Peak Travel Research Articles

Can a Driving Restriction Policy Improve Air Quality? Empirical Evidence from Chengdu

Automotive exhaust emissions contribute significantly to air pollution in developing countries. However, the effectiveness of driving restriction policies (DRPs) is unclear, and most research on China emphasizes Beijing. This study used Chengdu, a typical large city in China, to examine the impact of a DRP on air quality. To alleviate potential endogeneity threats, we employed a regression discontinuity design to verify the policy’s effect. The results show that the DRP significantly reduced air pollution levels, effectively improving air quality in restricted areas. The heterogeneity analysis found that (1) the DRP effectively reduced pollution in newly added and original areas, while the air quality in adjacent areas deteriorated; and (2) the DRP significantly improved air quality during peak travel periods but had no significant impact in other periods. Our results indicate that the DRP is an effective tool for urban environmental governance but presents potential negative aspects. Therefore, restricted areas and periods should be carefully considered when designing similar policies. This study provides significant insights into the governance of automotive exhaust emissions pollution for large cities in developing countries.

Optimising availability and geographical accessibility to emergency obstetric care within a sub-national social health insurance scheme in Nigeria.

Health insurance is a key instrument for a health system on its path to achieving universal health coverage (UHC) and protects individuals from catastrophic health expenditures, especially in health emergencies. However, there are other dimensions to care access beyond financial accessibility. In this study, we assess the geographical accessibility of comprehensive emergency obstetric care (CEmOC) within the Lagos State Health Insurance Scheme. We geocoded functional public and private CEmOC facilities, established facilities registered on the insurance panel as of December 2022, and assembled population distribution for women of childbearing age. We used Google Maps Platform's internal directions application programming interface to obtain driving times to facilities. State- and local government area (LGA)-level median travel time (MTT) and a number of CEmOC facilities reachable within 30 min were obtained for peak travel hours. Across Lagos State, MTT to the nearest public CEmOC was 25 min, reduced to 17 min with private facilities added to the insurance panel. MTT to the nearest public facility in LGAs ranged from 9 min (Lagos Island) to 51 min (Ojo) (median = 25 min). With private facilities added, MTT ranged from 5 min (Agege and Ajeromi-Ifelodun) to 36 min (Ibeju-Lekki) (median = 13 min). On average, no public CEmOC facility was reachable within 30 min of driving for women living in 6 of 20 LGAs. With private facilities included in the scheme, reachable facilities within 30 min remained zero in one LGA (Ibeju-Lekki). Our innovative approach offers policy-relevant evidence to optimise insurance coverage, support efforts in advancing UHC, ensure coverage for CEmOC, and improve health system performance.

Understanding the determinants of bike-sharing demand in the context of a medium-sized car-oriented city: The case study of Milton Keynes, UK

Emerging modes of shared mobility like bike-sharing may significantly contribute to increasing the share of sustainable travel in medium- and small-sized cities. However, research exploring the bike-sharing usage determinants specifically for them has been severely underrepresented in the literature. To address this gap, we conducted an analysis of bike-sharing rentals for over a one-year period in Milton Keynes, a medium-sized city of 288,000 residents. Being the largest of the planned settlements built under the UK government “New Towns” programme, Milton Keynes despite having the infrastructure to offer opportunities for modal shift has been repeatedly characterised as a car-centric city. The paper examines temporal variation by comparing the average number of bike rentals in different seasons and months. Analysis of peak rentals and travel directions suggests that throughout the year bikes are mainly used to travel to work. By contrast, recreational travel appears to happen mostly during the summer months. Regression analysis shows that the number of public transport stops, offices and schools are associated with a higher number of bike rentals, supporting the view that utilitarian travel is the main usage pattern. Our research also highlights the bike-sharing's potential to be adopted as a first/last-mile option that will complement and support public transit.

Measuring geographic access to emergency obstetric care: a comparison of travel time estimates modelled using Google Maps Directions API and AccessMod in three Nigerian conurbations.

Google Maps Directions Application Programming Interface (the API) and AccessMod tools are increasingly being used to estimate travel time to healthcare. However, no formal comparison of estimates from the tools has been conducted. We modelled and compared median travel time (MTT) to comprehensive emergency obstetric care (CEmOC) using both tools in three Nigerian conurbations (Kano, Port-Harcourt, and Lagos). We compiled spatial layers of CEmOC healthcare facilities, road network, elevation, and land cover and used a least-cost path algorithm within AccessMod to estimate MTT to the nearest CEmOC facility. Comparable MTT estimates were extracted using the API for peak and non-peak travel scenarios. We investigated the relationship between MTT estimates generated by both tools at raster celllevel (0.6 km resolution). We also aggregated the raster cell estimates to generate administratively relevant ward-level MTT. We compared ward-level estimates and identified wards within the same conurbation falling into different 15-minute incremental categories (<15/15-30/30-45/45-60/+60). Of the 189, 101 and 375 wards, 72.0%, 72.3% and 90.1% were categorised in the same 15- minute category in Kano, Port-Harcourt, and Lagos, respectively. Concordance decreased in wards with longer MTT. AccessMod MTT were longer than the API's in areas with ≥45min. At the raster cell-level, MTT had a strong positive correlation (≥0.8) in all conurbations. Adjusted R2 from a linear model (0.624-0.723) was high, increasing marginally in a piecewise linear model (0.677-0.807). In conclusion, at <45-minutes, ward-level estimates from the API and AccessMod are marginally different, however, at longer travel times substantial differences exist, which are amenable to conversion factors.

Peaked too soon? Analyzing the shifting patterns of PM peak period travel in Southern California

Daily vehicle travel collapsed with the onset of the COVID-19 pandemic in early 2020 but largely bounced back by late 2021. The pandemic caused dramatic changes to working, schooling, shopping, and leisure activities, and to the travel associated with them. Several of these changes have so far proven enduring. So, while overall vehicle travel had largely returned to pre-pandemic levels by late 2021, the underlying drivers of this travel have likely changed.To examine one element of this issue, we analyzed whether patterns of daily trip-making shifted temporally between the fall of 2019 and 2021 in the Greater Los Angeles megaregion. We used location-based service data to examine vehicle trip originations for each hour of the day at the U.S. census block group level in October 2019 and October 2021. We observed notable shifts in the timing of post-pandemic PM peak travel, so we examined changes in the ratio of mid-week trips originating in the early afternoon (12–3:59 PM) and the late afternoon/early evening (4–7:59 PM).We found a clear shift in the temporal distribution of PM trip-making, with relatively more late PM peak period trip-making prior to the pandemic, and more early PM peak trip-making in 2021. The peak afternoon/evening trip-making hour shifted from 5–5:59 PM to 3–3:59 PM. We also found that afternoon/evening trip-making in each year is largely explained by three workplace-area/school-area factors: (1) the number of schoolchildren in a block group (earlier); (2) block groups with large shares of potential remote workers (earlier), and (3) block groups with large shares of low-wage jobs and workers of color (later, except for Black workers in 2021). We found the earlier shift in PM peak travel between pre- and late-pandemic periods to be explained most by (1) higher shares of potential remote workers and (2) higher shares of low-wage jobs and workers of color. These findings suggest that the rise of working from home has likely led to a shift in PM peak travel earlier in the afternoon when school chauffeuring trips are most common. This is especially true for low-income workers and workers of color.

Unsupervised Learning-Based Exploration of Urban Rail Transit Passenger Flow and Travel Pattern Mining

This study delves into the realm of urban rail transit systems, leveraging unsupervised learning techniques to analyze passenger flow characteristics and unearth travel patterns. Focused on the dynamic and complex nature of urban rail networks, the research utilizes extensive datasets, primarily derived from Automated Fare Collection (AFC) systems, to provide a comprehensive analysis of passenger behaviors and movement trends. Employing advanced algorithms like DBSCAN, the study categorizes passengers into distinct groups, including tourists, shoppers, thieves, commuters, and station staff. These classifications reveal intricate patterns in travel behaviors, significantly contributing to a deeper understanding of urban transit dynamics. The findings offer valuable insights into peak travel times, popular routes, and station congestion, highlighting potential areas for operational improvements and infrastructure development. The study’s application of unsupervised learning in analyzing vast, unstructured data sets a precedent in urban transportation research, showcasing the potential of artificial intelligence in enhancing the efficiency and sustainability of urban transit systems. The insights garnered are pivotal not only for optimizing current operations but also for shaping future expansion and adaptation strategies, ensuring urban rail systems continue to meet the evolving needs of growing urban populations.

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. However, classical traffic anomaly detection methods often overlook the evolving dynamic associations between road network nodes, which leads to challenges in capturing the long-term temporal correlations, spatial characteristics, and abnormal node behaviors in datasets with high periodicity and trends, such as morning peak travel periods. In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. Specifically, we propose the mirror temporal convolutional module to enhance feature extraction capabilities and capture hidden node-to-node features in the traffic network. Morever, we propose the graph convolutional gate recurrent unit cell (GCGRU CELL) module. This module uses Gaussian kernel functions to map data into a high-dimensional space, and enables the identification of anomalous information and potential anomalies within the complex interdependencies of the traffic network, based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. Experimental results on the NYC dataset illustrate that our model works best compared to other models for traffic anomaly detection.

Unleashing the Potential of Boosting Techniques to Optimize Station-Pairs Passenger Flow Forecasting

Station-pair passenger flow forecast modeling is crucial for public transportation to address emerging needs. The accurate prediction and estimation will provide backbone support for various features of transport, viz. (a) Demand forecasting for resource allocation during peak travel periods; (b) Service planning to determine transportation frequency on specific routes; (c) Resource allocation for vehicles and capacity adjustments, including planning for new fleet size; (d) Infrastructure planning to identify areas requiring additional development; (e) Enhancing passenger experience with ample seating and amenities; and (f) Emergency planning for crowd control and passenger safety. Recent advancements in transit information station-pair estimation methods can be implemented with Data Science. Over the years, there has been significant progress in developing innovative modeling approaches to estimate travel behaviour, taking into account various factors such as timeslots, holidays, and other relevant features. In this study, we have synthesized ticket data from Thane Municipal Transport (TMT), collected from e-ticket machines, spanning from January 1 to June 23, 2023, covering all routes and comprising more than 2.80 crore tickets. This paper presents a comprehensive station-pair short-term passenger flow forecast by utilizing three boosting models: extreme Gradient Boosting (XGBoost), Light Gradient Boosting model (LightGBM), and Categorical Boosting model (CatBoost). Their implementation is well-suited based on statistical parameters. The study concludes by comparing the results of the models, considering computation time and statistical parameters, to validate their superiority. By looking into day to day requirements, the LightGBM model is most appropriate for implementation with an accurate result. The research findings contribute to providing valuable data support and a robust scientific foundation for managing passenger flow through route planning and dispatching in urban transit systems.

Assessment of the Current Bus Route Network in the Federal Capital City Abuja, Nigeria

The inadequacy of buses for the public transportation system in the Federal Capital City (FCC) of Abuja has resulted to the dominant use of private automobiles, which has created a lot of problems, such as, long waiting times, lateness to work, kidnapping and loss of man-hours. The effort of Government in providing high capacity public buses through Abuja Urban Mass Transit Company (AUMTCO), has not been satisfactory. Therefore, in this study, an evaluation of the existing bus route networks and the availability of public transport bus services in the FCC, including an assessment of the factors that determine the present choice of travel mode in the City is presented. The methodology adopted includes: manual traffic volume count, measurement of traffic data and scope of the current bus route network using GPSmap76Cx (GARMIN) receiver, and questionnaire administration. The results show that public bus is 0.12% of vehicular traffic volume in the metropolitan city, while private car has 86.81%, which carries significant consequences that impact different facets of its citizens' lives. The existing bus routes network does not cover Wuse II and Maitama districts. Mode choice influences by social demographic characteristics, such as; occupation, marital status, time spent going to work, bus inconvenience, time to leave home for work, and decision to patronize public mode if available are among the trip factors assessed. The mean peak travel speed on the bus routes ranges from 15km/hr to 23 km/hr, while average time spent from house to and at bus stop are 10 minutes and 20 minutes respectively for public bus. The results suggest that the existing bus route networks should be optimized to cover all the districts of the Federal Capital City for effective accessibility and services to commuters. This will improve the security indices of commuters in the Federal capital city.

Carbon Dioxide Monitoring Demonstrates Variations in the Quality of Ventilation on Public Transportation Buses and University Student Shuttle Vans and Identifies Effective Interventions.

There is a risk for transmission of severe acute respiratory syndrome 2 (SARS-CoV-2) and other respiratory viruses in motor vehicles, particularly if ventilation is inadequate. We used carbon dioxide monitoring to examine the quality of ventilation in several public transportation buses and in university student shuttle vans in the Cleveland metro area during peak and non-peak travel times. Carbon dioxide levels above 800 parts per million (ppm) were considered an indicator of suboptimal ventilation for the number of people present. In the shuttle vans, we evaluated the impact of an intervention to improve ventilation. In large articulated buses with 2 ventilation systems, carbon dioxide concentrations never exceeded 800 ppm, whereas in standard buses with 1 ventilation system concentrations rose above 800 ppm during peak travel times and on some trips during non-peak travel times. In shuttle vans, the ventilation system was not turned on during routine operation, and carbon dioxide levels rose above 800 ppm on all trips during peak and non-peak travel times. In the shuttle vans, an intervention involving operation of the existing ventilation system resulted in a significant reduction in carbon dioxide levels (mean concentration, 1,042 no intervention versus 785 with intervention; P < 0.001). Our findings demonstrate substantial variability in the quality of ventilation in public transportation buses and university shuttle vans. There is a need for efforts to assess and optimize ventilation in motor vehicles used for public transportation to reduce the risk for aerosol-mediated transmission of respiratory viruses. Carbon dioxide monitoring may provide a useful tool to assess and improve ventilation.

Research on the Collaborative Optimization of the Power Distribution Network and Traffic Network Based on Dynamic Traffic Allocation

With the increasing penetration rate of electric vehicles, the spatiotemporal coupling relationship between the power distribution network and traffic network is stronger than ever before. Under the dynamic wireless charging mode, traffic jam charging is introduced and the dynamic loading process of traffic flow is described using a cellular transmission model. The charging load is related to traffic flow and serves as a bond between the power distribution network and traffic network. The traffic flow achieves balanced allocation under dynamic user equilibrium conditions, and cooperatively optimizes the power flow of the power distribution network in conjunction with charging loads. Numerical analysis shows that this model can accurately depict the congestion situation during peak travel periods, and alleviate traffic congestion and distribution network voltage out of range.

Tourism demand with subtle seasonality: Recognition and forecasting

Existing studies on tourism seasonality have been mainly identified at annual or monthly level and the seasonality in tourism demand forecasting has always been addressed by modeling season patterns. However, annual or monthly seasonality is coarse-grained and can’t capture the subtle changes emerging both in tourism theory and practice. This study recognizes tourism seasonality based on intra-day patterns and inter-day similarity and suggests a novel approach to addressing seasonality in tourism demand forecasting. The proposed three-step method contains tourism seasonality recognition, tourism seasonality matching, and tourism demand forecasting. The empirical findings, based on two attractions in China, demonstrate that the proposed method based on dynamic time warping and density-peak clustering can precisely capture tourism seasonality at the daily level. The method can also detect special tourism periods or subtle changes in seasonality, such as staggered peak travel phenomenon. Superior forecasting performance with seasonality matching is also revealed. This study sheds new light on tourism seasonality recognition and contributes to forecasting methodology.

Online grocery delivery: Sustainable practice, or congestion generator and environmental burden?

Grocery delivery services have seen rapid growth in recent years. We develop a method to estimate changes in travel patterns caused by grocery delivery adoption and apply it to a case study in Seattle, WA. Results suggest that delivery can increase or decrease peak hour emissions (−0.9% to + 4.9%) and vehicle hours traveled (−4.2% to + 6.3%), depending most critically on (1) delivery center location, (2) whether deliveries displace dedicated or non-dedicated shopping trips, (3) the degree to which delivery replaces shopping trips or increases demand, (4) the number of deliveries that can be coordinated in a single route, and (5) delivery timing relative to peak travel periods. No scenarios tested where deliveries originate from the store where customers shop achieve emissions or energy use reductions; however, congestion reductions of 3.4% are still possible if the delivery trips are shifted to off-peak hours.

Impacts of electric vehicle policy on the New Zealand energy system: A retro-analysis

Policy in New Zealand supports a transition to Electric Vehicles (EVs), which are projected to comprise 40% of the light vehicle fleet by 2040. The impact of EVs cannot be decoupled from the power generation. Therefore, in this study, New Zealand’s energy system (transport and national power generation) is modelled in LEAP and the impacts of a 40% transition to EVs assessed with a multiple-scenario 2019-based retro-analysis. Two charging scenarios are simulated, modelling “Optimistic” and “Concentrated” charging. Two methods of generation expansion are modelled: “Mixed Renewables” and “Non-Renewables”. Results show existing generation can support a 16% EV transition with Concentrated charging, and a 43% transition with Optimistic charging, without decreasing the reserve margin. It is expected a 40% EV transition with a Non-Renewable power system expansion will reduce energy sector GHG emissions by 2% and costs by 7%, and a Mixed Renewables expansion will reduce emissions by 17%–18% and costs by 4%–5%. These results suggest an EV transition is an effective method for New Zealand to meet GHG emission targets, particularly with renewable generation expansion. Important areas of future research are identified, including: alternative EV charging strategies, peak travel events, embodied vehicle emissions, and the costs of power-system upgrades.

Survey evidence on COVID-19 and its impact on rail commuting patterns in Great Britain

The COVID-19 pandemic saw a large proportion of the working population in the United Kingdom working from home for an extend period of time. In September 2021, a substantial number continued to work from home on a regular basis, a stark contrast to the situation pre-pandemic. Prior to 2020, although there was a growing trend for increased home working, this was still not a widespread practice across UK businesses, with the majority of staff working full-time from the employment location.The aim of this paper is to report the main changes in commuting and working patterns among commuters in Great Britain (GB) brought about by the COVID-19 pandemic with a special emphasis on the rail commuting market. The paper is informed by a panel survey of 3,892 GB respondents who commuted regularly to work prior to the pandemic in two waves. The first wave was conducted in Summer 2020 and focused on respondents’ pre-pandemic travel patterns. The second wave was conducted in the Autumn 2021 and asked respondents about their current travel patterns. Although the focus was on rail travel, many of the conclusions of this paper are applicable to all modes of transport.Although a majority of pre-pandemic commuters have returned to commuting at least once per week to their place of work, there is a significant share of pre-pandemic commuters who are still working from home full-time. The return to the workplace has been slower among individuals who used to commute by rail compared to other modes. This is partially explained by current workplace arrangements being strongly linked with an individual’s occupation and mode of transport. Individuals in office-based occupations are much more likely to work from home compared to those who work in other occupations.The pandemic has brought additional challenges to transport systems in addition to the reduced ridership level. Some of the emerging issues include managing post-pandemic peak travel volumes across a curtailed working week and the associated funding for peak capacity. Nonetheless, the research has highlighted some potential strategies to boost a faster recovery for travel demand.

Analysis of influencing factors and anti-pinch measures for door pinch accidents in conventional public transportation

A safe and convenient bus service is important for improving the bus mode share. However, during the morning and evening peak travel periods, hurrying passengers may get caught in closing doors when they squeeze into a bus. We conducted a survey of bus door pinch accidents that occurred in Harbin, China for the past five years to investigate the factors contributing to such accidents. A total of 1465 samples were collected to explore the effects of passenger age, gender, and other factors on pinching accidents. The results of the logit regression analysis revealed that all six influencing factors significantly affect the occurrence of door pinch accidents. Juveniles, females, and slow-moving people are more susceptible to pinch accidents. Additionally, situations such as door congestion, rushing to get on or off the bus, and inappropriately located cameras increase the likelihood of such accidents. To further investigate the effects of variables on different scenarios, the accidents were classified into four categories based on their occurrence process and object and analyzed using multiple logistic regression. The results show that juveniles and females are more likely to be involved in accidents in which objects are pinched, slow-moving people are more prone to be pinched while boarding and rushing to get on or off the bus, and congestion at the doors increases the probability of all four types of pinch accidents. Therefore, we recommend modifying the door position, eliminating the front door seat, and adjusting the handrail distribution to alleviate door congestion. Moreover, the door-closing warning sound must be amplified to remind passengers that the door is about to close, potentially reducing the number of pinching accidents caused by rushing to get on or off the bus.

Evaluating Contraflow High-Occupancy Vehicle Lane Designs for Mitigating High-Occupancy Vehicle Lane Performance Degradation

High-occupancy vehicle (HOV) lane performance degradation has become more prevalent in many regions because of the growing travel demand and the increasing number of HOV lane-eligible vehicles. Conventional capacity expansion strategies, such as adding a second HOV lane, can be a promising solution. However, they can be difficult in areas where there is little room left to add new travel lanes in both directions. In that case, adding a contraflow HOV lane could be a good compromise, especially if the peak travel demands in the HOV lanes are tidal. In this work, we study the impact of adding a contraflow HOV lane on a section of the I-215 freeway, which connects two major cities in Riverside County, California. Two alternative designs, “full contraflow” and “partial contraflow” HOV lanes, are evaluated in the traffic microsimulation environment. The evaluation results show that in the case of the full contraflow HOV lane design, the average delay during peak hours in the southbound direction of the freeway would be reduced by 76% compared with the scenario with no additional HOV lane. The implementation of the full contraflow HOV lane to supplement the existing HOV lanes would also increase the average speed in the currently degraded HOV lane from 37.8 to 55.0 mph, which is significantly above 45 mph, the speed threshold for HOV lane performance degradation.

Do corporate social responsibility ratings have any effect on traffic congestion?

• Employs quantitative research and analysis methods. • Explores traffic congestion in light of CSR ratings and its translation to real-world outcomes. • An innovative idea on sustainable transportation and policy in CSR. • Enhancing CSR policy in mitigating traffic congestion is an area where private entities can have a significant local impact on a social issue affecting people, plant, and profit. Traffic congestion (TC) is a complex issue that has an adverse impact on the environment, business operations, livability, and health of a community. Supply-side TC mitigation measures increase transportation capacities while demand-side measures attempt to modify travel behavior so that the travel demand is reduced. As part of the demand-side, some corporations are investing in TC reduction through actions that contribute to improved walkability, reduced peak travel demand, and active commuting. While beneficial for their local communities, TC reduction efforts are not specifically tagged as a part of corporate social responsibility (CSR) endeavors. CSR involves reporting of a company’s stewardship towards its community and environmental impact. Research reflecting on the impact of CSR on TC has not been conducted. This study aims to fill this gap. Using corporations headquartered in the top traffic-congested cities in the United States, this study examines the relationship between TC and CSR. This research employed a hierarchical linear model with two datasets, Travel Time Index (TTI) and CSRHub ratings. Of the four CSR categories studied, community, employees, and environment ratings are significantly related to TTI, with employees and environment ratings having an inverse relationship to TTI. This shows that congestion has a strong impact on the environment and that companies, through their employee policies, can impact TC. The results also highlight the opportunities that companies have in potentially reducing their environmental impact by incorporating congestion reduction strategies as part of their CSR, either as a separate measure or as part of their environmental or employee CSR ratings. Our results are also a starting point for new tools/strategies that transportation policymakers and analysts can use to engage companies to help mitigate TC. A further study on other cities with major traffic problems may shed more light on CSR and TC.

Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19.

An innovative ADE-TFT interpretable tourism demand forecasting model was proposed to address the issue of the insufficient interpretability of existing tourism demand forecasting. This model effectively optimizes the parameters of the Temporal Fusion Transformer (TFT) using an adaptive differential evolution algorithm (ADE). TFT is a brand-new attention-based deep learning model that excels in prediction research by fusing high-performance prediction with time-dynamic interpretable analysis. The TFT model can produce explicable predictions of tourism demand, including attention analysis of time steps and the ranking of input factors’ relevance. While doing so, this study adds something unique to the literature on tourism by using historical tourism volume, monthly new confirmed cases of travel destinations, and big data from travel forums and search engines to increase the precision of forecasting tourist volume during the COVID-19 pandemic. The mood of travelers and the many subjects they spoke about throughout off-season and peak travel periods were examined using a convolutional neural network model. In addition, a novel technique for choosing keywords from Google Trends was suggested. In other words, the Latent Dirichlet Allocation topic model was used to categorize the major travel-related subjects of forum postings, after which the most relevant search terms for each topic were determined. According to the findings, it is possible to estimate tourism demand during the COVID-19 pandemic by combining quantitative and emotion-based characteristics.

A model and methodology for resilience assessment of interdependent rail networks – Case study of Great Britain's rail network

Rail networks entail multiple interdependencies which can initiate or propagate network failures with serious consequences for the movement of trains and passengers. In this study, we present a rail infrastructure system-of-systems model which can be used to simulate disruptions to the network's operations and propose a performance metric based on train and passenger delay minutes. We demonstrate the applicability of our model by evaluating the resilience of the southern region of Great Britain's rail network in scenarios of failure initiated in the traction power supply system. The results highlight the sensitivity of the rail network to a small number of traction assets concentrated in the London area, where we find that failure of one of the region's most critical electricity traction power grid could disrupt 75% of all trains in the southern region and 25% of all trains nationally. Our analysis demonstrates how the resilience of the rail network is influenced by the speed of backup restoration following a failure event as well as the network's vulnerability to failure at peak travel times with a delay during peak travel resulting in about 63% increase in passenger delay minutes compared with off-peak travel times for the same duration.