Sydney Coordinated Adaptive Traffic System Research Articles

SCATS (Sydney Coordinated Adaptive Traffic System) As A Solution To Overcome Traffic Congestion in Big Cities

The development of an urban area should be followed by the arrangement of adequate infrastructure. One of the important needs in an urban area is transportation. An increase in the number of means of transport often creates congestion, especially at intersections. The timing of cycles that do not lead tothe number of vehicles lining up but only based on a balanced division of time is one of the causes of congestion. Based on this background, a system of setting the flame cycle of the lamp u basedon the length of the queue is needed. This study aims to implement the Sydney Coordinat Adaptive Traffic System (SCATS) application as a solution to overcome traffic congestion in DKI Jakarta. The method used is to use quantitative methods with the 1997 Indonesian Road Capacity Manual (MKJI) approach. The results obtained include, the SCATS application can adaptively adjust the cycle time of sensor-based traffic lights. Traffictraffic settings can be manually controlled by the operator remotely using an internet communication network and can be conditioned with special cases or emergencies. Based on the results of the study, the SCATS application is expected to be implemented in all major cities in Indonesia.

A Data-Driven Functional Classification of Urban Roadways Based on Geometric Design, Traffic Characteristics, and Land Use Features

The functional classification system (FCS) of roads means categorizing roads based on their service characteristics. The two primary considerations in classifying highway and street networks are accessibility and mobility, where by increasing the role of one, the other’s role is reduced. In this paper, besides the conventional variables such as geometric design characteristics, parking lots, land use features, and accessibility; the Sydney Coordinated Adaptive Traffic System (SCATS) data following the real-time traffic flow and average speed of vehicles collected by Location-Based Services (LBS) are considered as new variables for estimating the FCS. Linear regression is used to model the importance of the variables. The chi-square test compared the observational and predicted speeds in the five categories of roads in Tehran, the capital of Iran. Results show that on-street parking has the highest impact and the land use variable has the lowest impact on speed that changes the FCS. Moreover, the presented classification was one to two categories compared with the conventional FCS presented in manuals in the case of Tehran’s transportation network as a developing city.

Arterial travel time estimation method using SCATS traffic data based on KNN-LSSVR model

In order to improve the effect of estimating travel time and provide more precise and reliable traffic information to traffic management department and travelers, we proposed an arterial travel time estimation method using Sydney Coordinated Adaptive Traffic System traffic data based on K-nearest neighbor–least squares support vector regression model. First, the virtual time series is constructed by analyzing the characteristics of the inconsistent time intervals of Sydney Coordinated Adaptive Traffic System traffic data. Second, the K-nearest neighbor method was used to search the K similarity patterns matching the current traffic pattern and obtain K travel time data. Then, the least squares support vector regression model was used to perform travel time estimation. Finally, case validation is carried out using the measured data of Sydney Coordinated Adaptive Traffic System traffic control system. The estimation results demonstrate that the travel time estimation accuracy of proposed method outperforms the other two methods.

Analyzing urban traffic demand distribution and the correlation between traffic flow and the built environment based on detector data and POIs

PurposeThis paper aims to determine the urban traffic flow spatiotemporal characteristics and correlation with the built environment using SCATS (Sydney Coordinated Adaptive Traffic System) and POIs (Point of Interests) data of Shenyang, China.MethodsA standard analysis framework based on these data is proposed in the paper. The study analyzes the traffic volume spatiotemporal distributions and built environment influence factors determined by the geographical detector. An improved gravity model using simple structural parameters (lanes number and road length) is proposed to estimate the traffic flows of day and peak hour scales for specific flow ranges.ResultsThe results show that the peak hours of different intersections and roads are heterogeneous and reveal trip time flexibility. The correlation between peak hour flows and day flows is significant in the multidimensional analysis. Based on the investigation of lanes, more interesting conclusions are found. In this case, when the numbers of lanes of intersections and roads are more than 14 and 4 respectively, the lane resources are wasted to a great extent. There is also a certain correlation between these factors. Proposed gravity model establishes the connection between structure and function of urban roads.ConclusionsFlexible work time and places will be effective methods to reduce traffic congestion. The day flows could be estimated via a traffic survey on peak hour flows, especially in developing cities. The traffic flow mainly concentrates in a relatively small part of city roads. The maximum service traffic volumes exhibit segmentation, we should reconsider the maximum optimal lanes number of intersections and roads under better performance and utilization rate of the network. The effect of lanes number on the service traffic volumes is found to be more significant compared with the other factors. Our conclusions will be helpful for policy-makers and sustainable urban planning.

On development of arterial fundamental diagrams based on surrogate density measures from adaptive traffic control systems utilizing stop-line detection

A promising framework for understanding flow-density relationship in traffic flow theory is the Fundamental Diagram, originally developed for uninterrupted traffic flow facilities. The concept has been extended to the Arterial Fundamental Diagram (AFD), which has shown that the same relationship holds on arterial streets. However, constructing an AFD is subject to considerable variability in the measured quantities, due to the highly cyclical nature of signalized intersections. In most cases, these diagrams are based on the data from upstream detectors, located away from traffic signals. Recent scientific literature has shown a value of using stop-line detection data to develop AFDs, opening a plethora of opportunities to further investigate traffic dynamics utilizing the data from adaptive traffic control systems (ATCSs). This can, however, be problematic for two major reasons. First, the data may come from detectors unfit to provide good-quality inputs to develop an AFD. Second, such ATCSs may use their own surrogate measures of density and traffic flow, primarily developed for the purpose of controlling traffic, which may be inappropriate for developing fundamental relationships. This study aims to address these issues by investigating appropriateness of using Degree of Saturation (DS), a density-like measure from Sydney Coordinated Adaptive Traffic System (SCATS), to develop an AFD. Empirical SCATS data shows an interesting pattern of the AFD, which cannot be explained by the data itself. Hence, we derive a new analytical model of DS based on the high-resolution signal and detection data, which reveals parameters that drive its behavior. Additionally, we develop the Cyclical Vehicle Arrival and Discharge Model to simulate SCATS-like operations and derive causal relationships between traffic flow variables and density-like performance measures in a controlled environment. The findings show that DS does not have to be a poor estimator of traffic conditions, but when it is combined with SCATS-measured traffic flows it gives a false representation of near-capacity and over-saturated conditions.

Use of Big Data to Evaluate and Improve Performance of Traffic Signal Systems in Resource-Constrained Countries

Deployment of an adaptive area traffic control system is expensive; physical sensors require installation, calibration, and regular maintenance. Because of the high level of technical and financial resources required, area traffic control systems found in developing countries often are minimally functioning. In Cebu City, Philippines, for example, the Sydney Coordinated Adaptive Traffic System was installed before 2000, and fewer than 35% of detectors were still functioning as of January 2015. To address this challenge, a study was designed to determine whether taxi company GPS data are sufficient to evaluate and improve traffic signal timing plans in resource-constrained environments. If this work is successful, the number of physical sensors required to support those systems may be reduced and thereby substantially lower the costs of installation and maintenance. Taxi GPS data provided by a regional taxi-hailing app were used to design and implement methodologies for evaluating the performance of traffic signal timing plans and for deriving updated fixed-dynamic plans, which are fixed plans (with periods based on observable congestion patterns rather than only time of day) iterated regularly until optimization is reached. To date, three rounds of iterations have been conducted to ensure the stability of the proposed signal timings. Results of exploratory analysis indicate that the algorithm is capable of generating reasonable green time splits, but cycle length adjustment must be considered in the future.

Quantifying the impact of adaptive traffic control systems on crash frequency and severity: Evidence from Oakland County, Michigan

IntroductionDespite seeing widespread usage worldwide, adaptive traffic control systems have experienced relatively little use in the United States. Of the systems used, the Sydney Coordinated Adaptive Traffic System (SCATS) is the most popular in America. Safety benefits of these systems are not as well understood nor as commonly documented. MethodThis study investigates the safety benefits of adaptive traffic control systems by using the large SCATS-based system in Oakland County, MI known as FAST-TRAC. This study uses data from FAST-TRAC-controlled intersections in Oakland County and compares a wide variety of geometric, traffic, and crash characteristics to similar intersections in metropolitan areas elsewhere in Michigan. Data from 498 signalized intersections are used to conduct a cross-sectional analysis. Negative binomial models are used to estimate models for three dependent crash variables. Multinomial logit models are used to estimate an injury severity model. A variable tracking the presence of FAST-TRAC controllers at intersections is used in all models to determine if a SCATS-based system has an impact on crash occurrences or crash severity. ResultsEstimates show that the presence of SCATS-based controllers at intersections is likely to reduce angle crashes by up to 19.3%. Severity results show a statistically significant increase in non-serious injuries, but not a significant reduction in incapacitating injuries or fatal accidents.

Self-Organising Traffic Lights (SOTL) Do Not Outperform Sydney Coordinated Adaptive Traffic System (SCATS)

This paper responds to the article of L. Zhang, Garoni and J. De Gier “A comparative study of Macroscopic Fundamental Diagrams of arterial road networks governed by adaptive traffic signal systems”. It is shown that the authors' results do not support their conclusion that Self-Organising Traffic Lights (SOTL) outperform Sydney Coordinated Adaptive Traffic System (SCATS). In the paper, we explain how several mathematical and logical flaws lead to wrong conclusion.

Formalizing computational intensity of big traffic data understanding and analysis for parallel computing

Nowadays, traffic data can be collected from different types of sensors widely-deployed in urban districts. Big traffic data understanding and analysis in intelligent transportation systems (ITS) turns out to be an urgent requirement. This requirement leads to the computation-intensive and data-intensive problems in ITS, which can be innovatively resolved by using Cyber-Infrastructure (CI). A generic process for the solution contains four steps: (1) formalized data understanding and representation, (2) computational intensity transformation, (3) computing tasks creation, and (4) CI resources allocation. In this paper, we firstly propose a computational domain theory to formally represent heterogeneous big traffic data based on the data understanding, and then use data-centric and operation-centric transformation functions to evaluate the computational intensity of traffic data analysis in different aspects. Afterwards, the computational intensity is leveraged to decompose the domain into sub-domains by octree structure. All the sub-domains create computing tasks which are scheduled to CI resources for parallel computing. Based on the evaluation of overall computational intensity, an example of fusing Sydney Coordinated Adaptive Traffic System (SCATS) data and Global Positioning System (GPS) data for traffic state estimation is parallelized and executed on CI resources to test the accuracy of domain decomposition and the efficiency of parallelized implementation. The experimental results show that the ITS computational domain is decomposed into load-balanced sub-domains, therefore facilitating significant acceleration for parallelized big traffic data fusion.

Application of Finite Mixture of Regression Model with Varying Mixing Probabilities to Estimation of Urban Arterial Travel Times

Travel time along an urban arterial is greatly affected by traffic signals. Most studies on urban travel time use statistical models to obtain the distribution directly without incorporating the effects of traffic signal timing. In this study, a finite mixture of regression model with varying mixing probabilities (weights) was proposed to gain a better understanding of urban travel time distribution through consideration of signal timing. Standard finite mixture models with constant mixing probabilities have a limited ability to adapt to underlying random structural changes for observed travel times. The model developed in this study can capture such dynamics by ( a) modeling the mixing probabilities as a function of the explanatory variables associated with signal timing and ( b) establishing a linear regression between the mean of each component and signal timing. The finite mixture of regression model was applied to the travel time data collected by the automatic vehicle identification system on one urban arterial with the Sydney coordinated adaptive traffic system (SCATS). The results demonstrate that the varying mixing probabilities can be used to classify the samples of travel time, and the mean values of components can capture the effects of signal timing. By comparing various types of mixture models, the proposed approach not only has a better statistical fitting performance but also provides useful information about travel time features.

Design optimization traffic light timing using the fuzzy logic at a Diphasic's Isolated intersection

In this paper, to overcome the problem of increasing traffic, using state space equations, a comprehensive model of urban traffic network are obtained in a isolated intersection. Then, two fuzzy logic controllers, one for optimized green time of traffic lights and one green phase is used for an extended time. In this system, types of sensors is video detection, they are placed strategically at incoming and outgoing legs (lines) and the controller utilize the information received from these sensors to make optimal decisions to minimize the goal function. The simulation results show that TAOSF (Traffic Actuated Optimization Signal Fuzzy control) had a great improvement in 6 different traffic conditions such as certain, uncertain, using random data, and in 4 different traffic conditions using the real case study of OSTANDARI intersection in Mashhad, Iran, in comparison with the fixed time controller, real operation of Australian SCATS (Sydney Coordinated Adaptive Traffic System) system, TASFC (Traffic Actuated Signal Fuzzy Control) system in previous work for estimation of the goal function which is minimizing the queue length and the delay time of the vehicles.

Statistical Study of the Impact of Adaptive Traffic Signal Control on Traffic and Transit Performance

The Sydney Coordinated Adaptive Traffic System (SCATS) is designed to improve mobility on congested corridors with variable demand; SCATS is expected to improve transit performance. Because many urban corridors are heavily used by transit routes, it is important to determine how SCATS affects transit performance on congested corridors. However, there is limited research on evaluating the impact of SCATS on transit performance. To the best of the authors’ knowledge, this research presents the first field evaluation of SCATS with transit signal priority in the United States. The case study is based on before-and-after traffic and transit data along Powell Boulevard, one of the most congested urban arterials in the Portland, Oregon, metropolitan region. Using permanent traffic data collection stations, bus automated vehicle location and automatic passenger count data, and transit signal priority request data, this work presents the results of statistical tests and regressions to determine the impacts of SCATS. Statistically significant differences were observed in travel times and SCATS-related regression parameters. Overall, travel time changes or improvements related to SCATS seemed to depend greatly on the direction of travel and time of day.

Statistical Study of the Impact of Adaptive Traffic Signal Control on Traffic and Transit Performance

The Sydney Coordinated Adaptive Traffic System (SCATS) is designed to improve mobility on congested corridors with variable demand; SCATS is expected to improve transit performance. Because many urban corridors are heavily used by transit routes, it is important to determine how SCATS affects transit performance on congested corridors. However, there is limited research on evaluating the impact of SCATS on transit performance. To the best of the authors’ knowledge, this research presents the first field evaluation of SCATS with transit signal priority in the United States. The case study is based on before-and-after traffic and transit data along Powell Boulevard, one of the most congested urban arterials in the Portland, Oregon, metropolitan region. Using permanent traffic data collection stations, bus automated vehicle location and automatic passenger count data, and transit signal priority request data, this work presents the results of statistical tests and regressions to determine the impacts of SCATS. Statistically significant differences were observed in travel times and SCATS-related regression parameters. Overall, travel time changes or improvements related to SCATS seemed to depend greatly on the direction of travel and time of day.

A Cyber-ITS Framework for Massive Traffic Data Analysis Using Cyber Infrastructure

Traffic data is commonly collected from widely deployed sensors in urban areas. This brings up a new research topic, data-driven intelligent transportation systems (ITSs), which means to integrate heterogeneous traffic data from different kinds of sensors and apply it for ITS applications. This research, taking into consideration the significant increase in the amount of traffic data and the complexity of data analysis, focuses mainly on the challenge of solving data-intensive and computation-intensive problems. As a solution to the problems, this paper proposes a Cyber-ITS framework to perform data analysis on Cyber Infrastructure (CI), by nature parallel-computing hardware and software systems, in the context of ITS. The techniques of the framework include data representation, domain decomposition, resource allocation, and parallel processing. All these techniques are based on data-driven and application-oriented models and are organized as a component-and-workflow-based model in order to achieve technical interoperability and data reusability. A case study of the Cyber-ITS framework is presented later based on a traffic state estimation application that uses the fusion of massive Sydney Coordinated Adaptive Traffic System (SCATS) data and GPS data. The results prove that the Cyber-ITS-based implementation can achieve a high accuracy rate of traffic state estimation and provide a significant computational speedup for the data fusion by parallel computing.

A Probe-Vehicle-Based Evaluation of Adaptive Traffic Signal Control

In 2005, the Cobb County Department of Transportation, Cobb County, GA, conducted an adaptive signal control pilot study implementing the Sydney Coordinated Adaptive Traffic System (SCATS) on 15 intersections. This paper presents the results of a before-and-after probe-vehicle-based operational comparison of optimized time-of-day (i.e., before control) and SCATS (i.e., after control) traffic control system performance. The focus of this operational analysis is the typical operating performance during the weekday peak, weekday off-peak, and weekend travel periods. Travel time data were collected using Global-Positioning-System (GPS)-equipped test vehicles. The results showed that both systems provided good performance, whereas neither the before time-of-day or after SCATS is clearly dominant, except on Cumberland Parkway, where SCATS control consistently provides equivalent or superior performance to that of the time-of-day control.

Long-Term Benefits of Adaptive Traffic Control under Varying Traffic Flows during Weekday Peak Hours

When adaptive traffic control systems (ATCSs) are evaluated, traffic signal engineers and practitioners often collect data for a few weeks before and after installation. Benefits are then estimated on the basis of this limited data set. The evaluation of an ATCS with microsimulation requires considerable collection of field data. However, once an ATCS is installed, an abundance of data is collected and stored by the ATCS itself. These data (mostly traffic volumes) can be used to recreate field variability of traffic conditions in a model and perform long-term ATCS evaluation studies. This paper reports on the projected long-term benefits of deploying an ATCS. Field traffic data were statistically processed and modeled in microsimulation. The Sydney coordinated adaptive traffic system (SCATS) and two time-of-day (TOD) plans were exposed to variability of field traffic flows modeled in VISSIM. A simple calculation was then performed to extrapolate results to a period of 10 years. Findings showed that SCATS outperformed existing TOD signal-timing plans by about 20% and was better than the best TOD plan that could be theoretically developed on the basis of collection of long-term data. Results from the study revealed that short-term analyses often obscured the true benefits of deploying an ATCS. A computation of the monetary value of achieved benefits showed that limited operational benefits reported in this paper, when projected over the long term, would exceed overall installation costs for SCATS in Park City, Utah. These benefits are expected to increase further with inclusion of the analysis periods that were not part of this study.

Arterial Performance Evaluation on an Adaptive Traffic Signal Control System

This paper documents the evaluation of Sydney Coordinated Adaptive Traffic System (SCATS) at a major signalized arterial in Las Vegas. The evaluation was based on various performance measures, including primarily travel times and stops at selected routes along the arterial. Extensive travel time runs were conducted during the weekday and weekend peak periods. Arterial performance was compared between SCATS and conventional time-of-day (TOD) coordination plans. Based on the data and analysis results, a general conclusion was reached that no significant improvement on arterial progression was achieved with SCATS under normal traffic conditions. However, this conclusion was made based on a major limitation of using video detection in the field, which may have significantly limited SCATS from achieving its best performance.

Comparison of Before–After versus Off–On Adaptive Traffic Control Evaluations

An adaptive traffic control system, the Sydney Coordinated Adaptive Traffic System (SCATS), was installed in Park City, Utah, to improve traffic performance at its network of signalized intersections. A field evaluation of the previous time-of-day actuated–coordinated signal timings was conducted before SCATS installation to compare the two systems. However, the post-SCATS field evaluation could not occur until two additional signals were installed and several other changes were made to the network. Two years after the original pre-SCATS field evaluation, the network was reevaluated with an off–on technique analogous to a before–after study. The signal timings and parameters in the off condition forced SCATS to use time-of-day actuated–coordinated control, similar to the before study but with timings readjusted for the additional signals and changed traffic conditions. The performance gains with SCATS on were measurably greater than those with SCATS off for travel time and number of stops and greater overall for stopped delay. These data provided the transportation agency with results anticipated by its traffic engineers. However, the original field evaluation data from 2 years earlier provided a less distinct conclusion. A methodology is presented to determine the relevance of an off–on study in place of a before–after study. The results show that “before” and “off ” data sets behave consistently 62.5% of the time. This value provides a basis of support for using off data, which better represent before signal timings on an after network. It also quantifies sensitivity to changes in the network, which are substantial in this case.

Experiences in deploying a wireless mesh network testbed for traffic control

Wireless mesh networks (WMN) have attracted considerable interest in recent years as a convenient, flexible and low-cost alternative to wired communication infrastructures in many contexts. However, the great majority of research on metropolitan-scale WMN has been centered around maximization of available bandwidth, suitable for non-real-time applications such as Internet access for the general public. On the other hand, the suitability of WMN for mission-critical infrastructure applications remains by and large unknown, as protocols typically employed in WMN are, for the most part, not designed for real-time communications. In this paper, we describe the Smart Transport and Roads Communications (STaRComm) project at National ICT Australia (NICTA), which sets a goal of designing a wireless mesh network architecture to solve the communication needs of the traffic control system in Sydney, Australia. This system, known as SCATS (Sydney Coordinated Adaptive Traffic System)and used in over 100 cities around the world, connects a hierarchy of several thousand devices -- from individual traffic light controllers to regional computers and the central Traffic Management Centre (TMC)-- and places stringent requirements on the reliability and latency of the data exchanges. We discuss our experience in the deployment of an initial testbed consisting of 7 mesh nodes placed at intersections with traffic lights, and share the results and insights learned from our measurements and initial trials in the process.

Analysis of intersection delay under real-time adaptive signal control

The United States Department of Transportation has recently begun implementation of the national demonstration project for suburban Advanced Traffic Management Systems (ATMS) utilizing the Sydney Coordinated Adaptive Traffic System (SCATS). SCATS is an automated, real time, traffic responsive signal control strategy. The expected benefit from the system comes from its ability to constantly modify signal timing patterns to most effectively accommodate changing traffic conditions. The objectives of this research study were to analyze the differences in certain delay parameters which would occur as a result of implementing SCATS signal control. The study employed a macroscopic simulation procedure to compute intersection delay under both a strategy that changed signal timings once per hour and SCATS signal control. A comparison of delay under both forms of control is presented. The study findings demonstrated mixed results regarding the benefit of SCATS control. A general conclusion of the study was that SCATS distributed the delay across competing approaches more evenly. However, in some cases this resulted in an increase in the total intersection delay. The observed delay change was attributed primarily to the saturation equalization objective of the SCATS control program. SCATS attempts to allocate green time to the intersection approaches based on the degree of saturation. Under this philosophy the system is able to balance the percentage of green time between all approaches, resulting in more uniform delay.