Changes concerning commute traffic distribution on a road network following the occurrence of a natural disaster – The example of a flood in the Mazovian Voivodeship (Eastern Poland)

Transportation Research Part D 65 (2018) 116–137

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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

Changes concerning commute traffic distribution on a road network following the occurrence of a natural disaster – The example of a flood in the Mazovian Voivodeship (Eastern Poland)

T

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Marta Borowska-Stefańskaa, , Adam Domagalskib, Szymon Wiśniewskia a b

Institute of the Built Environment and Spatial Policy, University of Łódź, Łódź, Poland, 90-142 Łódź, Kopcińskiego St. 31, Poland Graduate of the Faculty of Mathematics and Computer Science, University of Łódź, Łódź, Poland

A R T IC LE I N F O

ABS TRA CT

Keywords: Natural disasters Flood Traffic density Mazovian Voivodeship Poland GIS

The purpose of this article is to determine the size and spatial structure of changes in traffic density within the regional road network following an occurrence of a flood in the Mazovian Voivodeship, Poland. The use of the application developed for the purpose of this article – offers a possibility to react accordingly when there are non-typical obstructions (here: a flood). On the basis of the conducted study, it has been stated that the greatest changes in vehicle traffic density (the analysis of commute traffic) regard the capital of Mazovian Voivodeship, which – first of all – stems from the fact that it is Warsaw that the largest number of employees commute to. Secondly, it is influenced by the location of the capital city in relation to the river system. In the case of the analysed voivodeship and in ‘normal’ circumstances (no flood), commuting to work remains approximately within the 160-min isochrone. In the second variant, this time would extend nearly eightfold, and in the remaining scenarios fivefold. As far as ‘normal’ circumstances (no flood) and commuting in the Mazovian Voivodeship are concerned, the greatest load refers in particular to the following road classes: main road of accelerated traffic, main road and cumulative road. In this case, express and motorways play a marginal role. On the other hand, in the remaining scenarios, the importance of the class of main road of accelerated traffic decreases at the expense of the classes of main road and cumulative road.

1. Introduction In terms of its consequences, a flood is considered one of the most ominous forms of natural disaster (Hossain and Davies, 2004). As a result of global climate changes, these days we more and more often face extreme phenomena, including floods, which directly impact urban infrastructure (Suarez et al., 2005). Floods can be classified into four types based on characteristics of the flood event:

• a flash flood of a few hours duration, • a single event flood of long duration • multiple-event floods • seasonal floods (Petersen 2001, p. 11). ⁎

Corresponding author. E-mail addresses: [email protected] (M. Borowska-Stefańska), [email protected] (S. Wiśniewski).

https://doi.org/10.1016/j.trd.2018.08.008

1361-9209/ © 2018 Published by Elsevier Ltd.

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Floods, and flash floods, in particular, generate economic, environmental and social effects. Economic effects include, e.g., damage in infrastructure, the negative influence upon transportation and communications networks, an increase in fuel costs (Petersen, 2001), as well as time loss due to traffic delays (congestion) and the necessity to take alternative routes (Petersen, 2001; BorowskaStefańska and Wiśniewski, 2018). ‘What is more, the term “flood risk” is closely related to floods and flooded areas. This is a common product of hazard (physical and statistical aspects of a flood), exposure (who and what is at risk of a flood), and vulnerability (the susceptibility of spatial infrastructure to a threat and its ability to counteract and eliminate the consequences of a natural disaster) (Stenchion, 1997; Mileti, 1999; Merz and Thieken, 2004; Apel et al., 2009; Randolph, 2004). The correlation of these three elements contributes to the making of the so-called risk triangle (Crichton, 1999, 2007). Total risk means the predicted number of casualties and injured, and material damage and disruption to economic activity, related to any given natural disaster (UN DHA, 1992; Granger et al., 1999). In simple terms, it means the likelihood of a flood and its effects (Jenelius et al., 2006), which may be desired or undesired (Helm, 1996; Sayers et al., 2002). These definitions were applied within the Floods Directive (2007, Art. 2, Item 2), where the term “flood risk” is defined as ‘the combination of the probability of a flood event and of the potential adverse consequences for human health, the environment, cultural heritage and economic activity’(Borowska-Stefańska, 2015). In the literature are no generally accepted definitions of the concept vulnerability (Einarsson and Rausand, 1998; Berdica, 2002; Holmgren, 2004; Jenelius et al., 2006). Berdica (2002) defines vulnerability in the road transportation system as “a susceptibility to incidents that can result in considerable reductions in road network serviceability” (Berdica 2002, p. 119; Holmgren, 2004, p. 12). Serviceability of a link/route/ road network, in turn, ‘‘describes the possibility to use that link/route/road network during a given period’’. D’Este and Taylor (2003) state that a node is vulnerable ‘‘if loss (or substantial degradation) of a small number of links significantly diminishes theaccessibilityof the node, as measured by a standard index of accessibility’’(Jenelius et al., 2006, p. 540). The criticality of a certain component (link, node, groups of links and/or nodes) in the network involves both the probability of the component failing and the consequences of that failure for the system as a whole. The more critical the component, the more severe is the damage to the system when that component is lost. If the probability of an incident is high, the component (link, etc.) is weak, and if the consequences are great, the component is important. If it is both weak and important, the component is critical (cf. Nicholson and Du, 1994). In the literature, the term ‘vulnerability’, when used in reference to road transport networks, is confused with the term ‘reliability’. Immers et al. (2004) define reliability as the ‘‘degree of certainty with which a traveller is able to estimate his own travel time’’, which depends on the probability distribution and stability of travel times, on available information and on alternative travel options (Jenelius et al., 2006, p. 539–540). Concepts of mobility include both multiscale movements of people, objects, capital and information all over the world, and local processes related to daily mobility and moving within the public space (Hannam et al., 2006; Cresswell, 2011; Komornicki, 2011). Thus, mobility is part of human activity that consists in making choices about moving and travelling. In reference to the journey parameters, this means decisions related to determining the destination, route, time and any given mode of transport (Kruszyna, 2014). ‘Mobility studies include research on migration, tourism, residential mobility and urban daily mobility’ (Jirón, 2009, p. 2). In this article, the authors focus on the subject of daily mobility and the manner in which it is influenced by unusual circumstances, including a flood. Daily mobility is the totality of everyday and recurrent trips people make to commute to work or school, etc. It plays a crucial role in the development of spatial relationships, which is particularly true for strongly urbanised areas (Bartosiewicz and Pielesiak, 2014). In Poland, there is a substantial amount of literature on daily mobility related to obligatory migration (to work or school), but not to facultative purposes, which have been thoroughly researched in the foreign literature. In Poland, the earliest and most profound focus was placed on commuting to work, since related data began to be collected in the late 1950′s and then analysed by the Central Statistical Office (GUS) during national population and employee censuses (Taylor, 1999; Śleszyński, 2012). Newer publications covering this subject include studies by Kruszka (2010) Rosik et al. (2010), Śleszyński (2013), Wiśniewski (2013), and Kurek et al. (2015). As far as the foreign literature is concerned, it is noteworthy to mention publications by Levinson (1998), Sultana and Weber (2007), Suthanaya (2011), Landré (2012), Niedzielski and Boschmann (2014). Congestion is defined as a situation in which a larger number of purchasers apply for a certain good which cannot be supplied in the form of separate units (Żochowska and Karoń, 2012). In reference to transportation, congestion means a level of vehicle traffic density that exceeds the maximum capacity of any given road. Traffic jams lead to delays, they lower the safety level and, to a greater extent, contribute to polluting the environment (Papageorgiou et al., 2003). Specific mechanisms responsible for the occurrence of congestion within the transportation network vary depending on the type of road. Congestion on express or motorways (segments that normally allow for the unobstructed flow of traffic) occurs for different reasons on segments that are characterised by an intermittent flow of traffic, e.g. within a densely woven network of city roads (Żochowska and Karoń, 2012). At this point, such variables determining traffic as road capacity, speed, density and flow should be listed, as they shape traffic flows within the analysed area of the road network. They were included in the application tool, which is described in more detail below. It must also be emphasised that these variables are co-dependent and co-influential. Thus, a bad correlation between them may result in the occurrence of bottlenecks within the road network, which is particularly plausible in built-up areas (Rosik and Kowalczyk, 2015). It is commuter traffic around cities that poses the greatest issue regarding bottlenecks within the road network, as is also shown in the presented study. Road sections leading away from the largest urban agglomerations (in this case Warsaw), characterised by high traffic increases, are prone to cumulative congestion. The analysis of the location of bottlenecks which appear after a flood within the road system fits squarely into the thesis that the development of the network should not be based on larger transportation sequences of roads, but on shorter sections, mainly ring roads and access roads leading to cities (Komornicki et al., 2013a). The elimination of transportation bottlenecks consists in the eradication of traffic jams that appear on any given section of the network characterised by low capacity in the face of congestion. However, the elimination of a bottleneck is not tantamount to the elimination of congestion, 117

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since it may reappear as a result of so-called induced traffic, caused by a growing interest of network users in a modernised or newly built road section. The solution to this issue is projects characterised by a significant growth in capacity (the largest possible number of vehicles that the road cross-section can serve in any given time unit), which should be much higher than the potential increase in the number of vehicles triggered by induced traffic (Komornicki et al., 2013a). The highest congestion within a city occurs most frequently during morning and afternoon rush hours – when people commute to and from workplaces (Quarmby, 1967; Pooley and Turnbull, 2000; Suthanaya, 2011), and that may be even more problematic in the event of unusual circumstances, including floods. Obstructions within the transportation network can be divided into typical and non-typical (Chen et al., 2016). The typical ones occur as a result of the unevenness of demand for transport in time and space. They are recurrent in nature, within a specified amount of time, and are caused by an excessive demand for transportation that appears at the peaks of any individual cycles and against a limited capacity (recurring congestion). Recurring congestion is the outcome of factors that regularly or periodically impact the transportation system. Other causes include the underdevelopment of the transport infrastructure or its faulty design, wrongly configured traffic lights, and the presence of level crossings. The analysed phenomenon of recurring congestion is particularly burdensome at rush hour which, in some areas of the city, may take up a significant part of the day. This type of congestion may also demonstrate a high degree of randomisation, especially in reference to the duration and consequences of traffic obstructions. Mainly, typical obstructions are common within those elements of the transportation network whose capacity is insufficient to satisfy the transportation needs; these are the so-called ‘bottlenecks’ of the system. On the other hand, non-typical obstructions stem from peculiar momentary conditions or circumstances which may be caused by fortuitous events (e.g. failures of underground installations and devices, or breakdowns within transportation facilities), engineering works performed on the road or its immediate vicinity, and special events that require a different organisation of traffic to be implemented, since the road itself may be the venue of an event or may host a large gathering (Żochowska and Karoń, 2012). Other causes of non-typical obstructions include various types of traffic incidents (e.g. accidents, collisions, oil slicks on the road, etc.), unfavourable weather conditions (e.g. blizzards, floods, rockslides), a momentary worsening of road surface quality, and even terrorist attacks. These obstructions lead to the occurrence of random congestion (non-recurring congestion) (Skabardonis et al., 2003; Chung, 2012; Żochowska and Karoń, 2012). The likelihood and extension of this type of congestion varies depending on the kind of network and its susceptibility to obstructions, related to the efficiency of activities regarding risk management, the appropriacy of planning roadworks, the efficiency of removing obstacles that impede a smooth and free flow of traffic, etc. Although the majority of non-typical obstructions cause similar negative consequences for road traffic, not all of these are characterised by an identical level of randomisation and difficulty in their prior prediction. For instance, most failures and breakdowns are unforeseeable, and yet spots which are particularly dangerous – due to the high number of collisions and accidents – can be identified on the basis of statistical studies, stipulated geometric and traffic parameters, and the analysis of the implemented traffic-safety devices (Żochowska and Karoń, 2012). The situation is similar as far as floods are concerned – during a flood, traffic can be managed in a manner which minimises the impact this natural disaster has on the flow of vehicles. If we know which area may be flooded and we follow hydrological warnings, we can react in advance. In Poland, the threatened areas are presented on flood hazard maps, which were drawn up in late 2013, following the implementation of the stipulations within the Floods Directive (Moel et al., 2009; Borowska-Stefańska, 2016c). The purpose of this article is to determine the size and spatial structure of changes in traffic density within the regional road network, following the occurrence of a flood in the Mazovian Voivodeship, Poland ‘There are numerous studies which focus on the effects extreme weather conditions (including flood events) have on transportation systems. Such studies focus mainly upon the issue of evacuation (Church and Cova, 2000; Chen et al., 2012; Richter et al., 2013; Hsu and Peeta, 2014; Andrei et al., 2017; Yuan et al., 2017) and the impact weather has on the frequency of road accidents (Berge-Hayat et al., 2013; Amin et al., 2014; Theofilatos and Yannis, 2014). There is also a significant number of papers that refer to changes in traffic density and transport accessibility as consequences of a flood. (Suarez et al., 2005; Sohn, 2006; Shand et al., 2011; Penning-Rowsell et al., 2013; Pyatkova et al., 2015; Nasralla et al., 2016; Borowska-Stefańska and Wiśniewski, 2018). The existing literature to accessing the impact of flooding on transport disruptions do not take into account the complexity of interactions between the flood hazard and the transport system. “Typically, assumptions can include (TRB, 2010; Shand et al., 2011; Penning-Rowsell et al., 2013):

• traffic volumes and speeds are assumed to correspond to regional (or even national) average statistics, • a road is assumed to be completely closed when its crown is covered by water, regardless of depth, • traffic on open roads continues to flow smoothly, perhaps at a slightly reduced maximum speed, • traffic volumes do not exceed the design capacity of a road, • traffic conditions do not change over the course of the day, or seasonally; and, • diversion routes, and changes (or not) to driver behaviour as a result of the flood, are often assumed without any clear rationale” (Pregnolato et al., 2017, p. 69).

In the presented study, the authors’ own application was used to assess the impact of a flood on transportation systems in four various scenarios (and the assumptions were presented later in the paper. Modelling transport behaviour is an integral constituent of a complex process of travel modelling, which leads to the making of traffic prognoses (Żochowska, 2011). When we are facing nontypical events, a traffic prognosis can be based on the results of monitoring (measurements of traffic density and the time necessary to cover individual segments of the road) as well as historical data for any given period. Unfortunately, it is far more difficult to forecast traffic in the case of non-typical events (including floods). In such circumstances, the most fundamental purpose is to select the 118

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optimum traffic management strategy. Once the strategy has been chosen and its implementation commenced, it is imperative to feed appropriate data to the system that warns users about, for instance, unexpected traffic conditions in the area affected by any given event. Such warnings may be conveyed by variable message signs, the Internet, the radio, CB radio, etc. (Dybicz and Suchorzewski, 2012). Since the functions of traffic models are so diverse, two categories of models are applied. The first one is a model aiming at calculating current travel times (state ‘0’), which utilises current traffic data (density, speed and travel times on selected road sections). The results of such calculations are the basis of information that is later provided to users. The update and transfer of data should not take more than a few minutes. This model is called the Basic Model. However, in the case of events that influence the conditions of traffic flow, it is imperative to apply a more complex model, the purpose of which is to analyse variant strategies of traffic management. The selection of the most appropriate strategy is performed by the operator. This model is called the Strategic Model, and it can be built by means of the standard methodology of traffic modelling (Dybicz and Suchorzewski, 2012). Travel modelling and forecasting is a relatively young, but dynamically developing branch of science, which is tightly connected with the issue of planning transportation systems. (Dybicz, 2009). The model of journeys connected with commuting to work developed with the use of proprietary software may differ from the models obtained thanks to the work of commercial tools or traffic measurements. The article does not assume that it will present the real traffic of vehicles on the roads of the Masovian Voivodeship. Proprietary software will be subject to further improvements in this area. Nevertheless, it should be stressed that it enables the full implementation of the objective of the research that concerns determining the size and spatial structure of changes in traffic density within the regional road network following an occurrence of a flood.

2. Characterisation of the area of research As a result of the administrative division from 1999, Poland has 16 voivodeships (Borowska-Stefańska and Wiśniewski, 2017; Przybyła and Kachniarz, 2017). For statistical purposes, five levels of territorial units were introduced in the EU. Within the practice of regional policy, the basic unit is the NUTS 2 level, which is the equivalent of voivodeship in Poland (Bronisz et al., 2008; Giffinger et al., 2007; Adamowicz, 2011; Churski and Hauke, 2012). In this article, the largest and most densely populated (Table 1) region in Poland was selected for the purpose of further research, i.e. the Mazovian Voivodeship, with the capital city of Warsaw, which is also the capital city of Poland (Gorzelak et al., 2006). The Mazovian Voivodeship is located within the basin of the Vistula River and its central course that encompasses the Vistula (the largest Polish river with a total length of 1047 km, of which 320 km lie within the borders of the Mazovian Voivodeship) and its tributaries (Fig. 1). The basin of its central course is asymmetric and dominated by right-bank tributaries, the largest of which is the Narew River with its largest tributaries within the said territory: the Bug, the Wkra and the Orzyc. The biggest (as far as the size of the drainage basin is concerned) left-bank tributaries of the Vistula River include the Radomka and the Pilica (Borowska-Stefańska and Wiśniewski, 2018; Plan zarządzania ryzykiem powodziowym dla regionu wodnego Środkowej Wisły, 2015; Program ochrony środowiska…, 2012). The characteristics of the said voivodeship in the context of flood hazard areas and flood risk was presented in the work compiled by Borowska-Stefańska and Wiśniewski (2018). Information on the development and management of the floodplain within the Mazovian Voivodeship can be found on flood risk maps.

Table 1 Population and size of voivodeships in Poland. Source: elaborated on the basis of the Local Data Bank of the Central Statistics Office (2016). Region

Population (2016 r.)

Area (km2)

Lower Silesian Kuyavian-Pomeranian Lublin Lubusz Łódź Lesser Poland Masovian Opole Subcarpathian Podlaskie Pomeranian Silesian Świętokrzyskie Warmian-Masurian Greater Poland West Pomeranian

2,903,710 2,083,927 2,133,340 1,017,376 2,485,323 3,382,260 5,365,898 993,036 2,127,656 1,186,625 2,315,611 4,559,164 1,252,900 1,436,367 3,481,625 1,708,174

19,947 17,972 25,122 13,988 18,219 15,183 35,558 9,412 17,846 20,187 18,310 12,333 11,711 24,173 29,826 22,892

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Fig. 1. The drainage system, the flood hazard areas and the settlement network against the administrative division of Mazovian Voivodeship. Source: author’s own elaboration.

3. Materials and methods 3.1. Materials In order to complete the research goal, it was necessary to collect a wide spectrum of source data. Firstly, the procedure involved the preparation of materials referring to the road network within which measurements of vehicle traffic flow were taken. Data on road classes and their course was retrieved from the resources of the Geodesic and Cartographic Documentation Centre (CODGiK) in Warsaw. Thus, part of the data from the Database of Topographical Objects (BDOT) was included in the study to the extent to which it refers to the road network. This data is up-to-date as of early 2016. The provider supplies the data in the vector format with an extensive set of attributive information (e.g. the number of lanes and carriageways, building material, road class and category). The study includes the road network of the Mazovian Voivodeship with a 100-kilometre buffer around the said region (Fig. 2). The applied buffer is aimed to materialise the results of the analysis, since the administrative borders do not comprise a barrier when a travelling path is chosen. The value of 100 km was adopted arbitrarily, with the exception of the area stretching to the east of the Mazovian Voivodeship, as this region nearly borders Belarus and, thus, it was impossible to mark out a 100-kilometre buffer along

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Fig. 2. The road network of the Mazovian Voivodeship. Source: author’s own elaboration.

the whole length of the eastern frontier of the voivodeship. This circumstance, however, is reflected in real life, since the state border with Belarus does compose a significant barrier to vehicle transport. The second type of source data within the research is information on the administrative division of the Mazovian Voivodeship into cadastral units. This division is necessary to present the sources and destinations of job-related trips. The data on the administrative division was also retrieved from the resources of the Geodesic and Cartographic Documentation Centre (CODGiK). What is more, the administrative data was also used to obtain information on the location of all settlement units within the Mazovian Voivodeship (in the form of a central point for any given unit), including the number of residents for each unit. This data is up-to-date as of early 2017, and it was obtained from the resources of the Ministry of the Interior and Administration (MSWiA), the Central Statistical Office (GUS), and commune offices in the designated area where these units are located. 121

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Fig. 3. Commuting to work within the Mazovian Voivodeship in 2011. Source: author’s own elaboration.

Another type of source material included in the study is commute data prepared by the Central Statistical Office (GUS). It was the only data of this kind available for Poland and presents the situation in 2011. Although the authors recognise that the information may not be particularly up-to-date, there is no other comprehensive data on commuting to work in Poland that can be applied. And thus, it is worth emphasising here that, for the purpose of completing the research goal, the currency of the data is an issue of secondary importance, as its completeness is much more significant. The Central Statistical Office (GUS) provides this data in the tabular format, where each subsequent row contains information on the place (commune) of residence and workplace (commune), and the number of people that this relation applies to. This makes it possible to construct a structure of road traffic, which is generated by workplaces. Although this database encompasses the whole territory of Poland, only the relationships that run within Mazovian Voivodeship were taken into consideration for the purposes of this study, i.e. only trips which start and finish within the Mazovian Voivodeship were included (Fig. 3). The limitation of the internal relationship within the voivodeship stems exclusively from the restricted calculating capacity of the software used in the study and the possibility to perceive the returned results. The implementation of a broader scope of data would greatly prolong the analysis, and it would be difficult to present the reader with the volume of the returned results in a user-friendly manner.

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The very last class of source data, imperative for the study, is the information on the flood risk areas, which was retrieved from flood hazard maps (http://www.mapy.isok.gov.pl), drawn up in the course of implementing the stipulations of the Floods Directive to the Polish legal system (Borowska-Stefańska, 2016c). These maps show areas of different probability of flood occurrence. Researchers most commonly use the so-called 100-year flood zones (Qp1%, i.e. discharge of probability of occurrence p = 1%), which is why the authors of this article also decided to confine themselves to this range (Gutry-Korycka et al., 2006; Borowska-Stefańska and Wiśniewski, 2018). 3.2. Research proceedings The applied research goal as well as the collected source materials and the developed research tool translated into the following research procedure. First of all, a road network was prepared to conduct a simulation of vehicle flows. Each section of the network was assigned to one of the following road classes: motorway, expressway, fast traffic trunk road (GP class), major road (G class) and service road. Although Polish legislation also identifies other types of roads, e.g. local roads, they were excluded from the study due to their local (regional) nature. Next, the assignment of various sections of the road network to different classes made it possible to describe them by means of the following attributes that are necessary to analyse traffic flows: road cross-section, speed of free-flow traffic (V0) [km/h], and capacity (C) [veh/h]. The attributes were assigned in accordance with the Polish legislation. For instance, in the case of the fast traffic trunk road (GP class) with the cross section 2 × 2, the free-traffic speed equals 60 km/h, and the capacity is 1800 veh/h. The subsequent stage of the research was to construct a road-traffic structure for trips to work within the Mazovian Voivodeship, since it was imperative to provide the data retrieved from the Central Statistical Office (GUS) with a spatial dimension so that a subsequent simulation of trips could be possible. For this purpose, for each cadastral unit (exclusively municipal communes, exclusively rural communes, municipal and rural communes, urban areas within a municipal and rural commune, and rural areas within a municipal and rural commune) its central point was generated. Next, the centroid was given an identification number of the node within the road network, thus, making it possible to determine the starting point and the destination of any given trip to work. The data from the Central Statistical Office (GUS) also stipulates the number of people that each of the transport relationships refers to. Therefore, it was imperative to ‘translate’ the number of people into the number of vehicles that will use the road network. A simplifying premise was assumed that all trips are made exclusively by means of private cars. Naturally, the authors are aware that commuters have an opportunity to choose other modes of transport, but it is beyond the scope of this study to estimate the share of various individual means of transport. The stipulation of the number of vehicles was based on the motorisation rate (the number of cars per 1000 inhabitants). The Central Statistical Office (GUS) provides this data with accuracy for the powiats. Thus, the number of vehicles used for commuting was stipulated, diversified to the level of powiat. The authors are aware of the flaws of the applied research method, which is based on the motorisation rate, and the fact that significantly more accurate data is presented in reports of comprehensive traffic surveys, which are, however, only conducted in large urban agglomerations (e.g. the Warsaw Traffic Survey 2015). The research on the division of transportation tasks in various travel motivations makes it possible to determine an accurate percentage of people who use their private cars to commute to work (Warsaw: 36.3%). Such detailed data is not, however, available for other settlement units within the region. Therefore, in order to ensure cohesion in the analysis, the authors decided to apply the motorisation rate for all commutes. The next stage of the analysis is connected with the determination of flood hazard areas. As mentioned above, their range was stipulated on the basis of the flood hazard maps (www.mapy.isok.gov.pl), which later made it possible to determine sections of the road network that are out of service during a flood (Table 2). The study does not take into account the road sections within the onehundred-year flood zone, for which it was impossible to determine the lane ordinate, as well as those sections whose lanes were below the level of the flood water wave (as presented on the flood risk maps). In the subject literature, this issue is approached in various manners. According to Pregnolato (Pregnolato et al., 2017), it is common practice to assume that a road is out of service if its lane is flooded, regardless of the water depth. In the case of lower-scale analyses, it is possible to supplement this kind of missing data by, for instance, conducting field research. Four variants of the research were conducted. In the first scenario, it is assumed that a flood does not occur and all commute trips are completed. This is the basic variant which enabled comparisons of results returned for all successive scenarios. The second variant includes the occurrence of a flood throughout the territory of the Mazovian Voivodeship and the completion of all commute trips. This scenario is aimed to illustrate the initial phase of the natural disaster – the flood has already occurred, but no evacuation procedures have been commenced or implemented yet. In the third variant, it is assumed that the flood has occurred, no evacuation Table 2 The number and length of the road network (by road class) that is rendered out of service during a flood. Source: author’s own elaboration. Road class

Number of road sections

Total length [km]

motorway expressway main road of accelerated traffic main road cumulative road

1 15 146 402 1113

0,03 1,84 24,53 58,31 161,57

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procedures have been commenced or implemented, but those who live in the disaster-stricken communes will not be able to reach their workplaces, and the same will apply to those whose workplaces are actually located in the afflicted area. The last, fourth scenario is based on the assumption that there has been a flood, neither residents of the disaster-stricken communes nor employees whose workplaces are located in the flood-afflicted area will be able to commute to work, and it is necessary to evacuate the inhabitants of the towns and settlements within the flooded territory. It was also assumed that people are being evacuated to the nearest (timewise) commune that has not been flooded. The changes resulting from the application of these successive scenarios were illustrated with the total time necessary to perform all commute trips, the number of vehicles in the road network expressed in twominute intervals, the number of episodes when the road capacity was exceeded in two-minute intervals (the number of vehicles exceeds the capacity in any given time period during the research), and the structure of the road network load, arranged by road class. 3.3. The characterisation of the application 3.3.1. Introduction to starting the application On the market, there are a number of tools (e.g. VISUM, VISSIM) for modelling the load of a road network. Nevertheless, the authors decided to apply their own application, having considered two factors: firstly, the greatly limited access to commercial tools, due to high licence costs; and secondly (and much more importantly), the possibility to build a tool that allowed the completion of the assumed research objectives. This tool makes it possible to trace changes in the load of the road network in any given time interval stipulated by the authors (a dynamic approach), whereas standard software solutions offer a cumulative approach (a statistical approach). Unlike commercial applications, the authors’ software also makes it possible to enter any attribute data that characterises sections of the road network (edges and nodes). This application also enables the researcher to determine how detailed the analysis should be, since it offers the possibility to indicate the size of transportation packages (vehicle numbers). The user is supposed to enter csv files which contain data on nodes and edges of the road network that he or she is about to analyse into the application. Next, the user determines the strategy of designating the weight of individual edges (sections of the road network) by means of the available options and settings. All edges are described through the parameters that characterise them – e.g. travel time, technical condition, travel cost – and which can be used to determine their weight. In order to compute load levels of individual constituents of the network, the application groups individual vehicles into more abstract objects called ‘transport packages’. Prior to starting the application, the user is offered the possibility to determine the maximum size of such packages (the maximum number of vehicles within a package). The application uses a turn-based mechanism to work. Each turn lasts a specified period of time, when transport packages are moved along designated routes. The final input parameter which the user ought to enter is the duration of individual turns (time intervals). Once all input parameters have been entered, several auxiliary objects are generated, the most crucial of which are Weight Strategy and Path Calculator (Fig. 4). On the basis of the data from the provided csv files, and in accordance with the designated specifications, the system creates transport packages, establishes their position in the starting nodes and calculates the shortest (according to the initial guidelines) path to the destination node (these calculations are performed on the unburdened network, which guarantees that the route the package has to travel is the shortest). Simulations begin once all the packages have been generated and located within the network. 3.3.2. The main loop of the application From the list of all available transport packages available, the system selects those which: ● have not reached their destination yet, ● have already exceeded the time of their delay (since each node can determine the time after which the packages that start there should begin moving, it is possible to conduct a simulation in which new nodes enter the network with time). Next, this list is sorted so that the packages that have to cover the shortest route are at the top (this helps avoid the situation when the packages whose route is blocked, i.e. vehicles at the end of the traffic jam, are activated within the system before those packages that block the route, i.e. cars at the beginning of the traffic jam). During the next step, the iteration of the internal counter of turns within the application is increased. An internal counter of travel time is set up, following the input data provided by the user, for all the transport packages on the list, as stipulated in Point 2 above. This counter determines for how long any given package can remain in motion in any given turn. Next, all transport packages are shifted (see below: movement of transport packages). The packages that have reached their destinations are transferred onto a special list and do not participate in further calculations. A file containing a turn report is generated, containing the data on the load of individual nodes and edges of the network. 3.3.3. Movement of transport packages The movement of transport packages is divided into two phases. During the first one, the system strives to move all the packages along their routes, in accordance with the following algorithm: 1. Move a package along the element of the graph on which it is currently located (edge or node) for the available time, 2. If, once moved along, the package does not have any more movement time available, proceed to move the next package, 124

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preparation of input data

selection of parameters for determining the path

determining the size of transport packages and the length of the time interval

creation and positioning of transport packages

in the case of available time, move to the next node

if there is no time the next packet moves

movement of transport packages by the amount of available time

sorting transport packages

identifying blocked packages

building a list of blocked packages

positioning and moving packets from the blocked list

determining an alternative path of travel

connection of data to the software (e.g. GIS)

data export to the selected format

construction output database

restarting the algorithm

• edge database • node database

Fig. 4. The scheme of application functioning. Source: author’s own elaboration.

3. If, once moved along, the package has reached the end of the element on which it is currently located and has not expired the entirety of its movement time, the next node on the route of the package ought to be checked, 3.1. If the next node within the route is blocked (there are too many vehicles) or it does not exist (see: information below), the package should be added to the list of blocked packages and the movement of the next package should commence, 3.2. If the next node within the route is not blocked, one should move the package to its beginning and then proceed to Point 1. 4. Once all the packages from the list have been moved, one should select the ones which have been marked as blocked and proceed to Phase 2. If the list of blocked packages is not empty (it contains some blocked packages), the second phase of movement is activated: 1. calculate how many packages are blocked, 2. create a second list of blocked packages, 3. for each blocked package: 3.1. retrieve its current position, 3.2. move it along the current element on which it is located, 3.3. if the package still has some movement time available, check the next node within the current route, 3.4. if the node does not exist, add the package to the second list of blocked packages, 3.5. if there is a next node within the route, but it is blocked, add the package to the second list of blocked packages, 3.6. if there is a next node within the route and it is not blocked, move the package to the node and return to Point 3.1. After making an attempt to move all the packages, one ought to verify whether the first and the second collection of blocked packages have the same sizes. If the second list of blocked items is smaller than the first one (the one with which the algorithm was initiated), it means that one of the packages moved and might have blocked the movement of other blocked ones. In this case, one should proceed to Point 1 and continue with the algorithm. If both lists have the same size, it means that the blocked packages have no possibility to move any further. In such circumstances, each blocked package should be provided with a new route to the destination point. Once new routes have been set, one should recurrently activate for them (and only for them) the function of package movement (movement of transport packages, Point 1 of the algorithm). The implementation of Dijkstra's algorithm, which is used within the application, works on the assumption that if it is not possible 125

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Fig. 5. Load of the road network resulting from commuting to work in the Mazovian Voivodeship. Source: author’s own elaboration.

to mark out a route from point A to point B, since such a route does not exist or all roads connecting these points are blocked, the system will nominate the route as non-existent, and this will be the only case in which a transport package cannot have the next node on its path of movement.

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number of capacity over 40

250

35 200

30 25

150

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15 10

50

5

number of capacity over

thousands

number of vehicle

number of vehicle

0 2 8 14 20 26 32 38 44 50 56 62 68 74 80 86 92 98 104 110 116 122 128 134 140 146 152 158

0

time [minutes] Fig. 6. The number of vehicles and crossings of the road network capacity against the background of travel time to work in the Mazovian Voivodeship. Source: author’s own elaboration.

4. Results and discussion 4.1. Variant No. 1 The obtained data clearly shows that as far as commuting to work is concerned, the dominant role is played by Warsaw and some sub-regional centres, such as Płock (Fig. 5). The distinctive role of Warsaw as a commuting centre stems both from its social functions and the development of the job market (Śleszyński, 2013). Good accessibility to job markets is one of the key elements that counterbalance the spatial maladjustment of workplaces and homes (commuting to work). Changes within this scope are conditioned by the investment process in the infrastructure. The analyses of job market availability and commuting, conducted by Komornicki (Komornicki et al., 2013b) also confirm the existence of a spatially vast influence of the Warsaw market. At the same time, they prove that transport accessibility towards Warsaw is a constituent that conditions the situation on the job market in the northern, eastern and also southern part of the voivodeship. Only in the western part of the researched territory does the role of alternative local markets counterbalance the relatively long travel time to the capital city (Komornicki et al., 2013b). In the subject literature, it is generally assumed that the most intense commuting takes place within the 45-min isochrone (Wiśniewski, 2012 after: Sakanishi, 2006). As far as this particular voivodeship is concerned, commuting to work is enclosed within an approximately 160-min isochrone (Fig. 6). Undoubtedly, traffic is the densest in the initial stage of movement. The number of segments within the road network that are congested decreases with increase in travel time – at an early stage, the number of network segments afflicted by congestion equals 35 (Fig. 6). The calculation of an isochrone outside which commute traffic density decreases for any given centre depends on numerous variables, including remuneration and profitability of commuting, the difference in salary levels between the local job market (place of residence) and neighbouring job markets that require commuting to, the spatial distribution of job offers around the potential employee’s home, the possibility to obtain unemployment benefit and its amount, as well as individual factors (Wiśniewski, 2012 after: Rouwendal, 2004). As far as the ‘normal’ circumstances (no flood) of commuting within the Mazovian Voivodeship are concerned, the greatest load refers in particular to the following road classes: main road of accelerated traffic, main road and cumulative road. In this case, express

main road of accelerated traffic main road cumulative road expressway motorway

Fig. 7. The structure of road load according to the class during commuting to work in the Mazovian Voivodeship. Source: author’s own elaboration. 127

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Fig. 8. Load of the road network resulting from commuting to work in the Mazovian Voivodeship during floods. Source: author’s own elaboration.

and motorways play a marginal role (Fig. 7).

4.2. Variant No. 2 In the case of a flood occurring throughout the whole territory of the Mazovian Voivodeship when all trips to work are being made, the greatest changes in the size of traffic flows and the selection of an optimum route take place in Warsaw. It stems from the fact that the city is the centre with the highest commute rate and is divided into two parts by the Vistula River that flows across it. 128

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number of vehicle

number of capacity over

250

45 40

200

35 30

150

25 20

100

15 10

50

5

0 2 50 98 146 194 242 290 338 386 434 482 530 578 626 674 722 770 818 866 914 962 1010 1058 1106 1154 1202 1250

0

number of capacity over

number of vehicle

thousands

M. Borowska-Stefańska et al.

time [minutes] Fig. 9. The number of vehicles and crossings of the road network capacity against the background of travel time to work in the Mazovian Voivodeship during floods. Source: author’s own elaboration.

main road cumulative road main road of accelerated traffic motorway expressway Fig. 10. The structure of road load according to the class during commuting to work in the the Mazovian Voivodeship during floods. Source: author’s own elaboration.

Outside this area, the biggest changes in the load of the road network would mainly refer to the northern part of the voivodeship, which is influenced by such rivers as the Wkra, the Narew and the Bug, and the southern part of the province, which remains under the impact of the Vistula, the Pilica and the Radomka (Fig. 8). If the flood occurred simultaneously throughout the whole territory and all employees had to commute to work, this time would grow almost eightfold. And the number of congested road segments within the network would practically remain at a high level within the entirety of the said period (Fig. 9). In the analysed scenario, not only would the commute time increase substantially, but also the use of any given roads would change – mainly, there would be more traffic on the main roads, at the expense of other road classes (Fig. 10). While comparing commute traffic density on the roads which are and are not afflicted by a flood, it must be stated that the flooding of a substantial part of the Mazovian Voivodship would result in a significant reduction of traffic density on the main roads towards Warsaw, since this is where the Vistula River flows. On the other hand, however, traffic density will noticeably increase on the roads within the outer belt of the voivodeship, which is unaffected by the flood (Fig. 11). 4.3. Variant No. 3 If the flood occurs within the borders of the Mazovian Voivodeship and the flooded communes are excluded from commuting to work, the circumstance will change in reference to the range of commute trips, their duration, and road load. The densest traffic will then be recorded in the areas bordering Warsaw and some smaller towns, such as Mława, Ciechanów, and Ostrów Mazowiecki in the north, Siedlce and Mińsk Mazowiecki in the east, Radom in the south, and Żyrardów and Grodzisk Mazowiecki in the west (Fig. 12). In comparison to the ‘basic’ scenario, travel time to work will extend approximately fivefold, and yet the road network will not be overloaded (with the sole exception of one instance – at about the 780th minute – when there is an accumulation of commute trips from various locations at the destination point). This is so due to the fact that a substantial number of communes (136 out of all 314 within the Mazovian Voivodeship) are excluded from commuting as a result of the flood that occurs within their borders (Fig. 13). The substantial prolonging of short-distance trips following the flood within the Mazovian Voivodeship is also confirmed in the studies conducted by Borowska-Stefańska and Wiśniewski (2018). When road load is analysed in terms of road classes, however, it must be stated that their structure will not change in comparison 129

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Fig. 11. Changes in the network load caused by the flood in the the Mazovian Voivodeship. Source: author’s own elaboration.

with Variant No. 2. The class of main road will play the greatest role in movement, followed by the classes of cumulative road and main road of accelerated traffic (Fig. 14).

4.4. Variant No. 4 In this scenario, there is an additional issue of evacuation, on top of the commute traffic from the regions unafflicted by the flood, 130

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Fig. 12. Load of the road network resulting from commuting to work in the Mazovian Voivodeship during floods. Source: author’s own elaboration.

which translates into the greatest traffic density in the north of the voivodeship, as well as in its central, eastern and southern parts (Fig. 15). The travel time to work will be similar to that recorded in Variant No. 3, i.e. approximately fivefold longer than in the ‘basic’ scenario. Due to evacuation procedures, however, the number of vehicles on the road will remain high, between 1000 and 10,000 units (reaching as many as 100,000 units in the initial phase), particularly at about the 480th minute of all commute trips. This will not, however, be a value that could influence the overloading of the road network (Fig. 16). The road class-based structure of road load will be similar to the ones recorded in Variants 2 and 3, with a substantial load of the

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number of vehicle

number of capacity over

number of vehicle

12

10000

10 1000

8

100

6 4

10

2 0 2 32 62 92 122 152 182 212 242 272 302 332 362 392 422 452 482 512 542 572 602 632 662 692 722 752 782

1

number of capacity over

14

100000

time [minutes] Fig. 13. The number of vehicles and crossings of the road network capacity against the background of travel time to work in the Mazovian Voivodeship during floods. Source: author’s own elaboration.

main road cumulative road main road of accelerated traffic motorway expressway

Fig. 14. The structure of road load according to the class during commuting to work in the the Mazovian Voivodeship during floods. Source: author’s own elaboration.

following road classes: main road and cumulative road (Fig. 17). Fig. 18 presents the road load both when there is and when there is no necessity to evacuate the inhabitants of the flooded areas. In this case, it must be stated that due to evacuation procedures, traffic will increase mainly in the north and south of the voivodeship, which is related to the location of the residential development within the flooded areas. As can be seen in the study conducted by Andrei (Andrei et al., 2017), the largest numbers of residential buildings are located in spots where the floodplain is the widest (where the river is joined by its tributaries) – the upper portion of the Vistula River within the borders of the Mazovian Voivodeship and the central portions of the Bug and Narew. Apart from that, residential development within the flood zones of this voivodeship is located along the banks of smaller regional rivers, i.e. the Pilica, Radomka, Wkra, and Bzura (Fig. 18). 5. Conclusions As shown in the conducted studies, the occurrence of non-typical obstructions, including a flood, may have an enormous influence not only upon the volume of traffic on the roads, but also upon its fluency, and the selection of an optimum route. Therefore, it is essential to generate traffic simulations, since it allows us to prepare appropriately for an emergency situation and to manage the traffic during a flood in a manner which will help minimise its negative impact on traffic fluency. Firstly, the implementation of the Floods Directive (2007) in Poland resulted in the development of flood hazard maps, indicating the areas which may be flooded. Secondly, the volume of everyday commute trips can also be estimated, which – in consequence, and with the use of the application developed for the purpose of this article – offers a possibility to react accordingly when there are non-typical obstructions (here: a flood). This, in turn, is of crucial importance, since numerous studies show that it is only possible to provide flood victims with effective assistance if the arrival time of the very first emergency services and resources does not exceed 15 min (Drzymała et al., 2014; Borowska-Stefańska 2016a,b). On the basis of the conducted study, it has been stated that the greatest changes in vehicle traffic density (the analysis of commute traffic) regard the capital of Mazovian Voivodeship, which – first of all – stems from the fact that it is Warsaw that the largest number of employees commute to. Secondly, it is influenced by the location of the capital city in relation to the river system. The Vistula River flows across the area, dividing the city into right- and left-bank parts, which may result in numerous areas within the city and its immediate vicinity being closed to traffic if there be a flood. Thus, flooding within the area may result in a significant decrease in 132

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Fig. 15. Load of the road network resulting from commuting to work in the Mazovian Voivodeship during floods. Source: author’s own elaboration.

traffic density. The situation will, however, be the reverse in the peripheral territories of this voivodeship, since the growth of congestion in their case will be influenced by the radial layout of the river system (within the borders of the Mazovian Voivodeship, the Vistula River flows from the south east, through the central area, and towards the north west, collecting waters from its tributaries). In the case of the analysed voivodeship and in ‘normal’ circumstances (no flood), commuting to work remains approximately within the 160-min isochrone. In the second variant, this time would extend nearly eightfold, and in the remaining scenarios fivefold. It must also be mentioned that when there is no flood, the commuting congestion is generated maximally on 35 segments of the 133

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number of capacity over 20 18 16 14 12 10 8 6 4 2 0

number of vehicle

100000 10000 1000 100 10

2 32 62 92 122 152 182 212 242 272 302 332 362 392 422 452 482 512 542 572 602 632 662 692 722 752 782

1

number of capacity over

number of vehicle

time [minutes] Fig. 16. The number of vehicles and crossings of the road network capacity against the background of travel time to work in the Mazovian Voivodeship during floods. Source: author’s own elaboration.

main road cumulative road main road of accelerated expresway motorway Fig. 17. The structure of road load according to the class during commuting to work in the the Mazovian Voivodeship during floods. Source: author’s own elaboration.

network, whereas in Variant No. 2 it rises to 42, and in the other scenarios it virtually does not exist (with the exception of the final period of commute trips – at about the 780th minute – when there is a cumulation of commute trips from various locations at the destination point). This is so because, in the last two variants, movements within the borders of flooded territories that are impassable (with the exception of evacuation procedures) were excluded from the analysis. As far as ‘normal’ circumstances (no flood) and commuting in the Mazovian Voivodeship are concerned, the greatest load refers in particular to the following road classes: main road of accelerated traffic, main road and cumulative road. In this case, express and motorways play a marginal role. On the other hand, in the remaining scenarios, the importance of the class of main road of accelerated traffic decreases at the expense of the classes of main road and cumulative road. It must be emphasised that the conducted studies represent a significant simplification of reality, regarding both the floodplain itself – in which case not all scenarios of flooding, its depth and flow speed were taken into account – and the functioning of the transportation system, i.e. the study only took into consideration the estimated data on commuting, and the number of vehicles on the roads was also evaluated. Naturally, one ought to bear in mind that traffic (also during a flood) is generated not only by commuters and evacuation procedures. The study should be expanded to include other types of movement which may contribute to congestion. These were not taken into account therein, however, due to a lack of relevant data. Despite that, the article is not only scientific but also applicational in its nature. The returned results may be useful for communes which may be directly or indirectly stricken by a flood, for companies that operate in areas which might become difficult to access when covered with floodwaters, and for institutions responsible for reacting to floods, including – in particular – those which organise evacuation. The conclusions drawn from the research can be translated into a set of recommendations aimed at decision-makers responsible for the development of policy related to the management of flood risk and the development of transport infrastructure. This is evidence of the considerable significance of the project results for the development of spatial planning and transport geography. The research results justify the obligation to conduct analyses of changes in transport accessibility and the load of the road network for flood risk areas, and on various spatial scales. The obtained results may be a basis for formulating and implementing procedures of managing transport flows at individual spatial levels. This ought to be understood as, for instance, activities related to traffic management, such as mandatory detours displayed on variable message signs on express and motorways (for transregional trips) and changes in traffic organisation implemented by means of intelligent transport systems (for intraregional trips). Besides assisting

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Fig. 18. Changes in network load resulting from evacuation in the the Mazovian Voivodeship. Source: author’s own elaboration.

decision-makers and transport organisers at drafting operational strategies and policies, the results of the study may also indicate guidelines for educational programmes for residents of flood risk areas and territories in their immediate vicinity, aimed at shaping appropriate transport behaviour in emergency situations. Even if, at the regional and national level, such behaviour can be conditioned by means of the aforementioned technical and technological solutions, at the local scale, where the flood afflicts local communities directly and immediately, trained behaviour is crucial. Good practices in that matter would not only make it possible to 135

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mitigate the negative effects of the flood on human life and health, but they would also increase the effectiveness of emergency services as far as anti-flood protection is concerned. Further research into the issues presented within this paper should focus on, for instance, the implementation of modal split with regard to commuting. On the one hand, the introduction of diversity in modes of transport chosen by various commuters would make it possible to provide more detailed data on the number of vehicles used to commute to work, and on the other, it would also enable researchers to determine the influence of a flood on the functioning of other means of transport (e.g. rail). What is more, the diversification of travel motivations, impacted by a flood, would also represent a valuable expansion of the presented study. References Adamowicz, M., 2011. Wsparcie rozwoju regionalnego w warunkach uczestnictwa Polski w Unii Europejskiej. Roczniki Nauk Rolniczych, SERIA G 98 (1), 60–74. 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