Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms

Nowadays, many cities have problems with traffic congestion at certain peak hours, which produces more pollution, noise and stress for citizens. Neural networks (NN) and machine-learning (ML) approaches are increasingly used to solve real-world problems, overcoming analytical and statistical methods, due to their ability to deal with dynamic behavior over time and with a large number of parameters in massive data. In this paper, machine-learning (ML) and deep-learning (DL) algorithms are proposed for predicting traffic flow at an intersection, thus laying the groundwork for adaptive traffic control, either by remote control of traffic lights or by applying an algorithm that adjusts the timing according to the predicted flow. Therefore, this work only focuses on traffic flow prediction. Two public datasets are used to train, validate and test the proposed ML and DL models. The first one contains the number of vehicles sampled every five minutes at six intersections for 56 days using different sensors. For this research, four of the six intersections are used to train the ML and DL models. The Multilayer Perceptron Neural Network (MLP-NN) obtained better results (R-Squared and EV score of 0.93) and took less training time, followed closely by Gradient Boosting then Recurrent Neural Networks (RNNs), with good metrics results but the longer training time, and finally Random Forest, Linear Regression and Stochastic Gradient. All ML and DL algorithms scored good performance metrics, indicating that they are feasible for implementation on smart traffic light controllers.

A Road towards 6G Communication—A Review of 5G Antennas, Arrays, and Wearable Devices

Next-generation communication systems and wearable technologies aim to achieve high data rates, low energy consumption, and massive connections because of the extensive increase in the number of Internet-of-Things (IoT) and wearable devices. These devices will be employed for many services such as cellular, environment monitoring, telemedicine, biomedical, and smart traffic, etc. Therefore, it is challenging for the current communication devices to accommodate such a high number of services. This article summarizes the motivation and potential of the 6G communication system and discusses its key features. Afterward, the current state-of-the-art of 5G antenna technology, which includes existing 5G antennas and arrays and 5G wearable antennas, are summarized. The article also described the useful methods and techniques of exiting antenna design works that could mitigate the challenges and concerns of the emerging 5G and 6G applications. The key features and requirements of the wearable antennas for next-generation technology are also presented at the end of the paper.

Efficiency Assessment of a Signalized Roundabout and a Traffic Intersection in Baghdad City

In Baghdad city, Iraq, the traffic volumes have rapidly grown during the last 15 years. Road networks need to reevaluate and decide if they are operating properly or not regarding the increase in the number of vehicles. Al-Jadriyah intersection (a four-leg signalized intersection) and Kamal Junblat Square (a multi-lane roundabout), which are two important intersections in Baghdad city with high traffic volumes, were selected to be reevaluated by the SIDRA package in this research. Traffic volume and vehicle movement data were abstracted from videotapes by the Smart Traffic Analyzer (STA) Software. The performance measures include delay and LOS. The analysis results by SIDRA Intersection 8.0.1 show that the performance of the roundabout is better than the signalized intersection but experiences high delay, and low LOS. Therefore, alternatives are proposed to improve the performance for current and future traffic volumes with low-medium delays.

Older Adults’ Perceptions of Smart City Initiatives to Age in Community

This project explores older American adults’ perceptions of smart city initiatives for them to “age in community” particularly in the northeast region. As the U.S. population is aging, it is imperative that the American cities can support their citizens to live in their preferred community environments for as long as they want. While there are many definitions of a smart city, some exemplary smarty city initiatives can be characterized as actively utilizing information and sensor technologies to promote efficiency and sustainability of city-wide systems, ultimately enhancing the quality of citizens' life. This project examines, in particular, seven smart city initiatives that are implemented globally: smart streetlights, health and fall monitoring system, community ridesharing, enhanced CCTVs, “age-friendly map,” contact tracing app, and smart traffic system. By surveying those age 55 and older, with a representative sampling from the nine states in the northeast region, this project found that the vast majority of older Americans in this region would prefer to age in rural and suburban communities, and depending on where they prefer to age in (rural-exurban-suburban communities vs. urban-urban center communities) and gender (female vs. male), they perceive particular sets of smart city initiatives as more important for them to age in community. Furthermore, regardless of the community/location preference and demographic (gender, income level, and age) differences, 40% of the respondents expressed no concern of data or information privacy issues from these initiatives, opening some doors for the municipalities that plan to adopt some of these initiatives in the near future.