exploringtheimpactofroadtrafficimpedanceandbuilt ... ·...

SSS10 Proceedings of the 10th International Space Syntax Symposium

R Chaturvedi & K S Rajan Exploring the impact of road traffic impedance and built environment for vulnerability mapping of evacuation areas – Case study of Hyderabad city

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Exploring the impact of road traffic impedance and built environment for vulnerability mapping of evacuation areas Case study of Hyderabad city

Rajesh Chaturvedi Lab for Spatial Informatics, International Institute of Information Technology, Hyderabad, India [email protected] K S Rajan Lab for Spatial Informatics, International Institute of Information Technology, Hyderabad, India [email protected]

Abstract

This study identifies spaces vulnerable to a disaster in terms of impedance offered to evacuation. Evacuation for spaces is evidently dependent on combination of several spatial and demographic features. In order to develop an evacuation model or prioritize regions for special attention during calamity, it is necessary to understand interactions and interdependencies of numerous factors. In this study, we address vulnerability issue in terms of potential difficulties in evacuating a region from a spatial perspective. Arrangement of built-‐up areas and interactions of neighborhoods are studied based on topological as well as metric distances between them.

We couple building to building (point to point) accessibility considering metric distances & their respective on-‐ground areas with space syntax based axial analysis on urban streets. The approach creates multi-‐dimensional feature vector on top of buildings layer. Feature vectors consist of building on-‐ground areas, their reach, betweenness, and local integration & choice values of segments adjacent to which they are located for a metric radius of 500 meters and topological radius R10. Further, we use bivariate Local Indicators of Spatial Association (LISA) to identify the clusters and conclusively carry out knowledge based denomination of the areas in terms of their vulnerability.

The study reveals that road segments offering highest traffic impedance for planned grid like arrangements are parallel, of equal length and in close vicinity of each other whereas for non – grid wards are rather scattered. The clusters of buildings located on roads with lower accessibility are significantly less in number as well as smaller sized for grid like symmetries compared to non – grid arrangements.

The evaluation of areas from vulnerability perspective carried out in this study can form the basis of a generalized decision support system – framework to identify, rank and prioritize both the current and future space planning and emergency response.

Keywords

Disaster vulnerability, evacuation, buildings-‐network topology, clustering.



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1. Introduction

Metropolitan cities, such as Hyderabad, are usually crowded and have very dense urban cores. Any natural or man-‐made haphazard can cause chaos and confusion in such large accumulation of populations. The situation worsens if the chaotic area is not evacuated at the earliest and can lead to a disaster. Deficiency of planning and inadequacy of services may actually lead to aggravation of the disaster. Disasters can occur during processions, sporting event egress and ingress, riots, weather, religious events, entertainment events (Fruin, 1993). Few other disasters in built environment can be fire, collapse of buildings and settlements, flooding or waterlogging. All forms of such disasters have pervasive effects on crowds in terms of panic. The first activity to be carried out is to evacuate the affected area as soon as possible. In this paper, we have conducted an extensive study for city of Hyderabad in order to identify areas where efficiency of services needs to be better as compared to others as they are more risk prone from the perspective of the built environment and their interaction with neighborhoods. In order to map such vulnerable sites we have considered two major forces that offer impedance to evacuation, the first is high road traffic density and the second is complex geometries of built environment with high density of buildings and relatively poor connectivity to neighborhoods. This study assesses areas based on the extent of vulnerability at a systemic level, while analysis is not conducted on a micro level where each individual entity of the system is studied separately. Also, speeds of evacuation and planning/ routing paths in the event of calamity are out of the scope of this study.

2. Background

General Observations

Emergency evacuation is a challenging situation. The two forms of hindrances for safe exits from the emergency location or easy access of emergency response service to the target area are dense road traffic and complex built environments. Accumulations of vehicles on roads becomes a bottleneck for the passage whereas dense complex built environment with significantly low integration values slows down the pace of evacuation as there are more number of turns for very short distances as compared to long straight roads. Both of the stated factors cause slower egress rates. Predicting traffic at roads at different times of day is an ambiguous job as different models may suggest same road segments to be loaded with different volumes of traffic at the same time of the day. Glickman (1986) and Stern (1990), in the past, have suggested considerable discrepancies possible between daytime and nighttime population distributions, and thus major difficulty in obtaining or calculating the information. It has been suggested by Southworth (1991) that it is rather easier to plan night time evacuation using census records but planning an efficient evacuation plan and predicting road traffic during the day time remains tricky. This study tries to identify most vulnerable areas from a spatial perspective and utilizes arrangement of structures in built environment to predict busiest roads during the daytimes as well as poorly connected dense areas.

Simulations for disaster planning

Initially, simulation remained a preferred choice for evacuation planning. One of the earliest of such simulations was MASSVAC, which has capability to simulate highway networks and find efficient routes from impact area to a safe spots in the event of natural disasters (Hobeika, 1985). Further improvements in MASSVAC led to integration of a user equilibrium assignment algorithm to it (Hobeika and Changkyun, 1998). Another simulation in the series was TEVACS, developed to analyze large-‐scale evacuation specifically for large cities in Taiwan (Han, 1990).



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Vulnerability analysis based on neighborhoods

Considerable amount of work was being done in modeling evacuation which was directed on various geographical scales like cities or buildings as large and small areas respectively, however, Cova and Church (1997) thought to analyze evacuation challenges at the neighborhood scale and were the first to study interaction of built environments. Church and Cova (2000) suggested methods to identify neighborhoods that might be particularly vulnerable to evacuation difficulty and also explained ways to develop maps of potential evacuation difficulty, their theories were majorly based on demand to exit capacities in any network. Based on points put forth by Church and Cova, an application of a micro-‐scale traffic simulation model to a neighborhood to estimate the extent to which a possible evacuation problem exists was presented by Church and Sexton (2002).

Space syntax and disaster mitigation

Space syntax theory has been utilized to identify vulnerability indicators in several urban settings. Space syntax suggests that magnitude of losses from a disaster is largely dependent on urban layouts. The theory has been used to a great extent to analyze, understand, explain and predict some of the social phenomena related to different kinds and phases of disasters. A number of studies have been conducted at local scales focusing on the relationship between the constructions, vehicles, and pedestrians. These studies explain how the constructions enable or prevent free movement or evacuation in different scenarios. Various general and specific situations have been addressed viz. evacuations of hospitals (Alper et al., 2005), evacuation from high-‐standing buildings (Choi et al., 2007), and evacuation in scenarios of natural disasters such as earthquakes or tsunamis (Firat and Kubat, 2012, Fakhrurrazi and van Nes, 2012).

Limitations of space syntax methodology

Integration is the focus of space syntax analysis. Integration values at global and different local scales are of great importance in understanding how urban systems function because it has been observed over the years in several cities across the globe that how much movement passes down each unit (axial line) is highly influenced by integration value (Hillier, 1996). It has also been conveyed that integration R3 is used in investigating pedestrian movements, and R10 is often used to vehicular movements (Jones and Fanek, 1997). However, even after having established very strong foothold for urban analysis, space syntax analysis suffers few inconsistencies as explained by Ratti (2004). Space syntax is entirely dependent on topological representation and discards all metric information. Hillier (1999) claims that the existence of pervasive regularities in urban systems ensures that the axial map does not ignore the geometric properties of space but internalizes them; this argument is not very convincing to avoid metric information in identifying hotspots of vulnerability. Another inconsistency stated by Ratti (2004) is buildings don’t come into picture at all in space syntax analysis. According to space syntax analysis, structurally similar areas (without considering information like volumetric capacities of buildings) will have similar natural movement patterns (Hillier et al., 1993), however, this study assumes that two similar arrangements may have different movement patterns based on the volumetric capacities of the buildings in the area, and unlike Hillier (1993) do not assume that that urban attractors are a mere consequence of configuration.

3. Objective

The objective is to generate two kinds of building vulnerability maps for each local municipal corporate ward – one representing buildings adjacent to roads with high traffic densities and other representing



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clusters of buildings which appear in high density construction belts which are poorly accessible. This study, like Church and Cova (2000), suggests methodology to identify areas with higher vulnerability based on neighborhood interactions, such that any small disturbance may become severe and take form of a disaster as there exist potential difficulties in easily evacuating such places. As discussed in limitations section above, considering buildings as well as metric distances is very necessary for identifying areas at higher risk, this study combines both space syntax and urban network methodologies in accessing the vulnerabilities of an area.

4. Data and Preprocessing

The city of Hyderabad is divided in 18 Circles. Further, circles are subdivided in corporate wards amounting to a total of 160. We carry out localized analysis on buildings, road centerlines and axial lines ward-‐wise and identify risk prone areas in each ward. The GIS data was provided by Greater Hyderabad Municipal Corporation (GHMC). The vector data layers included for this study are Corporate Ward Boundaries, Building Footprints, Road Polygons, and Road Centerlines. The data is preprocessed in the first step. Each corporate ward is analyzed separately. In order to negate the edge effects in any form, we make a buffer of 500 meters around each corporate ward boundary and carry out analysis on buildings and road segments lying within the newly created boundaries. The preprocessing also includes conversion of road polygon shapes to polylines and further simplifying the polylines using modified Douglas-‐Peucker algorithm (built-‐in QGIS) with a tolerance limit of 0.02 for angular deviation in order to reduce the computation overhead and hence minimize the time taken to generate results.

Proposed Methodology

Polylines generated in preprocessing are used as visible spaces and thus utilized to generate axial line map for each ward (Turner et al., 2005 and Varoudis, 2012). The integration and choice values are calculated for R10 (Turner et al., 2005 and Varoudis, 2012) because roads segments with relatively higher integration and choice values at R10 are assumed to hold more traffic than others (Jones and Fanek,

Source Layer with line features

Data Preprocessing

Calculate reach and betweenness on buildings layer at radius – 500m for each ward separately

Spatial Join for space syntax based building characterization

Network dataset generation from centerlines

Axial line map generation from Polylines

Calculate integration and choice at R10 for each ward separately

Impedance offered by road traffic using intersection of highest choice and highest betweenness segments

Vulnerability Map (At Ward Scale)

Impedance offered by built environment using BiLISA for cluster identification

Target Layer with point features



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1997). As mentioned in the limitations section above, topological relationships alone do not guarantee identification of most busiest roads, therefore, apart from the study of axial line network connections consisting of arcs and nodes, this analysis also features buildings that are used as the spatial units of analysis for all measures. The buildings can be weighted according to their particular characteristics. Voluminous, populated, buildings can have a proportionately stronger effect on analysis observations, thus showing dominance of specified measure to weigh them. This study uses base areas of the buildings to weigh them. The buildings polygons are represented by their respective centroid point features for all centrality analysis, ensuring that each centroid lies within the polygon boundary of the building it represents. Network dataset are generated from actual road centerlines to carry out urban network analysis on building features. This study assumes 500 meters to be a minimum safe distance to move to from an impact area and an emergency response service to be available in a neighborhood with equal radius, hence reach and betweenness values are calculated for a radius of 500 meters (Sevtsuk and Mekonnen, 2012).

In order to study results generated from space syntax analysis and urban network analysis together, the two layers -‐ lines from space syntax analysis and points from urban network analysis are spatially joined on the basis of location such that a building point inherits integration and choice attributes of the axial line closest to it. This helps in characterizing buildings based on space syntax parameters. This study assumes shortest paths are the attractors for the natural movements of pedestrians as well as vehicles. However, the two analyses hint at different paths being chosen as space syntax suggests more integrated and topologically shorter paths are highly preferred whereas spatial network theory suggests shortest paths in terms of metric distances are the busiest ones. The next step in this study is to extract out all such paths that overlap in terms of being accessed most for R10 space syntax analysis as well as for 500 meters urban network analysis. The intersection of highest values of choice and betweenness serve the purpose. The two values are normalized and then interpolated together, maximum of which signifies the most risk prone roads in terms of impedance offered to evacuation by day traffic. Next, the impedance offered by built environment is governed by high density and high reach of buildings, with significantly lower integration values. An emergency response service has to operate at speeds largely deviated and significantly lower as compared with maximum possible speeds as there are turns for very short distances travelled. The three attributes that come into picture to identify such risk prone zones are density, reach, and integration values. The clusters of all such regions are extracted using bivariate LISA (local indicators of spatial autocorrelation) (Anselin, 1995 and Anselin et al., 2002). The weight matrix for cluster identification through LISA is prepared by k-‐nearest neighbor method and value of k is taken to be 10.

4. Literature

Space Syntax (Hillier and Hanson, 1984) In pace syntax analysis, each unit is called an axial line that can be defined as the longest line drawn through an arbitrary point in the spatial configuration. The close accessibility notion can be detailed out through a depth wise connection analysis using mean-‐depth analysis, which is the average number of units required to cross from one unit to the other.

𝐷 =∑𝑑. 𝑛𝑘 − 1

Where, D = mean depth d = depth n = number of unit spaces at a specific depth k = total unit spaces that comprise the system



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The measure of mean depth is a relative as to how a particular unit is located in the system; hence, the scale of symmetricity is defined with lowest and highest measures of mean depth which are 1 and k/2 respectively, where k is the total number of unit spaces of the system.

𝑅𝐴 =2 𝐷 − 1𝑘 − 2

Where, RA = Relative Asymmetry D = mean depth k = total unit spaces that comprise the system The relative asymmetries of two different compositions cannot be compared as they are made of unequal number of units. Therefore, real relative asymmetry of a unit space is the ratio between its relative asymmetry and a factor, commonly expressed as Dk factor that distinguishes the systems based on their sizes.

𝐷𝑘 =2 𝑘 𝑙𝑜𝑔2

𝑘 + 23 − 1 + 1

𝑘 − 1 𝑘 − 2

And,

𝑅𝑅𝐴 =𝑅𝐴𝐷𝑘

Where, RA = Relative Asymmetry RRA = Real Relative Asymmetry The integration of a unit space is reciprocal of RRA, and it describes how closely (or distantly) the unit is topologically accessible from all other units within a given system addressing its symmetricity and size. Choice is a dynamic measure of the flow through a space. A space has a strong choice value when many of the shortest paths, connecting all spaces to all spaces of a system, passes through it. Urban Network Analysis (Sevtsuk, 2010 and Sevtsuk and Mekonnen, 2012) Unlike space syntax theories, urban network analysis depends on an accurate consideration of distance and angularity between places. The reach measure calculates the number of buildings in surroundings of each building reaches within a given search radius on the network such that the reached buildings are at a shortest path distance of at most the given search radius. It is defined as follows:

𝑅𝑟 𝑖 = | 𝑗Є 𝐺 − 𝑖 :𝑑 𝑖, 𝑗 ≤ 𝑟 | Where,

Rr[i] = reach of a building ‘i’ within search radius ‘r’ d [i,j] = Shortest path distance between nodes ‘i’ and ‘j’ in graph G If the nodes are weighted, then reach is defined as follows:

𝑅𝑟 𝑖 = 𝑊 𝑗! Є !! ! ,! !,! !!



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Where,

Rr[i] = reach of a building ‘i’ within search radius ‘r’ W[j] = weight of node The betweenness of a building is defined as the fraction of shortest paths between pairs of other buildings in the network that pass by building (Freeman, 1977). The betweenness measure is defined as follows:

𝐵𝑟 𝑖 = 𝑛𝑗𝑘 𝑖 𝑛𝑗𝑘

𝑊 𝑗 !,! Є !! ! ,! !,! !!

Where,

Br[i] = betweenness of a building ‘i’ within search radius ‘r’ njk [i] = number of shortest paths from ‘j’ to ‘k’ that pass by ‘i’

njk = total number of shortest paths from ‘j’ to ‘k’

Bivariate Moran’s I and BiLISA (Anselin, 1995 and Anselin et al., 2002) The global bivariate Moran’s I statistic calculates spatial interdependency of two variables xk and xl (in

this study reach and integration at R10) in a same location. The equation for autocorrelation is given as follows:

𝐼𝑘 𝑙 =𝑧𝑘𝑤𝑧𝑙𝑛

Where, n = Number of observations w = Row-‐standardized spatial weight matrix 𝑧𝑘 = 𝑥𝑘 − 𝑥𝑘 /𝜎𝑘 𝑧𝑙 = 𝑥𝑙 − 𝑥𝑙 /𝜎𝑙 The weight matrix defines the neighbor set for each observation with non-‐zero elements for neighbor and zero for the others. The global Moran’s I fails to give any concrete information for the existence of clusters i.e., occurrence of localized groups showing similar characteristic properties. Local Indicators of Spatial Association (LISA) helps to identify the type of spatial correlation and provides a measure of association for each spatial unit. The bivariate LISA can be defined as follows:

𝐼!"! = 𝑧!!∑𝑤!"𝑧!!

Where, 𝐼!"! = Degree of linear association (positive or negative) between the values for variable xk at a given

location ‘i’ and the average of variable xl at neighboring location such as ‘j’s.

5. Results and Discussions

Typical Indian cities have arrangements that are highly unstructured, and one can find large diversity within short metric distances. Hyderabad being no exception, has certain areas where no grid like street structure is observed, few others where grids are very patchy and scattered. There are very few wards



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out of the existing 160 where entire ward is composed with grid like structure. Maximum number of wards fall in the category where partial grid – like structure is observed, grids are rather patchy. Majority of wards in old city do not have any grids at all. It is therefore very difficult to generalize outcomes with study of very few wards; each ward has to be studied independently to find risk prone areas. To display a glimpse of results, we have chosen 4 structurally different wards, such that all the three kinds of structural arrangements (no grid, partial grid, no grid) mentioned above are dealt with, one forming the urban core of the old city, with very low integration values for R10, two with patchy and scattered grid like street networks with relatively higher integration for R10 and last one being newest of all wards with entire network constructed in form of grids with very high integration values for R10.

Ward ID

Mean Area (in square meters)

Mean Integration R10 (Axial Lines)

Mean Integration R10 (Buildings)

Mean Choice R10

Mean Reach 500 m

Mean Betweenness 500m

43 98 1.102 1.28 6745 93184 2846866

67 58 1.051 1.17 13103 40898 2025220

82 134 1.210 1.30 5209 30223 829135

127 126 1.322 1.51 13117 20020 499776

Table 1: Mean of Area of Buildings, Integration R10 for axial lines, Choice R10, Reach 500m, Betweenness 500m for spatially joined layer

Table 1 gives the mean of calculated values for chosen wards. These values do not identify the vulnerable areas specifically but give an overall picture of the structure and arrangement of each ward. Mean area of buildings for ward 43 is 98 sq. m. and exceptional high mean reach and mean betweenness values suggest that buildings with large areas are closely packed and road segments in terms of shortest metric distances between any two buildings spaced at a maximum distance of 500 m pass through them. A similar pattern can be observed for poorly integrated ward 67 where mean area of buildings is 58 sq. m. which is significantly lower as compared with other wards but high mean reach and high mean betweenness values suggest ward buildings to be closely packed and roads with highest betweenness to be passing close to largest buildings of the ward. A grid like symmetry has large impact, this is evident from the fact that mean areas of buildings of ward 82 and 127 are 134 sq. m. and 126 sq. m. respectively but higher mean integration value and relatively lower mean reach and mean betweenness values suggest even distributions and significantly high accessibility which makes them less vulnerable.

Figure 1 shows the integration map of the four wards mentioned, ward 67 has average integration of 1.051 and is poorly accessible locally, wards 43 and 82 have relatively higher average integration values with 1.102 and 1.210 respectively, and ward 127 has very high average integration of 1.322. An interesting observation, when the axial line layer is spatially joined with buildings layer, the mean integration of wards increase, as that of ward 43 goes to 1.28, for ward 67 it goes to 1.17, for ward 82 it raises to 1.30, and for ward 127 it increases to 1.51. This observation conclusively shows that roads with higher integration also have larger number of buildings across them as compared to lower integration roads. We shall later see the impact of grids like network on how the traffic could be managed efficiently in the event of a disaster.

With a symmetric grid like structure, all axial lines have nearly same choice value for R10 as it is clearly observable in Figure 2, whereas in a non-‐grid environment, preference of few streets to be taken by vehicles or pedestrians is higher than other in terms of topologically shortest distance.

Figure 3 and 4 respectively display the reach and betweenness calculated for buildings for a radius of 500 meters. Reach is analogous to density map shown in Figure 5. However the differences arise due to the weighing of reach with respect to base areas of the buildings, hence relatively less dense areas having



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significantly larger buildings in close proximity would have proportionately larger reach values as seen in case of wards 67, 82 and 127 with patches of high reach. Figure 6 shows BiLISA cluster map that identifies buildings with lower integration and higher reach which are most risk prone, they are displayed with Low-‐High label. The other labels High-‐High (high integration and high choice), High-‐Low (high integration and low choice) and Low-‐Low (low integration and low choice) are in decreasing order of impedance offered to evacuation. Figure 7, 8, 9 and 10 are conditional maps (for wards 43, 67, 82 and 127 respectively) with integration values on X-‐axis and betweenness values on Y-‐axis with choice as the theme of the map. The labels represent quartile ranges based on 25th percentile, 50th percentile and 75th percentile. The upper outlier in top right map (enclosed in red cubical box), for each ward, gives the intersection of buildings occurring on roads with both high choice and high betweenness which is supposed to cause maximum hindrance to evacuation.



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In a grid like network, since all the segments have almost equal choice and betweenness values, therefore the identified roads that are assumed to carry highest load of traffic during the day time are parallel to each other, this denominates all such wards (or areas in wards) to be safe as there are equally comfortable alternate passages, with similar attractors, available for the egress, as in the case of ward 82 and 127 (Figure 9 and 10 respectively). Hence a grid like network is less vulnerable in terms of impedance offered by road traffic. In a distorted grid and non-‐grid networks, the impedance offered by buildings would be highest as they are always poorly connected and swift vehicular movement is always hampered, as in the case of 43 and 67 (Figure 6), as we can observe large and frequent clusters of high density, high reach and low integration.

6. Conclusions and Future Work

Through this comprehensive study, we identified all such areas in the city where a disastrous event can cause panic due to difficulties in swift evacuation process and impact of disaster can be higher. This study thus generates insights for interaction of neighborhoods in order to identify areas with high vulnerability.

We conclude that symmetry plays a major role in traffic behavior and interaction of neighborhoods, the parts of city which are oriented in a grid like structure have parallel and equal length (both in terms of choice R10 and betweenness 500 m) road segments for which, the results convey that impedance offered by traffic would be maximum, this fact suggests that such areas are safe enough as there are alternate paths available for rescue operation without a significant change in topological and metric distances. Also, grid like structures have high integration values which permits a rescue service like ambulance to operate at high speeds, whereas for non – grid like structures, low integration can cause slower access rates. The planned grid like wards also have very small clusters (in number as well as in size) of high density, high reach (500 m) of buildings and low integration (R10), this makes them safe in terms of impedance offered by built environment. However, both of the above discussed factors are exactly contrary in case of unplanned wards where grid – like structure is either minimal or absent totally. Deviating from highest preferred roads in terms of choice and betweenness significantly alters the routes such that both topological and metric distances increase and thus the evacuation time as well.



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Non grid – like wards also have large identified clusters with high density, high reach (500 m) of buildings and low integration (R10) simultaneously which makes them more vulnerable as compared to others.

It is also realized through this study that the results obtained by space syntax methodologies and urban network analysis have slight deviations from one another as one runs on principle of topological distances whereas other is governed by metric distances. Both are equally important they concurrently decide the route that a trip maker follows. Apart from this, buildings form an important part of network and one can’t depend completely on node – edge structure for traffic predictions. The study supports the claim that more voluminous and larger number of buildings appear on highly accessible roads.

We have efficiently been able to generate maps for individual municipal corporate wards such that one map displays all buildings across roads which are supposed to carry maximum traffic load during the daytime and other shows buildings present in dense construction zones with relatively poor integration. Both of these cause hindrance to easy evacuation process. However, we have identified differences in occurrence patterns for such regions in grid like set-‐ups as opposed to non – grid arrangements. Traffic densities are not constant throughout the daytime; hence a limitation of this study is that it doesn’t consider the dynamic nature of changing crowd on the roads. This can be done by weighing the results with respect to actual densities of traffic and identifying the peak times of traffic in individual areas.

Further works to this study may include identifying scales of symmetry between space syntax analysis and urban network analysis, as in this study, chosen topological distance and metric distance of 10 turns and 500 meters respectively distance helps in identifying segments which have both high reach and high betweenness values, however that might not the case always, changing scales (say 5 turns and 1 km) may actually have intersection set to be almost empty. In this, study 10 turns and 500 m fit perfectly but further study can be performed to identify scales of symmetries between the two theories.

Another future work to this study can be identification of safe spots in the proximity of all identified risk prone areas where emergency rescue systems could be installed or people could be migrated to those places if they happen to be large open spaces. As we have identified maximum obstruction paths, this study can also be extended to locating minimum hindrance paths which can be used for navigating the crowds from impact area to identified safe areas.

Our approach to identify risk prone areas can act as decision support system as where to deploy emergency response services, where to construct new roads, where to alter roads by widening them and areas where no more construction shall be possible.

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