Traffic Recovery Research Articles

The impact of COVID-19 vaccination rate on traffic recovery

At the beginning of the COVID-19 pandemic in the US, traffic sharply fell due to social distancing policies in many locations. Correspondingly, many regions observed an increase in traffic volume (traffic recovery) as the pandemic eased in 2022. We examine how vaccination rates influence traffic recovery in Los Angeles County (LAC), controlling for differences in case counts, demographics, and socioeconomic factors across areas with different vaccination rates. We use arterial road sensor data as a proxy for the traffic volume within each ZIP code, alongside their respective demographic and socioeconomic characteristics. We find that a higher vaccination rate is statistically significantly associated with a larger traffic recovery, a finding that remains consistent across all explored models. This implies that an increased vaccination rate could reduce the public’s perception of the risks of disease infection, leading to a larger traffic recovery. Moreover, we found that variables including population, income, race, work industry, and commuting preferences were correlated with vaccination rates. This highlights potential inequalities based on race, income, and industry sectors in the COVID-19 vaccination and a return to normal traffic flow.

Have low emission zones slowed urban traffic recovery after Covid-19?

This paper bridges the gap between the literature on the pandemic’s effects on mobility and the literature on the impact of low emission zones (LEZ). Using data for large European cities in the period 2018–2021, we examine whether LEZs may explain differences in the recovery patterns of traffic in European cities after the shock of Covid. Controlling for several city attributes, we examine whether LEZ cities are less congested before and after the pandemic in comparison to non-LEZ cities. LEZs may have been more effective in reducing congestion after the pandemic because the fleet renewal process has slowed down or, alternatively, LEZs may be a proxy of unobservable factors related with attitudes of governments and citizens toward a sustainable mobility. Our results validate the traffic-mitigating role of the LEZ after the Covid-19 pandemic, although such result only holds for the pioneering LEZ cities. Hence, the traffic-mitigating role of the LEZ after the Covid-19 pandemic seems to be related to unobservable attributes that influenced the early decision to implement a LEZ. In this regard, we also find that LEZs may have induced a change in local attributes related to sustainable mobility given that we do not find differences between LEZs decided at the local or regional level.

Network traffic recovery from link-load measurements using tensor triple decomposition strategy for third-order traffic tensors

Network traffic data is the pivot of input in many network tasks but its direct measurement can be insufferably costly. In this paper, we propose a network traffic recovery method which only requires the conveniently measurable link-load traffics. We arrange the traffic data as a third-order tensor and utilize the triple decomposition technique proposed very recently by Qi et al. (2021). The studied model is a differentialble unconstrained minimization problem, which can be efficiently solved by a Barzilai–Borwein (BB) gradient algorithm. We prove that the generated iteration sequence can globally converge to a certain stationary point of the objective function. The numerical simulations on three open-source traffic datasets demonstrate the superiority of our method in comparison with other state-of-the-art algorithms.

A transportation Revitalization index prediction model based on Spatial-Temporal attention mechanism

The global outbreak of COVID-19 has had a substantial impact on various sectors worldwide, including the economy, healthcare, entertainment, policy formulation, and international relations, with the transportation industry being particularly hard-hit. To curb the widespread transmission of the virus, many regions globally have implemented policies and measures to restrict transportation. These actions not only directly affect the transportation industry but also further impose a severe impact on the economy and societal development of various areas. In this context, the Transportation Revitalization Index (TRI) becomes particularly important. It can evaluate the degree of recovery of city traffic conditions after the pandemic, and accurate prediction of TRI can help governments and decision-makers respond more precisely to the challenges that the pandemic brings to the transportation industry. However, existing research primarily focuses on the direct correlation between TRI change data and COVID-19 pandemic data, without fully considering the dynamic spatial correlation features and time dependency features that affect the nonlinear changes of TRI. In light of the above situation, this study proposes a Deep Spatial-Temporal prediction model based on the Attention Mechanism (DeepST-AM). The DeepST-AM deeply integrates historical TRI data with multivariate pandemic information and uses a spatial–temporal attention mechanism to capture the deep and complex spatial–temporal information of urban data. To more accurately capture the long-term complex features of TRI data, this paper designs a Gaussian temporal convolution model dedicated to TRI data. To validate the effectiveness of DeepST-AM, researchers used real data from 29 core cities in China as samples and compared the performance of DeepST-AM with existing multiple methods on TRI prediction tasks. The experimental results showed that compared to other methods, the DeepST-AM proposed in this paper has a significant advantage in the long-term prediction tasks of TRI in terms of performance evaluation, indicator prediction, etc. In summary, this research provides a more accurate and comprehensive prediction model for the traffic recovery status after the pandemic, hoping to provide strong support for future decisions.

Failure recovery in the MANTRA architecture with an IPoWDM SONiC node and 400ZR/ZR+ pluggables

The utilization of IP over wavelength division multiplexing (IPoWDM) boxes in optical networks introduces coordination issues at the control plane level between the optical and packet domains. Indeed, IPoWDM boxes are technically packet devices, but the configuration of coherent optical pluggables requires knowledge of optical network state information (e.g., wavelength availability). MANTRA (metaverse ready architectures for open transport) aims to define a new SDN control architecture for managing IPoWDM, enhancing coordination between the packet and optical controllers. Within MANTRA, two distinct architectures were presented: Dual and Single. However, only the roles related to controller management are defined, while clear procedures are not specified for dealing with resource provisioning and failure recovery. This leads to the need for the definition and implementation of tailored procedures. Besides the characterization of the coherent optical pluggables performance, this paper presents and compares three procedures based on MANTRA to coordinate and control IPoWDM nodes in a multi-layer network performing failure recovery. The considered solutions have been designed and experimentally validated with a focus on the achievable traffic recovery performance considering IPoWDM configuration and communication within the SDN control architecture. An experimental testbed, comprising a sliced IPoWDM node running an extended version of the open-source SONiC network operating system, has been deployed to validate these solutions, with a comprehensive analysis of the time required to recover a real traffic flow that spans both the packet and optical domains.

Examining the Effects of Confirmed COVID-19 Cases and State Government Policies on Passenger Air Traffic Recovery by Proposing an OD Spatial Temporal Model

Examining the Effects of Confirmed COVID-19 Cases and State Government Policies on Passenger Air Traffic Recovery by Proposing an OD Spatial Temporal Model

Tensor-Based Traffic Recovery of Industrial Network by BCD-Inspired Neural Approximation to Latent Nonlinearity and Sparsity

Tensor-Based Traffic Recovery of Industrial Network by BCD-Inspired Neural Approximation to Latent Nonlinearity and Sparsity

Safer Traffic Recovery from the Pandemic in London – Spatiotemporal Data Mining of Car Crashes

AbstractIn the aim to provide evidence for deployment policies towards post-pandemic safer recovery from COVID-19, this study investigated the spatiotemporal patterns of age-involved car crashes and affecting factors, upon answering two main research questions: (1) “What are spatiotemporal patterns of car crashes and any observed changes in two years, 2019 and 2020, in London, and waht were the influential factors for these crashes?”; (2) “What are spatiotemporal patterns of casualty by age, and how do people’s daily activities affect the patterns pre- and during the pandemic”? Three approaches, spatial analysis (network Kernel Density Estimation, NetKDE), factor analysis, and spatiotemporal data mining (tensor decomposition), had been implemented to identify the temporal patterns of car crashes, detect hot spots, and to understand the effect on citizens’ daily activity on crash patterns pre- and during the pandemic. It had been found from the study that car crashes mainly clustered in the central part of London, especially busier areas around denser hubs of point-of-interest (POIs); the POIs, as an indicator for citizens’ daily activities and travel behaviours, can be of help to analyze their relationships with crash patterns, upon further assessment on interactions through the geographical detector; the casualty patterns varied by age group, with distinctive relationships between POIs and crash pattern for corresponding age group categorised. In all, the paper introduced new approaches for an in-depth analysis of car crashes and their casualty patterns in London to support London’s safer recovery from the pandemic by improving road safety.

A novel intelligent traffic recovery model for emergency vehicles based on context-aware reinforcement learning

Management of traffic emergencies has become very popular in recent years. However, timely response to emergencies and recovering from an emergency is an important problem in itself. The strategies in the current studies merely suggest that after an emergency vehicle passes, the state should iterate to the next phase. Therefore, this paper proposes a novel approach for recovering from an emergency situation at an intersection based on real scenarios. The proposed method is a combination of context-aware and Reinforcement Learning (RL) models that predicts better alternatives for different states rather than just iterating to the next phase. In this regard, a new algorithm, named Interrupt Algorithm, is proposed to predict proper actions for recovering the emergency situation. This algorithm uses a Q-learning-based model that learns from traffic context for an emergency situation and chooses viable action from an action set. The recovery actions are categorized as max, min, and avg, respectively. Test results show that our proposed model outperforms traffic flow over than standard single choice recovering action-based approach by approximately 80%. Based on this, it may be more beneficial to choose different actions and therefore, proposed algorithm with the help of RL presents a more dynamic emergency recovery model.

Research on the Investment Value of Guangzhou Baiyun International Airport Based on Multiples Valuation Method

Aviation has been an industry that has been widely watched by investors. As the upstream of the civil aviation industry chain, the airport industry is an important core asset of the entire industry chain, providing relevant services for global air transport companies and passengers. In recent years, China's aviation industry has been in a state of steady development. Until the outbreak of COVID-19 in 2020, the market focus turned to the impact of the speed of passenger traffic recovery during the epidemic on the recovery of airport performance. For core hub airports, the priority is whether asset values have changed. This paper analyzes Guangzhou Baiyun International Airport in China, and studies whether the investment value of the airport is fair through financial analysis, peer comparison, and multiple valuation methods. The study found that the airport's overall revenue decline in the past two years was mainly due to the epidemic, but it has performed better compared to industry players. Through multiples valuation, the theoretical stock value of Baiyun Airport in 2021 is 16.19 CNY higher than the actual value. The result indicated that the company's stock was undervalued and has good room for growth in the future, suggesting a purchasing opportunity. It is hoped that the research in this paper can expand the research on related aspects of the private airport industry, and also provide some help and reference for the valuation research of Guangzhou Baiyun International Airport.

Influence of COVID-19 on air travel - A scenario study toward future trusted aviation

This paper develops three scenarios for the aviation industry's recovery from COVID-19 until 2030 by utilizing the scenario methodology. Besides the short- and mid-term pandemic development, the study takes into account the industry's adaptation to changes in the market environment, e.g., toward sustainability and hygiene requirements. The resulting scenarios include the expected point in time of full air traffic recovery to pre-crisis levels. Subsequent implications suggest that most COVID-19-related hygiene measures along the travel chain disappear after the pandemic is contained. Some measures might serve as a differentiator between airline business models, while others are expected to become a new standard. Implications for environmental awareness and resulting operational and technical measures include changes in society's attitude toward traveling post-pandemic, especially in light of varying levels of environmental awareness. The presented scenarios help to identify the range of plausible development paths, thus building the basis for future model-based research.

STOCHASTIC MODEL FOR FORECASTING OF CRUISE OR FERRY SHIP ARRIVAL AT SEAPORT FOR INFRASTRUCTURE ASSESSMENT

The topic of the research is the development of new models and methods to assess the efficiency of port operations of sea passenger ports and terminals in order to effectively plan the work and accurate forecasting of infrastructure development. It is noted that changes in cruise and ferry route networks, especially relevant in the conditions of passenger traffic recovery after the gradual lifting of restrictions due to Covid-19, will directly affect port congestion. It is proposed to include into the sphere of decision-making on management of the sea passenger port the models based on consideration of probabilistic processes of cruise or ferry vessels calls. The Baltic Sea region is chosen as an object of research. The main modern directions in the sphere of sea passenger transportations confirming the gradual recovery of the route networks operation are presented. The flow of cruise and ferry vessels of the sea passenger port “Passenger port St. Petersburg “Marine Facade” is chosen as an object of research. The analysis of loading of berths, the analysis of the size of cruise and ferry ships, the route networks of ferry and cruise lines are carried out. As a result of the research a new stochastic model is presented and comparison with known distribution laws is carried out. Based on the data obtained, a confidence zone for making decisions on port congestion is determined. This area is formed on the basis of an assessment of the study of the dynamics of ship traffic nonstationary intensity, followed by the application of correlation-regression analysis and the characteristics of the random function describing the intervals between individual arrivals of cruise and ferry ships. The proposed model includes the ability to consider different priorities, primarily in terms of vessel length. Digital transport model is built in AnyLogic environment for port congestion study. The received data are used for optimization experiments in AnyLogic software environment with the purpose of complex estimation of the seaport operation for a year. Based on the optimization experiment and simulation, data are generated considering different distribution laws and used for decision making in case of uncertainty for infrastructure modernization. The presented model can be used for substantiation of management decisions both on strategic and tactical levels for assessing the efficiency of investment projects on development of sea passenger ports and terminals.

Resilience assessment of railway networks: Combining infrastructure restoration and transport management

During railways operations, unplanned events might occur which can result in rail traffic being heavily impacted. The paper proposes a passenger-centred resilience assessment for disruption scenarios which consist of multiple simultaneous disruptions. It combines train traffic operations, passenger flows and network restoration. To evaluate resilience, an optimization-based approach has been developed for solving the new infrastructure restoration and transport management (IRTM) problem. Additionally, this approach develops mitigation plans for the best infrastructure restoration and traffic recovery and it captures the time-dependent transport network performance during disruptions. The approach is general with respect to types of disruptions, and can be applied for evaluation against short disruptions (1–2 h) as well as more substantial ones (multiple days or weeks). The performance of the proposed approach has been demonstrated on a Dutch railway network. Furthermore, the resilience of the system is assessed against the critical infrastructure disruption scenarios in the network. This optimization-based approach shall enable decision makers to quantify accurately impacts of multiple disruptions by considering the created inconveniences to passengers in the railway operation due to these disruptions.

Analysis of Air Transport Stake Holders Perceptions and Motivations about Long Term Innovations Towards Sustainable Aviation Energy Paradigms

Aviation sustainability and GHG emissions reduction are academic and social trending topics. Air transport intensive energy use together with its constant pre-pandemic growth and the difficulties to evolve towards non-carbon-based energy paradigms, conform an environmentally “worrying frame”. Emissions reduction initiatives comprise aircraft technology and operation improvements, tax schemes, compensation and trading, efficient infrastructures, and sustainable aviation fuels. All of them have been subject of extensive academic analysis. Aspects that have been almost neglected in this analysis are the personal motivation and commitment from air transport stakeholders to pursue longer term technology developments towards sustainable energy paradigms. This paper explores those aspects by using a novel methodology where experts were interviewed prior to the COVID-19 pandemic (2017-2018) and during the pandemic (summer 2021), aiming at exploring the evolution of their beliefs regarding aviation sustainability, focusing in the expectations regarding aviation energy paradigm changes. Additionally, a survey was run among professionals of the aviation and energy sectors performing a quantitative analysis, triangulated with the interviews’ results. Aviation professionals keep relying on air traffic recovery and growth despite the COVID crisis. They are also conscious of the difficulties of the sector to enact the application of emissions reduction technologies and policies. However, there is not consensus in the strategies with the highest potential, although some tendencies have been identified: whilst enthusiasm about aircraft electrification has cooled down, a higher degree of reliance is put in new engine architectures. Fiscal measures are either poorly valued or not well understood. Aviation should leverage the consensus on new engine architectures to achieve tangible emissions contention in the mid-term, increase efforts to improve and explain emissions trading and compensation schemes, keep developing new technologies to unveil their real potential, and manage social expectations realistically, stressing its social and economic sustainability dimensions.

Metamodel-based metaheuristics in optimal responsive adaptation and recovery of traffic networks

ABSTRACT Different emerging threats highlighted the relevance of recovery and adaptation modelling in the functioning of societal systems. However, as modelling of systems becomes more complex, its effort increases challenging the practicality of the engineering analyses required for efficient recovery and adaptation. In the present work, metamodels are researched as a tool to enable these analyses in traffic networks. One of the main advantages of metamodeling is their synergy with the short decision times required in recovery and adaptation. A sequential global metamodeling technique is proposed and applied to three macroscopic day-to-day user-equilibrium models. Two reference contexts of application are researched: optimal recovery to a perturbation (with response times reduced by 98% with loss of accuracy lower than 1%) and adaptation under uncertainty with perturbation-dependent optimality. Results show that metamodeling-based metaheuristics enable fast resource-intensive engineering analyses of traffic recovery and adaptation, which may change the paradigm of decision-making in this field

How Does COVID-19 Affect Traffic on Highway Network: Evidence from Yunnan Province, China

The COVID-19 pandemic and antipandemic policies have significantly impacted highway transportation. Many studies have been conducted to quantify these impacts. However, quantitative analysis of the impacts on province-wide traffic in developing countries, such as China, is still inadequate. This paper tried to fill this gap by proposing equations to quantify the traffic variations of overall province-wide traffic and to analyze the intercity bus traffic variation and intercity bus usage, applying the K-means cluster method to conduct the analysis of traffic reductions in regions with different levels of economic development, and using the hypothesis testing for traffic recovery analysis. It is found that passenger vehicle traffic and truck traffic dropped by 59.67% and 68.19% during the outbreak, respectively. The intercity bus traffic on highways declined by 59.8% to 98.6%, and the intercity bus usage dropped by 55.6% on average. For traffic reductions in different regions, the higher the GDP per capita was, the more the traffic was affected by the pandemic. In regions with lower GDP per capita, traffic variations were minor. It is also found that the passenger vehicle traffic went through four stages in 99 days: the Decline Stage, Rapid Recovery Stage, Slow Recovery Stage, and Normal Stage, while truck traffic only experienced the Decline Stage, Rapid Recovery Stage, and Normal Stage and took 51 days to recover to the Normal Stage. In the Rapid Recovery Stage, the recovery rates were 15.6% and 12.9% per week for passenger vehicle traffic and truck traffic, respectively, and the recovery rate was only 2.1% for passenger vehicle traffic in the Slow Recovery Stage. Despite the recovery of traffic volumes, neither passenger-kilometers nor tonne-kilometers of freight in 2020 reached the same level as in 2019. These findings help the understanding of the pandemic’s impacts on highway traffic for researchers and can provide valuable references for decision-makers to develop antipandemic policies.

‘Wild Your weekends' promotion and its effect on traffic recovery during COVID-19 pandemic

China Eastern Airlines launched a ‘Wild Your Weekends' programme offering buyers an unlimited number of free trips during weekends, which is unprecedented in the Chinese airline market and also rare around the world. However, it is unclear whether such a promotion is effective in improving airlines’ liquidity. This study adopts the difference-in-differences (DID) method to empirically examine the impacts of this promotion programme on traffic volumes, ticket prices and revenues of China Eastern and its competitors. Our estimations suggest that this programme has overall helped China Eastern improve its liquidity. On one hand, the carrier was forced to lower prices on weekends, probably because passengers formed strong beliefs on China Eastern’s low price due to its promotion programme, and felt psychologically unfair by paying high prices as compared to the programme’s users. This decreased China Eastern’s revenue from non-programme passengers on weekends.

Jet fuel demand hit by Omicron; recovery delayed until 2023/2024

Jet fuel demand hit by Omicron; recovery delayed until 2023/2024

ИССЛЕДОВАНИЕ И ОЦЕНКА ВЛИЯНИЯ ПАНДЕМИИ СOVID – 19 НА ПАССАЖИРОПОТОК В АВИАЦИОННОЙ ОТРАСЛИ

This article presents the impact of the Covid-19 pandemic on passenger traffic in the aviation industry at the global, Russian and regional levels. International data on airline traffic and passenger traffic at airports are presented. Data on passenger traffic at Pulkovo Airport from 2019 to the present is presented. On the basis of the collected data, graphs of air traffic decline are presented A detailed analysis of passenger traffic flows to domestic and international destinations is performed, and the main development trends are identified. Based on Pulkovo Airport passenger traffic analysis, the main directions are highlighted, and points for passenger traffic increase and recovery of air transport industry in North-West Russia are identified. The presented analytical data is the basis for analysis of the existing route aviation networks, creating the basis for solving the tasks of forecasting development and making decisions on effective expansion of destinations. Despite international restrictions, the expansion of domestic destinations is one of the ways to increase passenger traffic and achieve pre-crisis levels. The analytical bottom line presented is compiled from available airline and airport monitoring information systems.

An accurate and practical algorithm for internet traffic recovery problem

It is challenging to recover the large-scale internet traffic data purely from the link-load measurements. With the rapid growth of the problem scale, it will be extremely difficult to sustain the recovery accuracy and the computational cost simultaneously. For the purpose of fast and accurately recovering the traffic data from link-load measurements, we establish a new Sparsity Low-Rank Recovery (SLRR) model based on the intrinsic properties of traffic data and propose an improved Schur Complement Based semi-proximal Alternating Direction Method of Multipliers (SCB-spADMM) to solve the SLRR model. The main contribution lies in the following two aspects. First, we fully exploit the spatial low-rank property and the sparsity of traffic data, which are barely considered in the literature. The model only relates to the traffics in a certain individual time interval. Thus, the scale of the problem is significantly reduced. Second, the global convergence of the proposed ADMM-type algorithm is established and all the intermediate variables’ optimums can be calculated analytically in each iteration. According to the numerical results on the classic datasets Abilene and GÉANT, our method achieves the best accuracy with a low computational cost. Moreover, in our newly released large-scale real-life network traffic dataset HOD, our method perfectly reaches the seconds-level feedback, which meets the essential requirement for practical scenarios.