Traffic Signal System Research Articles

Data-Driven Ranking of Coordinated Traffic Signal Systems for Maintenance and Retiming

Automated traffic signal performance measures (ATSPMs) have been deployed with increasing frequency. At present, the existing ATSPMs are focused on the performance of individual movements or intersections. As the number of ATSPM users has increased, a need for system-level metrics has emerged. This paper proposes a method of evaluating corridor performance at the system level using high-resolution data. The method is demonstrated for eight signalized corridors in Indiana, including 87 intersections. This method develops five subscores for the areas of communication, detection, safety, capacity allocation, and progression; these five interrelated aspects of performance are each given a category subscore based on quantitative performance measures, with scales appropriate to the context of the operation. An overall score for each corridor is determined from the lowest subscore of each of the five areas. This approach simplifies the analysis process, as opposed to examining several hundred individual movements as currently would be required using ATSPM tools that are commonly available at present. The methodology is presented as a prototype for further development and adaptation to individual agency objectives and data sources.

Participatory Design for Improving Pedestrian and Traffic Sign System at Anuban Ranong School

The objective of this study is to design and improve the pedestrian environment and traffic signs with the participatory design at Ranong Kindergarten School. The result of this study was brought to identify the promotion of pedestrian safety zone. In the participatory process, the four groups were started up for collaboration including teacher and student group, local community group, academics group, and private organization group. The important finding was the “Brave Walk Unit,” the volunteer students who are the driven mechanism to promote other students who live near the school to walk or ride the bicycle to school on their own.

Upgrade Evaluation of Traffic Signal Assets: High-resolution Performance Measurement Framework

Agencies that have large-scale traffic signal systems under their purview often have to face asset upgrade decisions. As one of the most advanced traffic control technologies, Adaptive Traffic Control Systems (ATCS) are among the options that must be taken into consideration. Having in mind the complexity of benefits and costs stemming from ATCS investments, there is a need for information-rich performance measures (PM) used in the evaluation and decision-making. However, individual PMs are often not suitable for evaluating the multidimensionality of ATCS operations, due the inherent variability of ATCS control parameters. To expand the range of PMs used in ATCS evaluation, this research develops a new PM, i.e., average arrivals on green ratio, and proposes a refinement of average delay PM to account for queue formation. The paper also presents an application framework for a multi-criteria analysis, assuming a combination of the proposed and existing PMs. In addition to presenting the analytical PM formulation, the evaluation methodology uses microsimulation for a case study comparison between actuated-coordinated and ATCS operations. The results include a comparison between previous and proposed PMs, based on the processed simulation data as well as field data. In conclusion, the proposed PMs have a high transferability potential, low data collection cost, and high data quality, thus being suitable for use in decision processes for signal asset investment. Finally, this research opens up further opportunities for advancing decision-support methods for traffic operations asset management.

Self-organization models of urban traffic lights based on digital infochemicals

This paper presents a self-organizing model to design effective traffic signaling strategies in order to reduce traffic congestion in urban areas. The proposed traffic signaling system is based on a pattern model of self-organization, i.e., digital infochemicals (DIs), which are analogous to chemical substances that convey information between interactive elements mediated via the environment. In the context of traffic systems, the DIs refer to information generated by vehicles and dissipated by the urban transportation infrastructure. Based on the exploratory analysis with one single intersection, we demonstrate that the DI-based strategy performs significantly better than both the fixed and trigger-based scheduling strategies in terms of queue length and waiting time under both fixed and dynamic traffic demands.

Classification of traffic signal system anomalies for environment tests of autonomous vehicles

Abstract In the future there will be a lot of changes and development concerning autonomous transport that will affect all participants of transport. There are still difficulties in organizing transport, but with the introduction of autonomous vehicles more challenges can be expected. Recognizing and tracking horizontal and vertical signs can cause a difficulties for drivers and, later, for autonomous systems. Environmental conditions, deformity and quality affect the perception of signals. The correct recognition results in safe travelling for everyone on the roads. Traffic signs are designed for people that is why the recognition process is harder for the machines. However, nowadays some developers try to create a traffic sign that autonomous vehicles can use. Computer identification needs further development, as it is necessary to consider cases where traffic signs are deformed or not properly placed. In the following investigation, the advantages and disadvantages of the different perception methods and their possibilities were gathered. A methodology for the classification of horizontal and vertical traffic signs anomalies that may help in designing better testing and validation environments for traffic sign recognition systems in the future was also proposed.

Peer-to-Peer Priority Signal Control Strategy in a Connected Vehicle Environment

This paper presents a methodology that enhances the priority signal control model in the multi-modal intelligent traffic signal system (MMITSS). To overcome the range limit of vehicle to infrastructure (V2I) and the intersection geometry message (MAP) distance limits, peer-to-peer intersection communications are utilized to send priority requests from adjacent intersections. Through integrated communication, the peer priority control strategy can create a signal plan for prioritized vehicles that considers longer term (headway) arrival times. Transit vehicles are considered in this study. The longer-term signal plan provides a flexible signal schedule that allows local phase actuation. The peer priority strategy is effective in reducing the number of stops and delay for priority eligible vehicles, while minimizing the negative impact on regular vehicles. To validate the strategy, a simulation experiment was designed to compare fully actuated control, coordination, and MMITSS priority control using two different VISSIM simulation networks (Arizona and Utah). The result shows that the peer-to-peer long term planning strategy can improve transit service reliability while limiting the adverse impact on other traffic.

Trajectory Length Prediction for Intelligent Traffic Signaling: A Data-Driven Approach

Ship trajectory length prediction is vital for intelligent traffic signaling in the controlled waterways of the Yangtze River. In current intelligent traffic signaling systems (ITSSs), ships are supposed to travel exactly along the central line of the Yangtze River, which is often not a valid assumption and has caused a number of problems. Over the past few years, traffic data have been accumulated exponentially, leading to the big data era. This trend allows more accurate prediction of ships’ travel trajectory length based on historical data. In this paper, ships’ historical trajectories are first grouped by using the fuzzy c-means clustering algorithm. The relationship between some known factors (i.e., ship speed, loading capacity, self-weight, maximum power, ship length, ship width, ship type, and water level) and the resultant memberships are then modeled using artificial neural networks. The trajectory length is then estimated by the sum of the predicted probabilities multiplied by the trajectory cluster centers’ length. To the best of our knowledge, this is the first time to predict the overall trajectory length of manually controlled ships. The experimental results show that the proposed method can reduce the probability of generating incorrect traffic control signals by 74.68% over existing ITSSs. This will significantly improve the efficiency of the Yangtze River traffic management system and increase the traffic capacity by reducing the traveling time.

Development of Traffic Flow Measurement System Using Fixed Point Cameras

Recently, traffic congestions caused by increase of vehicle possession and complicatedness of traffic system have induced several serious social problems. In order to solve these problems, a lot of attempts have been carried out in many areas including new type of traffic signal system employing fuzzy control or neural network system. In addition to that system, a visualized miniature traffic simulation system based on real road system has been developed to examine the performance of the new traffic signal system and its effectiveness has been proved in several problems, which cannot sufficiently model that is able to reproduce the real traffic behaviors. In this study, a traffic flow measurement system has been developed to extract traffic flow data by analyzing images from the fixed point cameras set up near intersections. The measurement system has been developed by optical flow and R-CNN, and its performance was evaluated based on the recognition rate of the number of cars passing the intersection and the recognition rate of matching for same vehicle and the accuracy of the means speed estimated by the difference of passage time at two intersections. The result showed that the new system has higher rate of matching for same vehicle than previous study.

A Study on the application simplified traffic signal system at the intersection of the city back road

A Study on the application simplified traffic signal system at the intersection of the city back road

Quantitative Analysis of Smooth Progression in Traffic Signal Systems

AbstractTraffic signal coordination has been widely used to provide smooth progression for platoons on signalized arterials to enhance performance measures such as travel times, number of stops, an...

Context‐sensitive, first‐principles approach to bicycle speed estimation

Bicycle speed estimation is important for geometric design, traffic signal operations, microsimulation models, and health and safety assessment, among other applications. Bicycle speeds can vary greatly with the characteristics and power output of the rider and with travel conditions, especially road grade. This study presents a mathematical framework to address the non-trivial and practical problem of estimating bicycle free-flow speeds in a way that is sensitive to cyclist and roadway attributes. A closed expression is derived from first principles to determine speed from bicyclist power output. The method is extended to the problem of speed estimation for bicycles with limited gearing. Results are consistent with speed surveys in the literature. Application of the method to clearance interval calculation demonstrates the importance of context-sensitive bicycle speed estimation for advanced traffic signal systems.

Strategic Methods for Modernizing Traffic Signal Maintenance Management and Quantifying the Impact of Maintenance Activities

AbstractIn a period of increasingly constrained budgets, traffic engineers must develop more cost-effective maintenance strategies for traffic signal systems. Existing literature suggests that many...

An Intelligent Vehicular Traffic Signal Control System with State Flow Chart Design and FPGA Prototyping

The problem of vehicular traffic congestion is a persistent constraint in the socio-economic development of Pakistan. This paper presents design and implementation of an intelligent traffic controller based on FPGA (Field Programmable Gate Array) to provide an efficient traffic management by optimizing functioning of traffic lights which will result in minimizing traffic congestion at intersections. The existent Traffic Signal system in Pakistan is fixed-time based and offers only Open Loop method for Traffic Control. The Intelligent Traffic Controller presented here uses feedback sensors to read the Traffic density present at a four way intersection to provide an efficient alternative for better supervisory Control of Traffic flow. The traffic density based control logic has been developed in a State Flow Chart for improved visualization of State Machine based operation, and implemented as a Subsystem in Simulink and transferred into VHDL (Hardware Description Language) code using HDL Coder for reducing development time and time to market, which are essential to capitalize Embedded Systems Market. The VHDL code is synthesized with Altera QUARTUS, simulated timing waveform is obtained to verify correctness of the algorithm for different Traffic Scenarios. For implementation purpose estimations were obtained for Cyclone-III and Stratix-III.

Improving Pedestrian Environment and Traffic Sign System with the Participatory Design at Anuban Ranong School

The objective of this study is to design and improve the pedestrian environment and traffic signs with the participatory design at Ranong Kindergarten School. The result of this study was brought to identify the promotion of pedestrian safety zone. In the participatory process, the four groups were started up for collaboration including teacher and student group, local community group, academics group, and private organization group. The important finding was the “Brave Walk Unit,” the volunteer students who are the driven mechanism to promote other students who live near the school to walk or ride the bicycle to school on their own.Keywords: Participatory Design; Pedestrian Environment; Traffic Signs System, School zoneISSN: 2398-4287© 2017. The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.

Operational Readiness of Traffic Signal System with Human Error under Environmental Conditions Using Neural Network Approach

Operational Readiness of Traffic Signal System with Human Error under Environmental Conditions Using Neural Network Approach

Density Based Traffic Signal System Using Image Processing

Density Based Traffic Signal System Using Image Processing

Drawing an Unwritten Common Law: The Normative Pictograms of Christiania

If we exclude a few very specific fields, such as the traffic sign system, pictorial normative representation does not seem to play an important role in legal communication. In this paper, I will illustrate an example of pictorial normative representation expressing a set of norms that are fundamental to a community's very existence: this is the case of Christiania Freetown's Common Law. Christiania is a self-proclaimed autonomous neighbourhood located in the centre of Copenhagen. The norms of Christiania's Common Law are pictorially represented and displayed on a number of posters throughout Christiania's territory. These normative images prima facie appear to share certain features with traffic signs, despite the fact that, as the examples will make it clear, they are different in kind. Indeed, while the physical support of a traffic sign and its location are a necessary condition for the norm to be binding, Christiania's Common Law seems to be in force independently of its iconic representation and its concrete material support. On the other hand, even if the meanings of the different pictograms are not connected with the physical place in which they are located, their deeper meaning can only be fully understood on the basis of Christiania's cultural and institutional background.

Implementation Of Adaptive Control Algorithm To Overcome The Traffic Congestion Problems Of Karachi

Traffic controlling and management is a severe issue of urban cities as well as on high ways in developing countries like South Asian countries but here particularly, in Pakistan. The traffic congestion problem is becoming more severe with time. The main reason behind all this is a drastic increase in number of running vehicles and no proper road infrastructure. Not only, the traffic congestion is a problem, whereas, the other problems are also associated with this issue are air pollution, wastage of resources like fuel, energy, time etc. are the foremost anomalies faced by every human being of third world countries. It is also one of the major reasons of global warming and exploitation of natural life system. Currently, in Pakistan traffic controlling is done through time based traffic control signal system normally. In this paper, we have proposed a system which dynamically controls and manage the road traffic through image processing on real time and will take the decision on actual grounds for traffic routes and on traffic signal timing. In this system, we utilize the latest technologies of image processing which collects, organize and transmits the information to existing system to incorporate the new real time data for traffic timing control. It will save the people from road accidents and unnecessary wastage of fuel resources and more importantly make the life little bit more relax then others.

Assessment of the Robustness of Signal Timing Plans in an Arterial Corridor Through Seasonal Variation of Traffic Flows

Arterial traffic signal systems, predominantly in the United States, deploy multiple signal timing plans to account for daily variability of traffic demand. Those types of traffic flow deviations should be anticipated when timing plans are designed and, therefore, serviced satisfactorily. When traffic flow patterns are no longer predictable, a predetermined time-of-day (TOD) plan may no longer be the optimal one. This research aimed to examine signal timing optimality by applying a method similar to the selection of a traffic responsive plan to recognize automatically the best timing plan suited to current traffic conditions. The proposed method attempted to determine whether the optimality of signal timing settings could have been effectively estimated when systematic detector counts of the major approach were available. The study used 4 months of data from field microwave detectors coupled with data of turning-movement counts obtained over several days. The findings show that TOD signal timing plans mainly depended on adequate data collection that best describes a specific set of traffic conditions. Thus, the designed plan was as optimal as the related traffic information was reliable, whereas a problem arose in the case of limited-availability and low-quality data. New technologies are capable of collecting and storing massive amounts of data. Even if the granularity of collected data is low, the data can be used to improve traffic performance (i.e., reduce corridor delay). This realization could be of particular importance to traffic agencies that have installed, or plan to install, new field devices.

Long-Term Ship Speed Prediction for Intelligent Traffic Signaling

Yangtze River is probably the world's busiest inland waterway. Ships need to be guided when passing through a controlled waterway based on their long-term speed prediction. Inaccurate ship speed prediction leads to nonoptimal traffic signaling, which may cause a significant traffic jam. For the existing intelligent traffic signaling system, the ship speed is assumed to be constant, which has caused many problems and issues. This paper proposes a novel algorithm to construct an improved multilayer perceptron (MLP) network for accurate long-term ship speed prediction, in which the hidden neurons of the MLP are optimized by the particle swarm optimization method. The effectiveness and efficiency of the method are guaranteed by using the orthogonal least squares method, which is the fast approach for the construction of the MLP network in a stepwise forward procedure. The model is driven by easily acquired dynamic data of the ships, including the speed and the position. The effectiveness of the proposed method is further confirmed by comparing with several traditional modeling techniques. To the best of our knowledge, this is the first time that a ship speed model is built for long-term prediction. The experimental results show that the developed model is in good agreement with the real-life data, with more than 97% accuracy. It will help to generate the optimal traffic commands for Yangtze River in an intelligent traffic signaling system.