Impact of weather conditions on macroscopic urban travel times

Weather conditions may significantly impact a series of everyday human decisions and activities. As a result, engineers seek to integrate weather-related data into traffic operations in order to improve the current state of practice. Travel times and speeds are two of the elements of a transportation system that may be greatly affected by the weather resulting in deterioration of roadway network performance. This study aims to investigate the impact of different intensities of rain, snow and temperature levels on macroscopic travel times in the Greater London area (UK) during the period 1 October–10 December 2009. The analysis was carried out for three 2-h periods on weekdays during the morning, afternoon and evening periods. Automatic Number Plate Recognition (ANPR) data obtained from more than 380 travel links are used in the analysis. The main finding is that the impact of rain and snow is a function of their intensity. Specifically, the ranges of the total travel time increase due to light, moderate and heavy rain are: 0.1–2.1%, 1.5–3.8%, and 4.0–6.0% respectively. Light snow results in travel time increases of 5.5–7.6%, whilst heavy snow causes the highest percentage delays spanning from 7.4% to 11.4%. Temperature has nearly negligible effects on travel times. It was also found that the longer links within outer London generally yield greater travel time decreases than those in inner London, and even higher decreases than the shortest links in central London. This research provides planners with additional information that can be used in traffic management to modify planning decisions and improve the transportation system control on a network scale under different weather conditions. In order to determine whether the weather effects are region-specific, continued research is needed to replicate this study in other areas that exhibit different characteristics.     

Assessment of non-recurrent congestion caused by precipitation using archived weather and traffic flow data

Since precipitation has a negative impact on traffic congestion, there have been various studies for modeling the relationship between precipitation and its impact on traffic flow. However, due to limitations on existing data, none of the previous studies have accounted for the estimation of the total delay caused by precipitation. The objective of this study is to estimate the non-recurrent traffic congestion on freeways caused by precipitation. To accomplish this objective, archived weather and traffic data for the year 2008 from the Korean Freeway Systems was collected and analyzed. As a result, non-recurrent traffic congestion was about 1.6 million vehicle-hours due to rainfall and 186,000 vehicle-hours due to snowfall in 2008. In addition, simple analyses were performed to describe the average non-recurrent traffic congestion per unit distance as a function of precipitation and a function of the time period of precipitation. Although precipitation events might not be handled by human efforts, these results will assist in making strategic plans such as active speed management and contingency planning for mitigating traffic congestion due to precipitation.     

An analysis of air mass effects on rail ridership in three US cities

This paper examines whether daily weather affects ridership in urban transportation systems. When examining human–weather relationships, it is often advantageous to examine air masses, which take into account the entire parcel of air over a region. Spatial synoptic classification characterizes air masses based upon numerous meteorological variables at a given location. Thus, rather than examining temperature or precipitation individually, here we compare daily ridership to synoptic air mass classifications for three urban rail systems: Chicago Transit Authority (CTA), Bay Area Rapid Transit (BART), and the Hudson–Bergen light-rail line in northern New Jersey. Air masses are found to have a significant impact on daily rail ridership, with usage typically increasing on dry, comfortable days and decreasing on moist, cool ones, particularly on weekends. Although the comfort of a particular air mass changes throughout the year, seasonality is not a significant factor with respect to the air mass–ridership relationship. The results of this study can benefit rail system managers who must predict daily ridership or in the development of cost-benefit analyses for station improvements.     

Impacts of adverse weather on Arctic road transport

Arctic regions are geographically peripheral and characterized by cold climate, constantly changing weather conditions and strong seasonal variations. This article examines variations in road traffic volume due to adverse weather in an arctic region, a topic that has received little attention in the transportation literature. The subject of the case study is northern Norway's Saltfjellet mountain pass, which is part of European Highway 6 (Ev6). A succinct econometric structural equation model was used to test hypotheses regarding the impacts of fluctuations in temperature, precipitation and wind speed on passenger and freight traffic volumes. The findings indicate that there was some reduction in traffic volume during adverse weather, particularly with respect to passenger traffic. However, the day-to-day variations in traffic volumes were relatively low at the studied section of road, although it is known that generalized transport costs increase significantly in adverse weather due to delays related to poor driving conditions and closed roads. The studied region is rural with limited access to alternative routes or transport modes, thus making this portion of the road particularly important for the communities in the region. Hence, the road users have few other options than using this high-cost road in order to maintain their activities. The use of standardized parameters in transport models to predict the effect of adverse weather on traffic volume, would not be appropriate in the studied context. However, it is recommended that the benefits of reducing the extra costs that adverse weather impose on traffic are estimated and included in road-project assessment tools to capture the burden and strain imposed on road users. This to ensure that appropriate decisions are made regarding the development and improvement of transportation facilities, particularly in rural areas.     

Impact of Long‐Term Weather on Domestic and Foreign Winter Tourism Demand

This study estimates the determinants of domestic and foreign tourism demand using data on 28 Austrian ski resorts for the winter seasons 1986–1987 to 2007–1908. Using the dynamic panel data analysis, we find that the effect of the weather variables (e.g. snow depth, cloudiness or sunshine) is quite small, with a change in one standard deviation of the variation over time in each weather variable, leading to a 2–3 % change in overnight stays. Furthermore, domestic tourists are more sensitive to changes in weather conditions than foreign tourists. By contrast, overnight stays of foreign visitors are much more responsive to changes in income than it is the case for domestic overnight stays. The occurrence of extreme snow‐deficient winters, such as the winter of 2006–2007, in the future period will reduce overnight stays of foreign and domestic visitors by 2 and 5 %, respectively. Copyright © 2011 John Wiley & Sons, Ltd.     

The weather and macroeconomic determinants of whale-watching tourism: a Markov regime-switching analysis

Taking in sail is essential for a whale-watching excursion, which makes the demand for whale-watching tourism sensitive to the weather. The purpose of this paper is to examine the implications of two potential demand-side determinants, that is, weather and macroeconomic conditions, on the business cycle of Taiwan’s whale-watching tourism industry. By exploiting a Markov regime-switching model, this study finds that temperature and relative price changes are crucial determinants of the demand for whale-watching tourism, no matter whether in the peaks or in the troughs. Nevertheless, the influences of sunshine hours, rainfall and real GDP per capita on the demand for whale-watching tourism depend heavily on the phases of business cycle. The empirical results provide some inspiration for sustainable management of Taiwan’s whale-watching tourism.     

Weekend effect in airfare pricing

Internet traffic during weekends is lighter than at weekdays, allowing airlines to adopt a distinctive pricing policy during the weekend. By analyzing the daily airfares for 1000 US domestic routes, this study tests whether a weekend effect exists in the level and dispersion of airfares. It finds a strong weekend effect for airfare dispersion, but not for price level. This suggests that different arrival timing of online consumers during the weekdays motivate airlines to adopt a distinctive pricing mechanism during weekends, by offering occasional discounts while maintaining the same fare level on average.     

Cycling in a changed climate

The use of bicycle is substantially affected by the weather patterns, which is expected to change in the future as a result of climate change. It is therefore important to understand the resulting potential changes in bicycle flows in order to accommodate adaptation planning for cycling. We propose a framework to model the changes in bicycle flow in London by developing a negative binomial count-data model and by incorporating future projected weather data from downscaled global climate models, a first such approach in this area. High temporal resolution (hourly) of our model allows us to decipher changes not only on an annual basis, but also on a seasonal and daily basis. We find that there will be a modest 0.5% increase in the average annual hourly bicycle flows in London’s network due to a changed climate. The increase is primarily driven by a higher temperature due to a changed climate, although the increase is tempered due to a higher rainfall. The annual average masks the differences of impacts between seasons though – bicycle flows are expected to increase during the summer and winter months (by 1.6%), decrease during the spring (by 2%) and remain nearly unchanged during the autumn. Leisure cycling will be more affected by a changed climate, with an increase of around 7% during the weekend and holiday cycle flows in the summer months.     

Weather risk management at the Olympic Winter Games

The globally celebrated Olympic Winter Games (OWG) are highly dependent on suitable snow and ice conditions to support elite-level competitions. To determine the range of weather impacts on the Games, this study examines the official Olympic post-Games reports from 1924 to 2010. Impacts include preparations for the Games, holding outdoor opening–closing ceremonies, outdoor sporting competitions, spectator comfort, transportation, and television broadcasts. The study also examines the range of historical adaptations that have developed to manage weather risks at the OWG. Three adaptation eras are identified, spanning the history of the games: emergent adaptation, technological transition, and advanced adaptation. Analysis reveals that while weather-induced impacts have always been a part of the Games, these impacts would be far greater if not for technical climatic adaptations. With the average daytime temperature of host locations steadily increasing from 0.4°C at the Games held in the 1920–1950s to 7.8°C at the Games held in the twenty-first century, it would be difficult to imagine recent host cities/regions successfully delivering the diverse Games programme exclusively on natural ice and snow. The connection between the evolving needs for weather risk management strategies by Olympic organisers and the growth of the Olympics in size and scope is also discussed.     

Taking another look at cycling demand modeling: A comparison between two cities in Canada and New Zealand

Cities around the world are investing in their cycle network as a way to reduce traffic congestions and improve population health. For effective infrastructure investment decision-making, it is critical to understand the factors affecting cycling demand. This study investigates the combined effects of spatial and temporal factors on the demand of cycling in Auckland, New Zealand and Kelowna, Canada. A latent segmentation-based negative binomial (LSNB) model is developed utilizing daily cycling counts over a year from Auckland and Kelowna. The LSNB model captures unobserved heterogeneity based on the temporal attributes, and land use and neighborhood characteristics of the cycling facilities. The models confirm the influence of weather, bicycle facility type, built environment, traffic, land use, and accessibility attributes. The model results suggest that higher temperature, lower rainfall, increased length of shared paths, lower AADT, and closer distance to bus stop are likely to increase cycling demand in Auckland. The model also confirms heterogeneity. For example, road-connectivity index and bike index vary across the urban and suburban areas of Auckland. For Kelowna, higher temperature, lower rainfall, lower snowfall, and closer distance to water bodies are likely to increase cycling demand. In the case of heterogeneity, the effects of road-connectivity index, bike index, and AADT varies over the urban and suburban areas of Kelowna. Analysis suggests that increasing bike index is one of the critical factors to increase cycling demand in the suburban areas of Auckland and Kelowna.     

An investigation of air accidents in Nigeria using the Human Factors Analysis and Classification System (HFACS) framework

Increasing air traffic growth has been achieved along with substantial improvement in safety globally. While air traffic is equally growing in Nigeria, safety levels do not appear to be growing side by side. This was gleaned from the spate of accidents and fatalities recorded in the last couple of decades. The study therefore set out to assess safety performance in Nigeria's air transport industry by comparing accidents and fatality rates with global average levels during the period 1985–2008. A content analysis of the accident reports was done using the Human Factors Analysis Classification System (HFACS) as a conceptual framework; this was augmented with results of industry experts assessment of the Nigerian aviation industry. Their assessments were also discussed in the context of the conceptual framework. Accidents and passenger fatality rates in Nigeria were higher than global average figures for most of the years included in the analysis period. Findings on aircraft ages show that these are also higher than world average levels. The aviation industry experts' assessment presented various challenges which include inadequate airport facilities, absence of timely meteorological information and dearth of skilled personnel in Nigeria's aviation industry. The content analysis of the accident reports using the HFACS shows that skill based errors; physical environment and inadequate supervision are the most frequently occurring categories influencing accident occurrences. The Chi-square and Fishers's test used to analyze significant relationships in the HFACS categories obtained in the accident reports showed five pairs of significant associations between adjacent categories. Based on these associations, Supervisory Violations:-Crew Resource Management:- Decision Errors path is deemed the most potent for accident occurrences. Findings from the research point to the need to address personnel skill, physical environment issues (mostly weather related) and supervisory competence.     

Analysis of airport weather impact on on-time performance of arrival flights for the Brazilian domestic air transportation system

Air traffic operations are significantly impacted by weather conditions. These external factors may impose operational constraints and generate demand-capacity imbalances, leading to reduced on-time performance, additional airline costs and inconveniences to passengers. Efficient management of such disruptions requires an understanding of the main causes of flight delays towards increasing their predictability. In this study, we investigate the impacts of airport surface weather conditions on the likelihood of flight delays for the Brazilian domestic air transportation system. We use historical flight schedule, on-time performance and weather data and estimate a logit model to analyze how different meteorological variables at the airport of destination affect the probability of a delayed arrival. We obtain empirical evidence for the impacts of low ceiling and visibility conditions, precipitation and wind gusts on the likelihood of arrival delays for the set of Brazilian airports analyzed.     

How does weather affect bikeshare use? A comparative analysis of forty cities across climate zones

This study examines the effect of weather on bikeshare use. We employ data from forty Public Bicycle Sharing Programs located in forty cities (16 countries) across five different climate zones, spanning tropical to boreal climates. Our curated dataset is longitudinal and consists of nearly 100 million cycling trips. Key findings include: (a) the most significant variable, particularly on weekdays, is the time of day, followed by precipitation; (b) in most cities, usage increases on weekdays and weekends up to a point around 27 to 28 °C, before declining; (c) usage by hour usually follows a bimodal or trimodal daily pattern on weekdays, except for schemes which are too small to serve a commuter function (weekend and weekday usage is similar in small schemes); (d) weekend usage peaks at around 2 to 3 pm in most schemes, except those in hotter climates where the peak is around 5 pm; (e) precipitation negatively affects female ridership more than male ridership; and, (f) a changing climate is likely to affect cycling by boosting ridership in cold climates and lowering ridership in warm climates, but the effects will likely be small. In the spirit of reproducibility, all data and R code are publicly available.     

The future of the Olympic Winter Games in an era of climate change

The Olympic Winter Games (OWG) stands as a symbol of international cross-cultural exchange through elite-level sport. As a mega-event with a significant reliance on a specific range of weather conditions for outdoor competitions, the OWG have developed several technologies and strategies to manage weather risk. Can these climatic adaptations cope with future climate change? Based on an analysis of two key climate indicators (probability of a minimum temperature of ≤0°C, and probability of a snow depth of ≥30 centimetres with advanced snowmaking capacity), this paper examines how projected changes to climate will impact the ability of the 19 previous host cities/regions to provide suitable conditions for outdoor competitions in the future. The results indicate that while the 19 former OWG hosts all have a suitable climate in the 1981–2010 period, only 11 or 10 (low–high-emission scenarios) remain climatically suitable in the 2050s, with as few as 6 in the high-emission scenario of the 2080s. The analysis reveals that climate change has important implications for the future geography of OWG host cities/regions as well as broader implications for participation in winter sport.     

Extreme weather,public transport ridership and moderating effect of bus stop shelters

This paper examines how extreme weather conditions influence urban public transport ridership with a particular focus on the role of bus stop shelters. Using bus ridership data from the Salt Lake City metropolitan area, we find that extreme weather such as very high and low temperatures, and heavy rainfall reduces public transport ridership, while bus stop shelters have a modest effect on mitigating ridership losses resulting from these adverse weather conditions. The moderating effect of shelters is more pronounced on weekdays, and for bus stops with lower service frequency and fewer transfers. Our research also shows that the installation of bus shelters correlates with a variety of factors including service frequency, land use types, and local socioeconomic and demographic characteristics. Overall, our findings suggest that public transport amenities with weather-proof attributes have the potential to retain and attract more ridership on extreme weather days.     

What is ‘neighborhood walkability’? How the built environment differently correlates with walking for different purposes and with walking on weekdays and weekends

Residential environments are associated with people's walking behavior. Transit-related, non-transit-related, and recreational walking may be differently associated with residential environments on weekdays and weekends, but empirical evidence is scarce. We therefore examined 1) to which extent these types of walking correlated with natural and built environmental characteristics of residential neighborhoods, 2) how these correlations differ for walking on weekdays and weekends, and 3) what substitution and complementarity effects between different types of walking exist. Our sample comprised 92,298 people aged ≥18 years from the pooled Dutch National Travel Survey 2010–2014. Multivariate Tobit regression models were used to assess the associations between the natural and built environment and the three types of walking (in average minutes per day). Our models accounted for cross-correlations between the walking types. Our results showed that denser residential areas encouraged both longer transit-related and non-transit-related transport walking on weekdays and weekends, whereas lower density neighborhoods were positively associated with recreational walking on weekdays. Shorter distances to public transport were only significantly associated with transit-related transport walking on weekdays. Shorter distances to daily facilities were positively associated with non-transit-related transport on weekdays. No significant associations between built environment and recreational walking were found on weekends. Additionally, some compensation effects between different types of walking seem to be at play: during weekends, recreational walking was inversely correlated with transit-related transport walking. Residential environments seem to affect walking types in a different way, suggesting that one size fits all policies might be less effective. Intervention strategies should be tailored for each walking type separately.     

Effects of winter precipitation on automobile collisions,injuries, and fatalities in the United States

To better understand the links between winter precipitation (snow, sleet, and freezing rain) and travel risk, data on weather conditions and vehicle crashes, injuries and fatalities are gathered for 13 U.S. cities. A matched pair analysis is used to construct event-control pairs to determine the relative risk of crash, injury, and fatality. Winter precipitation is associated with a 19% increase in traffic crashes and a 13% increase in injuries compared to dry conditions. The type of winter precipitation (snowfall vs. freezing rain, ice pellets, or sleet) had no significant impact on the relative risk of crash. The relative risk of crash was significantly higher during the evening (1800–2359 local time) than during other times of the day. More intense precipitation led to increased relative risk of crash and injury compared to less intense precipitation. Relative risk of crash, injury, or fatality was not significantly higher during the first three winter precipitation events of the year as compared to subsequent events. The relative risk of both winter precipitation crash and injury showed no significant trend during the 1998–2008 period. Sensitivity of U.S. cities to winter precipitation varies from city to city in a manner that is not easily explained. Future research will be required to determine which safety interventions are most effective in each city and revise or expand safety programs appropriately.     

Short-term forecasts on individual accessibility in bus system based on neural network model

Precise forecasts on individual accessibility in bus system can help make policies to accommodate fluctuating bus travel demand and promoting social equity. In this study, we propose a three-stage method for short-term forecasts on individual accessibility in bus system based on neural network (NN) model. In the first stage, a NN model is designed to tackle the nonlinear mapping between passengers' bus trip appearances in historical periods and those in the predicted period. A rate function, which considers bus trip generation rates of passengers, is then applied using outputs of the designed NN model. In the second stage, probabilities of origin-destinations (ODs) chosen by passengers in the predicted period are calculated. In the third stage, land use information combined with results of previous two stages are used to obtain the individual accessibility in bus system in the predicted period. Compared to individual accessibility calculated by real data, it is found that the average errors of predicted results by the proposed method in weekdays and at weekends are only 8.37% and 10.13%, respectively. The results also demonstrate the capability of combining a NN model, traffic data and land use information to forecast the future spatial distribution of individual accessibility in transport system.     

Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning

In recent years, convective weather has been the cause of significant delays in the European airspace. With climate experts anticipating the frequency and intensity of convective weather to increase in the future, it is necessary to find solutions that mitigate the impact of convective weather events on the airspace system. Analysis of historical air traffic and weather data will provide valuable insight on how to deal with disruptive convective events in the future. We propose a methodology for processing and integrating historic traffic and weather data to enable the use of machine learning algorithms to predict network performance during adverse weather. In this paper we develop regression and classification supervised learning algorithms to predict airspace performance characteristics such as entry count, number of flights impacted by weather regulations, and if a weather regulation is active. Examples using data from the Maastricht Upper Area Control Centre are presented with varying levels of predictive performance by the machine learning algorithms. Data sources include Demand Data Repository from EUROCONTROL and the Rapid Developing Thunderstorm product from EUMETSAT.     

Modelling and analysing effects of complex seasonality and weather on an area's daily transit ridership rate

Adverse weather is generally perceived as deterrent for public transit uses. This has also been highlighted in previous literatures. In contrary, our previous study found no association between weather and transit ridership while investigating the underlying temporal influences behind variation in daily ridership across the sub-tropical city of Brisbane, Australia. This contraindication led to the primary focus of this research. This research acknowledged that Inclusion of weather variables in conjunction with other relatively strong independent variables might result in washout of the weather effects on ridership. Variables such as rainfall do not recur on a daily basis throughout the year. Thus, generalising their effect on ridership with other independent variables that consistently influence ridership may create a similar problem. Hence, weather variables were converted into their normalised factors and combined with other independent variables while formulated the optimised the daily ridership rate estimation model. Several models were developed concerning various combinations of weather variables and through rigorous analysis it was identified that only the rain variable has noticeable effect on daily ridership. Evidently, this study functions as an update of our former study by directing towards a new approach to the analysis of the relationship between weather and transit ridership.