The use of traffic modelling to inform a flood evacuation policy for Lincolnshire and Norfolk

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1 Comprehensive Flood Risk Management Klijn & Schweckendiek (eds) 2013 Taylor & Francis Group, London, ISBN The use of traffic modelling to inform a flood evacuation policy for Lincolnshire and Norfolk A.F. Tagg HR Wallingford, Wallingford, UK B. Kolen & J. Leenders HKV Consultants, Lelystad, The Netherlands H. Chen ITS, University of Leeds, Leeds, UK D. Powell Lincolnshire County Council, Lincoln, UK ABSTRACT: The counties of Lincolnshire and Norfolk have some of the highest flood risk in the country, due to low-lying land combined with a particular hazard from storm surge in the North Sea, such as occurred in January Evacuation is seen as an appropriate means to reduce loss of life. However, the decision to evacuate will lead to significant disruption of business and family life, and require significant resources. This paper describes the use of tiered traffic models to determine how well the road network will perform during an evacuation and the impact of different management options. The models outputs provided the time required for evacuation and the areas of highest congestion, amongst others. Even with an optimised evacuation, a total time of 36 hours would still be needed for the self-evacuating population to reach safety. The results are being used to inform the continuing development of the mass evacuation plan for the region 1 INTRODUCTION The East coast of England, due to its geographical location, faces a particular hazard from storm surge in the North Sea, such as occurred in January Combined with the low-lying nature of much of the land bordering The Wash, this means that the counties of Lincolnshire and Norfolk have some of the highest flood risk in the country. There is, therefore, the real threat of sea inundation along a significant stretch of the East coast, resulting from overtopping or breaching of the sea defences or natural land edge. Figure 1 below illustrates the large flooded area (Environment Agency flood zone 3 0.5% annual flood) for Lincolnshire. Given the high probability of flooding and the severity of a major inundation, evacuation is seen as an appropriate means to reduce loss of life and other impacts. Lincolnshire and Norfolk would be both badly affected by a major surge flood and would find it difficult to both move people out and move equipment in. For example the Cabinet Office has defined the H19 planning scenario (equivalent to the January 1953 event) by. Up to 0.4 m people (including tourists) in coastal villages and towns evacuated from flooded sites. People stranded over a large area and up to 40,000 people in need of rescue. Up to 40,000 people needing assistance with sheltering for up to 12 months Planning for the H19 scenario has resulted in a number of studies being instigated by resilience groups along the east coast. This includes an assessment of rescue capability at regional and county levels (Di Mauro 2010). The evacuation study described here formed another of these initiatives, and was managed by a steering group made up of resilience representatives from the two counties. Although evacuation has the potential to save lives and movable goods it will be costly in time, money, and organisational credibility, especially if the flood impacts are less severe than expected or do not materialise. Therefore this leads to the need to balance the confidence in a warning and the time required to evacuate and for decision makers to compare the potential benefits of an evacuation with the consequences produced by the disruption. There is a fundamental issue of when to determine that a major flood event is likely and 1541

2 Figure 1. Flood zones in Lincolnshire and The Wash coast. to begin evacuation of parts of the East coast, given that this is the only option to minimize exposure to the flood for the majority of the affected population The decision to evacuate, considering horizontal as well as vertical evacuation, will obviously lead to significant disruption of business and family life, and require significant resources from the immediate and surrounding areas (fire, police, local authority, Environment Agency, army etc.) to facilitate the movement of people. There will also be additional resources to receive and shelter the evacuees which could affect other neighbouring local authorities. Against this are the consequences in delaying the decision to evacuate, which could mean that it is then not possible to evacuate everyone, that the evacuation is more chaotic and dangerous, and that more people have to be received in locations that lie within the flooded area and loss of life might result. These issues highlight the need to understand better the logistical effort required to effectively and safely evacuate the public from high risk areas. One particular logistical challenge is how well the road network will perform during an evacuation, and how the capacity of the roads in Lincolnshire and Norfolk would restrict the free flow of traffic in the event of a major evacuation from the coast. The outputs from traffic modelling will enable emergency planners to better understand where congestion may occur on the road network, the time required for an evacuation, the optimum points for decisions and options for evacuation management, amongst other beneficial information. The calculation of this optimum decision point depends on the likely origins and destinations for the affected areas and the capacity of the road network that the evacuees would have to use. In addition to these physical factors, it is necessary to consider less tangible influences such as the willingness to cooperate with the evacuation plan, the decision-making process and unforeseen events such as accidents or breakdowns. The required time for an evacuation strategy depends on the time needed for people to evacuate a flooded area (see Figure 2): Departure: the time needed for people to leave their home and enter the traffic network. This time is influenced by warnings, but also by social behaviour. In this study, therefore, a departure curve is assumed. Travel: the time needed to travel to an exit point. This time depends on the amount of congestion on the roads and the chosen route. Exit: the time needed to leave at an exit. This time depends on limitations produced by reduced capacities outside the evacuation zone (such as day-to-day use of the road network). 2 MODELLING APPROACHES AND ASSUMPTIONS There are several traffic modelling tools that can be used to inform the decisions described above. As this was the first assessment of mass evacuation, Figure 2. The evacuation process split into a process of departure, travel and exit (Zuilekom & Zuidgeest 2008). 1542

3 a tiered approach to modelling was adopted. This was selected to provide a greater flexibility for the councils and resilience professionals. This allows for a range of interventions to be tested with the higher-level approach, which can be implemented simply and quickly. From this analysis, specific options can then be tested with the more complex model, which provides enhanced detail on the congestion across the whole road network. This tiered approach consisted of undertaking traffic and evacuation modelling at two different scales: a macro-scale (large scale) and a meso-scale (medium scale). The two different scales of modelling were used to provide specific outputs and test different elements of the system, although they worked with common assumptions. 2.1 Macro model The Evacuation Calculator is a tool that calculates the traffic production for categories of evacuees. Friso, et al. (2011) gives a summary of the principles of the Evacuation Calculator. A full description of the working principles of the Evacuation Calculator is given in Zuilekom & Zuidgeest (2008) and Zuilekom et al. (2005). The use of the Evacuation Calculator for the Dutch context for several strategies of evacuation is described by Kolen (2012). By combining a departure profile (which indicates the departures of people over time), the origin-destination-matrix (OD-matrix), the distance matrix and assumptions for the capacities of the exit points, it can give an estimate of the evacuation time. The OD-matrix can be processed by static macroscopic or macroscopic/microscopic dynamic assignment methods. The user is free in defining socio-economic data, production coefficients, departure profile and capacity of exits. The Evacuation Calculator can distribute the number of trips for all the origin zones over the different exits available using different strategies for traffic management. In this study, the two main strategies were: Nearest exit: People will leave at the nearest exit point in this strategy, regardless of road capacity and use of this exit. This strategy gives priority to the minimisation of car kilometres. There will be no crossing flows at intersections so that the chance of queues and accidents at intersections will be reduced. However, the capacity of the network will not be used optimally. Traffic management: Exits are used proportionally according to their capacity, crossing traffic flows at intersections are avoided and car-kilometres are minimised (giving proportional use of exits). In this way directed, convergent, non-crossing traffic flows to the exit points are realised. Zones are assigned to one or more exit points, so-called outflow areas. 2.2 Meso model The meso-scale modelling is based on traffic assignment theory which analyses how the capacity of the roads in Lincolnshire and Norfolk would be best used to transport the people and equipment between the forecast area of inundation and a designated place of safety (e.g. large reception centres). The transport planning software Omni- TRANS was used in this project for its flexible job engine for the management and execution of the model calculations, and user-friendly interface for editing, analysing and presenting data and results ( It should however be noted that the number of simulations and their calibrations does take significant computer time. This underlines the importance of prioritising the essential simulation scenarios as discussed and agreed with the client. The typical outputs for a meso-model include link flows, density, congestion measures (e.g. ratio of actual flow and capacity), route journey time, and the optimum route between a given origin and destination. 2.3 Origin and destination definition Based on the flood hazard areas of Lincolnshire and Norfolk, the affected wards were identified and hence the population origins. Two main evacuation areas were used: (1) Norfolk East and (2) Lincolnshire, The Wash and Norfolk West. The total number of people that evacuate in area 1 is about 137,000. In this area, there are about 2,200 caravan people taken into account. The total number of people that evacuate in area 2 is about 373,000. Figure 3(A) below illustrates the population per ward for area 2. The red stars in Figure 3 are the exit points. Once people have passed these exit points, people have left the threatened area and are considered to be able to find a safe place. 2.4 Assumptions As far as possible, a consistent set of assumptions were used in both modelling studies, so that general conclusions remained valid. The key assumptions, affecting the simulations, were: 80% of population self-evacuate Average traffic speed of 20 km/hr Road capacity of 1200 vehicles/hour for B roads and 1500 vehicles/hour for A roads. Capacity reduced by 20% to allow for winter conditions 1543

4 A: B: Number of cars that passed an exit point Traffic Management Nearest exit Time [hours] Figure 3. Exit points for Area 2 and evacuation times. Outflow reduction factor of 0.2 to account for clearing of traffic from exits S-shaped departure curve, with timescales of between 8 and 16 hours Traffic load of 2.09 people per car (assumed that all cars will leave the area) Flood event of 0.1% annual exceedance probability (due to coastal surge). Both models tested the sensitivity to some of these assumptions, such as the shape or duration of the departure curve, which provides information on whether careful management of the departure delivers benefits in terms of less congestion and reduced evacuation times. Some of these sensitivity tests are described below. 3 RESULTS 3.1 Scenario 1: Nearest exit and traffic management This paper contains a sample of the outputs generated from the two models, to illustrate the information that was provided to the emergency planners in the two counties. The base scenario was run in the macro model, looking at the impact of different transport strategies. Figure 3(A) shows the main evacuation area (Area 2) and the exit points, and 3(B) the number of people who reach an exit within 48 hours. The percentage of people which reach safety for each of the three time horizons is given in Table 1. So with no organisation of the departure from the affected wards to nominated exits, between 30 to 36% of the people will not be able to reach safety within 48 hours; viewed as the maximum evacuation time available. With improved allocation of the traffic, everyone can evacuate safely within the 48 hour window. 3.2 Sensitivity tests The macro model was able to test the sensitivity to several of the modelling assumptions. These are summarised in Table Scenario 2: Optimised use of exits Based on the results of the traffic management strategy, the macro model was run with an optimised allocation of population to the exit points, as shown in Figure 4. The number of people reaching safety is summarised in Table 3. Although there is no improvement in the results for Norfolk East, an additional 5% of people are able to evacuate within 24 hours. Overall, these results would indicate that some 36 hours are required for a safe evacuation away from the flood hazard area. 3.4 Meso model Although configured in a slightly different way, the meso model produced similar results when looking Table 1. Percentage of evacuated population reaching an exit point Scenario 1. Evacuation area 1 12H 24H 48H Evacuation area 2 12H 24H 48H Nearest exit, hours Traffic management hours 1544

5 Table 2. Sensitivity tests for scenario 1 (macro model). Option Description Summary findings Departure curve 8 hours vs. 16 hours No. of people to evacuate 1000 year event vs. 200 year event Outflow factor Factors assessed between 0.2 (realistic) and 1.0 Minimal impact on About 6% more people reach evacuation time exit point after 24 hours Less time is required Everyone reaches an exit to evacuate fewer after 24 hours in 200 year people but only if event, compared to 86% using traffic management for 1000 year event strategy Outflow factor has Approximately 94% of a strong effect on population can evacuate the time needed for in 24 hours with outflow evacuation factor of 1.0 Figure 4. Example Optimised allocation of wards to exit points. Table 3. Percentage of evacuated population reaching an exit point Scenario 2. Evacuation area 1 Evacuation area 2 12H 24H 48H 12H 24H 48H Departure 16 hours, outflow factor of 0.2 at the various traffic strategies. For example, the attractions to each exit point are shown in blue in Figure 5 when people choose the nearest exit. This is for a static departure i.e. people all leave at one time. This subjective justification resulted in a large amount of traffic going to a few exit points such as 161 (Lowestoft), 170 (Gayton) and 178 (Donington). Consequently, the traffic simulation of scenario one indicates that this would cause excessive congestion in the local networks. Exit points 204, 205 and 206 are on the national strategic network and included in the simulation in order to assess the impact of the spread of the evacuation traffic beyond the local road network. To overcome this problem, the optimisation scenario 2 was applied to provide alternative more efficient routes. Individual wards are explicitly connected to specific exit points. For example, instead of using the shortest distance to evacuate all people in Boston to exit 178, some of the population is directed to go to other exits nearby such as 181. The same strategy was applied to 161 and 170 in scenario 2, which resulted in the new distribution of trips at the exit points as indicated in green in Figure 5. Figure 6 shows what the traffic conditions at exit point 178 in Donington would be for the static and nearest exit case, if it is used as the exit point for 1545

6 Figure 5. Number of trips attracted to exit points (static departure). Figure 6. Predicted traffic conditions at exit point 178 in scenario one. 1546

7 Figure 7. Most congested regions (static assignment, scenario 1). the evacuation trips from the wards in Boston. The colours indicate the degree of flow saturation (i.e. the ratio of the actual flow on a link and the link capacity) and the bandwidth displays the traffic volume, which can exceed the link capacity. Essentially the bandwidth can be thought of as several cars stacked on top of each other trying to move along the highway. The power of the spatial representation of the congestion inherent in the meso modelling approach is given in Figure 7, which shows the road use throughput the whole study area. The location of the most congested areas, within the dotted lines, allows the emergency planners and highway authority to consider what mitigation measures may be needed to facilitate such a major evacuation. 4 CONCLUSIONS The modelling for this study showed that it is advisable to put in place a traffic management strategy in the case of a mass evacuation from the East coast. That is, it is preferable to direct people to specific exit points based on available road capacities, rather than letting them choose their favourite or best known exit route from the flooded area. Moreover, it is likely that an effective evacuation will need to be planned on an inter-regional or national basis. The current recommended evacuation routes based on the traffic management option in the macro model are shown in Figure 3. Improvements can be made to these routes with regard to the amount of time required to evacuate everyone from the flooded area as discussed below. Coordination will be needed to keep roads outside of flooded areas and on the national network as fluid as possible. This will help reduce evacuation time by preventing a traffic backlog into the flooded area. If this strategy is properly implemented, then it is possible to provide a shorter departure curve and reduce warning times required to evacuate everyone from the area at risk. However a consequence is that people in the not threatened areas are also affected by the consequences of evacuation. The need to implement these measures is a complex decision that has to take the costs and benefits into account. If feasible, it is recommended to evacuate some people as early as possible to make use of available 1547

8 road capacity. However, in this case, there is a risk of shadow evacuation which could increase the population that evacuates. This is one of the dilemmas facing decision-makers, who have to balance more uncertain flood forecasts yet longer warning times, against more certain forecasts but insufficient time to evacuate. All this should be considered within any strategy. It is recommended that further exit points and road management are included in the management strategy in order to provide an improved spread of traffic across roads where capacity is still available. In addition, it is recommended that optimum routes are reassessed at a finer geographical level (e.g. as detailed as the post-code level) since this modelling has been undertaken at the Ward level. Finally, it is important that any designated evacuation routes are communicated to the public, and that appropriate signage is used. These aspects were considered in this traffic study although they have not been discussed in this paper. The results of the sensitivity testing carried out with both models provide valuable information on the factors to consider in the evacuation plan for the region. This includes the outflow time needed for people to move from the exit points and onto the wider road network. In the case of a major event affecting more than Lincolnshire and Norfolk, this would require national management of the event. 5 DISSEMINATION AND FUTURE STEPS The traffic modelling and other project activities delivered a large amount of information for consideration by the Steering Group. As noted previously, it was understood that such a study had not been tackled before in England & Wales, and therefore an important component of the wider study was the dissemination of these outputs to the emergency management community along the whole of the east coast. A workshop was therefore held on the 9 September 2011, to present the main project findings, to hopefully obtain acceptance of the technical approach, and to then consider what the implications of the findings were for the improvement of evacuation planning across the region. The workshop was successful in achieving all of these objectives, with acceptance of both the approach and the general findings. Work is now continuing on taking the project recommendations forward, including the commissioning of similar studies in other counties. REFERENCES Di Mauro, M East Coast Flood Inundation Mapping Phase II: a review of potential resource demand in the event of a major East Coast Flood. HR Wallingford Report No EX6244. Friso, K., Zuilekom, K.M. van and Kolen, B Evaluation of the national concept of traffic management for mass, ICNS-2011 conference, May , Venice/Mestre, Italy. Kolen, B Time needed to evacuate the Netherlands in the event of large-scale flooding: strategies and consequences. Disasters. DOI: /j x Zuilekom, K.M. van; and Zuidgeest, M.H.P A decision support system for the preventive evacuation of people in a dike-ring area; page ; Geospatial Information Technology for Emergency Response/ editors Zlatanova, S; Li, J. Taylor & Francis/Balkema; Leiden, The Netherlands. Zuilekom, K.M. van, Maarseveen, M.F.A.M. van and Doef, M.R. van A decision support system for preventive evacuation of people, Geo-information for disaster management, Zlatanova, P. Oosterom, S. van and Fendel, E.M. (eds.), , Springer, Berlin Heidelberg. 1548