Traffic Signal Performance Measures Research Articles

Vehicle and Pedestrian Traffic Signal Performance Measures Using LiDAR-Derived Trajectory Data.

Light Detection and Ranging (LiDAR) sensors at signalized intersections can accurately track the movement of virtually all objects passing through at high sampling rates. This study presents methodologies to estimate vehicle and pedestrian traffic signal performance measures using LiDAR trajectory data. Over 15,000,000 vehicle and 170,000 pedestrian waypoints detected during a 24 h period at an intersection in Utah are analyzed to describe the proposed techniques. Sampled trajectories are linear referenced to generate Purdue Probe Diagrams (PPDs). Vehicle-based PPDs are used to estimate movement level turning counts, 85th percentile queue lengths (85QL), arrivals on green (AOG), highway capacity manual (HCM) level of service (LOS), split failures (SF), and downstream blockage (DSB) by time of day (TOD). Pedestrian-based PPDs are used to estimate wait times and the proportion of people that traverse multiple crosswalks. Although vehicle signal performance can be estimated from several days of aggregated connected vehicle (CV) data, LiDAR data provides the ability to measure performance in real time. Furthermore, LiDAR can measure pedestrian speeds. At the studied location, the 15th percentile pedestrian walking speed was estimated to be 3.9 ft/s. The ability to directly measure these pedestrian speeds allows agencies to consider alternative crossing times than those suggested by the Manual on Uniform Traffic Control Devices (MUTCD).

Automated Traffic Signal Performance Measures (ATSPMs) in the Loop Simulation: A Digital Twin Approach

In this paper, we present a digital twin approach to the enhancement of traffic signal performance monitoring and congestion identification using automated traffic signal performance measure (ATSPM) systems. The objective of this effort is to use the high-fidelity microscopic simulation engine to generate simulated traffic signal events and connected vehicle data and allow the ATSPM systems to generate various traffic signal measures of effectiveness (MOEs) for a forensic traffic signal evaluation. Real-world ATSPM systems are driven by the traffic signal logs generated during operations. Therefore, they are primarily applied to traffic signal operations in the field. However, traffic signal design at present still follows the traditional method based on averaged delays, stops, and so forth, while more and more agencies have begun to evaluate the implemented traffic signal systems’ performance using the novel ATSPM MOEs. The proposed ATSPMs-in-the-loop simulation system fills this gap by using the ATSPM systems to evaluate the proposed traffic signal timings at the design stage. The benefits of this system include providing full-spectrum decision support for traffic signal management from design to operation and facilitating agencies to develop new insights on identifying traffic signal problems using the ATSPM MOEs. Another feature of the ATSPMs-in-the-loop simulation system is that it can use the emerging connected vehicle data set to generate new traffic signal MOEs. In the case study, we demonstrate how to use the proposed system to identify the potential issues of detector layouts and bottlenecks. Additional features of this ATSPM digital twin include allowing external components to interact with this platform via standard protocols in traffic control systems and connected vehicles to serve more purposes.

Deriving Verified Vehicle Trajectories from LiDAR Sensor Data to Evaluate Traffic Signal Performance

Advances and cost reductions in Light Detection and Ranging (LiDAR) sensor technology have allowed for their implementation in detecting vehicles, cyclists, and pedestrians at signalized intersections. Most LiDAR use cases have focused on safety analyses using its high-fidelity tracking capabilities. This study presents a methodology to transform LiDAR data into localized, verified, and linear-referenced trajectories to derive Purdue Probe Diagrams (PPDs). The following four performance measures are then derived from the PPDs: arrivals on green (AOG), split failures (SF), downstream blockage (DSB), and control delay level of service (LOS). Noise is filtered for each detected vehicle by iteratively projecting each sample’s future location and keeping the subsequent sample that is close enough to the estimated destination. Then, a far side is defined for the analyzed intersection’s movement to linear reference sampled trajectories and to remove those that do not cross through that point. The technique is demonstrated by using over one hour of LiDAR data at an intersection in Utah to derive PPDs. Signal performance is then estimated from these PPDs. The results are compared to those obtained from comparable PPDs derived from connected vehicle (CV) trajectory data. The generated PPDs from both data sources are similar, with relatively modest differences of 1% AOG and a 1.39 s/veh control delay. Practitioners can use the presented methodology to estimate trajectory-based traffic signal performance measures from their deployed LiDAR sensors. The paper concludes by recommending that unfiltered LiDAR data are used for deriving PPDs and extending the detection zones to cover the largest observed queues to improve performance estimation reliability.

Predicting Directional Traffic Volume at Intersections with Automated Traffic Signal Performance Measures Data Using Machine Learning Algorithms

Predicting Directional Traffic Volume at Intersections with Automated Traffic Signal Performance Measures Data Using Machine Learning Algorithms

Development and assessment of trajectory-based arterial through percent arrivals on red for arterial signal coordination performance evaluation

Efficient traffic signal system management plays a pivotal role in reducing traffic congestion and improving traffic mobility on urban roads. The applications of the Automated Traffic Signal Performance Measures (ATSPM) revolutionize the way of proactively managing and evaluating traffic signal systems through a suite of performance measures. The Percent Arrivals on Red (PAoR) is one of the commonly used progression performance measures in the ATSPM to characterize vehicle arrivals at the intersection. However, the accuracy of PAoR to assess arterial signal coordination is restricted by configuration limitations of advance detectors and remains to be further explored. To address this problem, this research proposes an easy-to-use trajectory-based performance measure, Arterial Through Percent Arrivals on Red (ATPAoR), for arterial signal coordination performance evaluation and presents the general procedures to calculate ATPAoR from connected vehicle data. A case study is carried out to implement the proposed ATPAoR and investigate the relationship between the ATPAoR and the PAoR. It is found that the combination of the time–space diagram (TSD) and arterial through-vehicle trajectories is effective in the actual arterial signal coordination performance visualization, ATPAoR result interpretation, and potential timing improvement recommendations. The PAoR is found to be greater than the ATPAoR in undersaturated conditions, and the PAoR above 60% is recommended to identify poor arterial signal coordination design. The historical TSD can be utilized to verify the accuracy of the PAoR to evaluate the actual arterial signal coordination when vehicle trajectory data is unavailable.

A Methodology to Detect Traffic Data Anomalies in Automated Traffic Signal Performance Measures

Automated traffic signal performance measures (ATSPMs) have garnered significant attention for their ability to collect and evaluate real-time and historical data at signalized intersections. ATSPM data are widely utilized by traffic engineers, planners, and researchers in various application scenarios. In working with ATSPM data in Utah, it was discovered that five types of ATSPM data anomalies (data switching, data shifting, data missing under 6 months, data missing over 6 months, and irregular curves) were present in the data. To address the data issues, this paper presents a method that enables transportation agencies to automatically detect data anomalies in their ATSPM datasets. The proposed method utilizes the moving average and standard deviation of a moving window to calculate the z-score for traffic volume data points at each timestamp. Anomalies are flagged when the z-score exceeds 2, which is based on the data falling within two standard deviations of the mean. The results demonstrate that this method effectively identifies anomalies within ATSPM systems, thereby enhancing the usability of data for engineers, planners, and all ATSPM users. By employing this method, transportation agencies can improve the efficiency of their ATSPM systems, leading to more accurate and reliable data for analysis.

Use of High-Resolution Signal Controller Data to Measure Transit Signal Priority Performance: A Case Study in the Boston Region

Automated traffic signal performance measures (ATSPMs) have been developed to organize data within a traffic signal, which will then be used to evaluate and monitor signal timing strategies. Past research has summarized the use of ATSPMs for performance-based management of a traffic signal system. The National Cooperative Highway Research Program Research Report 954 focused on many measures useful for general traffic, but did not consider transit agencies as specific stakeholders. ATSPMs do provide insights into measuring the number of preemption events, and the percentage of transit vehicles arriving on green, but additional information is needed to determine the effectiveness of transit signal priority or other transit preferential treatment strategies. This paper proposes a method for evaluating transit performance at signalized intersections more fully using the high-resolution data available within modern traffic signal controllers. The addition of transit-specific inputs within the ATSPM enumerations is used to produce transit-specific signal performance measures designed to be useful to practitioners. The methodology can be followed by other agencies seeking to enhance the capabilities of existing automatic vehicle location systems to further describe the performance at each signalized intersection. By incorporating transit into the ATSPM system, an agency can better assess the delay of buses, which can contribute to schedule adherence, on-time performance, and overall bus service. The methodology is presented as a prototype for further development and adaptation to individual agency objectives and data sources.

Comparison of Arrivals on Green Estimations from Vehicle Detection and Connected Vehicle Data

Many agencies use Automated Traffic Signal Performance Measures (ATSPMs), which require vehicle detection and communication equipment, to evaluate traffic signal efficiency. ATSPMs usually rely on projections based on spatially limited vehicle trajectory samples to estimate performance. Recently, connected vehicle (CV) data has become available that provides entire vehicle trajectories that can be used to generate accurate signal performance measures without the need for projections or infrastructure upgrades. This paper analyzes over 50 intersections in Utah to evaluate how traditional detector-based arrivals on green (AOG) calculations compare with their CV-based calculations. The effects that saturation and queue- lengths have on estimations are analyzed. In general, there is close correlation between computations when queues are short and undersaturated conditions exist. However, if queues extend past the advance detector, detector-based calculations tend to overestimate AOG since vehicles are detected during green but may stop before. This impact is particularly large when the approach is oversaturated, which led to overestimations of around 40% in this study. In addition, detector-based estimations can underestimate AOG in undersaturated scenarios with short queues if vehicles reach the advance detector on red but reduce their speed afterwards, allowing them to not stop. In some cases, the detector-based technique underestimated AOG by over 50%. The findings can help practitioners understand how detector-based estimations vary by traffic conditions. This is particularly important as the industry moves toward a hybrid blend of detector- and CV-based signal performance measures. It is recommended that CV trajectories be used to measure AOG during periods with long queues, oversaturated conditions, or both.

Systemwide Identification of Signal Retiming Opportunities with Connected Vehicle Data to Reduce Split Failures

Road networks are significantly affected by traffic signal operations, which contribute from 5% to 10% of all traffic delay in the U.S.A. It is important for agencies to be able to systematically monitor traffic signal performance and have procedures to identify locations as proactively as possible where the redistribution of green time has the greatest potential of reducing congestion. Currently, Automated Traffic Signal Performance Measures (ATSPMs) require extensive communication networks and detectors on all phases at each intersection to effectively monitor the traffic signal performance for every traffic movement. This study utilizes commercially available connected vehicle (CV) trajectory data to identify overcapacity movements where there is availability of unused green time and no downstream blockage by an adjacent signal. Over 100 intersections, 400,000 trajectories, and 6,800,000 Global Positioning System points are evaluated using this method to prioritize signal retiming opportunities in a proposed Relative Performance Diagram (RPD) by assessing split failures and downstream blockage of all traffic movements. Out of the intersections identified as having opportunities for operational improvements, three were selected for retiming. A post-retiming review of the traffic signal performance measures showed up to 30% reductions in split failures, 53 s/veh reductions in control delay, and 21% increase in arrivals on green at the benefited movements. The techniques easily scale to any intersection where CV data is available without the need for vehicle detection or communication technology.

Modeling red-light running behavior using high-resolution event-based data: a finite mixture modeling approach

To effectively reduce the number of red-light violations and crashes, it is crucial to explore RLR behavior at local intersections, understand the contributing factors, and identify the riskiest intersections by estimating RLR frequency. In this study, a finite mixture modeling method was utilized to understand the contributing factors to RLR behavior and estimate this violating behavior. To develop the RLR estimation models, performance metrics and signal phasing data were collected from the Automated Traffic Signal Performance Measures (ATSPMs) system in two jurisdictions in Arizona: Pima County and the Town of Marana. The results from calibrated models showed that an increase in traffic flow, intersection delay, number of approach lanes, and split failure is associated with an increase in the likelihood of observing red-light violations. In addition, it was found that an increase in cycle length is associated with a decrease in the likelihood of observing the red-light violation. The results of comparing the proposed RLR estimation method with several conventional methods, the Poisson Generalized Linear Model (PGLM), Zero-inflated Poisson Regression Model (ZIPM), and Zero-inflated Negative Binomial Regression Model (ZINB) showed the proposed method outperforms all the models in terms of both model fit and accuracy. The application of the proposed method could be used to analyze the intersections with the highest number of red-light violations. Furthermore, the presented transferability results can be advantageous to transportation agencies within Arizona and urban areas with similar characteristics by providing insight into which model specifications may provide the best RLR estimation accuracy.

Arterial Vehicle Trajectory Reconstruction Based on Stopbar Video Sensor for Automated Traffic Signal Performance Measures

Arterial Vehicle Trajectory Reconstruction Based on Stopbar Video Sensor for Automated Traffic Signal Performance Measures

Investigating the Probability of Potential Actions Performed by Drivers Experiencing Daytime Sun Glare-Induced Blindness

There has been extensive visual-disability-focused safety research resulting from various factors/conditions that have a significant impact on a driver’s ability to maneuver safely. One such critical condition among the major environmental issues that contribute to traffic crashes is daytime sun glare-induced temporary visual impairment. Nevertheless, the contribution of relevant factors to the choice of potential actions at the time of such crashes has not been widely studied. Approximately 3,000 crashes per year in the United States are attributed to sun glare, which may indicate that the driver had no control over the situation. Thus, this study investigated several factors to identify crashes that occurred, with acceptable certainty, under the direct influence of sun glare over 4 years (2014 to 2017) in Florida—“the Sunshine State.” A multinomial logistic regression model was developed to formulate relationships between crash-related contributing factors and to evaluate the likelihood of alternative actions being adopted by drivers experiencing sun glare. The results indicated that running red lights/stop signs was the most likely action one might take while experiencing this adverse condition, particularly on local roadways. A further consequence of sun glare is that drivers tend to follow the car in front too closely along segments with higher annual average daily traffic. The findings could help safety officials support the use of emerging intelligent transportation system technologies (i.e., automated traffic signal performance measures and cooperative intersection collision avoidance systems) to alert drivers of the possibility of a temporary vision impairment exactly when high-glare conditions exist.

Do existing split failure metrics accurately reflect pedestrian operation at signalized intersections?

Automated Traffic Signal Performance Measures (ATSPMs) uses high-resolution data to develop operational performance measures for signalized intersections. Split Failure (SFs) is one of the primary metrics to identify intersections with operational issues. These SFs are determined by measuring vehicle occupancy for a given movement during its green time (i.e., Green Occupancy Ratio or GOR) and immediately after the signal turns red (i.e., Red Occupancy Ratio or ROR5). While the SF metric is a great tool for signal operations and rebalancing green times, it focuses entirely on vehicular measures and ignores the treatment of pedestrians at the intersection. Prioritizing vehicular movements may lead to excess pedestrian delay, which may cause pedestrians to violate traffic signals.To address this issue, this paper examines the relationship between pedestrian delay and GOR or ROR5. The main objective was to identify whether the GOR and ROR5 could be adequately used as a proxy for pedestrian delays. To achieve this goal, high-resolution data was collected from the ATSPMs database at a signalized intersection in Salt Lake City, Utah. To predict GOR or ROR5 as a function of pedestrian delay, a linear regression model was developed. The results reveal that there is very weak to no relationship between these metrics. This implies that using only GOR or ROR5 in quantifying signal performance does not meaningfully capture pedestrian delay and thus might overemphasize vehicle movements. Specific pedestrian delay metrics should be included in a signal operation analysis to identify operational issues.

A Data-Driven Intersection Geometry Mapping Technique to Enhance the Scalability of Trajectory-Based Traffic Signal Performance Measures

A Data-Driven Intersection Geometry Mapping Technique to Enhance the Scalability of Trajectory-Based Traffic Signal Performance Measures

Evaluating Signal Systems Using Automated Traffic Signal Performance Measures

Automated traffic signal performance measures (ATSPMs) are used to collect data concerning the current and historical performance of signalized intersections. However, transportation agencies are not using ATSPM data to the full extent of this “big data” resource, because the volume of information can overwhelm traditional identification and prioritization techniques. This paper presents a method that summarizes multiple dimensions of intersection- and corridor-level performance using ATSPM data and returns information that can be used for prioritization of intersections and corridors for further analysis. The method was developed and applied to analyze three signalized corridors in Utah, consisting of 20 total intersections. Four performance measures were used to develop threshold values for evaluation: platoon ratio, split failures, arrivals on green, and red-light violations. The performance measures were scaled and classified using k-means cluster analysis and expert input. The results of this analysis produced a score for each intersection and corridor determined from the average of the four measures, weighted by expert input. The methodology is presented as a prototype that can be developed with more performance measures and more extensive corridors for future studies.

Development and Evaluation of Performance Measures for Capacity Utilization of Traffic Signals

Emerging high-resolution datasets allow for developing traffic signal performance measures (TSPMs) which can provide a better understanding of traffic signal operations. One of the specific objectives of signal operations is related to the measurement of capacity utilization. A fundamental measure for capacity utilization is the “degree of saturation” ( DS), and it has both volume-based and occupancy-based formulations. The former is known as “volume over capacity ratio” ( V/C), whereas the latter is known as “green occupancy ratio” ( GOR). When V/C is used to observe capacity utilization, it is not known whether the capacity is utilized from interrupted flow, free flow, or a combination of two. Similarly, the amount of time in the cycle utilized by volume (characterized with the first and last vehicle arrival time) is not revealed within V/C. On the other hand, GOR“overlooks” cases when a high number of vehicles travel without stopping or fewer vehicles depart the signal with significant startup delays and similar. In addition, because of a lack of scientific attention, GOR has often been misinterpreted as some similar, yet different TSPM. Therefore, if only V/C or GOR are used to monitor signal performance, the understanding of utilized capacity will be incomplete or misleading. This study proposes three TSPMs: “queued volume in volume to capacity ratio” ( QViV/C), “cycle utilization” ( CLU), and “volume-occupancy capacity utilization” ( VOCU) to overcome the limitations of V/C and GOR. Also, by evaluating existing occupancy-based measures, it was found that they can be used to estimate capacity utilization, including oversaturated conditions.

Massively parallelizable approach for evaluating signalized arterial performance using probe-based data

Effective performance of arterial corridors is essential to community safety and vitality. Considering the dynamic nature of traffic demand, efficient management of these corridors require frequent updating of the traffic signal timings through various strategies. Agency resources for these activities are commonly scarce and are primarily by public complaints. This study provides a workflow using probe-based data to measure and compare different segments on arterial corridors in terms of the traffic signal performance measures that can capture travel time dynamics across signalized intersections. The proposed methodology identifies a group of dynamic days followed by evaluation of travel rate based upon remaining non-dynamic days. Dynamic days represent the variability of traffic on a segment. Consequently, a corridor having high number of dynamic segments along with poor performance during normal days would be a candidate for adaptive control. Further, to handle the large-scale data source collected from city-wide or statewide traffic signals, the study adopts parallel computation-based strategy using MapReduce technique. A case study was conducted on 11 corridors within Des Moines, Iowa, to demonstrate the efficacy of the proposed approach, which identified two arterial corridors, Merle Hay Road and University Avenue, to be suitable for adaptive traffic signal control.

Correlation Analysis for Exploring the Relationship Between Probe Vehicle Data and Event-Based Traffic Signal Performance Measures

Probe vehicle data have been extensively used to assess the performance of roadway systems, particularly the interstate highway system. High-resolution data from traffic signal controllers is another data source used for automated traffic signal performance measures (ATSPM), which is increasingly used to evaluate signal operation. As high-resolution data require additional resources and may not always be available, probe vehicle data are sometimes used to evaluate signalized corridor operation. However, there has been little previous research comparing probe vehicle data on signalized corridors with ATSPM. In this study, we compared the average speeds from probe vehicle data with ATSPMs to examine the degree of correlation between the two datasets. Different scenarios including segments with random arrivals and platoons were considered for parts of US 20 in Dubuque, Iowa. Regression analysis was performed with average speed as the dependent variable to check the correlation between the two datasets. Four different signal performance measures, namely the percent on green, volume-to-capacity ratio, percent of green duration, and average delay, were used as independent variables. Two sets of categorical variables representing time-of-day and day-of-week variables were also added. It was found that there exists good correlation between the datasets, supporting the use of probe vehicle data for corridor-level analysis in the absence of high-resolution data. Additionally, the durations of the intervals used for data aggregation were varied to check its impact on the correlation. Higher levels of aggregation resulted in better correlation between the two datasets.

A novel model to jointly estimate delay and arrival patterns by using high-resolution signal and detection data

Delay is one of the most important traffic signal performance measures. In coordinated networks, understanding the characteristics of vehicle arrivals is important for coordination purposes and to properly estimate delays. When observed on a cyclical basis in real-time, distinctive arrival patterns can lead to similar delays, which may go undetected by contemporary delay models. This study proposes a set of enhancements to the Incremental Queue Accumulation (IQA) delay model to overcome the limitations of current models. Additionally, this study proposes a hybrid signal performance measure that combines delay and arrival patterns to depict signal performance truthfully. The enhancements to IQA are realised through an algorithm for the identification of distinctive vehicle arrival groups based on high-resolution signal and detection data. The results demonstrate that the proposed model provides reliable delay estimates (MAPE score in range 4.3–11.2%) while reporting a number of traffic arrival characteristics that are not available from the benchmarked models.

Subcycle Waveform Modeling of Traffic Intersections Using Recurrent Attention Networks

Traffic flow dynamics in the vicinity of urban arterial intersections is a complex and nonlinear phenomenon, influenced by factors such as signal timing plan, road geometry, driver behaviors, etc. Predicting such flow dynamics is an important task for urban traffic signal control and planning. Current methods use microscopic simulation for studying the impact of a large number of signal timing plans at each of the intersections. A major drawback of microscopic simulation is that they are they are based on source destination traffic generation models and cannot incorporate the high resolution loop detector data such as that are provided by automated traffic signal performance measures (ATSPM) based systems. The arrival (or departure) information of each vehicle on a detector can be thought of as a time series waveform. Given the high granularity of ATSPM data, this waveform can be used to several interesting analyses. The waveforms can be used to derive information on platoon dispersion as vehicles progress across the corridor. Also, these waveforms can be modelled to understand how the vehicles progress across the corridor for a variety of signal timing plans. In this paper, we show that deep neural networks based machine learning systems can be used to effectively leverage the waveforms collected at multiple sensors (stopbar and advanced) on the intersection to model the traffic dynamics both at an intersection and across intersections. We show that modelling of these waveforms can be useful to understand traffic flow dynamics under different signal timing plans and can be potentially integrated into signal timing optimization software. Further, these methods are three to four orders of magnitudes faster than using microscopic simulations.