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.     

A global model for estimating the block time of commercial passenger aircraft

This paper uses a two-stage statistical model to estimate the block time of commercial passenger aircraft. The model considers many of the factors contributing to airport congestion and provides a basis for future development of multivariate statistical models of the flight operation process. The model is tested using 2 million US domestic flights by six airlines in 2004. Model analysis provided insight regarding the relative impact of weather conditions and airport utilization on block time. In particular, population, arrival time, airport utilization, ice, and the interaction of poor weather conditions and traffic were found to be significant predictors of block time.     

关于铁路运输企业路外伤亡诉讼案件的调查与思考

铁路运输企业路外伤亡诉讼案件的妥善解决,是关系铁路运输安全畅通的重要因素.通过对铁路运输企业路外伤亡诉讼案件解决方案情况的调查,分析当前法律、司法体制及时代背景,总结铁路运输企业处理路外伤亡案件面临的问题,提出强化证据意识、快速处理、调诉衔接、事故防范、加快法律适用研究等方面的建议.     

Methods of estimating destination price competitiveness: a case of horses for courses?

Price competitiveness is a key factor in the overall tourism competitiveness of a country or a destination. Given its importance to overall destination competitiveness, various indicators of prices have been developed. Different indicators shed light on different aspects of competitiveness, and the measures which are most useful depend on what questions are being explored. The authors explore several of the different indicators aimed at measuring destination price competitiveness, describing the strengths and weaknesses of using each. Some of the key aspects which condition which indicators should be used are: the need for accuracy and tourism-specific detail versus timeliness; the need for cross-country (or cross-destination) comparisons of the prices tourists are actually paying; the need for estimates of changes in relative price competitiveness over time and the need to provide overall summary measures of a country's price competitiveness at a point of time or changes in it over time. The specific measure used will therefore depend on the particular analytical and practical need of researchers and destination managers.     

Examining the impact of weather variability on non-commuters’ daily activity–travel patterns in different regions of Sweden

By jointly modelling the routine and leisure activity–travel engagements of non-commuters in different regions of Sweden, this paper explores the interactions between time allocation, travel demand and mode choice under different weather conditions. Combined weather and travel survey datasets that span a period of over 13 years were analysed. Simultaneous Tobit models were applied to explore the interactions among these activity–travel indicators, whilst municipalities’ unique conditions and heterogeneities between different time-points were taken into account. The model results reveal the trade-offs between routine and leisure activities in terms of activity duration, number of trips and travel time. Positive mutual endogeneity was found between slow-mode share in routine and leisure trips. The results also highlight the trade-offs between routine and leisure activities under abnormal weather conditions. Regional differences between weather effects are substantial due to differences in direct, indirect and total marginal effects. Between-municipality variability constitutes a considerable part of the variability in activity duration and travel time. Between-municipality variability in leisure activity duration and leisure travel time is larger in northern Sweden, while that of routine activity duration and routine travel time is larger in central Sweden, after weather and social demographics have been controlled.     

Optimal route decision with a geometric ground-airborne hybrid model under weather uncertainty

Adverse weather is the dominant cause of delays in the National Airspace System (NAS). Since the future weather condition is only predictable with a certain degree of accuracy, managing traffic in the weather-affected airspace is a challenging task. In this paper, we propose a geometric model to generate an optimal combination of ground delay and route choice to hedge against weather risk. The geometric recourse model (GRM) is a strategic Probabilistic Air Traffic Management (PATM) model that generates optimal route choice, incorporating route hedging and en-route recourse to respond to weather change: hedged routes are routes other than the nominal or the detour one, and recourse occurs when the weather restricted airspace becomes flyable and aircraft are re-routed to fly direct to the destination. Among several variations of the GRM, we focus on the hybrid Dual Recourse Model (DRM), which allows ground delay as well as route hedging and recourses, when the weather clearance time follows a uniform distribution. The formulation of the hybrid DRM involves two decision variables - ground delay and route choice - and four parameters: storm location, storm size, maximum storm duration time, and ground-airborne cost ratio. The objective function has two components: expected total ground delay cost and expected total airborne cost. We propose a solution algorithm that guarantees to find the global optimum of the hybrid-DRM. Based on the numerical analysis, we find that ground-holding is effective only when combined with the nominal route. Otherwise, it is optimal to fly on the route determined by the DRM without ground delay. We also find the formula of the threshold ground-airborne cost ratio, which we call the Critical Cost Ratio (CCR), that determines the efficacy of ground delay: the higher the CCR, the more effective the strategies involving ground delay. We conclude that both ground delay and route hedging should be considered together to produce the best ATM decisions.     

Ride-sourcing compared to its public-transit alternative using big trip data

Ride-sourcing risks increasing GHG emissions by replacing public transit (PT) for some trips therefore, understanding the relation of ride-sourcing to PT in urban mobility is crucial. This study explores the competition between ride-sourcing and PT through the lens of big data analysis. This research uses 4.3 million ride-sourcing trip records collected from Chengdu, China over a month, dividing these into two categories, transit-competing (48.2%) and non-transit-competing (51.8%). Here, a ride-sourcing trip is labelled transit-competing if and only if it occurs during the day and there is a PT alternative such that the walking distance associated with it is less than 800 m for access and egress alike. We construct a glass-box model to characterise the two ride-sourcing trip categories based on trip attributes and the built environment from the enriched trip data. This study provides a good overview of not only the main factors affecting the relationship between ride-sourcing and PT, but also the interactions between those factors. The built environment, as characterised by points of interest (POIs) and transit-stop density, is the most important aspect followed by travel time, number of transfers, weather, and a series of interactions between them. Competition is more likely to arise if: (1) the travel time by ride-sourcing <15 min or the travel time by PT is disproportionately longer than ride-sourcing; (2) the PT alternative requires multiple transfers, especially for the trips happening within the transition area between the central city and the outskirts; (3) the weather is good; (4) land use is high-density and high-diversity; (5) transit access is good, especially for the areas featuring a large number of business and much real estate. Based on the main findings, we discuss a few recommendations for transport planning and policymaking.     

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.     

A model for projecting flight delays during irregular operation conditions

On-time performance of airlines schedule is key factor in maintaining current customer satisfaction and attracting new ones. This requires management of the different operation resources (crew/aircraft) to ensure their on-time readiness for each flight in the planned schedule. However, flight schedules are often subjected to irregularity. In particular, weather accounts for nearly 75% of system delays. Due to the tight connection among airlines resources, these delays could dramatically propagate over time and space unless the proper recovery actions are taken. This paper presents a model which projects flight delays and alerts for possible future breaks during irregular operation conditions. The results of applying the model at the operation control center of a major airlines company in the US are presented.     

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.     

基于满意度分析的高速铁路列车运行调整优化研究

为了研究高速铁路列车在恶劣天气影响下导致区段临时限速情景下的列车运行调整问题,文中选择总晚点时间和晚点列数2个指标,分别转化为列车延误满意度和正点率满意度,建立线性满意度、梯形满意度和传统多目标线性加权模型,通过改变列车到发时刻、运行径路、次序等实现高效、准点的列车运行调整。最后设计一个限速场景,运用软件求解,通过改变总晚点时间和晚点列数的权重,得到模型的帕累托解,结果表明满意度模型的帕累托解优于线性加权模型。此外,算例验证了梯形满意度线性加权模型从计算时间上优于传统多目标线性加权模型,提出的满意优化模型可以辅助调度员在发布限速命令后尽快获得满意的调度方案。     

Risk factors associated with flying in adverse weather: From the passengers' point of view

Risk factors on the demand side of aviation safety are rarely addressed as the focus of research is most often placed on the supply side of aviation service. Even though airlines have the prerogative to cancel flights in the face of adverse weather, stranded passengers have to be reckoned with in the event of cancellation. Passengers' awareness of the risks associated with flying in adverse weather may help mitigate the confrontation as well as induce passengers to take the lead in adopting preventive measures. The aim of this article is to study the attitudes air travelers say they have towards flying in adverse weather, as well as risk factors regarding cancellation of trips in adverse weather. A conceptual framework is presented which outlines the links between risk factors and preventive measures. Data generated from a quantitative survey of 1145 air travelers, conducted in July 2015 at Taiwan's Kaohsiung International Airport, is used to examine this framework using correlation analysis and one-way analysis of variance (one-way ANOVA). The factors of trust, risk perception and domestic destination are found to be positively related to passengers' stated attitude toward taking preventive measures. Income, purpose of the trip and direct experience generate ambiguous results. The outcome also suggests the following paradox: while pilots make the final decision regarding aviation safety in adverse weather, they appear to enjoy the lowest level of passenger trust.     

The effects of area-wide road speed and curvature on traffic casualties in England

Transport provides a range of benefits to society in terms of mobility, access and economic growth. There are however negative impacts of transport, not least in terms of environmental degradation, damage to property, traffic accidents and loss of life. This paper focuses on road traffic accidents, the reduction of which is an important aim of transport policy world wide. The primary objective of this paper is to develop a series of relationships using spatially disaggregated area-level cross-sectional data between different traffic casualties, road traffic speed and road curvature by controlling for other contributing factors associated with area characteristics. The spatial units of the analysis are the 8019 census wards in England. Ward-level casualty data are disaggregated by severity of the casualty (such as fatalities, serious injuries and slight injuries) and by the severity of the casualty related to various road users.The results suggest that increased average speed within a ward is positively associated with total fatalities and serious injuries; and road curvature is found to be negatively associated with road accidents.     

Welcome to the Wet Tropics: the importance of weather in reef tourism resilience1

As one of Australia's iconic tourism attractions and one of the seven natural wonders of the world, the Great Barrier Reef (GBR) is an important economic, social and natural resource for Queensland's Tropical North. However, the long-term prognosis for the health of the reef and by implication, the industries dependent on it, is not positive. So far much attention has focussed on the health and resilience of the reef ecosystem, as a foundation for a resilient tourism industry. In this study we explore how weather conditions have affected the reef experiences of 1000 tourists to the Cairns/Port Douglas region, suggesting that this may also be an important indicator of change on the reef. The results suggest that poor weather has a more pronounced effect on experiences than good weather and reinforce the likelihood that seasickness, cold and wet conditions, reduced water visibility, and difficult snorkelling/diving conditions will reduce overall levels of satisfaction. Poor weather was found to have a direct effect on satisfaction scores, the likelihood that reef and tour expectations were not realised, and lowered perceived value for money. These are important considerations for the reef centred tourism industry that is currently facing strong environment pressures from climate change.     

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.     

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.     

论铁路旅客伤亡事故的赔偿责任

7·23铁路动车组列车特别重大事故发生后,伤亡旅客的赔偿标准成为人们争议的焦点。针对赔偿数额的法律依据,在上位法缺失的情况下,限额赔偿制度的合法性等问题,从旅客伤亡赔偿责任的性质入手,分析探讨旅客伤亡赔偿责任的法律适用条款,并就7·23事故赔偿标准的合法性进行解读,提出对铁路部门的警示,从而有效推进铁路法律、法规的修改和完善。     

Motorway speeds in wet weather: the comparative influence of porous and conventional asphalt surfacings

This paper examines motorists’ speeds in wet weather compared with non-hazardous dry conditions (the control) for two separate survey locations on the M4 motorway, south Wales. One site was surfaced with conventional asphalt, whilst the other location had a porous asphalt wearing course. The aim of this study is to establish whether drivers compensate for the additional risks posed in rain by reducing their speed in wet weather. Drivers are slowing down in wet weather, but only marginally; and although the speed reductions proved statistically significant, they are insufficient to compensate for the additional wet weather risks imposed. Such findings have broader implications for the government’s road safety targets.     

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.     

A GIS-based Bayesian approach for analyzing spatial–temporal patterns of intra-city motor vehicle crashes

This paper develops a GIS-based Bayesian approach for intra-city motor vehicle crash analysis. Five-year crash data for Harris County (primarily the City of Houston), Texas are analyzed using a geographic information system (GIS), and spatial–temporal patterns of relative crash risks are identified based on a Bayesian approach. This approach is used to identify and rank roadway segments with potentially high risks for crashes so that preventive actions can be taken to reduce the risks in these segments. Results demonstrate the approach is useful in estimating the relative crash risks, eliminating the instability of estimates while maintaining overall safety trends. The 3-D posterior risk maps show risky roadway segments where safety improvements need to be implemented. Results of GIS-based Bayesian mapping are also useful for travelers to choose relatively safer routes.