Climate Change, Adverse Weather Conditions, and Transport: A Literature Survey Mark J. Koetsea, Piet Rietveldb September 2007 a

Corresponding author, Vrije Universiteit, Department of Spatial Economics, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands. Tel: + 31 (0)20 598 6168, E-mail: [email protected]

b

Vrije Universiteit, Department of Spatial Economics, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands. Tel: + 31 (0)20 598 6097, E-mail: [email protected]

1.

Introduction

Climate change is by now almost invariably considered an issue of global interest. Still, the extent to which climate change represents a problem is still a heavily debated issue; calculations on future damages associated with climate change, and therefore also judgements about mitigation costs to be made now, differ widely. An example is the Stern report, which claims that ‘the benefits of strong, early action considerably outweigh the costs’ (see Stern, 2007, p. II). Specifically, assuming no mitigation efforts, the report estimates a permanent decrease in annual global GDP of between 5% and 20%, thereby claiming justification for large mitigation efforts right now. The report has received wide attention, but part of the reactions were very critical. For instance, Tol (2006) argues that for “water, agriculture, health and insurance, the Stern review consistently selects the most pessimistic study in the literature” (see also Lomborg, 2006). Another criticism comes from Nordhaus (2006). He focuses on the unusually low social discount rate of 0.1% used in the report. Since a near-zero discount rate gives a large weight to climate change damages in the distant future, GDP losses are large even when distant future damages are small. Using a discount rate that is more generally accepted, Nordhaus shows that the extremely low discount rate used in The Stern Review is the main reason for the unusually large damage estimates. Social discount rate discussions will probably continue for some time. Whatever the outcome, they remain of vital importance to assess potential damages related to climate change for the various sectors in the economy. The Stern report analyses damages for, among others, the water, agricultural,

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health and insurance sectors1. A sector that receives fairly little (explicit) attention is the transport sector. This is not entirely surprising, since to date the consequences of changing weather conditions and climate change for the transport sector have not received much attention in the literature. Still, it is widely known that transport systems on the whole perform worse under adverse and extreme weather conditions. This is especially true in densely populated regions, where one single event may lead to a chain of reactions that influence large parts of the transport system. In this paper we therefore give an overview of the literature on the impact of climate change and changes in weather conditions on the transport sector. This paper reflects a changing orientation in research and policy in the field of climate change and transport. Until recently the overwhelming majority of research outputs in the field of climate and transport was on mitigation, the central question being the effectiveness and efficiency of measures to reduce the environmental burden of transport (see, for example, Hensher and Button, 2003; IPCC, 2007).2 More recently, a tendency can be observed that policy makers accept the fact of climate change and explore adaptation strategies such as the implementation of policy measures to reduce potential damage costs related to climate change. An important observation is that adaptation measures and mitigation strategies are interrelated: large adaptation opportunities have implications for the urgency to implement mitigation measures. There are several ways to examine the influence of climate change on transport. One possible route would be to compare transport systems between regions with very different climate conditions, for example by comparing transport in Spain with transport in Norway. Differences in performance of road, rail and waterway transport systems give an indication of the potential impacts of climate change. One of the difficulties of this approach is that differences between countries are the result of a whole range of factors where in addition to climate also other factors play a role, such as the level of economic development, and the spatial and other physical conditions. Another approach to analyse the influence of climate would be to consider the seasonal variations in transport and travel behaviour. Variations in travel behaviour and performance of transport systems between seasons can be partly explained by weather variations. For freight transport, variations in demand will be related to seasonal cycles in sectors such as agriculture. For passenger transport one also has to take into account nonweather seasonal effects such as Christmas holidays and the holiday calendar of schools, which are

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It is worth noting that the effects of climate change on welfare are not necessarily negative; for example, higher temperatures imply lower costs for heating and higher agricultural productivities in moderate and cold zones. A tendency may be expected that the balance of the two is negative in countries in warm zones and positive in cold zones. Also in the transport sector a mixture of negative and positive effects may be expected. In both cases they will lead to adaptation strategies at the supply and demand side of the markets for transport services. 2 A review of approaches to reduce emissions in the transport sector, with a focus on car use, road freight and aviation, is provided in Chapman (2007).

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partly related to weather. A third way to address climate issues would be to consider the instantaneous relationship between weather and travel behaviour. This may be expected to lead to clearly visible adjustments, but one should be aware that these are typically short term adjustments. We will come back to this in the next section. One must be aware that climate and weather will not only affect the demand side on transport markets, but also the supply side. Most of the contributions in this paper address the demand side, but one should be aware that at the supply side there may also be adjustments. A long run adjustment is that the design of infrastructure is such that it copes with the relevant features of weather conditions such as performance under extreme weather conditions in terms of high or low temperatures, heavy rainfall, fog, heavy wind, etc. Supply may also be affected at short notice by weather variations, for example when railway companies and airports stop operations due to extreme wind conditions. In the review below we will distinguish between freight and passenger transport. Most studies on climate and weather concern passenger transport. This makes sense, since behavioural reactions tend to be larger here than in freight transport. However, given the nature of transport as a derived demand, in the long run the patterns of trade flows will be affected by climate changes when these will lead to changes in location patterns of production and consumption. In a similar vein seasonal variations may occur. Further, freight transport will be affected when climate or weather changes lead to changes in generalised costs of transport, directly or indirectly. An example is that when extreme weather leads to accidents on roads both passenger and freight transport will suffer. The remainder of this paper is organised as follows. First we discuss transport demand in Section 2. Since most research on the effects of weather conditions has been done in the area of road transport, we focus on road transport in Section 3. Evidence on the impact of weather on other transport modes is reviewed in Section 4. In Section 5 we set out the main consequences of climate change for future weather conditions, with a focus on the situation in The Netherlands. Subsequently, Section 6 describes potential effects of changes in weather conditions on the transport sector. Section 7 concludes.

2.

Weather impacts on transport demand

In this section we focus on the impact of weather conditions on transport demand; studies that focus directly on the impact of climate change or seasonal variation in weather are scarce. We furthermore concentrate on transport demand by passengers; aspects of demand for freight transport will be dealt with in section 4. For transport activities, adverse weather conditions lead among others to an increase in the average travel time, an increase in the spread of travel times, and to an increased probability of accidents. Hence the generalised costs of transport are affected. Behavioural reactions may occur in various

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ways. We can order them according to the well known basic dimensions of trip generation, trip distribution, modal choice, route choice, temporal choice, and speed choice (Ortúzar and Willumsen, 2001). Trip generation concerns the number of trips made per person per day for various purposes. It is plausible that under adverse weather conditions people consider cancelling certain trips, depending on the purpose of the trip. Trip distribution refers to the choice of destination of trips. In the short run, trip distribution due to adverse weather may occur when people decide to do shopping nearby rather than further away due to adverse weather. In the long run, a less attractive climate, leading to higher generalised transport costs may lead to changes in average commuting distances. Mode choice is the third type of behavioural change we consider. For instance, when rain slows down traffic and increases congestion on roads, car drivers might be inclined to shift to public transport. A further option is that people adjust their route choice based on expectations about changes in generalised transport costs of route choice alternatives. The fifth alternative we consider is that travellers decide to change time of departure. The viability of this option is highly dependent on the degree of flexibility of the trip and on the weather forecast for the rest of the day. Obviously, non-scheduled trips are easily postponed, while scheduled trips such as most commuting trips are not. The last dimension of change concerns speed choice. This choice element can be considered as an instrument for car users to correct for the risk changes that occur under extreme weather conditions (this aspect will be covered in section 3). We note that the above behavioural adjustments are the result of choices of travellers. There is another type of behavioural change that can take place, namely, decisions of suppliers of transport services to adjust the level of service to the weather conditions. This aspect will be discussed in more detail in section 4. We now turn to a review of the literature on the impact of climate change on transport demand. There is fairly little empirical evidence on changes in travel behaviour due to adverse weather conditions. Most of them focus on modal change. Several studies focus on bicycle use under diverse weather patterns. Van Boggelen (2007) shows that for The Netherlands the yearly volume of per capita bicycle kilometres in the period 1986-2006 increases with 1.8 for every day with temperatures higher than 25 °C, and decreases by 1.3 for every day with temperatures lower than 0 °C and by 1.7 for every day with more than six hours of precipitation. Richardson (2000) finds similar effects for the number of cycling trips; rainfall and both low and very high temperatures decrease the number of cycling trips. These patterns appear to be fairly general; low temperatures, strong wind and precipitation have a negative impact on the use of the bicycle (see, e.g., Goetzke and Rave, 2006; Emmerson et al., 1998; Winters et al., 2007). An exception is Nankervis (1999), who finds a negligible effect of precipitation on bicycle use in Melbourne. However, he solely uses students in his research, who can be expected to have few substitution possibilities and therefore are more or less bound to use the bike for transportation, even when the weather is bad. Bergström and Magnusson (2003) perform a survey among a thousand employees of four major companies in two Swedish cities. They find that there is a

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large decrease in the number of bicycle trips (–47%) and a large increase in car use (+27%) for commuting purposes during winter (see also Öberg et al., 1996). Moreover, temperature and precipitation were among the most important factors for those who cycled to work in summer but not in winter. Although bad weather certainly causes a reduction in the number of people who use the bike for commuting purposes, there is strong evidence that recreational cycling is more affected by bad weather than utilitarian cycling (see Richardson, 2000; Bergström and Magnusson, 2003). Most studies discussed use a time series approach. A cross sectional comparison is carried out by Rietveld and Daniel (2004), who find that bicycle use in The Netherlands depends on wind speed: municipalities with strong winds report lower annual bicycle use than municipalities with moderate wind speeds. Khattak and De Palma (1997) conduct a detailed survey among Brussels commuters in 1992 in order to analyse, among others, their mode choice decisions under various circumstances. The results show that 69% of the respondents, next to their primary transportation mode, have access to an alternative transportation mode, but that only 5% actually switches between transportation modes according to season. This suggests that changes in weather patterns from summer to winter have only a small impact on modal choice. More specifically, since only a very small percentage of the commuters use a bike to get to work, the results suggest that substitution between the car and public transport from summer to winter is limited. However, responses to the more detailed questions on travel decisions under adverse weather show a different picture. They reveal that more than half of the automobile users change their mode, their departure time or route choice under adverse weather conditions. Of these three possibilities, changes in departure time were more often mentioned to be an important option in adverse weather. Furthermore, an ordered probit analysis on the factors that influence ‘mode change due to adverse weather’ is presented. Interesting here is that people with children in day care change mode far less often, suggesting that flexibility of the activity is a very important factor for change in modal choice. Also worth noting is that the use of weather forecasts have only a small increasing and statistically insignificant impact on the probability of a change in mode choice. A similar pattern emerges from an ordered probit analysis on ‘changes in departure time due to adverse weather’, with the added feature that greater flexibility in arrival time and departure at work time has a large impact on changing departure time.3 Since the sample used for these ordered probit analyses is rather small (N = 166) and includes car users only (i.e., car is primary transport mode), caution is required in generalising the results.4

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In general the results suggest that people with greater flexibility in arrival times at work do not change their departure time due to adverse weather because it does not matter whether they arrive late or not. Greater flexibility in leaving work early does lead to changes in departure times, suggesting that in adverse weather people who can will leave home and work earlier to avoid the morning and evening peaks. 4 De Palma and Rochat (1999) conduct a similar survey among Geneva commuters. The patterns found are like the ones in Khattak and De Palma (1997). Weather leads to changes in mode choice, route choice and departure

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Results from a revealed preference study by Aaheim and Hauge (2005) on the impact of weather on travel habits in Bergen (Norway) in 2000 suggests that the impact of weather on substitution between public and private transport is relatively small. The study also shows that travel distance decreases with precipitation, except for trips with commuting purposes. Therefore, although precipitation has a direct negative effect on the proportion of walking and biking trips, it has an indirect positive effect because of its decreasing effect on trip distance. The authors conclude that in some cases the indirect positive effect outweighs the direct negative effect. However, the study has several important drawbacks. Among others, it uses daily instead of hourly data on weather conditions, and observations are from 2.5 months only. Results should therefore be interpreted with caution. We conclude that most studies surveyed above focus on mode choice. Destination choice, route choice and departure time are issues that are under researched. We furthermore note that most studies focus on the impact of current weather conditions, although there are also some studies where seasonal patterns are studied. When one is interested in the potential long run effects of climate change for transport, the latter is probably more relevant, because seasonal patterns tell us more about some of the long run adjustments to climate change. For example, models on instantaneous responses to adverse weather do not take on board the question whether the number of trips is actually reduced, or that people just make the trip at a later time during the day or the week.

3.

Road transport

After the general introduction to the theme in section 2 we now discuss results for road transport in particular. We pay special attention to the effects of weather on road accidents and the secondary effects on traffic flow and congestion. The second part of the section is on the functioning of the road system under extreme events leading to long periods that roads cannot be used. 3.1

Road safety, traffic flow and congestion

It is clear that (changes in) weather conditions have an effect on road safety. Several weather variables appear to be important. Stern and Zehavi (1990) investigate the relationship between hot weather and traffic accidents. They conclude that the risk of an accident increases with increasing heat-stress conditions. The largest increase was found to be in the category single-vehicle accidents (see also Maycock, 1995; Welch et al., 1970; Cantilli, 1974; McDonald, 1984). Also fog and wind may have an increasing effect on the number of accidents (see, e.g., Edwards, 1996; Hermans et al., 2006). However, by far the most important variable is precipitation. Empirical evidence on the impact of rain and snow on the frequency and severity of road accidents is abundant. Although studies employ a wide variety time choice, with the latter being most important. Furthermore, weather forecasts again did not play a substantial role.

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of methods (least squares, Poisson and negative binomial regressions, matched-pair approach, mean differences, wet pavement indices) and display a fairly wide variety of outcomes in a quantitative sense, most of them indicate a positive relationship between precipitation and frequency (severity) of road accidents (see, e.g., Eisenberg, 2004; Shankar et al., 2004, 1995; Bos, 2001; Edwards, 1996; Levine et al., 1995; Jones et al., 1991; Brodsky and Hakkert, 1988; Satterthwaite, 1976). Rather extreme increases in road accidents and injuries due to precipitation are found by Andrey et al. (2002) using data from mid-sized Canadian cities. On average, precipitation increases the number of accidents by 75% and the number of related injuries by 45%, with snowfall having a more substantial effect than rainfall. Several issues appear to mediate the impact of weather on accidents. Eisenberg (2004) shows that lagged precipitation, i.e., rainfall the day or days before, substantially reduces the impact of precipitation on road safety. This implies that rainfall leads to a stronger increase in the number of fatal accidents after a dry spell. The latter is most likely caused by the fact that precipitation clears the oil that accumulates on roads during dry periods, thereby making roads slippery. It is also possible that people adjust their driving behaviour slowly, implying relatively risky driving behaviour in rainy conditions after a dry spell. A similar lagged precipitation effect is found by Levine et al. (1995) and Brodsky and Hakkert (1988). Although accident frequency is always found to increase due to precipitation, the effect of precipitation on accident severity is not as pronounced. For instance, using negative binomial regressions, Eisenberg and Warner (2005) estimate the effects of snowfall on US traffic crash rates between 1975 and 2000. They find that snow days had more nonfatal-injury crashes and property-damage-only crashes, but fewer fatal crashes than dry days. Andrey et al. (2001) also find that the increase in the probability of an injury due to rain and snow is lower than the increase in the probability of an accident. Results by Fridstrøm (1999, Chapter 6) show a similar pattern; increases in the number of snow days in Norway increases the number of injury accidents but decreases the number of fatalities per accident. For rainfall both number of accidents and number of fatalities decrease. The mediating effect here is likely that precipitation reduces the speed of traffic (Van der Vlist et al., 2004), thereby reducing the severity of an accident when it occurs.5 In turn, road accidents affect traffic speed and traffic flow, making the relation between weather, road safety and traffic flow and traffic speed an interesting but complex one (see Figure 1). Direct evidence on this relationship is scarce, however. A notable exception is a study by Golob and Recker (2003), who identify relationships between accident characteristics, traffic and traffic-flow characteristics, and weather and lighting conditions. They find that weather and lighting conditions are

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Where traffic speed is likely a relevant mediating factor in accident severity, traffic volume appears to be more relevant in accident probability per time period (see Van der Vlist et al., 2004).

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related to accident characteristics directly and indirectly through their impact on traffic characteristics, such as traffic flow and traffic speed. Traffic speed Weather conditions

Traffic flow Accident frequency and severity

Figure 1: Relationship between weather, road safety, traffic speed and traffic flow Other studies in this area focus primarily on partial relationships, such as the impact of adverse weather on traffic speed and traffic flow. For instance, figures in Martin et al. (2000) range from 10% speed reduction in ‘wet conditions’ to 25% speed reduction in ‘wet and slushy conditions’. Traffic flow reduction figures are somewhat lower, probably because distance between vehicles is reduced somewhat when traffic speed decreases. Similarly, Ibrahim and Hall (1994) use a dummy variable technique to analyse the effects of adverse weather on the speed-flow and flow-occupancy relationships (see also Hall and Barrow, 1988). They find a substantial impact of heavy snow and, to a somewhat lesser extent, heavy rain, causing reductions in the free-flow speed of 38-50 km/hour and 5-10 km/hour, respectively. Using an extensive dataset, Agarwal et al. (2005) find that heavy rain and heavy snow reduce road capacity by 10-17% and 19-27%, respectively. The impact on traffic speed was measured at 4-7% and 11-15%, respectively. Finally, Stern et al. (2003) regress travel times on several Washington D.C. road segments on various weather variables. Although the average increase in travel time was measured at 14%, variation is high and increases in travel time exceeding 20% are far from uncommon (the maximum measured increase was 26%, see Table 1). In conclusion, although the estimates from different studies are difficult to compare in magnitude, it is clear that the impact of intense precipitation on capacity, traffic speed and traffic flow can be substantial. Table 1: Observed percentage increases in travel time due to adverse weather conditions Percentage increase in travel time 5-10% 10-15% 15-20% >20% Total

Number of cases in % 29 % 36 % 18 % 17 % 100 %

Source: Own calculations based on Stern et al., 2003, Table 2 (total number of observations is 66)

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Related to reductions in traffic speed, precipitation also increases the frequency and severity of traffic jams. As shown in AVV (2005), approximately one third of the increase in (the severity of) traffic jams in 2004 compared to 2003 can be explained by changes in weather conditions (mainly an increase in snow and rain). The same study gives the number of traffic jams with their associated causes in the years 2001 to 2004. These numbers show that road accidents accounted for 11.7% and 13.6% of all traffic jams in 2004 and 2003, respectively. Although the impact appears substantial, some caution is required because the figures only deal with the number of traffic jams and do not include their severity. On the other hand, MuConsult (2004) shows that around 11% of vehicle time loss, a measure which does include severity, can be accounted to road accidents. The impact of weather on vehicle time loss is around 3%. AVV (2006a) provides further insight into the welfare consequences associated with vehicle time loss due to traffic accidents. They show that, despite the fact that the number of traffic accidents has decreased, the costs associated with vehicle time loss have increased from € 88 million in 1997 to € 100 million in 2000 and € 125 million in 2003. Although these figures are respectable, the report also shows that costs of accident related traffic jams constitute only 1% of total accident costs. Changes in traffic volume can be interpreted as demand for transport. Parry (2000) notes that during days with snow, inessential journeys are postponed or curtailed. Although this is confirmed by the available empirical evidence, empirical findings disagree on the magnitude of the effect. Al Hassan and Barker (1999) find an average reduction of traffic volume in Scotland of approximately 15% when roads are covered with snow and a reduction of 4.6% on days with the highest rainfall. Keay and Simmonds (2005) find an overall reduction in traffic volume in Melbourne of 1.35% on wet days in winter and of 2.11% on wet days in spring.6 Their results also show an overall volume reduction of 23% for 2-10 mm of rain during daytime, with reductions in spring somewhat larger than those in winter. Fridstrøm (1999, Chapter 4) analyses determinants of vehicle kilometres in Norway and finds a strong seasonal impact. The more minutes of light per day and the higher the mean monthly temperature, the more vehicle kilometres are driven (elasticities are 0.141 and 0.068, respectively). The number of days of snowfall per month has a negative and statistically significant effect on vehicle kilometres; the elasticity is –.025. Snowfall appears to have more substantial consequences for freight transport; the elasticity is –.067 for vehicle kilometres by heavy vehicles. Because functional forms for the relationships under investigation are largely unknown but likely non-linear, Box-Cox parameters were estimated. In each case the estimated parameter indicates that the elasticity increases with the initial level of the relevant variable, e.g., a one percent increase in snowfall frequency causes more substantial percent decreases in vehicle kilometres at higher initial snowfall frequencies. In contrast, Hanbali

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Hogema (1996) presents similar findings, with reductions in traffic volume of 2-3% on wet days opposed to dry days. However, the differences between dry and wet days were statistically insignificant without exception.

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and Kuemmel (1993) analyse the impact of winter storms on traffic volume, and find that the decrease in traffic volume (in %) is large and nearly proportional to the amount of snowfall.7 The results also suggest that the reduction in traffic volume is dependent on the importance of the trip; traffic volume reduction during peak hours was less than during off-peak hours, and less during weekday hours than during weekend hours. As such, trip purpose and specifically the distinction between business related transport and leisure transport appears to be an important segmentation in the transport market. Finally, of the few econometric studies that have been done on the relationship between traffic flow and road accidents, most find a nearly proportional relationship (see, e.g., Vitaliano and Held, 1991). However, Dickerson et al. (2000) argue that not controlling for geographical and road specific heterogeneity might bias the empirical estimates. Controlling for these factors they show that the relationship may in fact be non-proportional. Specifically, they find a near-proportional relationship at low and moderate traffic flows, and a substantial increasing marginal accident rate at high traffic flows. Note that, although Vitaliano and Held do incorporate speed limits, both studies do not control for actual traffic speed, which might be an important moderating factor in the accident-flow relationship. In conclusion, adverse weather conditions have an increasing effect on the number, and to a lesser extent on the severity, of road accidents. It also causes traffic to slow down and increases the number and intensity of traffic jams, leading to substantial time loss by road users. However, although the effects of weather on road safety, traffic speed and traffic volume and congestion have been investigated individually, knowledge on their interaction is scarce. 3.2

Extreme events: Storm and Flooding

Few studies have assessed the impact of extreme weather events, such as flooding and storms and hurricanes, on transport and transport infrastructure. However, the few studies that do show that there may be substantial effects. For instance, in CVS (1994) economic losses from transport delays due to the January 25/26 Storm in the Netherlands in 1990 are estimated at 4.5 million Euro. Furthermore, sea-level rise and increased risk of coastal flooding may cause structural damage to both rail and road transport infrastructure in coastal areas (Parry, 2000). This is especially relevant for low-lying areas in coastal zones or alongside rivers that also have a high density of road and rail infrastructure. Suarez et al. (2005) investigate the impact of flooding and climate change on urban transportation in the Boston Metro Area. The area is interesting because it is situated along the coast and has numerous river systems. In the study the effects of flooding on system performance are simulated using the Urban Transportation Modelling System (UTMS). The results show almost a doubling of delays and lost trips. The

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Specifically, traffic flow reduction during weekdays for < 25 mm of snow is 7-17%, for 25-75 mm of snow it is 11-25%, for 75-150 mm of snow it is 18-43%, for 150-225 mm of snow it is 35-49%, and for 225-375 mm of snow it is 41-53%.

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authors argue that these negative effects are too insubstantial to warrant large adaptations to infrastructure. On the other hand, they also note that applying the model to cities that have more low-lying areas may produce more dramatic results.8 In AVV (2006b) the economic costs associated with changes in traffic and transport due to a single flooding incident in The Netherlands are estimated. The study analyses mobility effects based on predictions of the LMS model (Gunn, 1994). The analyses are meant as a quick scan in order to assess the order of magnitude of the effects. For instance, transport effects of evacuations and calamity tourism are not taken into account. The study distinguishes between four transport scenario’s, which vary with respect to the time after the flooding incident and with respect to assumptions on the behavioural effects. The first scenario analyses the first days after the incident, for which it is assumed that the only possible behavioural reaction is to change route choice. In this scenario destination traffic is taken out of the equation, so changes in route choice hold for non-destination traffic only. Total mobility in The Netherlands increases with 1.4% (8.5 million extra vehicle kilometres) but travel times increase with 40%, which is high. The latter can be attributed to the fact that people can only change their route choice and have no other possibilities for substitution. The second scenario is termed the long term equilibrium and is meant to address the lower boundary in economic costs. The scenario assumes that the directly damaged area is permanently unusable. Every behavioural reaction is possible in this scenario, i.e., people may change their route choice, destination choice, mode choice and departure time. Population of the damaged area is distributed among the neighbouring municipalities, employment in agriculture and retail disappears, employment in industry is halved and redistributed within a radius of 50 kilometres, while employment in services remain unchanged in the damages area. Directly affected freight transport remains unchanged in order to clearly address the lower cost boundary in this scenario. The third scenario differs from scenario two in that commuters are is assumed to be limited in their behavioural response. Therefore, commuters may only change their route choice, as was the case in scenario one. Finally, the fourth scenario is meant as a sensitivity analysis to scenario three. The difference is that in scenario four, one of the affected highways is quickly restored.

To arrive at the

economic costs associated with these scenarios different values of time (in 2010 Euro) are used for different trip purposes (freight € 40.63, commuting € 8.38, business € 29.05, other € 5.79) and extra costs per kilometre (gas etc.) are calculated. Results are presented in Table 2. Depending on the scenario the costs vary from € 414 million for the long term equilibrium scenario, to € 1.1 billion in scenario 3. Al-

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Examples of such cities are Tampa, Cincinnati, and especially New Orleans, as has become painfully clear.

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though the figures show some variation, they are all many times higher than previous estimates of around € 5 million (see AVV, 2006b).9 Table 2: Monetary costs of losses in travel time and extra kilometres driven due to a single flooding incident in The Netherlands for four scenarios (in millions of 2010 Euro) Freight transport Commuting Business trips Other trips Extra kilometres Total

Scenario 1 289 191 415 146 54 1,095

Scenario 2 163 51 142 58 0 414

Scenario 3 402 318 227 93 74 1,114

Scenario 4 278 263 170 69 65 845

Source: AVV (2006b)

These studies tell us little about the impact of climate change directly. Indirectly, however, sea level rise and increases in precipitation and wind speeds due to climate change will increase the probability of flooding incidents in the future, ceteris paribus. As the AVV (2006b) study shows, increases in transport costs due to a single flooding incident can be considerable, especially in densely populated areas like the western part of The Netherlands. Further evidence on increased risk of sea level rise and flooding incidents is provided by Jacob et al. (2000), who estimate that, at a 3 feet sea level rise by the year 2001, the frequency of coastal surging and related flooding will increase by a factor 2 to 10, with an average of 3. Needless to say the frequency of extreme events will increase accordingly, the costs of which are substantial to say the least.

4.

Other transport modes

In comparison to road transport, the effects of climate change and weather on the other modes of transport have been scarcely documented. In the following three subsections we review the available evidence on rail transport, inland shipping and air transport, respectively. 4.1

Rail transport: Infrastructure failure and accidents

In Duinmeijer and Bouwknegt (2004) the frequency and distribution of rail infrastructure failures due to adverse weather conditions in the Netherlands in 2003 are reported. Weather appears to cause approximately 5% of all infrastructure failures (8,279 in 2003 in The Netherlands), which is limited but

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Some examples of the impact of floods on rail transport in the US can be found in Rossetti (2002). A telling example is the Midwestern river floods of 1993 with 4,000 miles of track either flooded or idled and over $ 200 million in estimated losses.

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far from negligible. Most of the weather-related failures are caused by high temperatures, icing, storm and lightning. However, within the reporting system of Prorail it is assumed that when, for instance, temperature is between certain values it cannot cause a failure. When this assumption would be removed, and therefore failures would be reported differently, the number of failures attributable to adverse weather conditions would likely double to around 10% (personal communication). A study by Rossetti (2002) shows that for 66 out of 5,700 accidents and incidents in the US between 1993 and 2002 the reported primary cause was weather, a figure much lower than that for The Netherlands. Alternatively, when looking at the weather conditions at the time of the accident, snow, fog and rain seem to account for 131, 81 and 411 accidents, respectively. This would amount to approximately 10% of all failures, which would be more in accordance with the Dutch situation. The main causes of weather-related problems in both countries are, however, very different. Clearly, more in-depth research is needed in this area. 4.2

Inland shipping: Economic loss due to low water levels

Changes in temperature and precipitation have consequences for water levels in rivers and thereby for the inland shipping sector. Specifically, low water levels in the rivers may disrupt transport over water as especially in the river Rhine basin many goods (bulk freight) are transported by inland waterways. Low water levels will force inland waterway vessels to use only part of their maximum capacity, which will considerably increase transportation costs. Not much research has been done in this area. In an early study Marchand et al. (1988) use a hydrologic model to predict changes in water levels and water level variation due to climate change for the year 2035. By applying an extensive transport model they subsequently simulate the consequences of these changes for average annual shipping costs in the Great Lakes – St. Lawrence river system in Canada. Using a climate change with a doubling of CO2 emissions, they show that mean annual shipping costs from 1979 to 2035 may increase by 5% using a static economic scenario, 10% using economic forecasts with a 1% increase in shipping demand, and 27% using economic forecasts with a 2% increase in shipping demand. Moreover, they find a large increase in the frequency of extreme costs. Results from this 1988 study may be criticised because climate change scenarios around that time may not have been as advanced as they are now. In a recent study on the consequences of a doubling of CO2 emissions for shipping in the Great Lakes river system, Millerd (2005) estimates that increases in average operating costs may indeed be substantially higher. Specifically, he estimates that average operating costs from 2001 to 2030 increase by 18% to 42% depending on the industrial sector, with an average of approximately 30%. Using slightly different climate change scenarios, i.e., scenarios in which greenhouse gasses are gradually increased annually, the estimates are substantially lower. Average annual operating costs in 2030 are estimated

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to increase by 3% to 14% depending on the industrial sector, with an average of approximately 8%. Estimates for 2050 range from 6% to 22%, with an average of 13%.10 Shifting our attention to Europe, Jonkeren et al. (2006) analyse freight prices of approximately 2800 shipping trips on the river Rhine in the period 1986-2004. Approximately 70% of inland shipping in the EU is transported on the Rhine. Water levels are measured at Kaub, which at low water levels is the bottleneck for most trips. Further, since water levels have no effect on freight prices arranged through long-term contracts, only transport enterprises that operate on the spot market are included in the dataset. Applying regression analysis to explain on the spot freight prices per ton transported, the study clearly shows increasing freight prices at decreasing water levels. It is estimated that in the period 1986-2004 there has been an annual average welfare loss of € 28 million due to low water levels in the river Rhine. The estimated loss in 2003 was as high as € 91 million due to the very dry summer in that year. Although these results are based on historical data they have clear consequences for the inland shipping sector under climate change. Climate change scenarios (see also Section 5) show that the incidence of low water levels will increase, making inland shipping less attractive relative to road or rail transport, ceteris paribus, potentially causing a modal shift from water transport to rail and road transport. 4.3

Air transport: Delays, cancellations and accidents

For the aviation sector wind speeds are important because of their impacts on safety. Extreme wind speeds imply that aircrafts are not allowed to land at a certain airport and hence have to land at alternative airports. This has large cost implications, both for the airlines and the travellers. In a similar vein, high winds imply that the departure of aircrafts will be delayed. Wind speeds and their directions also have implications on the use of runways. Strong cross winds have an impact on the probability of accidents. For example, one of the larger aviation accidents at Schiphol after the El-AL Boeing catastrophe in 1992 was due to a landing of a Transavia plane in 1997 with very strong cross winds. In a country like the Netherlands it is important for airports that sufficient runway capacity is available under various wind directions. An underestimate of wind speeds and their directions may mean that wrong decisions are taken on the design of airports in terms of the capacity and orientation of runways. CPB (2002) has estimated that a ‘wrongly’ configured airport like Schiphol – implying that the number of hours that the airport cannot be used is unnecessarily long – may lead to disadvantages for the aviation sector of amounts between 300 million and 1 billion Euro. However, wind is not the only factor. A

10

In an earlier study on the same area, Millerd (1996) simulates individual shipments in 1989 for several commodity groups and for a scenario in which emission of carbon dioxide is two times as high. He finds a substantial negative impact of over 2 million dollars on average a year, with over 90% of the damage occurring within grain and coal transport.

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good example of economic loss due to other types of bad weather is San Francisco International Airport. A study by Eads et al. (2000) shows that poor visibility in the summer months and rain storms in the winter months lead to substantial delays and numerous cancellations. In Table 3 some figures are reported. Compared to good weather, cancellations per day increase by a factor 2-3 when weather is bad in the morning, and by a factor 3-4 when weather is bad all day. Similar figures hold for the number of delay minutes per flight operated. These figures illustrate that the impact of weather can be substantial. However, since the construction of San Francisco Airport differs from other major airports in the United States, caution is required in generalising the results. At San Francisco the parallel runways are much closer than at other airports, which is why visibility is required by the Ministry of Transport and why lack of visibility leads to capacity restrictions. Most other airports in the US do not have this restriction and certain types of weather have less of an impact there. Still, weather plays a crucial role in the US aviation sector. It is estimated to cause 70% of all delays while also being an important contributing factor in 23% of all aviation accidents. Total annual monetary costs of accident damage and injuries, delays and unexpected operating costs are estimated at $ 3 billion in US aviation (see Kulesa, 2002). Table 3: Cancellations per day and delay minutes per flight at San Francisco International Airport in various types of weather in 1997, 1998 and 1999

Cancellations per day Delay minutes per flight operated

Year 1997 1998 1999 1997 1998 1999

Good all day 6 8 8 20 23 20

Weather prevailing at SFO Bad morning weather Bad weather all day 10 19 20 35 18 32 34 56 45 89 40 82

Source: Adaptation of Table 4 in Eads et al. (2000)

5.

Changes in weather conditions: A case study for The Netherlands

Although some general consequences of climate change can be discerned on a global scale, changes on a regional scale are surrounded with considerable uncertainty. Model imperfections, unknown future greenhouse gas emissions, and internal variability of the climate system are three important sources of uncertainty, of which the former appears to become more important the shorter the time horizon considered (see KNMI, 2006, p. 69). Although some global changes, such as average mean temperature, are fairly predictable at short horizons, transformations of global to regional changes are needed in order to assess changes in weather patterns on a regional scale. The relationship between global average temperature change and regional climate changes is highly uncertain as well. Below we

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will address climate change scenarios for the Netherlands. Or course, for other countries different scenarios will be relevant. We choose to present the case of the Netherlands since it illustrates well the degree of uncertainty on climate change, whilst at the same time the range of outcomes is also relevant for other countries in moderate climate zones. Considering the various sources of uncertainty, the Royal Netherlands Meteorological Institute (KNMI) has analysed climate change consequences for weather conditions in The Netherlands in 2050 for four climate change scenarios.11 The first crucial parameter in the construction of these scenarios is the increase in average global temperature. The output of global climate change models or general circulation models (GCM) displays an increase in average global temperature between 1990 and 2050 of 1°C and 2°C (see KNMI, 2006, Figure 3-2). The second important parameter for assessing climate change effects in The Netherlands is wind circulation change in Europe (see KNMI, 2006, Figure 3-4). The two variations chosen were strong and weak changes in wind circulation. Ultimately, the following four scenarios were distinguished: M

1°C increase in average global temperature in 2050 relative to 1990 and weak changes in air flow patterns in Western Europe;

M+

1°C increase in average global temperature in 2050 relative to 1990, softer and wetter winters due to an increase in western winds, warmer and dryer summers due an increase in eastern winds;

W

2°C increase in average global temperature in 2050 relative to 1990 and weak changes in air flow patterns in Western Europe;

W+

2°C increase in average global temperature in 2050 relative to 1990, softer and wetter winters due to an increase in western winds, warmer and dryer summers due an increase in eastern winds.

For analysing changes in weather conditions due to climate change in The Netherlands, the output of general circulation models is used as input in regional climate change models (RCM), after which empirical/statistical downscaling is applied, using specific local observations for The Netherlands (see KNMI, 2006). The resulting consequences of climate change for weather conditions in The Netherlands for the four scenarios are summarised in Table 4. Table 4: Changes in temperature and precipitation in The Netherlands for 2050 relative to 1990 for the four KNMI 2006 climate change scenarios 11

Other regional climate change scenarios have been developed for many smaller and larger regions in the world, e.g., the US (MacCracken et al., 2003), the UK (Hulme et al., 2002), Switzerland (Frei, 2004) and Southern Africa (Arnell et al., 2005).

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Variable Summer Mean temperature Temperature 10% warmest days Temperature 10% coldest days Mean precipitation (%) Wet day frequency (%) Mean wet day precipitation (%) Median wet day precipitation (%) Precipitation on 1% wettest days (%) Winter Mean temperature Temperature 10% warmest days Temperature 10% coldest days Mean precipitation (%) Wet day frequency (%) Mean wet day precipitation (%) Median wet day precipitation (%) Precipitation on 1% wettest days (%)

M

M+

W

W+

0.9 1.0 0.9 2.8 –1.6 4.6 –2.5 12.4

1.4 1.8 1.1 –9.5 –9.6 0.1 –6.2 6.2

1.7 2.0 1.8 5.5 –3.3 9.1 –5.1 24.8

2.8 3.6 2.2 –19.0 –19.3 0.3 –12.4 12.3

0.9 0.8 1.0 3.6 0.1 3.6 3.4 4.3

1.1 1.0 1.4 7.0 0.9 6.0 7.3 5.6

1.8 1.7 2.0 7.3 0.2 7.1 6.8 8.6

2.3 1.9 2.8 14.2 1.9 12.1 14.7 11.2

Source: Slightly adapted version of Table 4.6 in KNMI (2006)

The figures in the table show that the increase in average global temperature causes an increase in summer and winter temperatures. The four scenarios show an increase in mean temperature that varies from 0.9 to 2.3 °C in winter and from 0.9 to 2.8 °C in summer. Winters will be between 4% and 14% wetter. Since wet day frequency changes are relatively small, the average increase in precipitation is caused by the sharp increase in mean wet day precipitation, especially when changes in circulation are strong. Changes in precipitation on extremely wet days are comparable to changes on an average wet day. Although precipitation on wet days during summer will also increase, the number of wet days during summer will decrease. On average, summer precipitation will increase by 3% to 6% when there are no changes in air flow patterns, while summer precipitation will decrease by 10% to 19% when there is an increase in eastern wind. Striking are the changes in precipitation on extremely wet days, especially when changes in circulation are weak. Predictions on wind speed and direction are highly uncertain and display large spatial variation. The general tendency is a small increase in maximum wind speeds. Implications for changes in North Sea surges due to extreme winds would therefore be limited according to these scenarios. Finally, sea levels are estimated to rise by around 10 cm in 2050, and by about 25–30 cm in 2100.

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6.

Potential consequences of climate change for the Dutch transport sector

Changes in temperature and precipitation in summer and winter may lead to modal shifts. Based on the scarce available empirical evidence, however, such changes are likely small. Changing departure time, for instance, seems to be a more important behavioural change in response to bad weather than modal choice change. However, more research is needed in order to be able to draw more definite conclusions. One of the more important changes we may observe are changes in bicycle use. Specifically, decrease in precipitation frequency during summer may increase the use of the bicycle, while increases in the amount of precipitation may decrease bicycle use, both in summer and winter. The generic increase in temperature will likely increase the use of the bicycle as well, but the net effect of these changes remains uncertain. The impact of changes in precipitation on road safety in summer is ambiguous as well. A decrease in the number of wet days increases road safety, while an increase in the amount of precipitation at wet days and extremely wet days decreases road safety. The increase in frequency and duration of dry spells may also increase accident frequency once it starts raining. An additional issue is the impact on accident severity. Since precipitation generally slows down traffic, accident severity may decrease once an accident actually occurs. The net effect of climate change on traffic safety and accident casualties and injuries is ambiguous. The increase in mean precipitation during winter but especially the substantial increase in precipitation on the 1% wettest days implies that, without changes in capacity, rivers will have more trouble in carrying down water. The probability of flooding will therefore likely increase, ceteris paribus. Next to the fact that this has consequences for the inland navigation sector (see below), it may also cause substantial economic damages in terms of increased travel times on road networks (see Table 2). An interesting exercise is to analyse the consequences of climate change for disruptions in infrastructure networks, not only because they represent direct economic losses, but also because it may give us insight into changes in the competitive positions of the various modes in freight transport. During winter congestion on road networks will unambiguously increase in response to an increase in precipitation. Moreover, increased precipitation implies that accidents will happen more often, causing an even further increase in congestion and traffic jams. During summer the consequences are less certain because there are opposing effects. A decrease in the number of wet days and median wet day precipitation will decrease congestion, while the large increase in precipitation at the 1% wettest days will substantially increase congestion. The impact of mean precipitation depends on the preferred scenario. Ultimately, congestion will decrease during summer, except on very wet days, on which the amount of precipitation will be so large that substantial disruptions to the system may be expected.

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With respect to rail infrastructure, current empirical evidence suggests that approximately 10% of all failures is weather related. Looking at the predicted changes in weather patterns in 2050 we may expect that higher temperatures reduce rail infrastructure failures due to icing, but increase failures due to high temperatures. Changes in maximum wind speed are small, so the effects on failures due to extreme winds are likely small as well. Extreme rainfall may in turn increase the number of failures. For the inland navigation sector it is clear that the increasing variation in precipitation implies a substantial increase in uncertainty around water levels. An increase in the frequency and duration of dry spells implies lower water levels, while an increase in extreme precipitation implies higher high water levels. The economic consequences are larger variation in and higher freight prices and a decrease in the reliability of transport. A consequence of these effects is a possible shift towards freight transport by road and rail, ceteris paribus. Finally, considering the small increases in maximum wind speed, consequences for air transport are likely relatively small. Important to note, however, is that consequences for individual airports are to a great extent dependent on changes in wind direction, which are more difficult to predict. Climate change consequences for the air transport sector furthermore depend on issues not described in KNMI scenarios, such as fog and visibility. In conclusion, consequences for air transport are very much airport specific and highly uncertain.

7.

Discussion and further research

We find that the consequences of climate change on the transport system are not easy to pin down. In particular in the field of traffic safety we observe several feedbacks of increases in the generalised costs of transport. Nevertheless we may conclude that adverse weather conditions have an increasing effect on the number of road accidents. The effect on the severity of road accidents is ambiguous. Adverse weather also leads to lower speeds in road transport, implying substantial time loss by road users. Another finding is that large weather related catastrophes, such as the Katrina induced floods in New Orleans in 2005, may lead to large direct damages, but also, depending on the network structures, to large indirect costs in the transport system. This has important implications for the design of transport network structures and for ways to devise infrastructures that are less sensitive to disruptions. For freight transport, natural waterways appear to be rather sensitive to climate change. Here too, there is a need for further research as a basis for optimal adjustments to make waterways less vulnerable to extreme weather conditions. A basic uncertainty in this field concerns the development of climate during the next decades. Table 4 above shows that climate researchers take into account considerable variations in outcomes for the next fifty years. However, our review shows that there are also substantial uncertainties on how

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users of transport systems will change their behaviour in response to increases in generalised costs, implying that it is also highly uncertain what effects we may expect. In particular, knowledge on the interactions between the effects of weather conditions on road safety, traffic speed and traffic volume and congestion is scarce. The large majority of the studies on the impact of climate and weather focus on instantaneous or short term impacts. Less attention is paid to impacts at the seasonal level, or the long run effects as they can be detected by comparing regions that operate under different climate conditions. Research into these directions is recommended to develop a fuller view on the impacts of climate change on transport. As we stand, the uncertainty with respect to changes in weather patterns but especially with respect to the impact of these changes on transport sector makes it difficult to assess the impact of climate change on transport. Particularly problematic is the quantification of the effects and the fact that both positive and negative consequences of changes in weather exist at the same time, often making the net effect ambiguous. Acknowledgements This research is supported through the BSIK programme ‘Climate Changes Spatial Planning’. We thank Jos van Ommeren for useful comments and suggestions. References Aaheim HA, KE Hauge, 2006, Impacts of Climate Change on Travel Habits: A National Assessment Based on Individual Choices, CICERO Report No. 2005:07, CICERO, Oslo. Agarwal M, TH Maze, R Souleyrette, 2005, Impacts of Weather on Urban Freeway Traffic Flow Characteristics and Facility Capacity, in: Proceedings of the 2005 Mid-Continent Transportation Research Symposium Al Hassan Y, DJ Barker, 1999, The Impact of Unseasonable or Extreme Weather on Traffic Activity within Lothian Region, Scotland, Journal of Transport Geography 7, 209–213. Andrey J, J Suggett, B Mills, M Leahy, 2001, Weather-Related Road Accident Risks in Mid-Sized Canadian Cities, Canadian Multidisciplinary Road Safety Conference XII Proceedings, June 1113, London. Arnell NW, DA Hudson, R Jones, 2005, Climate Change Scenarios from a Regional Climate Model: Estimating Change in Runoff in Southern Africa, Journal of Geophysical ResearchAtmospheres 108, No, D16, 4519 AVV, 2005, Filemonitor 2004 (Traffic Jam Monitor 2004), AVV, Ministry of Transport, Public Works and Water Management, Rotterdam. AVV, 2006a, Kosten Verkeersongevallen in Nederland (Costs of Trafic Accidents in The Netherlands), AVV, Ministry of Transport, Public Works and Water Management, Rotterdam. AVV, 2006b, Economische Waardering van Mobiliteitseffecten van een Dijkdoorbraak (Economic Valuation of Mobility Effects of a Flood), AVV, Ministry of Transport, Public Works and Water Management, Rotterdam. Bergström A, R Magnusson, 2003, Potential for Transferring Car Trips to Bicycle during Winter, Transportation Research Part A 37, 649–666.

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Bos JMJ, 2001, Door Weer en Wind: Gevolgen van Perioden met Extreem Weer voor de Verkeersveiligheid (Through Wind and Weather: Consequences of Extreme Weather for Traffic Safety), R-2001-23, SWOV, Leidschendam, The Netherlands. Brodsky H, AS Hakkert, 1988, Risk of a Road Accident in Rainy Weather, Accident Analysis and Prevention 20, 161–176. Cantilli JE, 1974, Programming Environment Improvements in Public Transportation, Lexington Books, Toronto. Chapman L, 2007, Transport and Climate Change: A Review, Journal of Transport Geography 15, 354–367. CPB, 2002, Gevolgen van Uitbreiding Schiphol (Consequences of Extension of Schiphol Airport), CPB, Den Haag. CVS, 1994, Data on the January 25/26 1990 Storm Losses, CVS (Centre for Insurance Statistics), Zoetermeer. De Palma A, D Rochat, 1999, Understanding Individual Travel Decisions: Results from a Commuters Survey in Geneva, Transportation 26, 263–281. Dickerson A, J Peirson, R Vickerman, 2000, Road Accidents and Traffic Flows: An Econometric Investigation, Economica 67, 101–121. Duinmeijer AGP, R Bouwknegt, 2004, Betrouwbaarheid Railinfrastructuur 2003 (Reliability Rail Infrastructure 2003), Prorail, Utrecht. Eads GC, M Kiefer, S Mendiratta, P McKnight, E Laing, MA Kemp, 2000, Reducing WeatherRelated Delays and Cancellations at San Francisco International Airport, CRA Report No. D01868-00, prepared for San Francisco International Airport, Charles River Associates, Boston, Massachusetts. Edwards JB, 1996, Weather-Related Road Accidents in England and Wales: A Spatial Analysis, Journal of Transport Geography 4, 201–212. Eisenberg D, 2004, The Mixed Effects of Precipitation on Traffic Crashes, Accident Analysis and Prevention 36, 637–647. Eisenberg D, KA Warner, 2005, Effects of Snowfalls on Motor Vehicle Collisions, Injuries, and Fatalities, American Journal of Public Health 95, 120–124. Emmerson P, TJ Ryley, DG Davies, 1998, The Impact of Weather on Cycle Flows, Transportation Research Labatory, Berkshire, England. Frei C, 2004, Die Klimazukunft der Schweiz - Eine Probabilistische Projektion (The Climate Future of Switzerland - A Probabilistic Projection), Institute for Atmospheric and Climate Science, ETH, Zürich. Fridstrøm L, 1999, Econometric Models of Road Use, Accidents, and Road Investment Decisions, Dissertation Institute of Transport Economics, Oslo, Norway. Goetzke F, T Rave, 2006, Bicycle Use in Germany: Explaining Differences between Municipalities through Network Effects, University of West-Virginia, Morgantown, USA. Golob TF, WW Recker, 2003, Relationships among Urban Freeway Accidents, Traffic Flow, Weather, and Lighting Conditions, Journal of Transportation Engineering 129, 342–353. Gunn H, 1994, The Netherlands National Model: A Review of Seven Years of Application, International Transactions in Operational Research 1, 125–133. Hall FL, D Barrow, 1988, Effect of Weather Conditions on the Relationship Between Flow and Occupancy on Freeways, Transportation Research Record 1194, 55–63. Hanbali RM, DA Kuemmel, 1993, Traffic Volume Recuctions Due to Winter Storm Conditions, Transportation Research Record 1387, 159–164. Hermans E, T Brijs, T Stiers, C Offermans, 2006, The Impact of Weather Conditions on Road Safety Investigated on an Hourly Basis, Transportation Research Board Annual Meeting 2006, CDROM Paper 06-1120. Hogema JH, 1996, Effects of Rain on Daily Traffic Volume and on Driving Behaviour, TNO-report TM-96-B019, TNO Human Factors Research Institute, Soesterberg.

21

Hulme M, G Jenkins, X Lu, JR Turnpenny, TD Mitchell, RG Jones, J Lowe, JM Murphy, D Hassell, P Boorman, R McDonald, S Hill, 2002, Climate Change Scenarios for the United Kingdom: The UKPCIPo2 Scientific Report, Tyndall Centre for Climate Change Research, Norwich, UK. Ibrahim AT, FL Hall, 1994, Effect of Adverse Weather Conditions on Speed-Flow-Occupancy Relationships, Transportation Research Record 1457, 184–191. IPCC, 2007, Climate Change 2007 - Mitigation of Climate Change, Cambridge University Press, Cambridge/New York. Jacob KH, N Edelblum, J Arnold, 2000, Climate Change and a Global City: An Assessment of the Metropolitan East Coast (MEC) Region, in: Lamont-Doherty Earth Observatory, Columbia University, New York Jones B, L Janssen, F Mannering, 1991, Analysis of the Frequency and Duration of Freeway Accidents in Seattle, Accident Analysis and Prevention 23, 239–255. Jonkeren O, P Rietveld, J van Ommeren, 2006, Welfare Effects of Low Water Levels on the River Rhine Through the Inland Waterway Transport Sector, Department of Spatial Economics, Vrije Universiteit, Amsterdam. Keay K, I Simmonds, 2005, The Association of Rainfall and other Weather Variables with Road Traffic Volume in Melbourne, Australia, Accident Analysis and Prevention 37, 109–124. Khattak AJ, A de Palma, 1997, The Impact of Adverse Weather Conditions on the Propensity to Change Travel Decisions: A Survey of Brussels Commuters, Transportation Research Part A 31, 181–203. KNMI, 2006, Climate Change Scenarios 2006 for the Netherlands, WR 2006-01, KNMI, De Bilt, The Netherlands. Kulesa G, 2002, Weather and Aviation: How Does Weather Afect the Safety and Operations of Airports and Aviation, and How Does FAA Work to Manage Weather-Related Effects?, The Potential Impacts of Climate Change on Transportation Workshop, October 1-2, 2002, DOT Center for Climate Change and Environmental Forecasting. Levine N, KE Kim, LH Nitz, 1995, Daily Fluctuations in Honolulu Motor Vehicle Accidents, Accident Analysis and Prevention 27, 785–796. Lomborg B, 2006, Stern Review: The Dodgy Numbers Behind the Latest Warming Scare, Wall Street Journal, November 2. MacCracken MC, EJ Barron, DR Easterling, BS Felzer, TR Karl, 2003, Climate Change Scenarios for the U.S. National Assessment, Bulletin of the American Meteorological Society 84, 1711–1723. Marchand D, M Sanderson, D Howe, C Alpaugh, 1988, Climatic Change and Great Lakes Levels: The Impact on Shipping, Climatic Change 12, 107–133. Martin PT, J Perrin, B Hansen, I Quintana, 2000, Inclement Weather Signal Timings, UTL Research Report MPC01-120, UTL, University of Utah, Salt Lake City. Maycock G, 1995, Driver Sleepiness as a Factor in Car and HGV Accidents, TRRL Report 169, TRRL, Crowthorne, Berhshire, UK. McDonald N, 1984, Fatigue, Safety and the Truck Driver, Taylor and Francis Ltd., London. Millerd F, 1996, The Impact of Water Level Changes on Commercial Navigation in the Great Lakes and St. Lawrence River, Canadian Journal of Regional Science 19 Millerd F, 2005, The Economic Impact of Climate Change on Canadian Commercial Navigation on the Great Lakes, Canadian Water Resources Journal 30, 269–280. MuConsult, 2004, Verklarende Analyse Verkeersprestatie Hoofdwegennet 2000-2003, AVV, Rotterdam. Nankervis M, 1999, The Effect of Weather and Climate on Bicycle Commuting, Transportation Research Part A 33, 417–431. Nordhaus W, 2006, The Stern Review on the Economics of Climate Change, http://nordhaus.econ.yale.edu/SternReviewD2.pdf.

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Öberg G, G Nilsson, H Velin, P Wretling, M Berntman, K Brundell-Freij, C Hydén, A Ståhl, 1996, Single Accidents Among Pedestrians and Cyclists, VIT Meddelande 799 A, Swedish National Road and Transport Research Institute, Linköping, Sweden. Ortúzar J de D, LG Willumsen, 2001, Modelling Transport, Third Edition, John Wiley & Sons, Ltd, West Sussex, England. Parry ML, 2000, Assessment of Potential Effects and Adaptations for Climate Change in Europe: The Europe ACACIA Project, Jackson Environment Institute, University of East Anglia, Norwich, UK. Richardson AJ, 2000, Seasonal and Weather Impacts on Urban Cycling Trips, TUTI Report 1-2000, The Urban Transport Institute, Victoria, Australia. Rossetti MA, 2002, Potential Impacts of Climate Change on Railroads, The Potential Impacts of Climate Change on Transportation Workshop, October 1-2, 2002, DOT Center for Climate Change and Environmental Forecasting (http://climate.volpe.dot.gov/workshop1002/). Satterthwaite SP, 1976, An Assessment of Seasonal and Weather Effects on the Frequency of Road Accidents in California, Accident Analysis and Prevention 8, 87–96. Shankar VN, S Chayanan, S Sittikariya, M Shyu, NK Juwa, JC Milton, 2004, Marginal Impacts of Design, Traffic, Weather, and Related Interactions on Roadside Crashes, Transportation Research Record, 156–163. Shankar VN, F Mannering, W Barfield, 1995, Effect of Roadway Geometrics and Environmental Factors on Rural Freeway Accident Frequencies, Accident Analysis and Prevention 27, 371– 389. Stern AD, V Shah, LC Goodwin, P Pisano, 2003, Analysis of Weather Impacts on Traffic Flow in Metropolitan Washington DC, Federal Highway Administration (FHWA), Washington DC, USA. Stern E, Y Zehavi, 1990, Road Safety and Hot Weather: A Study in Applied Transport Geography, Transactions of the Institute of British Geographers 15, 102–111. Stern N, 2007, The Economics of Climate Change: The Stern Review, Cambridge University Press, Cambridge. Suarez P, W Anderson, V Mahal, TR Lakshmanan, 2005, Impacts of Flooding and Climate Change on Urban Transportation: A Systemwide Performance Assessment of the Boston Metro Area, Transportation Research Part D 10, 231–244. Tol RSJ, 2006, The Stern Review of the Economics of Climate Change: A Comment, http://www.fnu.zmaw.de/fileadmin/fnu-files/reports/sternreview.pdf. Van Boggelen O, 2007, De Invloed van het Weer op het Fietsgebruik en het Aantal Fietsslachtoffers (The Impact of Weather on Bicycle Use and Fatal Bicycle Accidents), Publication no. 15, Fietsberaad, Rotterdam. Van der Vlist M, P van Beek, M Olde Klater, 2004, Betrouwbaarheid van Reistijden (Reliability of Travel Times), Goudappel Coffeng, commissioned by AVV, Rotterdam. Vitaliano DF, J Held, 1991, Road Accident External Effects: An Empirical Assessment, Applied Economics 23, 373–378. Welch JA, RJ Vaughan, DC Andreassend, LA Folovary, 1970, Weather Conditions and Road Accidents, in: Proceedings - Australian Road Research Conference, Vol. 5, pp. 190–208. Winters M, MC Friesen, M Koehoorn, K Teschke, 2007, Utilitarian Bicycling: A Multilevel Analysis of Climate and Personal Influences, American Journal of Preventive Medicine 32, 52–58.

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