ANFIS traffic signal controller for an isolated intersection

Traffic signal controlling is one of the solutions to reduce the traffic congestion in cities. To set appropriate green times for traffic signal lights, we have applied Adaptive Neuro-Fuzzy Inference System (ANFIS) method in traffic signal controllers. ANFIS traffic signal controller is used for controlling traffic congestion of a single intersection with the purpose of minimizing travel delay time. The ANFIS traffic controller is an intelligent controller that learns to set an appropriate green time for each phase of traffic signal lights at the start of the phase and based on the traffic information. The controller uses genetic algorithm to tune ANFIS parameters during learning time. The results of the experiments show higher performance of the ANFIS traffic signal controller compared to three other traffic controllers that are developed as benchmarks. One of the benchmarks is GA-FLC (Araghi et al., 2014), next one is a fixed-FLC, and a fixed-time controller with three different values for green phase. Results show the higher performance of ANFIS controller.

Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network

Traffic congestion in urban roads is one of the biggest challenges of 21 century. Despite a myriad of research work in the last two decades, optimization of traffic signals in network level is still an open research problem. This paper for the first time employs advanced cuckoo search optimization algorithm for optimally tuning parameters of intelligent controllers. Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are two intelligent controllers implemented in this study. For the sake of comparison, we also implement Q-learning and fixed-time controllers as benchmarks. Comprehensive simulation scenarios are designed and executed for a traffic network composed of nine four-way intersections. Obtained results for a few scenarios demonstrate the optimality of trained intelligent controllers using the cuckoo search method. The average performance of NN, ANFIS, and Q-learning controllers against the fixed-time controller are 44%, 39%, and 35%, respectively.

Applications of redundancy analysis for the detection of chemical response patterns to air pollution in lichen

The lichens Ramalina celastri (Spreng.) Krog & Swinsc., Punctelia microsticta (Müll. Arg.) Krog and Canomaculina pilosa (Stizenb.) Elix & Hale were transplanted simultaneously to 17 urban-industrial sites in a northwestern area of Córdoba city, Argentina. The transplantation sites were set according to different environmental conditions: traffic, industries, tree cover, building height, topographic level, position in the block and distances from the river and from the power plant. Three months later, chlorophyll a, chlorophyll b, phaeophytin a, soluble proteins, hydroperoxy conjugated dienes, malondialdehyde concentration and sulfur accumulation were determined, and a pollution index was calculated for each sampling site. Redundancy analysis was applied to detect the variation pattern of the lichen variables that can be 'best' explained by the environmental variables considered. The present study provides information about both the specific pattern response of each species to atmospheric pollution, and environmental conditions that determine it. As regards pollutants emission sources R. celastri showed a chemical response associated mainly with pollutant released by the power plant and traffic. P. microsticta and C. pilosa responded mainly to industrial sources. Regarding environmental conditions that affect the spreading of air pollutants and their incidence on the bioindicator, the topographic level and tree cover surrounding the sampling site were found to be important for R. celastri, tree cover surrounding the sampling site and the building height affected P. microsticta, while building height did so for C. pilosa.

A review on computational intelligence methods for controlling traffic signal timing

Urban traffic as one of the most important challenges in modern city life needs practically effective and efficient solutions. Artificial intelligence methods have gained popularity for optimal traffic light control. In this paper, a review of most important works in the field of controlling traffic signal timing, in particular studies focusing on Q-learning, neural network, and fuzzy logic system are presented. As per existing literature, the intelligent methods show a higher performance compared to traditional controlling methods. However, a study that compares the performance of different learning methods is not published yet. In this paper, the aforementioned computational intelligence methods and a fixed-time method are implemented to set signals times and minimize total delays for an isolated intersection. These methods are developed and compared on a same platform. The intersection is treated as an intelligent agent that learns to propose an appropriate green time for each phase. The appropriate green time for all the intelligent controllers are estimated based on the received traffic information. A comprehensive comparison is made between the performance of Q-learning, neural network, and fuzzy logic system controller for two different scenarios. The three intelligent learning controllers present close performances with multiple replication orders in two scenarios. On average Q-learning has 66%, neural network 71%, and fuzzy logic has 74% higher performance compared to the fixed-time controller.

Design of an optimal ANFIS traffic signal controller by using cuckoo search for an isolated intersection

An optimal design of Adaptive Neuro-Fuzzy Inference System (ANFIS) traffic signal controller is presented in this paper. The proposed controller aims to adjust a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. The ANFIS controller is trained, to learned how to set green times for each traffic phase. This intelligent controller uses the Cuckoo Search (CS) algorithm to tune its parameters during the learning pried. Evaluating the performance of the proposed controller in comparison with the performance of a FLS controller (FLC) with predefined rules and membership functions, and also three fixed-Time controllers, illustrates the better performance of the optimal ANFIS controller against the other benchmark controllers.

Design of distributed adaptive neural traffic signal timing controller by cuckoo search optimization

This paper focuses on designing an adaptive controller for controlling traffic signal timing. Urban traffic is an inevitable part in modern cities and traffic signal controllers are effective tools to control it. In this regard, this paper proposes a distributed neural network (NN) controller for traffic signal timing. This controller applies cuckoo search (CS) optimization methods to find the optimal parameters in design of an adaptive traffic signal timing control system. The evaluation of the performance of the designed controller is done in a multi-intersection traffic network. The developed controller shows a promising improvement in reducing travel delay time compared to traditional fixed-time control systems.

Characterising indoor air temperature and humidity in Australian homes

Information about indoor air temperatures in residential buildings is of interest for a range of reasons, e.g. the health and comfort of occupants, energy demand for space heating and cooling. To date there have been few long term studies that measure and characterise indoor air temperatures in Australian homes. New primary research undertaken by the authors measured temperatures in 273 homes over the period 2011 to 2014 in seven climate zones, from Melbourne in the south to Cairns in the north of Australia. Humidity data was also collected in 20 homes. This paper is a description of the data collected and the subsequent analysis.

Indoor temperatures were compared with outdoor temperatures and a mathematical model was fitted to the data. In general, monthly average indoor temperatures were found to be 2 degreesC higher than monthly average outdoor temperatures, apart from periods with consistently cold weather, where the monthly average outdoor temperature was less than 20 degreesC, which were found to have larger differences. The indoor temperature model developed has been compared with data measured by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) in 438 homes in three Australian cities. The model developed using project measurements are highly consistent with the CSIRO data.

Further data collection compared indoor and outdoor humidity in 20 houses in Sydney and Melbourne. The indoor humidity ratio was found to be, on average, slightly higher than outdoors, but indoor levels generally track outdoor levels quite closely. This is likely due to the high air exchange rate in most houses.