposing number of lanes, cycle lengths and green time splits. Validation of the model based on complex Highway Capacity Manual procedures, indicates that the proposed model provides similar correlation to observed capacities. Finally, a nomograph is developed which presents the model in a simple form for interpretation and application by practicing traffic engineers, when required to determine left-turn phasing options. This procedure allows simple determination based on minimum input data needs similar to the cross product determination, without the need for complex hand calculations or computing requirements of the Highway Capacity Manual.

An Importance-Performance Analysis(IPA) for Bus Users Travel Time by Using Structural Equation Model(SEM)

현재 수립된 대부분의 대중교통관련 계획에서는 투자우선순위를 결정함에 있어 중요도(importance) 분석 없이 시설물 조사와 설문 조사를 통해 시설물에 대한 설치율과 만족도(performance)가 낮은 항목을 우선적으로 고려하여 투자순위를 결정하고 있다. 본 연구에서는 구조방정식모형(SEM: Structural Equation Model)을 활용하여 버스 이용자의 통행시간에 대한 중요도와 만족도를 동시에 고려할 수 있는 중요도-만족도 분석(IPA: Importance-Performance Analysis) 기법을 제시하였다. 수도권과 비수도권을 대상으로 버스이용자에 대한 IPA 분석을 실시한 결과 수도권의 경우 우선적인 개선이 필요한 항목으로는 버스정차횟수, 교차로수, 배차간격, 승차대기시간 및 신호체계 순서로 분석되었고, 비수도권의 경우 수도권의 역순인 신호체계, 승차대기시간, 배차간격, 버스전용차로 및 교차로수 순서로 분석되었다. In most public transportation related master plans, decisions for investment priorities are initially made by facilities with lower installation rates or lower satisfaction (performance) levels. In general, the decisions are made without conducting importance factor analysis. In this study, a combined method of importance-performance analysis (IPA) model for bus users related in travel time is proposed by using Structural Equation Model (SEM). The results of the IPA for Metropolitan users show that the categories need improvement are number of bus stops, number of intersections, headways, waiting times for boarding and traffic signal operations in order. On the other hand, Non-Metropolitan uses show that the categories need improvement are traffic signal operations, waiting times for boarding, headways, bus exclusive lanes and number of intersections that is in reverse order to Metropolitan users.

Fine-Tuning Time-of-Day Transitions for Arterial Traffic Signals

Time-of-day operation (i.e., the implementation of different signal settings at different times of the day) is commonly used in traffic signal operation to accommodate time-dependent traffic patterns. In the current practice of signal retiming, traffic engineers rely largely on engineering judgments to determine the proper time-of-day transitions; this approach may lead to inefficient operations. For such a deficiency to be reduced, in this research an easy-to-use approach was proposed to fine-tune time-of-day transitions for signalized arterials. The optimal transition points were determined through the evaluation of total delays on the basis of a Highway Capacity Manual (HCM) formula. To account for the impact of the coordination and actuation of arterial signals, the researchers extended HCM formula with a simple but effective model to estimate the green duration of actuated traffic signals. The proposed approach was demonstrated and validated with a case study that used traffic data collected from the SMART SIGNAL system.

Online Implementation and Evaluation of Weather-Responsive Coordinated Signal Timing Operations

This paper presents the development and application of weather-responsive traffic management strategies and tools to support coordinated signal timing operations with traffic estimation and prediction system (TREPS) models. First, a systematic framework for implementing and evaluating traffic signal operations under severe weather conditions was developed, and activities for planning, preparing, and deploying signal operations were identified in real-time traffic management center (TMC) operations. Next, weather-responsive coordinated signal plans were designed and evaluated with the TREPS method and a locally calibrated network. Online implementation and evaluation was conducted in Salt Lake City, Utah—the first documented online application of TREPS to support coordinated signal operation in inclement weather. The analysis results confirm that the deployed TREPS, which is based on DYNASMART-X, is able to help TMC operators test appropriate signal timing plans proactively under different weather forecasts before deployment and is capable of using real-time measurements to improve the quality and accuracy of the system's estimations and future predictions through detectors and roadside sensor coverage.

Traffic Signal Battery Backup Systems

A stable supply of power is a critical element in reliable traffic signal operation. Battery backup systems (BBSs) are often used to prevent traffic signals from experiencing power disruption. BBSs are designed through the use of engineering judgment because of a lack of operational requirements and well-defined performance measures. New high-resolution traffic controllers have the ability to record event-based data for power failures and traffic counts at a resolution of 0.1 s. With these high-resolution data, this paper proposes performance-based investment programming that uses the average annual signal downtime (AASD) over the analysis period as a performance measure. The AASD is stochastically estimated through the use of hazard-based duration models developed with power failure data. A volume and functional class–weighted stochastic optimization scheme is then presented for a BBS planning project, in which battery capacity is sized to minimize the AASD for a network under given budget constraints.

Traffic Signal System Misconceptions across three Cohorts

Both research evidence and theories of situated knowledge suggest that students are not prepared for the engineering workforce upon graduation from engineering programs. Concept inventory results from diverse fields also suggest that students do not understand fundamental concepts of engineering, mathematics, and science. These concerns may result from different knowledge deficiencies: one from a lack of conceptual understanding and the other from a lack of applied knowledge. In an attempt to explain the patterns in misconceptions across three cohorts, the research goals of this paper are to identify misconceptions (knowledge about phenomena that are persistent and incorrect) related to traffic signal operations and design across the cohorts of novice engineering students, expert engineering students, and practicing engineers. Results indicate three misconception patterns (decreasing, increasing, and no change) across the three cohorts. The pattern of decreasing misconception can be explained by a traditional model of learning that suggests improved understanding with additional instruction and student time on task. The pattern of increasing misconception appeared for concepts that are particularly complex and confounding; practicing engineers produce much more complex answers that are mostly correct but include leaps and speculations not yet proven in the literature. Misconception frequencies that stay the same tend to include topics that do not have required national standards or that are buried in automated processes. The process of identifying and documenting misconceptions that exist across these cohorts is a necessary step in the development of a data-driven curriculum. An example of a conceptual exercise developed from four misconceptions identified in this study is also demonstrated.

Estimation of Delay and Fuel Loss during Idling of Vehicles at Signalised Intersection in Ahmedabad

The fuel consumption of the vehicles is increasing day by day as a result of enhanced trip lengths, personal mode of transport and congested intersections. When the vehicles are waiting for their turn to cross the intersection at signals, the drivers normally keep their vehicle's engine on and a result of this extra fuel is consumed. This small amount of fuel wasted aggregated over a number of cycles per day, number of days per year and number of signalized intersections results in a huge quantity of fuel. Measuring delay is important in computing the level of service provided to road users at signalized intersection. Intersection delays may include queue delay and control delay. The signalized intersection capacity and LOS estimation procedures are built around the concept of average control delay per vehicle. Control delay is the portion of the total delay attributed to traffic signal operation for signalized intersections and this delay can be categorized into deceleration delay, stopped delay and acceleration delay. In India mainly two methods were using to estimate control delay those are field measurement and theoretical/analytical measurement. Field measurement of control delay includes the use of test-car observations, path tracing of individual vehicles, and the recording of arrival and departure volumes at Intersection but this expensive for long period. Analytical delay models of traffic systems are structured in a demand-supply framework and these models deals with the macroscopic method to estimate delay measurements. Whereas micro simulation based models are capable to estimate the individual delay of each vehicle at intersection and these models works on car following theory. The main objective of present study is to estimate delay and fuel loss during idling vehicles at signalized intersections in Ahmedabad city. For estimation of delay at intersections, typical corridor on drive in road in Ahmedabad city has been considered. Drive in road is one the busiest urban arterial corridor and this corridor connects the traffic coming from Thaltej intersection to Vijay Char Rasta Intersection.16hours classified traffic volume count surveys were conducted at four intersections falls in study corridor to observe the traffic volume and traffic composition. GPS based Velocity-Box (V-Box) apparatus was fitted in car to identify the existing speed and delay characteristics on the study corridor. VISSIM micro simulation software was adopted to simulate the traffic movement on the study corridor and to estimate the total delay including idling delay at the intersection. Base Case model was developed for the study corridor and the model has been validated on statistical grounds by using GEH statistics. The GEH is a widely used statistics for comparing the modelled values and observed values evolved through simulation tools. After validation, the developed model has been deployed to estimate the idling delay of each vehicle type at the intersection and this was compared with the observed data collected through floating car method. Fuel loss due to idling of vehicles at each intersection was estimated by considering available fuel loss data exists with CSIR-CRRI. Mitigation measures such as grade separated intersection were proposed in this study to reduce the fuel consumption on the study corridor. Further these mitigation measures were implemented in VISSIM model to quantify the reduction in fuel loss due to idling at each intersection. Comparative evaluation was made between base case and proposed scenario for the study corridor. It can be concluded that the micro simulation based technique is the most suited approach to estimate idling delay at the intersection. It was concluded from the study that minor geometric improvements and stabilizing the signal timings suggested for the corridor seems to reduce the delays and idling time drastically on the study corridor.

Traffic Signal Control with Connected Vehicles

The operation of traffic signals is currently limited by the data available from traditional point sensors. Point detectors can provide only limited vehicle information at a fixed location. The most advanced adaptive control strategies are often not implemented in the field because of their operational complexity and high-resolution detection requirements. However, a new initiative known as connected vehicles allows the wireless transmission of the positions, headings, and speeds of vehicles for use by the traffic controller. A new traffic control algorithm, the predictive microscopic simulation algorithm, which uses these new, more robust data, was developed. The decentralized, fully adaptive traffic control algorithm uses a rolling-horizon strategy in which the phasing is chosen to optimize an objective function over a 15-s period in the future. The objective function uses either delay only or a combination of delay, stops, and decelerations. To measure the objective function, the algorithm uses a microscopic simulation driven by present vehicle positions, headings, and speeds. The algorithm is relatively simple, does not require point detectors or signal-to-signal communication, and is completely responsive to immediate vehicle demands. To ensure drivers' privacy, the algorithm does not store individual or aggregate vehicle locations. Results from a simulation showed that the algorithm maintained or improved performance compared with that of a state-of-the-practice coordinated actuated timing plan optimized by Synchro at low and midlevel volumes, but that performance worsened under saturated and oversaturated conditions. Testing also showed that the algorithm had improved performance during periods of unexpected high demand and the ability to respond automatically to year-to-year growth without retiming.

Time-Variant Travel Time Prediction Model and Its Application in a Cooperative Traffic Control System

This paper presents a travel time prediction model and an application of this model to vehicle cooperative control systems. The travel time prediction model considers vehicle delay because of downstream queue formation and traffic signal operation. For estimation of traffic signal–induced delay, this model uses a time-variant nonlinear probability function that maps the vehicle arrival time at a downstream stop line to the probability that it will receive a green signal on arrival. The probability function is determined iteratively. The travel time prediction model can be applied to any phase-based traffic controller. The road vehicle cooperative control system is developed on the basis of vehicle-to-infrastructure communication. The control system optimizes signal timing according to data transmitted in real time and sends speed adjustment recommendations to selected vehicles on the road. Results from numerical tests validate the accuracy of the travel time prediction model and demonstrate the benefits of the cooperative control system.

Revisiting the Cycle Length

During oversaturation, a popular objective in traffic signal operations is to maximize throughput to keep traffic moving. As cycle lengths are increased, the proportion of lost time used to transition between signal phases is reduced. This factor is often a rationale for programming long cycle lengths into signal timing plans. An investigation of the impact of cycle length revisited the concept of the use of critical lane analysis to calculate throughput and applied the technique to data collected at an oversaturated intersection in Indianapolis, Indiana. Traffic volumes were measured for 10 weeks while various cycle lengths, ranging from 80 to 135 s, were tested at the intersection. During saturated conditions, no clear increase in the sum of critical lane throughput was observed, even when the cycle length increased by more than 50%. At 135 s, there was a slight reduction in the total critical lane sum volume. These findings concur with a recent study by Denney et al. The decrease in throughput during the longer cycle lengths is attributed to the reduction of saturation flow during long green times. Possible results of use of a time-dependent saturation flow rate are discussed. Additionally, critical lane analysis may have applications to evaluation and ranking of intersections within corridors as under, near, or over saturation.

Performance Diagnosis Tool for Arterial Traffic Signals

Efficient operation of traffic signals is greatly beneficial to drivers. Because of the intensive labor required, most traffic signals in the United States are retimed once every 3 to 5 years or more, even though signal retiming has a very high benefit–cost ratio. Such a practice may miss opportunities for operational improvements and lead to unnecessary delays. One of the major obstacles to improving the practice is the lack of data collection capability and a convenient performance monitoring tool for signalized arterials. To fill this gap, this paper proposes a performance diagnosis tool for arterial traffic signal systems. The tool aims at identifying necessary parameter changes and assisting agencies in periodically fine-tuning signal timing parameters. A flexible and low-cost data collection unit is developed to equip traffic signal cabinets with event-based data collection capability. Three major parameters of traffic signals (offset, green split, and cycle length) are evaluated in the data center by diagnosis modules that use event-based traffic data. The development of the data collection unit and the diagnosis methodologies are described in detail. The implementation of the tool is illustrated by field data analysis at intersections on Trunk Highway 13 in Burnsville, Minnesota.

Performance Diagnosis Tool for Arterial Traffic Signals

Efficient operation of traffic signals is greatly beneficial to drivers. Because of the intensive labor required, most traffic signals in the United States are retimed once every 3 to 5 years or more, even though signal retiming has a very high benefit–cost ratio. Such a practice may miss opportunities for operational improvements and lead to unnecessary delays. One of the major obstacles to improving the practice is the lack of data collection capability and a convenient performance monitoring tool for signalized arterials. To fill this gap, this paper proposes a performance diagnosis tool for arterial traffic signal systems. The tool aims at identifying necessary parameter changes and assisting agencies in periodically fine-tuning signal timing parameters. A flexible and low-cost data collection unit is developed to equip traffic signal cabinets with event-based data collection capability. Three major parameters of traffic signals (offset, green split, and cycle length) are evaluated in the data center by diagnosis modules that use event-based traffic data. The development of the data collection unit and the diagnosis methodologies are described in detail. The implementation of the tool is illustrated by field data analysis at intersections on Trunk Highway 13 in Burnsville, Minnesota.

Evaluation of Mobility and Safety of Operating an Overlap Phase on a Shared-Left-Turn Lane Using a Microscopic Traffic Simulation Model

Government agencies including the national police agency have executed diverse efforts including continuous improvements of traffic facilities and operation methods, education, enforcements in order to improve traffic operation systems; nevertheless there have been continuous criticisms on irrationality in traffic signal and road facility operation. One of the reasons may be the lack of systematic preliminary evaluations on various alternatives. However, there was no appropriate tool to evaluate the mobility and safety of thus alternatives in a systematic way. Therefore, this study proposes the systematic use of microscopic traffic simulation models as a comprehensive evaluation tool. In addition, this study verified the potential of using a microscopic traffic simulation model using the case of operating an overlap phase on a shared-left-turn lane through a systematic way where the evaluation was conducted through data collection, building networks, calibrating microscopic simulation models, producing performance measures, evaluating mobility and safety, and so on. As a result, the operation of overlap phase on a shared-left-turn lane showed no big difference from other operation scenarios such as leading left-turn on exclusive left turn lane in terms of mobility. However the operation of overlap phase on a shared-left-turn lane decreased safety by increasing potential conflicts.

Economic Analysis of Using a Renewable Wind Power System at a Signalized Intersection

The transportation sector consumes about 28% of the total energy expended by all sectors in the United States, according to the U.S. Energy Information Administration. This article proposes a renewable wind power system (RWPS) as an alternative power source for a signalized traffic intersection. The RWPS can be mounted on existing transportation infrastructure to provide green energy. Large-scale implementation of such a system has the potential to change the role of the public right-of-way from an energy consumer to an energy producer. This article provides a framework to investigate the physical and economic feasibility of installing an RWPS. Methodologies to conduct a structural analysis, site selection, and economic analysis are developed. A case study for an intersection in Lincoln, Nebraska, is used to demonstrate the application of evaluation procedures. The benefits of an RWPS are twofold: (a) the power generated by such a system can support the operation of traffic signals and any excess power produced can be sold back to the power grid; and (b) an RWPS provides a source of backup power in case of grid failure and thus increases the traffic network reliability. This article presents the methodologies to determine the economic value of an RWPS for both cases just described. The costs and benefits of providing RWPS are stated in dollar values. The decision to install an RWPS at an intersection can be made using a benefit–cost ratio. The case study shows that the RWPS is economically feasible at the subject intersection. The results also show that intersections with frequent power failures will have higher benefit–cost ratios. In the event of budget constraints, the methodologies developed in this article can be used to prioritize the investments.

Stochastic Optimization for Coordinated Actuated Traffic Signal Systems

AbstractExisting state-of-the-practice traffic signal timing-optimization programs rely on macroscopic and deterministic models to represent traffic flow, including coordinated actuated traffic signal systems. One distinct shortcoming of such an approach is its inability to account for the stochastic nature of traffic, such as the variability in traffic demand, driver behavior, vehicular interarrival times, vehicle mix, and so forth. In addition, the existing traffic signal timing-optimization programs for coordinated actuated traffic signal systems still focus on four basic traffic signal timing parameters (i.e., cycle length, green times or force-off points, offsets, and phase sequences). Studies have shown that actuated signal settings such as minimum green time, vehicle extension, and recall mode are also important parameters in traffic signal operations. This study presents the development of a stochastic-optimization method for coordinated actuated traffic signal systems. The proposed method account...

Impacts of Transit Priority on Signal Coordination: Case Study of Toronto, Ontario, Canada

This study introduces the current application of transit signal priority (TSP) in the City of Toronto, Ontario, Canada, and focuses on the following: traffic signal operations, constraints of the current signal control system and TSP technology, evaluation metrics, and models for simulating transit priority operation. In Toronto, 335 traffic signals have transit priority, 321 of which are under the control of the main traffic signal system (MTSS). Active transit priority unconditionally allows an extension of up to 30 s per cycle in addition to the normal signal green time on transit routes. The extension generates complaints about traffic delays for vehicles on side streets, long pedestrian wait times, and poor signal coordination. Field tests were conducted on a section of a downtown Toronto arterial bus route. Actual MTSS logs and signpost data obtained by the transit agency during field tests were used to investigate three scenarios along Bathurst Street at six signalized intersections. An analysis of signal timing changes that encompassed selection and determination of traffic operation performance measures was conducted on a per-cycle basis in the morning peak, afternoon peak, and off-peak periods. Green-band effectiveness was one of the evaluation measures used to assess signal progression. Subsequently, a performance assessment frame based on a model for the analytic hierarchy process was built to determine the best scenario and to help develop simulation models. The analysis shows that the TSP strategy of unconditional extension up to 30 s in signal green time is not recommended for use with the existing system of traffic signal control. The approaches described can apply to different transit routes with variable situations in Toronto.