Cyber-Physical Modeling of Intelligent Traffic Controllers Using Simulator for Urban Mobility (SUMO)

Transportation problems have always been a global concern. The challenges in traffic congestion were easily observed during pre-pandemic times. However, traffic congestion still persists even during the COVID-19 pandemic (2020 and present) where there has been less number of vehicles because of travel restrictions. The emergence of wireless communication technologies and intelligent transportation systems (ITS) pave the way for solving some of the problems found in the transportation industry. Subsequently, traffic control systems are used at various intersections to manage the flow of traffic and reduce car collisions. However, some intersections are better off without these traffic control systems. The proposed study will analyze a T-junction road in five different setups using different types of traffic controllers. The simulation tool used is SUMO. The study found that an adaptive or vehicle-actuated traffic controller is the ideal method for regulating traffic flow in a T-junction with a one-way or two-way main road. It was observed in the simulation that it reduced the potential car collisions in the non-TL junction. However, the average speed and completion time of the road network was affected by the method. © 2022 IEEE.

Urban Mobility in the Postpandemic Stage: A Comprehensive Investigation of a Variety of Cities in China

The global outbreak of coronavirus disease 2019 (COVID-19) has affected the urban mobility of nations around the world. The pandemic may even have a potentially lasting impact on travel behaviors during the post-pandemic stage. China has basically stopped the spread of COVID-19 and reopened the economy, providing an unprecedented environment for investigating post-pandemic travel behaviors. This study conducts multiple investigations to show the changes in travel behaviors in the post-pandemic stage, on the basis of empirical travel data in a variety of cities in China. Specifically, this study demonstrates the changes in road network travel speed in 57 case cities and the changes in subway ridership in 26 case cities. Comprehensive comparisons can indicate the potential modal share in the post-pandemic stage. Further, this study conducts a case analysis of Beijing, where the city has experienced two waves of COVID-19. The variations in travel speed in the road network of Beijing at different stages of the pandemic help reveal the public's responses towards the varying severity of the pandemic. Finally, a case study of the Yuhang district in Hangzhou is conducted to demonstrate the changes in traffic volume and vehicle travel distance amid the post-pandemic stage based on license plate recognition data. Results indicate a decline in subway trips in the post-pandemic stage among case cities. The vehicular traffic in cities with subways has recovered in peak hours on weekdays and has been even more congested than the pre-pandemic levels;whereas the vehicular traffic in cities without subways has not rebounded to pre-pandemic levels. This situation implies a potential modal shift from public transportation to private vehicular travel modes. Results also indicate that commuting traffic is sensitive to the severity of the pandemic. This may be because countermeasures, e.g., work-from-home and suspension of non-essential businesses, will be implemented if the pandemic restarts. The travel speed in non-peak hours and on non-workdays is higher than pre-pandemic levels, indicating that non-essential travel demand may be reduced and the public's vigilance towards the pandemic may continue to the post-pandemic stage. These findings can help improve policymaking strategies in the post-pandemic new normal. © 2023 American Society of Civil Engineers.

Mapping the Suitability of Existing and Probable Isolation Facilities

This study aimed to map the accessibility of the existing isolation facilities in Cabagan, Isabela, and propose probable locations suitable for establishing isolation facilities using the Geographic Information System (GIS). Digital datasets of the current isolation facilities were used in the study, along with factors such as land uses, hazards, landfills, and road networks that should be taken into consideration when choosing potential locations for isolation facilities. These factors follow the guidelines set by the Department of Health (DOH). The processing and generation of layers related to the criteria were done using GIS techniques, specifically overlay analysis tools. In order to project an appropriate map of potential isolation facilities in Cabagan, Isabela, the layers were combined and overlaid. The existing isolation facilities are accessible to Milagros Albano District Hospital (MADH) since all of them are adjacent to national or barangay roads. More than half, or 65.38%, of the isolation facilities, belong to areas with low to moderate susceptibility to flooding, and 26.92% are in areas with high susceptibility to flooding. Furthermore, all isolation facilities are open to the public, with 53.85% of existing isolation facilities in residential areas, 7.69% in commercial areas, and 38.46% in agricultural areas. The suitability map of proposed isolation facilities was successfully generated, showing that 100% of the proposed isolation facilities are accessible from any road network in the municipality with low and moderate susceptibility to flooding and low susceptibility to landslides. © 2022 IEEE.

A Simulation-Heuristic Approach to Optimally Design Drone Delivery Systems in Rural Areas

In recent years, drone delivery has become one of the most widely adopted emerging technologies. Under the current Covid-19 pandemic, drones greatly improve logistics, especially in rural areas, where inefficient road networks and long distances between customers reduce the delivery capacity of conventional ground vehicles. Considering the limited flight range of drones, charging stations play essential roles in the rural delivery system. In this study, we utilize simulation to optimize the drone delivery system design, in order to minimize the cost of serving the maximum capacity of customers. As facility siting is usually difficult to optimize, we propose a novel simulation-heuristic framework that continuously improves the objective to find near-optimal solutions. In addition, we conduct a case study using real-world data collected from Knox County, Tennessee. The results suggest that the proposed approach saves over 15% on total costs compared with the benchmark. © 2022 IEEE.

A Parcel Delivery Scheduling Scheme in Road Networks

Due to COVID-19, ordering food through online shopping increased. Accordingly, the use of logistics and delivery services is also increasing. As the number of parcels to be delivered gets bigger, the efficiency of the delivery mechanism and battery efficiency becomes important. The problem of finding the route traveling several destinations at once is called as Traveling Salesman Problem (TSP). There are several algorithms suggested to solve it in polynomial time. Among them, this paper experimented to compare the performance of two algorithms, the greedy algorithm, and the branch-and-bound algorithm. We used the Simulation of Urban Mobility (SUMO) program to test the vehicle running based on the calculated route by two algorithms. The average running time and charging time are recorded to evaluate the performance. Through this experiment, we found out that the branch-and-bound algorithm provides in a faster route selection and consumes less battery than the greedy algorithm. © 2022 IEEE.

Transportation System Operating Characteristics Analysis of Beijing during COVID-19

Traffic operation has shown abnormal characteristics during COVID-19. This paper obtains traffic data from multiple fields in Beijing for the whole year of 2020, combines traffic operation data with the number of confirmed cases, and deeply explores the operating characteristics of road networks, public transportation, and intercity transportation at various stages during the major epidemic. The results showed that travel demand decreased significantly during the epidemic period. From the perspective of urban road network traffic pressure, the demand for rigid travel in peak hours during the epidemic recovery period is relatively large. Based on this research, it can provide decision support for the government to formulate relevant prevention and control measures and policies, thereby improving the ability of urban traffic to respond to public health emergencies. © ASCE.

The COVID-19 Pandemic's Impact on Traffic Fatalities in Utah

During the COVID-19 pandemic, the road network in the state of Utah experienced a major drop in traffic volume. During this time, there was also a decrease in the number of crashes. However, while there was less traffic and fewer crashes, the number of fatal crashes increased. This study aims to identify trends and changes in traffic fatalities during the pandemic. The data shows that traffic fatalities in 2020 increased by 20.8% from 2019 and by 14.6% from the average of the last 10 years. The different attributes of the fatal crashes were investigated. The types of attributes that increased significantly during the pandemic include intersection-related, older driver-related, motorcycle-related, and speed-related fatal crashes. Other types of fatal crashes decreased including work zone-related, distracted driving-related, teenage driver-related, and pedestrian-related fatal crashes. © 2022 IEEE.

Roads and Goal 8 of the 2030 Sustainable Development Agenda

Through the experience of a trip to Rwanda, the author describes the role of roads in achieving the Sustainable Development Goals of the United Nations 2030 Agenda. Specifically, the article focuses on goal 8 "Decent work and economic growthThe author also reviews the theories of the last 50 years which try to explain how transport influences the general development of a society Growth, employment, inclusion, training, health, safety, sustainable tourism, appearance of financial services ... All these are the benefits of a quality road network that allows agile and efficient mobility The article concludes by referring to the negative consequences that the pandemic has had and will have on the economic evolution of the poorest countries of the planet.,,,,,,. © 2021 Asociacion Espanola de la Carretera. All rights reserved.

Comparing the Impact of Road Networks on COVID-19 Severity between Delta and Omicron Variants: A Study Based on Greater Sydney (Australia) Suburbs.

The Omicron and Delta variants of COVID-19 have recently become the most dominant virus strains worldwide. A recent study on the Delta variant found that a suburban road network provides a reliable proxy for human mobility to explore COVID-19 severity. This study first examines the impact of road networks on COVID-19 severity for the Omicron variant using the infection and road connections data from Greater Sydney, Australia. We then compare the findings of this study with a recent study that used the infection data of the Delta variant for the same region. In analysing the road network, we used four centrality measures (degree, closeness, betweenness and eigenvector) and the coreness measure. We developed two multiple linear regression models for Delta and Omicron variants using the same set of independent and dependent variables. Only eigenvector is a statistically significant predictor for COVID-19 severity for the Omicron variant. On the other hand, both degree and eigenvector are statistically significant predictors for the Delta variant, as found in a recent study considered for comparison. We further found a statistical difference (p < 0.05) between the R-squared values for these two multiple linear regression models. Our findings point to an important difference in the transmission nature of Delta and Omicron variants, which could provide practical insights into understanding their infectious nature and developing appropriate control strategies accordingly.

Correlation and Spatial Analysis between the number of Confirmed Cases of the COVID-19 and Traffic Volume based on Taxi Movement Data

The spread and damage of COVID-19 are putting significant pressure on the world, including Korea. Most countries place restrictions on movement and gathering to minimize contact between citizens and these policies have brought new changes to social patterns. This study generated traffic volume data on the scale of a road network using taxi movement data collected in the early stages of the COVID-19 third pandemic to analyze the impact of COVID-19 on movement patterns. After that, correlation analysis was performed with the data of confirmed cases in Daegu Metropolitan City and Local Moran's I was applied to analyze the effect of spatial characteristics. As a result, in terms of the overall road network, the number of confirmed cases showed a negative correlation with taxi driving and at least -0.615. It was confirmed that citizens' movement anxiety was reflected as the number of confirmed cases increased. The commercial and industrial areas in the center of the city confirmed the cold spot with a negative correlation and low-low local Mona's I. However, the road network around medical institutions such as hospitals and spaces with spatial characteristics such as residential complexes was high-high. In the future, this analysis could be used for preventive measures for policymakers due to COVID-19. © 2021 Korean Society of Surveying. All rights reserved.

Suburban Road Networks to Explore COVID-19 Vulnerability and Severity.

The Delta variant of COVID-19 has been found to be extremely difficult to contain worldwide. The complex dynamics of human mobility and the variable intensity of local outbreaks make measuring the factors of COVID-19 transmission a challenge. The inter-suburb road connection details provide a reliable proxy of the moving options for people between suburbs for a given region. By using such data from Greater Sydney, Australia, this study explored the impact of suburban road networks on two COVID-19-related outcomes measures. The first measure is COVID-19 vulnerability, which gives a low score to a more vulnerable suburb. A suburb is more vulnerable if it has the first COVID-19 case earlier and vice versa. The second measure is COVID-19 severity, which is proportionate to the number of COVID-19-positive cases for a suburb. To analyze the suburban road network, we considered four centrality measures (degree, closeness, betweenness and eigenvector) and core-periphery structure. We found that the degree centrality measure of the suburban road network was a strong and statistically significant predictor for both COVID-19 vulnerability and severity. Closeness centrality and eigenvector centrality were also statistically significant predictors for COVID-19 vulnerability and severity, respectively. The findings of this study could provide practical insights to stakeholders and policymakers to develop timely strategies and policies to prevent and contain any highly infectious pandemics, including the Delta variant of COVID-19.