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.

Joint optimization of rule placement and traffic engineering for QoS provisioning in software defined network

Software-Defined Network (SDN) is a promising network paradigm that separates the control plane and data plane in the network. It has shown great advantages in simplifying network management such that new functions can be easily supported without physical access to the network switches. However, Ternary Content Addressable Memory (TCAM), as a critical hardware storing rules for high-speed packet processing in SDN-enabled devices, can be supplied to each device with very limited quantity because it is expensive and energy-consuming. To efficiently use TCAM resources, we propose a rule multiplexing scheme, in which the same set of rules deployed on each node apply to the whole flow of a session going through but towards different paths. Based on this scheme, we study the rule placement problem with the objective of minimizing rule space occupation for multiple unicast sessions under QoS constraints. We formulate the optimization problem jointly considering routing engineering and rule placement under both existing and our rule multiplexing schemes. Via an extensive review of the state-of-the-art work, to the best of our knowledge, we are the first to study the non-routing-rule placement problem. Finally, extensive simulations are conducted to show that our proposals significantly outperform existing solutions.

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.

Distributed Q-learning controller for a multi-intersection traffic network

This paper proposes a Q-learning based controller for a network of multi intersections. According to the increasing amount of traffic congestion in modern cities, using an efficient control system is demanding. The proposed controller designed to adjust the green time for traffic signals by the aim of reducing the vehicles’ travel delay time in a multi-intersection network. The designed system is a distributed traffic timing control model, applies individual controller for each intersection. Each controller adjusts its own intersection’s congestion while attempt to reduce the travel delay time in whole traffic network. The results of experiments indicate the satisfied efficiency of the developed distributed Q-learning controller.

A child-centered scale of informal social control for Latino parents of preschool-age children: development and validation

Perceived neighborhood informal social control may determine whether parents allow their young children to be physically active in the neighborhood. We developed and validated a scale of neighborhood child-centered informal social control appropriate for Latino parents of preschool-age children. The scale was administered to 240 Latino parents, mainly mothers, recruited from neighborhoods cross-stratified by objectively measured crime and traffic safety. Participants completed measures of community cohesion, perceived signs of physical and social disorder, traffic safety and hazards, and perceived stranger danger. A subsample was reassessed 1 week later to determine test-retest reliability. Confirmatory factor analyses (CFAs) were conducted to examine the fit of the data to a priori measurement models. Construct validity was assessed by estimating the associations of the scale with the other measures. The scale showed good test-retest reliability, and factorial and construct validity. The scale needs to be cross-validated on other samples and Latino fathers.

A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.

Spatial coordinated medium sharing: Optimal access control management in drive-thru internet

Driven by the ever-growing expectation of ubiquitous connectivity and the widespread adoption of IEEE 802.11 networks, it is not only highly demanded but also entirely possible for in-motion vehicles to establish convenient Internet access to roadside WiFi access points (APs) than ever before, which is referred to as Drive-Thru Internet. The performance of Drive-Thru Internet, however, would suffer from the high vehicle mobility, severe channel contentions, and instinct issues of the IEEE 802.11 MAC as it was originally designed for static scenarios. As an effort to address these problems, in this paper, we develop a unified analytical framework to evaluate the performance of Drive-Thru Internet, which can accommodate various vehicular traffic flow states, and to be compatible with IEEE 802.11a/b/g networks with a distributed coordination function (DCF). We first develop the mathematical analysis to evaluate the mean saturated throughput of vehicles and the transmitted data volume of a vehicle per drive-thru. We show that the throughput performance of Drive-Thru Internet can be enhanced by selecting an optimal transmission region within an AP's coverage for the coordinated medium sharing of all vehicles. We then develop a spatial access control management approach accordingly, which ensures the airtime fairness for medium sharing and boosts the throughput performance of Drive-Thru Internet in a practical, efficient, and distributed manner. Simulation results show that our optimal access control management approach can efficiently work in IEEE 802.11b and 802.11g networks. The maximal transmitted data volume per drive-thru can be enhanced by 113.1% and 59.5% for IEEE 802.11b and IEEE 802.11g networks with a DCF, respectively, compared with the normal IEEE 802.11 medium access with a DCF.

A congestion control scheme based on fuzzy logic in wireless body area networks

One of the major challenges in healthcare wireless body area network (WBAN) applications is to control congestion. Unpredictable traffic load, many-to-one communication nature and limited bandwidth occupancy are among major reasons that can cause congestion in such applications. Congestion has negative impacts on the overall network performance such as packet losses, increasing end-to-end delay and wasting energy consumption due to a large number of retransmissions. In life-critical applications, any delay in transmitting vital signals may lead to death of a patient. Therefore, in order to enhance the network quality of service (QoS), developing a solution for congestion estimation and control is imperative. In this paper, we propose a new congestion detection and control protocol for remote monitoring of patients health status using WBANs. The proposed system is able to detect congestion by considering local information such as buffer capacity and node rate. In case of congestion, the proposed system differentiates between vital signals and assigns priorities to them based on their level of importance. As a result, the proposed approach provides a better quality of service for transmitting highly important vital signs.

Movement-based incentive for cellular traffic offloading through D2D communications

Due to the various applications for smartphones, mobile data traffic is growing at an unprecedented rate. The cellular network is suffering from traffic overloaded currently. Offloading part of the cellular traffic through opportunistic contact between mobile devices is a promising solution to solve the overload problem. However, due to the uneven distribution of devices and regular mobility of smartphone users, the contacts between mobile devices are opportunistic, the cellular traffic offloading approach results in poor performance, i.e., the relay user contacts with other mobile users with small probability. In this paper, we are the first to propose a movement-based incentive mechanism for cellular traffic offloading, where we control the mobility of relay users to improve the performance of traffic offloading. The movement-based incentive mechanism contains a relay user selection algorithm and a payment determination algorithm. Comparing with existing solutions, our proposed movement-based incentive mechanism has better performance.

Control layer resource management in SDN-IoT networks using multi-objective constraint

Software Defined Networking (SDN) and Internet of Things (IoT) integration has thrown many critical challenges. Specifically, in heterogeneous SDN-IoT ecosystem, optimized resources utilization and effective management at the control layer is very difficult. This mainly affects the application specific Quality of Service (QoS) and energy consumption of the IoT network. Motivated from this, we propose a new Resource Management (RM) method at the control layer, in distributed SDN-IoT networks. This paper starts with reasons that why at control layer RM is more complex in the SDN-IoT ecosystem. After-that, we highlight motivated examples that necessitate to investigate new RM methods in SDN-IoT context. Further, we propose a novel method to compute controller performance. Theoretical analysis is conducted to prove that the proposed method is better than the existing methods.