Predicting traffic sound levels in Cairo before, during, and after the COVID-19 lockdown using Predictor-LimA software

This study develops a replicable urban toolkit for decision-makers to improve the quality of life in Cairo for residents and visitors affected by traffic noise. Our case study in Cairo was selected using the Aviation Design Environmental Tool (AEDT) used worldwide for airports. To simulate the COVID-19 era and days after, the noise contour mapping was performed using the Predictor-LimA software at eight receiver locations at six building heights with three assumptions of 100 %, 70 %, and 50 % traffic flow. The case study ends with lessons that can be used in regional planning, urban planning, and design to raise public awareness of noise effects in public spaces. Our analysis confirmed a deep relationship between traffic flow and noise, so controlling urban activities by reducing unnecessary uses is beneficial. We recommend that urban planners and designers incorporate noise prediction into outdoor environments' planning and design processes. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Traffic Flow Forecasting in the COVID-19: A Deep Spatial-temporal Model Based on DiscreteWavelet Transformation

Traffic flowprediction has always been the focus of research in the field of Intelligent Transportation Systems, which is conducive to the more reasonable allocation of basic transportation resources and formulation of transportation policies. The spread of COVID-19 has seriously affected the normal order in the transportation sector. With the increase in the number of infected people and the government's anti-epidemic policy, human outgoing activities have gradually decreased, resulting in increasingly obvious discreteness and irregularities in traffic flow data. This article proposes a deep-space time traffic flow prediction model based on discrete wavelet transform (DSTM-DWT) to overcome the highly discrete and irregular nature of the new crown epidemic. First, DSTM-DWT decomposes traffic flow into discrete attributes, such as flow trend, discrete amplitude, and discrete baseline. Second, we design the spatial relationship of the transportation network as a graph and integrate the new crown pneumonia epidemic data into the characteristics of each transportation node. Then, we use the graph convolutional network to calculate the spatial correlation of each node, and the temporal convolutional network to calculate the temporal correlation of the data. In order to solve the problem of high discreteness of traffic flow data during the epidemic, this article proposes a graph memory network (GMN), which is used to convert discrete magnitudes separated by discrete wavelet transform into highdimensional discrete features. Finally, use DWT to segment the predicted traffic data, and then perform the inverse discrete wavelet transform between the newly segmented traffic trend and discrete baseline and the discrete model predicted by GMN to obtain the final traffic flow prediction result. In simulation experiments, this work was compared with the existing advanced baselines to verify the superiority of DSTM-DWT.

Traffic-driven SIR epidemic spread dynamics on scale-free networks

Traffic flow affects the transmission and distribution of pathogens. The large-scale traffic flow that emerges with the rapid development of global economic integration plays a significant role in the epidemic spread. In order to more accurately indicate the time characteristics of the traffic-driven epidemic spread, new parameters are added to represent the change of the infection rate parameter over time on the traffic-driven Susceptible-Infected-Recovered (SIR) epidemic spread model. Based on the collected epidemic data in Hebei Province, a linear regression method is performed to estimate the infection rate parameter and an improved traffic-driven SIR epidemic spread dynamics model is established. The impact of different link-closure rules, traffic flow and average degree on the epidemic spread is studied. The maximum instantaneous number of infected nodes and the maximum number of ever infected nodes are obtained through simulation. Compared to the simulation results of the links being closed between large-degree nodes, closing the links between small-degree nodes can effectively inhibit the epidemic spread. In addition, reducing traffic flow and increasing the average degree of the network can also slow the epidemic outbreak. The study provides the practical scientific basis for epidemic prevention departments to conduct traffic control during epidemic outbreaks.

Assessing the impact of architectural and behavioral interventions for controlling indoor COVID-19 infection risk: An agent-based approach

The COVID-19 pandemic changed our lives, forcing us to reconsider our built environment. In some buildings with high traffic flow, infected individuals release viral particles during movement. The complex interactions between humans, building, and viruses make it difficult to predict indoor infection risk by traditional computational fluid dynamics methods. The paper developed a spatially-explicit agent-based model to simulate indoor respiratory pathogen transmission for buildings with frequent movement of people. The social force model simulating pedestrian movement and a simple forcing method simulating indoor airflow were coupled in an agent-based modeling environment. The impact of architectural and behavioral interventions on the indoor infection risk was then compared by simulating a supermarket case. We found that wearing a mask was the most effective single intervention, with all people wearing masks reducing the percentage of infections to 0.08%. Among the combined interventions, the combination of customer control is the most effective and can reduce the percentage of infections to 0.04%. In addition, the extremely strict combination of all the interventions makes the supermarket free of new infections during its 8-hour operation. The approach can help architects, managers, or the government better understand the effect of nonpharmaceutical interventions to reduce the infection risk and improve the level of indoor safety.

A New Big Data Approach to Understanding General Traffic Impacts on Bus Passenger Delays

This paper presents a new method to quantify the potential user time savings if the urban bus is given preferential treatment, changing from mixed traffic to an exclusive bus lane, using a big data approach. The main advantage of the proposal is the use of the high amount of information that is automatically collected by sensors and management systems in many different situations with a high degree of spatial and temporal detail. These data allow ready adjustment of calculations to the specific reality measured in each case. In this way, we propose a novel methodology of general application to estimate the potential passenger savings instead of using simulation or analytical methods already present in the literature. For that purpose, in the first place, a travel time prediction model per vehicle trip has been developed. It has been calibrated and validated with a historical series of observations in real-world situations. This model is based on multiple linear regression. The estimated bus delay is obtained by comparing the estimated bus travel time with the bus travel time under free-flow conditions. Finally, estimated bus passenger time savings would be obtained if an exclusive bus lane had been implemented. An estimation of the passenger's route in each vehicle trip is considered to avoid average value simplifications in this calculation. A case study is conducted in A Coruña, Spain, to prove the methodology's applicability. The results showed that 18.7% of the analyzed bus trips underwent a delay exceeding 3 min in a 2,448 m long corridor, and more than 33,000 h per year could have been saved with an exclusive bus lane. Understanding the impact of different factors on transit and the benefits of a priority bus system on passengers can help city councils and transit agencies to know which investments to prioritize given their limited budget.

Mobility Dynamics amid COVID-19 with a Case Study in Tennessee.

The year 2020 has marked the spread of a global pandemic, COVID-19, challenging many aspects of our daily lives. Different organizations have been involved in controlling this outbreak. The social distancing intervention is deemed to be the most effective policy in reducing face-to-face contact and slowing down the rate of infections. Stay-at-home and shelter-in-place orders have been implemented in different states and cities, affecting daily traffic patterns. Social distancing interventions and fear of the disease resulted in a traffic decline in cities and counties. However, after stay-at-home orders ended and some public places reopened, traffic gradually started to revert to pre-pandemic levels. It can be shown that counties have diverse patterns in the decline and recovery phases. This study analyzes county-level mobility change after the pandemic, explores the contributing factors, and identifies possible spatial heterogeneity. To this end, 95 counties in Tennessee have been selected as the study area to perform geographically weighted regressions (GWR) models. The results show that density on non-freeway roads, median household income, percent of unemployment, population density, percent of people over age 65, percent of people under age 18, percent of work from home, and mean time to work are significantly correlated with vehicle miles traveled change magnitude in both decline and recovery phases. Also, the GWR estimation captures the spatial heterogeneity and local variation in coefficients among counties. Finally, the results imply that the recovery phase could be estimated depending on the identified spatial attributes. The proposed model can help agencies and researchers estimate and manage decline and recovery based on spatial factors in similar events in the future.

Hourly forecasting of traffic flow rates using spatial temporal graph neural networks.

Traffic congestion forms a large problem in many major metropolitan regions around the world, leading to delays and societal costs. As people resume travel upon relaxation of COVID-19 restrictions and personal mobility returns to levels prior to the pandemic, policy makers need tools to understand new patterns in the daily transportation system. In this paper we use a Spatial Temporal Graph Neural Network (STGNN) to train data collected by 34 traffic sensors around Amsterdam, in order to forecast traffic flow rates on an hourly aggregation level for a quarter. Our results show that STGNN did not outperform a baseline seasonal naive model overall, however for sensors that are located closer to each other in the road network, the STGNN model did indeed perform better.

Optimizing Energy Consumption in Smart Cities' Mobility: Electric Vehicles, Algorithms, and Collaborative Economy

Mobility and transportation activities in smart cities require an increasing amount of energy. With the frequent energy crises arising worldwide and the need for a more sustainable and environmental friendly economy, optimizing energy consumption in these growing activities becomes a must. This work reviews the latest works in this matter and discusses several challenges that emerge from the aforementioned social and industrial demands. The paper analyzes how collaborative concepts and the increasing use of electric vehicles can contribute to reduce energy consumption practices, as well as intelligent x-heuristic algorithms that can be employed to achieve this fundamental goal. In addition, the paper analyzes computational results from previous works on mobility and transportation in smart cities applying x-heuristics algorithms. Finally, a novel computational experiment, involving a ridesharing example, is carried out to illustrate the benefits that can be obtained by employing these algorithms.

Impact of COVID-19 on vacation recreational walking: Evidence from an urban coastal destination

This study examined the impact of COVID-19 on recreational walking while on vacation, a relevant activity among tourists especially in urban destinations. We surveyed visitors to Costa Daurada, an urban coastal destination in Catalonia in August 2020. Only 5% of participants reported lower recreational walking levels compared to normal circumstances;75% reported similar levels;and 20% reported higher levels. Higher recreational walking levels were associated with visiting local amenities or strolling and with a higher level of perceived safety from COVID-19 when walking. Higher perceived overcrowding was associated with less recreational walking. The pandemic may have altered recreational behavior while on vacation in urban settings especially by increasing outdoor activities such as recreational walking. If persistent, such behavioral changes could have relevant implications for both the tourism and recreation sectors and for local policymakers who aim to promote walkability while managing tourist pedestrian flows.

Bike Sharing and Health

Civil Aviation is a vital sector of the global economy. The US National Airspace System (NAS) provides a network of airspace, air navigation facilities, equipment, services, airports, technical information, and personnel needed for the operation of civil aviation in the United States. The capacity of the airspace in the current system is primarily limited by the ability of the air traffic controllers to maintain situational awareness and provide separation services to the aircraft. Currently, there is an unprecedented decline of between 56% and 60% in air traffic demand due to the outbreak of corona virus (COVID-19) pandemic in China and its rapid spread to the rest of the world. Before the pandemic, the 2020 FAA forecast predicted the air traffic demand to grow over the next 20 years with an annual growth rate of about 2%. This anticipated increase in traffic will put a further strain on the airports and the airspace;it will result in large delays, airline schedule breakdown, and adverse environmental impact. The system needs to address developments and anticipated explosive growth in low speed and low-cost urban air mobility vehicles. This article provides an overview of Air Traffic Management (ATM) in the United States, brief review of current aviation operations, research under development, and technology and infrastructure upgrades currently being deployed to enable the current aviation system to meet the needs of future aviation systems. Air traffic operations need to be harmonized across all parts of the globe to achieve standardization and efficiency of operations. © 2021 Elsevier Ltd. All rights reserved

Air quality changes in Taiwan over the past decades and during the COVID-19 crisis

Over the past decades, Taiwan has achieved remarkable goals in air pollution reduction with the concentrations of several common air pollutants such as CO, NOx, PM10, PM2.5, and SO2 going down. In contrast to these achievements, the mitigation of O3 remains extremely tough due to the complexity of its formation process involving synergistic effects of precursor reductions and meteorological influences. During the local COVID-19 crises in Taiwan and the Level 3 alert in 2021, air pollutants directly emitted from the traffic such as CO and NOx present clear relationships with the drop of the recorded freeway traffic volume due to the alert, while PM10 and PM2.5 which are also relevant to the traffic do not show indications of being greatly influenced by the decrease of the traffic flow. Although road traffic is not regarded as a main source of SO2 by current understanding, the unusual SO2 variation patterns found in this study suggest a prolonged impact for months from the changes of travel behavior during the epidemic. In contrast, the epidemic did not exert influences on industrial SO2 concentration which accounts for a large portion of total SO2 in Taiwan, and a similar scenario is also seen in each type of O3 monitoring. Although some results discussed in this study are not in line with current consensuses and understandings in terms of the nation of certain air pollutants, these findings may disclose new perspectives which could be a potential benefit to air quality improvement projects in the future.

Levels and drivers of urban black carbon and health risk assessment during pre- and COVID19 lockdown in Augsburg, Germany

This study aimed to evaluate the levels and phenomenology of equivalent black carbon (eBC) at the city center of Augsburg, Germany (01/2018 to 12/2020). Furthermore, the potential health risk of eBC based on equivalent numbers of passively smoked cigarettes (PSC) was also evaluated, with special emphasis on the impact caused by the COVID19 lockdown restriction measures. As it could be expected, peak concentrations of eBC were commonly recorded in morning (06:00–8:00 LT) and night (19:00–22:00 LT) in all seasons, coinciding with traffic rush hours and atmospheric stagnation. The variability of eBC was highly influenced by diurnal variations in traffic and meteorology (air temperature (T), mixing-layer height (MLH), wind speed (WS)) across days and seasons. Furthermore, a marked "weekend effect” was evidenced, with an average eBC decrease of ∼35% due to lower traffic flow. During the COVID19 lockdown period, an average ∼60% reduction of the traffic flow resulted in ∼30% eBC decrease, as the health risks of eBC exposure was markedly reduced during this period. The implementation of a multilinear regression analysis allowed to explain for 53% of the variability in measured eBC, indicating that the several factors (e.g., traffic and meteorology) may contribute simultaneously to this proportion. Overall, this study will provide valuable input to the policy makers to mitigate eBC pollutant and its adverse effect on environment and human health. © 2022 Elsevier Ltd

Modeling, equilibrium, and demand management for mobility and delivery services in Mobility-as-a-Service ecosystems

Mobility-as-a-Service (MaaS) is an emerging business model integrating various travel modes into a single mobility service accessible on demand. Besides the on-demand mobility services, instant delivery services have increased rapidly and particularly boomed during the coronavirus (COVID-19) pandemic, requiring online orders to be delivered timely. In this study, to deal with the redundant mobility resources and high costs of instant delivery services, we model an MaaS ecosystem that provides mobility and instant delivery services by sharing the same multimodal transport system. We derive a two-class bundle choice user equilibrium (BUE) for mobility and delivery users in the MaaS ecosystems. We propose a bilateral surcharge-reward scheme (BSRS) to manage the integrated mobility and delivery demand in different incentive scenarios. We further formulate a bilevel programming problem to optimize the proposed BSRS, where the upper level problem aims to minimize the total system equilibrium costs of mobility and delivery users, and the lower level problem is the derived two-class BUE with BSRS. We analyze the optimal operational strategies of the BSRS and develop a solution algorithm for the proposed bilevel programming problem based on the system performance under BSRS. Numerical studies conducted with real-world data validate the theoretical analysis, highlight the computational efficiency of the proposed algorithm, and indicate the benefits of the BSRS in managing the integrated mobility and delivery demand and reducing total system equilibrium costs of the MaaS ecosystems.

Exploring a new method to identify hidden danger space in the post-pandemic period: Taking Beian city in China as a case

COVID-19 has become a major public health emergency in the world, which seriously affects the normal operation of cities. Epidemic prevention and control is not only needed in big cities, but also in small and medium-sized cities. In view of this, the paper takes Beian city, China as the research area. This study establishes a street network model through spatial syntax, and predicts the crossing potential and arrival potential of its street network. This will play a reference role for traffic flow control in Beian city. The article uses emerging data. Through GIS spatial analysis method, we identify the hidden danger space of city. Therefore, this summarizes the places where people are easy to gather and some problems of the current situation of the city. The results show that: (1) Beian bridge and Wuyuer street have a good traffic potential. The intersection of Longjiang Road and Beidahuang street and the intersection of Tianyuan North Road and Baocheng road have good accessibility. (2) The intersection of Ping'an Street and Shanghai road is a potential hidden danger space of the city, and the focus of epidemic prevention and control. (3) The coverage rate of urban community medical services to residential land is 58.61%, and the existing medical infrastructure is insufficient. Under public health emergencies, the paper will argue a new development ideas for health and safety small town planning by visualizing the hidden danger space of the city. [ FROM AUTHOR]

Predicting traffic sound levels in Cairo before, during, and after the COVID-19 lockdown using Predictor-LimA software

This study develops a replicable urban toolkit for decision-makers to improve the quality of life in Cairo for residents and visitors affected by traffic noise. Our case study in Cairo was selected using the Aviation Design Environmental Tool (AEDT) used worldwide for airports. To simulate the COVID-19 era and days after, the noise contour mapping was performed using the Predictor-LimA software at eight receiver locations at six building heights with three assumptions of 100 %, 70 %, and 50 % traffic flow. The case study ends with lessons that can be used in regional planning, urban planning, and design to raise public awareness of noise effects in public spaces. Our analysis confirmed a deep relationship between traffic flow and noise, so controlling urban activities by reducing unnecessary uses is beneficial. We recommend that urban planners and designers incorporate noise prediction into outdoor environments' planning and design processes.

Dynamic Emergency Transit Forecasting with IoT Sequential Data

Medical emergency transit counts minutes as real human lives. It is important to plan emergency transport routes according to real-time traffic flow status which leads to the the essential requirement of correct dynamic traffic prediction. Many Internet of Things (IoT) devices have been employed to assist emergency transit. Dynamic traffic flow patterns can be better predicted using data given by those devices. In small cities, however, the data are sent into separated management offices or just saved inside edge devices due to system compatibility or the cost of mobile network to computer centres. This condition leads to small and local datasets. Making full use of small local data to conduct prediction is one way to solve local emergency planning problems. In this work, we design a dynamic graph structure to work with Graph Neural Network (GNN) algorithm to forecast traffic flow levels considering this scenario. The proposed graph considers both geographical and time information with the potential to grow within a local mobile communication network. The commonly used Extreme Gradient Boosting (XGBoost) is included in the comparison. Experimental results show that our new design provides high prediction efficiency and accuracy.

Air traffic flow management under emission policies: Analyzing the impact of sustainable aviation fuel and different carbon prices

As part of the global efforts to make aviation activities more environmentally friendly, the worldwide goal is to achieve a 50% reduction in the 2005 emissions by 2050. In this context, aviation emissions represent a critical challenge to aviation activities, especially with the increasing travel demand up to the beginning of the COVID-19 crisis, starting in 2020. One of the potential drivers that would help the aviation industry reduce its emissions is the use of sustainable aviation fuel (SAF). In this study, we analyzed the impact of SAF from an air traffic flow management (ATFM) perspective, considering delay and re-routing costs. We developed an optimization model that considers, in addition to the traditional ATFM costs, fuel costs and carbon dioxide emissions. We investigated the impact of accounting for these two new aspects, that is, fuel costs and emissions, on ATFM performance, and we compared SAF with conventional fuel. The analysis of a real case study revealed that, in addition to delay and re-routing costs, fuel cost should be included in the ATFM model so that the resulting solution becomes economically and environmentally realistic for airlines. The increase in the fuel cost and network delays when using SAF requires setting an appropriate carbon price under an emission policy, such as the carbon offsetting and reduction scheme for international flights policy, to make SAF more attractive. Furthermore, flexible re-routing programs for flights operated using SAF make it advantageous from an ATFM perspective.

A bifurcation of the peak: new patterns of traffic peaking during the COVID-19 era.

This paper analyzes the emergence of two well-defined peaks during the morning peak period in the traffic flow diurnal curve. It selects six California cities as research targets, and uses California employment and household travel survey data to explain how and why this phenomenon has risen during the pandemic. The final result explains that the double-humped phenomenon results from the change in the composition of commuters during the morning peak period after the outbreak.

Dimensioning of Cycle Lanes Based on the Assessment of Comfort for Cyclists

In a century where mobility is becoming more sustainable in terms of energy transition, emissions reduction, and a healthy quality of life, the use of bicycles is increasing and has many advantages over other modes of transport that have been underused. The bicycle is an excellent alternative for short distances of up to five kilometers. In combination with public transportation, it can also successfully compete with motorized transport for longer distances. For the adequate development of cycling, it is necessary to create the right conditions in terms of accessibility and road safety. This means planning appropriate cycling infrastructure where cyclists feel comfortable and safe, which can lead to additional increased use in bicycles for everyday trips. Comfort for cyclists is a concept supported by road safety, a pleasant environment, connectivity, and the attractiveness of cycling infrastructure. In other words, cyclists respond to the physical, psychological, and sociological aspects of the cycling experience that are also related to the cycling infrastructure and environment: where I am, what I see and perceive, and how I feel. This paper presents the concept of the level of service for cyclists (BLOS) as a unified method for defining the comfort of cyclists. This paper presents the method for determining the level of service or comfort for bicyclists as a function of road width, width of the cycling area, traffic volume, and the speed and structure of motorized traffic flow. The result of BLOS, the mathematical model used, is graphically presented and allows decision-makers and designers of cycling infrastructure to easily assess the suitability of cycling infrastructure. Different diagrams for different input data are presented in the paper.

Vulnerability Evaluation and Improvement Method of Civil Aviation Navigation Network

Due to events such as natural disasters and navigation equipment failures, enormous calamity may be caused by the interruption of the navigation network which is a guarantee for the flight safety of civil aviation aircraft. The navigation network consists of the navigation stations as nodes and the routes between them as edges. Different nodes have different effects on the vulnerability of the network due to their different abilities to maintain the stability of the network topology and the normal function of the network. To quantify this difference and identify key nodes that have a greater impact on the vulnerability of the navigation network, an indicator to assess the importance of a navigation station is proposed which combines the structural importance reflected by node topology centrality and functional importance reflected by node weight. The structural importance of a node corresponds to its topology features including local dominance of the node and its global influence, and the important contribution to both adjacent and nonadjacent nodes from this node, while the functional importance is indicated by the flight flow serviced by the node during a fixed period of time. Vulnerability evaluation shows that the navigation network is more vulnerable when subject to the intentional attack of nodes with higher comprehensive node importance than an intentional attack of nodes with a larger value of indicators used in previous literature. Finally, the vulnerability of the navigation network is improved through changing the topology of the most critical node and balancing the node importance of the whole network.