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.

Traffic Intersection Lane Control Using Radio Frequency Identification and 5G Communication

This article deals with automated urban traffic management, and proposes a new comprehensive infrastructure solution for dynamic traffic direction switching at intersection lines. It was assumed that the currently used solutions based on video monitoring are unreliable. Therefore, the Radio Frequency IDentification (RFID) technique was introduced, in which vehicles are counted and, if necessary, identified in order to estimate the flows on individual lanes. The data is acquired in real time using fifth-generation wireless communications (5G). The Pots and Ising models derived from the theory of statistical physics were used in a novel way to determine the state of direction traffic lights. The models were verified by simulations using data collected from real traffic observations. The results were presented for two exemplary intersections.

At the Traffic Intersection, Stopping, or Walking? Pedestrian Path Prediction Based on KPOF-GPDM for Driving Assistance

Pedestrian detection has always been a research hotspot in the Advanced Driving Assistance System (ADAS) with great progress in recent years. However, for the ADAS, we not only need to detect the behavior of pedestrians in front of the vehicle but also predict future action and the motion trajectory. Therefore, in this paper, we propose a human key point combined optical flow network (KPOF-Net) in the vehicle ADAS for the occlusion situation in the actual scene. When the vehicle encounters a blocked pedestrian at a traffic intersection, we used self-flow to estimate the global optical flow in the image sequence and then proposed a White Edge Cutting (WEC) algorithm to remove obstructions and simply modified the generative adversarial network to initialize pedestrians behind the obstructions. Next, we extracted pedestrian optical flow information and human joint point information in parallel, among which we trained four human key point models suitable for traffic intersections. At last, KPOF-GPDM fusion was proposed to predict the future status and walking trajectories of pedestrians, which combined optical flow information with human key point information. In the experiment, we did not merely compare our method with other four representative approaches in the same scene sequences. We also verified the accuracy of the pedestrian motion state and motion trajectory prediction of the system after fusion of human joint points and optical flow information. Taking into account the real-time performance of the system, in the low-speed and barrier-free environment, the comparative analysis only uses optical flow information, human joint point information, and KPOF-Net three prediction models. The results show that (1) in the same traffic environment, our proposed KPOF-Net can predict the change of pedestrian motion state about 5 frames (about 0.26 s) ahead of other excellent systems; (2) at the same time, our system predicts the trajectory of the pedestrian more accurately than the other four systems, which can achieve more stable minimum error ±0.04 m; (3) in a low-speed, barrier-free experimental environment, our proposed trajectory prediction model that integrates human joint points and optical flow information has higher prediction accuracy and smaller fluctuations than a single-information prediction model, and it can be well applied to automobiles’ ADAS.

Reading Vehicular Messages from Smart Road Signs: A Novel Method to Support Vehicle-to-Infrastructure in Rural Settings

Vehicle-to-infrastructure (V2I) communication is essential for reliable deployment of connected automated vehicle technology, contributing to the advanced safety and optimization of our transportation networks. However, supplying and maintaining necessary wireless infrastructure is a challenging task, particularly when it comes to rural areas. This study proposes a novel methodology that uses artificial intelligence, machine vision, and smart traffic signs to support V2I in areas where availability of wireless communication infrastructure is limited. The objective of this paper is to investigate the operational challenges of the proposed low-cost solution in different V2I applications, including a MapData message in an unsignalized traffic intersection, traveler information message in a work zone, and a red-light violation warning with the help of a smart sign. The proposed system showed some important advantages, such as invulnerability to third-party alterations and robust operation under harsh environmental conditions.

Assessment of Air Pollution Tolerance Index (APTI) for Selected Ornamental Plants in Urban Metropolis of Northern India

Present study aims to evaluate the tolerance and sensitivity of some selected common ornamental plants like Dracaena deremensis, Tagetes erecta and Dianthus caryophyllus by the Air Pollution Tolerance Index (APTI) at selected sites in an urban metropolis of Northern India, Delhi city. Air pollutant concentrations were monitored at Site I (nearby traffic intersection with less vegetation) and Site II (far by from traffic intersection with dense vegetation) and selected plant species were examined for their biochemical response during winter months (November 16, 2016 – February17, 2017). Based on the results, Dracaena deremensis and Tagetes erecta were segregated under tolerant whereas, Dianthus caryophyllus was under the sensitive category. Therefore, Dracaena deremensis and Tagetes erecta can be used as natural air filters or sinks for mitigation of air pollution and Dianthus caryophyllus as biomonitors or bioindicators.