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.     

New method to estimate local and system-wide effects of level rail crossings on network traffic flow

This paper makes local and aggregate estimates of the effects of 152 level crossings in the Melbourne metropolitan area, Australia on traffic congestion in the morning peak period (7 am–9 am). A new method, including micro-simulation of a range of rail crossing configurations, is used to inform a network model which makes aggregate estimates of impacts on all traffic.Relationships between train frequency and percentage change in vehicle travel time and volume were identified. These equations can predict change in travel time/traffic flow caused by rail level crossings based on rail crossing closures and train frequency.Overall, Melbourne's level crossings result in an average increase in travel time of 16.1% for vehicle traffic on links with a level crossing. However on average a level crossing reduces the volume of vehicles on these links by 5.9% as a result of traffic diversion. These values are higher in middle suburbs where train arrivals and crossing closure times at level crossings are more frequent.The aggregate effect of all 152 level rail crossings on all traffic in Melbourne is a travel time change from 1.81 to 1.82 min/km (an increase of around 0.3%). The number of congested links in Melbourne increases by 0.9% while the total delay increases by 0.7%. These network wide effects are not large compared to localised effects because road links affected directly by crossings represent a very small part of the overall network. Additionally, network effects also include traffic diversion impacts which will counteract some of the immediate impacts on a localised scale. However, it is significant that the very small element of the system can have even a measurable effect on aggregate traffic congestion.     

The impact of unseasonable or extreme weather on traffic activity within Lothian region,Scotland

Little has been reported on the variation in traffic activity with either unseasonable or extreme weather conditions. This paper reports on an investigation into such relationships for the former Lothian Region (now split into four administrative districts), in Scotland.Daily traffic activity together with meteorological data was gathered for the period between 1987 and 1991. This data was used to develop a simple model to predict traffic activity based on yearly, monthly and daily variations. A similar model was developed to predict meteorological variables based on monthly variations. The differences in the observed and predicted values were used in the 50% test to determine whether or not unseasonable weather was linked to changes in traffic activity. The changes in the average weekday traffic activity, although significant, were less than 3% for unseasonable sunshine hours, maximum temperature, minimum temperature and rainfall. However at weekends there were reductions of more than 4% in the average traffic activity on both the days with the highest rainfalls and the days with lower than expected minimum temperatures.Similar comparisons were made to determine whether or not there were links between traffic activity and extreme weather conditions. There were similar slight but significant changes in average weekday traffic activity with the extremes of rainfall, sunshine hours, maximum temperature and minimum temperature. However there was an average reduction of 10% in weekday traffic activity when snow was lying. At weekends there was an average reduction of 4% in traffic activity on the days with the highest rainfall and an average reduction of 15% on the days when snow was lying.     

Space-time dynamics: A modeling approach for commuting departure time on linked datasets

Commuters' departure time related decisions are important in time geography. Analytic tools have been proposed to capture the inherent choice determinants both in time and space. Although the dynamic aspects of the problem have been identified, most of the existing studies are based on static models. In this paper, a dynamic modeling framework is proposed to explore the relationship between commuters' departure time choices and the evolution of en route traffic. A data linkage method is developed to create an integrated dataset that enables the observation of commuters' reaction to changes in travel time and traffic conditions over time. A regional household travel survey is linked to travel information obtained from the Google Maps application program interface (API), creating a synthetic longitudinal dataset. Two decision rules are applied to model commuters' response to the evolution of traffic. The results indicate that travel time, distance to work location, flexibility in working schedule, expected arrival time, and commuters' sociodemographic influence departure time choices. It is also found that accounting for dynamics improves model fit and out-of-sample predictions. Both the dynamic model and the proposed data linkage method contribute to the understanding of human activities in space and time and can be used to enhance transportation demand analysis and urban policy studies.     

Tourist Perceptions of Climate Change: A Study of International Tourists in Zanzibar

A study was conducted in Zanzibar, Tanzania, in order to understand tourist perceptions of climate change, the importance of climate for travel decisions, and the likely consequences of ongoing climate change for travel decisions. The results show that climatic characteristics of destinations are important, though not the only factor shaping travel decisions. Under a scenario of climate change, certain climate variables, such as more rain, storms, and higher humidity are also likely to negatively influence travel decisions, rather than higher temperatures alone, which are not necessarily perceived as negative. Regarding the contribution of travel to climate change, the study reveals that leisure tourists in Zanzibar are largely unaware of their impact on the natural environment, while the analysis of stated travel behaviour shows that they frequently travel by air. Overall, the results indicate that travel flows might change in more complex ways than currently assumed, and that a small share of high-intensity air travellers is responsible for a considerable environmental impact in terms of climate change.     

Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks

The trip patterns on an urban network can be represented by two main variables: origin-destination flows (OD flows), defined as the number of trips between two locations over a given time period, and traffic volumes, defined as the number of vehicles that cross a street over a given time interval. Past research on the dynamic of traffic assignment and OD estimation suggested that the traveler's decisions vary on a day-to-day basis and that their most recent decisions may affect their current travel decisions. Based on these assumptions, this study analyzed the autocorrelation of a set of day-to-day series of traffic volumes and OD flows generated from a large collection of traffic sensors, identifying the data's correlation structure over different locations and OD pairs in an urban network. To this end, a method for data treatment of the 2017 dataset from the traffic monitoring system of Fortaleza, Brazil, was employed, which consisted in the following major steps: data cleaning due to equipment failure, definition of traffic profiles for typical and atypical months, definition of daily traffic periods, selection of suitable devices to obtain OD flows, and detection of outliers in the time series. The traffic profiles and the daily traffic periods were defined by applying clustering techniques. The analysis of autocorrelation was performed after controlling for seasonal effects in the data by applying regression analysis. This study contributes to understand how the dynamic of trip patterns varies over space due to the spatial distribution of the city's activities and the network's spatial centrality. The analysis of 144 sets of traffic volumes throughout 2017 suggests that the autocorrelation of traffic volumes should be higher in congested central areas where multiple options of route are available. It seems that, for large congested networks, which present many uncertain factors (e.g., accidents, variable weather, multiple paths, etc.), part of the users do not have complete knowledge of the network's performance, and must rely on experience and habit to decide their routes, especially at more centralized locations of the network. The analysis of serial correlation in the series of sample OD flows between regions showed that the city's central area, where more commercial and service-related activities take place, seems to influence the dynamic of OD flows, probably due to the occurrence of more non-commuting trips to the central area of the city.     

Network structure and travel patterns: explaining the geographical disparities of road network vulnerability

Inevitably, links in the road network are sometimes disrupted because of adverse weather, technical failures or major accidents. Link closures may have different economic and societal consequences depending on in which regions they occur (regional importance), and users may be affected differently depending on where they travel (regional exposure). In this paper we investigate in what way these geographical disparities depend on the road network structure and travel patterns. We propose aggregate supply-side (link redundancy, network scale, road density, population density) and demand-side (user travel time, traffic load) indicators and combine them in statistical regression models. Using the Swedish road network as a case study, we find that regional importance is largely determined by the network structure and the average traffic load in the region, whereas regional exposure is largely determined by the network structure and the average user travel time. Our findings show that the long-term vulnerability disparities stem from fundamental properties of the transport system and the population densities. Quantitatively, they show how vulnerability depends on different variables, which is of interest for robust network design.     

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.     

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.     

The end of travel time matrices: Individual travel times in integrated land use/transport models

To reduce inaccuracies due to insufficient spatial resolution of models, it has been suggested to use smaller raster cells instead of larger zones. Increasing the number of zones, however, increases the size of a matrix to store travel times, called skim tables in transport modeling. Those become difficult to create, to store and to read, while most of the origin-destination pairs are calculated and stored but never used. At the same time, such approaches do not solve inaccuracies due to lack of temporal resolution. This paper analyzes the use of personalized travel times at the finest spatial resolution possible (at x/y coordinates) and a detailed temporal resolution for synthetic agents. The approach is tested in the context of an existing integrated land use/transport model (ILUT) where travel times affect, among others, household relocation decisions. In this paper, person-level individual travel times are compared to traditional skim-based travel times to identify the extent of errors caused by spatial and temporal aggregation and how they affect relocation decisions in the model. It was shown that skim-based travel times fail to capture the spatial and temporal variations of travel times available at a microscopic scale of an agent-based ILUT model. Skims may provide acceptable averages for car travel times if a dense network and small zones are used. Transit travel times, however, suffer from temporal and spatial aggregation of skims. When analyzing travel-time-dependent relocation decisions in the land use model, transit captive households tend to react more sensitively to the transit level of service when individual travel times are used. The findings add to the existing literature a quantification of spatial biases in ILUT models and present a novel approach to overcome them. The presented methodology eliminates the impact of the chosen zone system on model results, and thereby, avoids biases caused by the modifiable spatial unit problem.     

Estimating the influence of crowding and travel time variability on accessibility to jobs in a large public transport network using smart card big data

Accessibility metrics are gaining momentum in public transportation planning and policy-making. However, critical user experience issues such as crowding discomfort and travel time unreliability are still not considered in those accessibility indicators. This paper aims to apply a methodology to build spatiotemporal crowding data and estimate travel time variability in a congested public transport network to improve accessibility calculations. It relies on using multiple big data sources available in most transit systems such as smart card and automatic vehicle location (AVL) data. São Paulo, Brazil, is used as a case study to show the impact of crowding and travel time variability on accessibility to jobs. Our results evidence a population-weighted average reduction of 56.8% in accessibility to jobs in a regular workday morning peak due to crowding discomfort, as well as reductions of 6.2% due to travel time unreliability and 59.2% when both are combined. The findings of this study can be of invaluable help to public transport planners and policymakers, as they show the importance of including both aspects in accessibility indicators for better decision making. Despite some limitations due to data quality and consistency throughout the study period, the proposed approach offers a new way to leverage big data in public transport to enhance policy decisions.     

Travel and tourism competitiveness index: The impact of air transportation,railways transportation,travel and transport services on international inbound and outbound tourism

The objective of the study is to examine the impact of air transportation, railways transportation, travel and transport services on international inbound and outbound tourism in a panel of 19 tourists - oriented countries, over a period of 1990–2014. By applying principal component analysis, the study constructs travel and tourism competitiveness index for inbound and outbound tourism. The main constructs of inbound tourism index include international tourists' arrival, tourism receipts, receipts of passengers' transports items and travel items while the constructs of the outbound index include international tourists' departure, tourism expenditures, and expenditures for passengers transport and travel items. The result of panel Fully Modified OLS (FMOLS) regression shows that the presence of air transportation, railways transportation, and trade openness positively affect inbound tourism index, while travel and transport services negatively affect tourism competitiveness index. The causality results confirm the bidirectional relationship between inbound tourism, air transportation, railways passengers carried, trade openness and travel and transport services, while there is a unidirectional causality running from inbound index to railway goods transported, from air transport freight to trade factor, and from travel services to air transport freight. Outbound tourism index confirmed the bidirectional causality relationship with air transportation, railways transportation, and travel and transport services, while the causality running from outbound index to trade factor, from air transport passenger carried to travel services, and from railway goods transported to trade and transport services, which support the unidirectional causality relationship between them. The variance decomposition results show that air transportation freight is the contributor that largely influences inbound-outbound tourism, while railways passengers carried and trade openness has the least share to influence inbound and outbound tourism index for the next 10-year period. The impulse response function indicates that air transportation, railways transportation, trade openness and travel services will positively impact on inbound truism while travel and transport services will positively affect outbound tourism for the next 10-year period. The study concludes with the importance of transportation sector that deem desirable to promote tourism worldwide. The concentration of different modes of transportation including air transportation, railways transportation, and travel and transport system would helpful to advance international tourism.     

Making plans or “just thinking about the trip”? Understanding people’s travel planning in practice

ICT solutions have been proposed as a means for changing environmentally unfavourable traffic behaviour by providing better, real-time and more accessible travel information. However, prevailing models of travel choice and travel behaviour tend to overemphasise the impact and importance of information and the individualistic perspective. The issue of choice and travel planning in everyday life situations, and how information is used and acted on in these processes, was examined in a qualitative study in Stockholm, Sweden. Practice Theory was used as the theoretical framework for the study. Interviews were supplemented with an explorative diary and photo assignment to bring unreflected choices and actions of planning travel to the conscious level.The results showed that travel planning involves the immediate situation where planning and decisions are made, but also aspirations, cognitive/time/material limitations, social norms and social relations that extend widely in time and space. Definitions of travel planning and travel information based on the situated practices of planning are suggested. In the muddle of everyday life, travel planning takes place in the brief moments where circumstances at different levels – time, place, the social realm - interact and are considered or directly acted upon. In the development of new ICT-based travel information services, the role of technology in changing normal practices should be considered.     

Ex-post environmental and traffic assessment of a speed reduction strategy in Madrid's inner ring-road

Since urban traffic is a major source of CO₂ and NO_x emissions, cities play a key role averting climate change and combating air pollution. Most researchers agree on the need of designing comprehensive mitigation strategies instead of applying isolated measures. Nevertheless, it is important to understand the specific impact and scope of each measure to look for the most effective synergies among them. In 2004, the Madrid City Council launched a plan to re-design its inner ring-road to move traffic out of the city centre. For safety reasons the planned speed limit for the full-renovated South-West section was finally reduced from 90 km/h to 70 km/h. Besides contributing to traffic safety, this strategy could also be seen as positive to the environment due to the associated reduced fuel consumption and lower emissions. However, lower speed limits have lower rates of community acceptance due to its impact on average travel times at the individual level. This paper conducts an ex-post evaluation of this speed reduction strategy to explore its environmental and traffic performance impacts. The results support the thesis that, in this velocity range, lower speed limits present important opportunities for reducing GHG and air pollution in the section affected by the measure, without substantially altering traffic performance. The implementation of the new speed limit policy produces a 14.4% and 16.4% reduction in CO₂ and NO_x emissions respectively, while global travel time remains virtually constant and the saturation rate decreases slightly. Besides, this cost-effective measure reveals great potential to reduce air pollution in highly populated urban areas located next to urban highways. This work provides local policy makers and city managers with useful insights regarding potential co-benefits of traffic optimization and speed reduction management to reduce mobile source emissions in urban environments.     

The influence of personality traits on airport public transport access mode choice: A hybrid latent class choice modeling approach

Many objective and subjective factors affect individual tendencies. Such subjective factors include personality traits, attitudes, identities, perceptions, and feelings. The choice of transportation mode is an individual tendency that is considered important in policy-making decisions, and it can affect sustainable transportation, particularly in metropolitan areas. The present study’s main aim is to determine the impact of the Big Five Personality Factors on individual preferences toward public transportation modes. We use data from a survey conducted in January and February of 2015 at Imam Khomeini International Airport (IKIA). Passengers were asked to indicate their preferred mode of transportation to access the IKIA and to respond to questions on the NEO Five-Factor Inventory. Based on 557 valid responses, hybrid discrete latent class modeling was conducted to understand the heterogeneity in the respondents’ individual preferences regarding the Big Five Personality Factors and their preferences toward public modes of transportation. The results indicated that individuals who display neuroticism were more likely than the others to be concerned about carrying heavy luggage and about inclement weather conditions when using public transportation. In addition, interesting results indicated that conscientious individuals likely paid more attention to travel cost than to any other attribute of public transportation, and the model of the conscientious latent personality trait was a better fit to the data. Finally, this paper examined the taste heterogeneity of each personality trait and the results indicate the usefulness of considering personality traits in mode choice models for richer insights toward sustainable transportation.     

Spatial impact analysis of trans-Yangtze highway fixed links: A case study of the Yangtze River Delta,China

As an important infrastructure connecting straits or rivers, the construction of fixed links has become an effective measure for improving traffic conditions and promoting socio-economic development in many countries and regions around the world. Thus, it has become a significant topic in the field of transport geography. Taking the Yangtze River Delta as its case area, this study proposes a spatial impact model of trans-Yangtze highway fixed links, consisting of three components: an accessibility model, modified gravity model, and traffic utilisation model, which are used to analyse, respectively, cross-Yangtze accessibility and changes to transport structure, cross-Yangtze urban interaction and changes to economic structure, and the utilisation relationship of fixed links. Making use of existing fixed links while planning and building new ones has become the basis for achieving regional integration and sub-regional cooperation in this area, the aim of which is to create the barrier-free circulation of elements. The results prove that this spatial impact model coincides with reality. The increase in the number of fixed links has significantly shortened cross-river travel time and facilitated a unification of north-south highway networks. It has also promoted the formation of a high-connection urban network along the river. Northern urban nodes have joined the southern economic circle and finally achieved north-south integration. There are complex relationships between fixed links and their hinterlands, showing obvious utilisation gaps between the links. The complete system of trans-Yangtze fixed links is comprised of several river crossing facilities and connected highways. The function of each link is related to its role in the regional highway network. From a small number of fixed links to the realisation of multiple trans-Yangtze bridges and tunnels with easy access to multiple arterial highways, a ‘many-to-many’ spatial pattern is created that ultimately leads to the evolution of regional transport and economic structures.     

Dynamic accessibility: Incorporating day-to-day travel time reliability into accessibility measurement

Travel times and hence accessibility in urban areas are susceptible to traffic disruption caused by events such as congestion, roadworks and traffic accidents. Being highly valued by travellers, travel time reliability affects their participation in activities and thus, plays a decisive role in accessibility. The aim of the study was to develop an approach to integrate travel time reliability into the measurement of accessibility. To achieve this, we extended a commonly-used accessibility indicator to include day-to-day variability in travel times. In a case study of the accessibility to the newly-built Queen Elizabeth University Hospital (QEUH) in Glasgow, we used real-time travel times with high temporal resolution collected over a long period of time to demonstrate the applicability and the utility value of this approach compared to the standard accessibility measurement. Our results revealed that travel time reliability varied both temporally and spatially, and zones experienced relatively high levels of accessibility loss due to higher travel time variability. The proposed approach provides a more realistic representation of actual network performance, allows for assessing the effect of travel time reliability on accessibility throughout the day and will help transport planners to trace equity impacts on accessibility due to travel time unreliability.     

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.     

How did COVID-19 impact air transportation? A first peek through the lens of complex networks

The current outbreak of COVID-19 is an unprecedented event in air transportation. This is probably the first time that global aviation contributed to the planet-wide spread of a pandemic, with casualties in over two hundred countries. As of August 23rd, 2020, the number of infected cases has topped 23 million, reportedly relating to more than 800,000 deaths worldwide. However, there is also a second side of the pandemic: it has led to an unmatched singularity in the global air transportation system. In what could be considered a highly uncoordinated, almost chaotic manner, countries have closed their borders, and people are reluctant/unable to travel due to country-specific lock-down measures. Accordingly, aviation is one of the industries that has been suffering most due to the consequences of the pandemic outbreak, despite probably being one of its largest initial drivers. In this study, we investigate the impact of COVID-19 on global air transportation at different scales, ranging from worldwide airport networks where airports are nodes and links between airports exist when direct flights exist, to international country networks where countries are contracted as nodes, and to domestic airport networks for representative countries/regions. We focus on the spatial-temporal evolutionary dynamics of COVID-19 in air transportation networks. Our study provides a comprehensive empirical analysis on the impact of the COVID-19 pandemic on aviation from a complex system perspective using network science tools.