Scaling of average receiving time on double-weighted polygon networks

In this paper, we introduce a class of double-weighted polygon networks with two different meanings of weighted factors [Formula: see text] and [Formula: see text], which represent path-difficulty and path-length, respectively, based on actual traffic networks. Picking an arbitrary node from the hub nodes set as the trap node, and the double-weighted polygon networks are divided into [Formula: see text] blocks by combining with the iterative method. According to biased random walks, the calculation expression of average receiving time (ART) of any polygon networks is given by using the intermediate quantity the mean first-passage time (MFPT), which is applicable to any [Formula: see text] ([Formula: see text]) polygon networks. What is more, we display the specific calculation process and results of ART of the double-weighted quadrilateral networks, indicating that ART grows exponentially with respect to the networks order and the exponent is [Formula: see text] which grows with the product of [Formula: see text]. When [Formula: see text] increases, ART increases linearly ([Formula: see text]) or sublinearly ([Formula: see text]) with the size of networks, and the smaller value of [Formula: see text], the higher transportation efficiency.

Detection and Optimization of Traffic Networks Based on Voronoi Diagram

Traffic peak is an important parameter of modern transport systems. It can be used to calculate the indices of road congestion, which has become a common problem worldwide. With accurate information about traffic peaks, transportation administrators can make better decisions to optimize the traffic networks and therefore enhance the performance of transportation systems. We present a traffic peak detection method, which constructs the Voronoi diagram of the input traffic flow data and computes the prominence of candidate peak points using the diagram. Salient peaks are selected based on the prominence. The algorithm takes O(n log n) time and linear space, where n is the size of the input time series. As compared with the existing algorithms, our approach works directly on noisy data and detects salient peaks without a smoothing prestep and thus avoids the dilemma in choosing an appropriate smoothing scale and prevents the occurrence of removing/degrading real peaks during smoothing step. The prominence of candidate peaks offers the subsequent analysis the flexibility to choose peaks at any scale. Experiments illustrated that the proposed method outperforms the existing smoothing-based methods in sensitivity, positive predictivity, and accuracy.

Modeling and Analysis of Mixed Traffic Networks with Human-driven and Autonomous Vehicles

The emergence of connected and automated vehicles (CAV) indicates improved traffic mobility in future traffic transportation systems. This study addresses the research gap in macroscopic traffic modeling of mixed traffic networks where CAV and human-driven vehicles coexist. CAV behavior is explicitly included in the proposed traffic network model, and the vehicle number non-conservation problem is overcome by describing the approaching and departure vehicle number in discrete time. The proposed model is verified in typical CAV cooperation scenarios. The performance of CAV coordination is analyzed in road, intersection and network scenario. Total travel time of the vehicles in the network is proved to be reduced when coordination are applied. Simulation results validate the accuracy of the proposed model and the effectiveness of the proposed algorithm.

Analysis of the Possibility to Detect Road Vehicles via Bluetooth Technology

As road traffic networks become more congested and information systems are implemented to manage traffic flows, real-time data gathering becomes increasingly important. Classic detectors are placed in one point of the network and are able to provide information only from that area. As useful as this is, it lacks the big picture of the routes the vehicles usually travel. There are applications developed to help individuals make their way into the road network, but these are no solutions that deal with the cause of traffic; rather, they counteract the effects. It becomes obvious that a proper management system, with knowledge of all the relevant aspects will better serve all travelers. The detection solution proposed in this paper is based on Bluetooth detectors. This system is able to match detected devices in the road network, filter the results, and generate a vehicle count that is proved to follow RADAR detection results.

IMPACT OF COVID-19 ON EUROPEAN AND TURKEY AIR TRAFFIC NETWORKS

Covid-19, which was defined as a result of research conducted in a group of patients developing respiratory symptoms in late December 2019 in Wuhan province of China, spread to other countries in a very short time by infecting from people to people. On January 30, 2020, the World Health Organization declared the “International Public Health Emergency” due to the Covid-19 pandemic. Travel restrictions have been imposed by countries to prevent the pandemic. With these restrictions, air traffic has come to a halt, only health, humanitarian, military, repatriation and cargo flights have been carried out. Due to the Covid-19, more than 6 million traffic losses occurred in the European air traffic network. There were 0.2 million flight losses in the 9/11 attacks and 0.8 million flight losses in the great financial crisis. It is not known how long the recovery will take to reach traffic data in 2019. In this study, the impact of Covid-19 on the European and Turkey air traffic networks in 2020 was analyzed compared to the 2019 data. Within the scope of forecasts published by aviation authorities, assessments regarding the recovery process are also included. It is predicted that the impact of the Covid-19 pandemic on the air traffic networks will not be compensated for a long time in line with current data and predictions for the pandemic.