THE OPTIMIZATION OF A STREET NETWORK MODEL FOR EMERGENCY RESPONSE ANALYSIS WITHIN A GEOGRAPHICAL INFORMATION SYSTEM. A Thesis.

Transcription

1 THE OPTIMIZATION OF A STREET NETWORK MODEL FOR EMERGENCY RESPONSE ANALYSIS WITHIN A GEOGRAPHICAL INFORMATION SYSTEM A Thesis Presented to the Faculty of San Diego State University In Partial Fulfillment of the Requirements for the Degree Master of Science in Geography by Jeremy Hamm Fall 2010

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3 iii Copyright 2010 by Jeremy Hamm All Rights Reserved

4 iv DEDICATION This thesis is dedicated to no one in particular.

5 v ABSTRACT OF THE THESIS The Optimization of a Street Network Model for Emergency Response Analysis within a Geographic Information System by Jeremy Hamm Master of Science in Geography San Diego State University, 2010 Emergency response is affected by both static and dynamic variables within a street network. Static variables are constant and do not change while dynamic variables change over time. When variables are input into emergency response, modeling the prediction of travel time will be more accurate which results in lives saved. This study focuses on modeling both static and dynamic variables within a street network model. This model incorporates the static variables of turn penalties, slope adjustment, and the dynamic variable of weather impact. The variables have been tested both individually and in conjunction with one another. The goal of this study is to develop a GIS model which predicts travel time for emergency vehicles with multiple variables included. The output travel times from this model were compared with actual travel time records in order to determine if the included variables have any impact on emergency response time. The model was developed using a combination of Python scripting and the Network Analyst extension in ArcGIS Desktop. Weather data in this study was downloaded from the National Weather Service. Other GIS data was provided by The Omega Group and a variety of other sources. Analysis of model results was accomplished with Correlation Analysis within Microsoft Excel. This statistical method determines how well the model output correlates with the actual travel times. The time recording methods showed no discernable impact on the model results. The static variables of turn penalties and slope adjustment showed only minor changes in accuracy, mostly below one percent. The dynamic weather variable improved the model accuracy significantly in all three study areas. The conclusion is that weather conditions effect emergency response time significantly. Turn penalties and slope adjustment do not have a large affect emergency response time.

6 vi TABLE OF CONTENTS PAGE ABSTRACT...v LIST OF TABLES... viii LIST OF FIGURES... ix ACKNOWLEDGEMENTS...x CHAPTER 1 INTRODUCTION Problem Statement Research Questions BACKGROUND AND LITERATURE REVIEW GIS-T Emergency Management with GIS Network Modeling within a GIS METHODOLOGY Study Areas GIS Tools and Statistical Packages Data Model Design Turn Penalties Slope Adjustment Weather Conditions Mean Sum of Squared Residuals MODEL CREATION Base Map Data Variable Data Preparing Base Map Data Preparing Variable Data Turn Penalties...34

7 vii Slope Adjustment Weather Calculation Testing the Model MODEL EXECUTION AND RESULTS Importing the Results Wilson, North Carolina Results Wilson Turn Penalty Results Wilson Slope Adjustment Results Wilson Weather Impact Results Wilson Compound Variable Results Aurora, Colorado Results Aurora Turn Penalty Results Aurora Slope Adjustment Results Aurora Weather Impact Results Aurora Compound Variable Results Elgin, Illinois Results Elgin Turn Penalty Results Elgin Slope Adjustment Results Elgin Weather Impact Results Elgin Compound Variable Results Travel Time Prediction Model Accuracy Travel Time Recording Methods Wilson, North Carolina Time Recording Methods Aurora, Colorado Time Recording Methods Elgin, Illinois Time Recording Methods SUMMARY...53 REFERENCES...56 APPENDIX A PYTHON CODE FOR WEATHER EXTRACTION...58 B MODELBUILDER MODEL FOR SLOPE ADJUSTMENT...61

8 viii LIST OF TABLES PAGE Table 3.1. GIS Tools Table...17 Table 3.2. Data Layers...19 Table 4.1. Turn Penalties...31

9 ix LIST OF FIGURES PAGE Figure 2.1. Tele atlas data collection. Source: Retrieved April 6, 2010 from: ( ge.jpg)....5 Figure 2.2. Developmental travel time forecasting model. Source: You, & Kim. (2000). Development and evaluation of a hybrid travel time forecasting model. Transportation Research Part C 8, Figure 2.3. Three types of GIS transportation models. Source: You, & Kim. (2000). Development and evaluation of a hybrid travel time forecasting model. Transportation Research Part C 8, Figure 2.4. Tualatin Valley Fire and Rescue response map. Source: Amdahl, G. (2001a). Tualatin valley fire and rescue. Disaster response: GIS for public safety, Figure 2.5. Dynamic segmentation. Source: Goodchild, M. F. (1998). Geographic information systems and disaggregate transportation modeling. Geographical Systems, 5(1-2), Figure 3.1. Methodological flowchart Figure 3.2. Wilson, North Carolina. Source: The Omega Group Figure 3.3. Aurora, Colorado. Source: The Omega Group Figure 3.4. Elgin, Illinois. Source: The Omega Group Figure 3.5. Base streets layer. Source: Elgin Fire Department, Illinois Figure 3.6. Incident table. Source: Aurora Fire Department, Colorado Figure 3.7. Street network model diagram Figure 3.8. Closest facility tool in network analyst. Source: Aurora Fire Department, Colorado Figure 3.9. Turn penalty script Figure Slope time multiplier table (Price, 2008) Figure Slope calculation model Figure Z value extraction. Source: Price, M. (2008). Slopes, Sharp Turns and Speed. ArcUser, Figure Incidents layer with precipitation data. Source: The Omega Group....27

10 Figure Incidents layer with precipitation data excluded. Source: The Omega Group Figure 4.1. Street centerline, fire stations and incidents layer Figure 4.2. Digital elevation model of Elgin, Illinois Figure 4.3. Elgin, Illinois ArcMap document Figure 4.4. Geodatabase Data Hierarchy Figure 4.5. Evaluators dialogue box Figure 4.6. Visual Basic script dialogue Box Figure 4.7. Slope calculation code Figure 4.8. Network analyst toolbar Figure 4.9. Complete GIS model for predicting travel time with the included variables Figure 5.1. Travel time calculation Figure 5.2. The routes layer Figure 5.3. Wilson, North Carolina results graph Figure 5.4. Aurora, Colorado results graph Figure 5.5. Elgin, Illinois results graph Figure 5.6. Wilson, North Carolina model preformance scatter plot Figure 5.7. Wilson, North Carolina model preformance histogram Figure 5.8. Aurora, Colorado model preformance scatter plot Figure 5.9. Aurora, Colorado model preformance histrogram Figure Elgin, Illinois model preformance scatter plot Figure Elgin, Illinois model preformance histogram Figure B.1. Modelbuilder model for slope...62 x

11 xi ACKNOWLEDGEMENTS I wish to first and foremost thank The Omega Group for their time and effort in aiding this research. Without their support this thesis would not have been possible. I would also like to thank the respective fire Chief s from the City of Aurora, Colorado, the City of Elgin, Illinois and the City of Wilson, North Carolina. I wish to also thank Dr. Ming Tsou for his support throughout this research process.

12 1 CHAPTER 1 INTRODUCTION Efficiency within emergency management is crucial for saving lives and property. Efficiency of emergency management covers planning the location of a new fire station, to routing fire engines to an incident. If emergency responders do not have the right technology, lives and property could be lost and resources will be wasted. Emergency management is defined as the discipline and profession of applying science, technology, planning, and management to deal with extreme events that can injure or kill large numbers of people, do extensive damage to property, and disrupt community life (Cova, 1999). Within this study the extreme events are defined as any event the fire department responds to. This includes any call for service regardless of the cause. Fire department response time is determined by factors such as the time it takes to make turns along a street network, topography of the street network, and weather conditions within the street network. It is important to understand and predict the effects of multiple variables within a street network in order to achieve maximum model accuracy. A Geographical Information System (GIS) can be used to manage and route emergency vehicles. A GIS can also be used for predicting response times based on the above network variables. This can be done using specific software which integrates transportation variables with a street network. The process of analyzing transportation using a GIS is known as Geographical Information Systems for Transportation or GIS-T. This technology is essential for analyzing spatial data within the context of transportation problems (Waters, 1999). With this technology it is possible to create a street network with maximum accuracy. This study utilized the Closest Facility function of the Network Analyst extension in ArcGIS to conduct the analysis. This thesis selected three cities to use as case studies. These cities are Wilson, North Carolina, Aurora, Colorado, and Elgin, Illinois. The street network GIS model has been created using ArcGIS. The model consists of multiple scripts and sections integrated to predict travel time with the selected variables.

13 2 1.1 PROBLEM STATEMENT Traditional GIS-T and street network models only consider travel speeds when conducting network analysis. This study has combined two static variables of turn penalties and slope, and one dynamic variable of weather conditions in the created model. Modeling each variable within a street network for emergency response is important for achieving maximum accuracy. The objective of this study is to discover how variables within a street network affect the accuracy of emergency response models. 1.2 RESEARCH QUESTIONS Specific questions to be addressed in this research are listed below: Question 1: How does variability of recording emergency response time methods impact street network model results? The travel time between fire stations and incidents is measured differently in each region. Some stations have automated systems to record response times, and others record them manually. The variation in recording methods could change the model result and impact the accuracy of the model. The fire chief in each study area have been contacted about the recording methods used for their particular area. Based on the response and analysis we can analyze which region has the most accurate records and also the best recording method. Question 2: How can model accuracy be improved with the addition of dynamic weather variables? The weather is a variable which is in constant change. This change has an impact on the speed of emergency responders. The model accuracy should improve when the weather days are extracted from the model. A weather day is any day which receives precipitation. Question 3: What is the relationship between dynamic and static variables within a street network model? In order to predict response times accurately, both static and dynamic variables must be included. Each type of variable affects the street network model differently. This study has discovered how the chosen static and dynamic variables impact emergency response. This study has also discovered how the static and dynamic variables affect each other when modeling emergency response.

14 3 This study has created a street network model in which both static and dynamic variables are input. The goal of this study was to discover the impact both static and dynamic variables had on the emergency response times. This includes determining if the recording methods for travel times have any impact on travel time accuracy. The street network model is created using ArcGIS and consists of multiple scripts and sections. The model with be tested in three different study areas using the same variables.

15 4 CHAPTER 2 BACKGROUND AND LITERATURE REVIEW 2.1 GIS-T Transportation problems can be modeled using a Geographic Information System (GIS). Traffic and network analysis becomes easier and more cost effective with the use of GIS. This branch of GIS has evolved to direct, plan and assess transportation problems. Transportation problems can include vehicle routing, service area for vehicle deployment or the cost for taking a particular route on a network. It is known as GIS for transportation or GIS-T (Goodchild, 1998). GIS-T involves both geographic regions and transportation systems (Miller & Shaw, 2001). GIS-T is ideal for transportation planning and analysis because all transportation issues involve spatial information. GIS-T includes transportation planning for emergency vehicles as well (Lang, 1999). The use of GIS in transportation dates back to the early 1960s. Duane Marble of Northwestern University was one of the first to develop an elementary form of GIS-T (Goodchild, 2000). GIS-T was developed from advances in database techniques similar to other GIS functions. Advances in programming techniques lead to the development of new algorithms for shortest path analysis and routing problems within a GIS. New software packages began to emerge. These new software packages used the new algorithms to compute shortest path analysis, resource allocation and many other analysis techniques along a network (Walters, 1999). TransCAD is an early version of GIS-T software packages. This product, created by the Caliper Corporation, integrates GIS with a wide selection of transportation planning tools. With this software base street network maps were created. Simple analysis was then done with the included tools (Lieberman, 1991). A second example of an early GIS-T software package was ARC/INFO. ARC/INFO, created by Environmental Systems Research Institute, was a generalized system for processing geographic information. It is based on a relatively simple model of geographic space - the coverage - and contains an extensive set of geoprocessing tools which operate on coverages.

16 5 (Morehouse, 1989, p. 266). Both software packages along with the development of new algorithms provided the foundation for GIS-T. GIS-T uses a spatial database to store information on the attributes of a transportation network. These attributes can include physical features, planning related attributes and geometric characteristics (Walters, 1999). Other spatial attributes in a GIS-T include locations for analysis usually stored as coordinates in longitude and latitude (Lang, 1999). Sources for spatial information in the network structure come from a variety of sources. For GIS-T in North America street feature classes may be obtained from census files such as TIGER (American) provided by the US Bureau of the Census. Street feature classes may also be purchased from third party vendors such as Tele Atlas or MapInfo (Walters, 1999). Tele Atlas is one of the original creators of digital maps (see Figure 2.1). Figure 2.1. Tele atlas data collection. Source: Retrieved April 6, 2010 from: ( tlasnorthamericaproductimage.jpg). When adopting GIS-T in a street network, complex spatial analysis can be accomplished by editing link attributes and performing network analysis functions. For example, the length of a segment in the network can be divided by the speed limit of the segment to provide the travel time for each individual segment. If the travel time is known for each street segment, complex analysis can be completed. Station coverage areas can be determined based on street segment speed limit and length. The analysis will produce

17 6 polygons based on the predicted amount of time it will take to travel within each polygon (Walters, 1999). An example of a GIS used in a traffic study is found in the journal article Development and Evaluation of a Hybrid Travel Time Forecasting Model by Jinsoo You and John Kim (see Figure 2.2). The goal of this study was to develop and test a travel time forecasting model with a GIS. This study determined three roles which GIS will play in the creation of the model. The first is data management. The travel time forecasting model requires different types of data. Examples of this data are; traffic speed, traffic volume, occupancy rate and number of lanes in the street network. The GIS used in this study must be able to manage the data and make it useful. The second function GIS must provide is technology management. Within the hybrid travel time forecasting model the GIS must be able to integrate data such as GPS communication and raw traffic data input from loop sensors. The final function the GIS must provide is information management. Information management within the GIS is needed to validate the results from the model. Information management is also the goal for displaying the results on the internet and other mediums (You & Kim, 2000). Figure 2.2. Developmental travel time forecasting model. Source: You, & Kim. (2000). Development and evaluation of a hybrid travel time forecasting model. Transportation Research Part C 8, The travel time forecasting model usually has three different types of GIS integration. The first is the forecasting model including GIS. The second is the forecasting model connected to GIS. The final is the forecasting model within a GIS (see Figure 2.3). The first

18 7 Figure 2.3. Three types of GIS transportation models. Source: You, & Kim. (2000). Development and evaluation of a hybrid travel time forecasting model. Transportation Research Part C 8, type of model, which includes GIS functions, is difficult to build and program. Many vendors and software providers offer object oriented GIS function libraries. These include MapObjects, NetEngine, MGE, GISDK, MapX and MapBasic. With these functions users can perform spatial and attribute based queries and also communicate with external programming environments such as Visual Basic and Visual C++. The second type of travel forecasting model software uses customized software interfaces to interact with GIS data such as an ESRI shapefile. Two applications which fall into this category are TP+ and Tranplan DBC, both of which are able to connect with ESRI shapefile format. An example of a category two application can be found in Mesa County Colorado where they have developed a transportation model with MINUTP and Arc/View. The final category or type 3 applications are transportation models built inside a GIS. This is done by using built-in macro languages most commonly Visual Basic for Applications. It is generally accepted that these applications are less accurate than stand alone applications similar to category one applications (You, & Kim, 2000).

19 8 The model in You and Kim s study is comprised of 5 modules; graphical user interface (GUI), real-time traffic data collection, database, forecasting and machine learning modules. The GUI accepts the user input and any other parameter the model requires (see Figure 2.3). Real-time traffic data collection comes from looping sensors or probe vehicles, this data is sent back to the database. The database accepts input from the sensors and catalogues the data into various tables. The forecasting and machine learning modules increase the accuracy if the model. This is done by generating training samples and learning parameters afterward with the generated training samples (You & Kim, 2000). When the model has been implemented the performance can be evaluated. There are three different statistical methods. The first is called Root Mean Square Error. This method is used to describe the difference between the observed and forecast values for each forecasting output. The second type of statistical method used to analyze the data is called Mean Absolute Percent Error. This method is used to understand the overall performance of the model. The final method used to analyze the output is called correlation analysis. Correlation coefficients are calculated to explain the relationship between observed and forecast values. The closer to one the correlation coefficient is the better the relationship between the two values (You, & Kim, 2000). This study has shown how the application of GIS to transportation can predict travel time on a street network. This is done by creating several GIS models and populating them with traffic data. 2.2 EMERGENCY MANAGEMENT WITH GIS The goal of emergency management is saving lives and property. This goal can be accomplished in a variety of ways including routing emergency vehicles, coordinating personnel response, and analyzing and reducing response times. Hoetmer defines emergency management as the discipline and profession of applying science, technology, planning, and management to deal with extreme events that can injure or kill large numbers of people, do extensive damage to property, and disrupt community life (Drabek & Hoetmer, 1991). Emergency situations require dealing with spatial data. For example if a fire department is deciding on which route to take to an incident, the spatial layout of the street network is how the route will be decided upon. A GIS, which supports geographical inquiry and, ultimately,

20 9 spatial decision making, is critical for solving these types of problems. Many emergency managers are now including GIS as an integral part of emergency management (Cova, 1999). Emergency responders can now use a GIS to accomplish these tasks. An example of this is found in Beaverton, Oregon. At this district GIS has been used in many ways and seen improvements such as response time reduction and cost reduction. One way GIS has been utilized at this location is by assisting the department in maintaining state mandated standards for the surrounding communities. As the community grows and new buildings are constructed the GIS acts as a storehouse for this information. The GIS which Tualatin Valley Fire and Rescue uses can map and visualize new development making sure it meets standards. This includes tall buildings, buildings which are particularly prone to fire and buildings which house hazardous material (Amdahl, 2001a). A second use Tualatin Valley Fire and Rescue have for a GIS is to allocate resources effectively (see Figure 2.4). Fire stations are required, by law, to have a ladder truck located two and a half miles from all buildings two stories or greater in height. With the use of their GIS Tualatin Valley Fire and Rescue was able to determine which buildings fell under this category. Another way in which Tualatin Valley Fire and Rescue has used a GIS to manage resources is through the planning of new stations to accommodate urban growth. In the late 20 th century new development rendered several fire stations useless due to their limited area coverage. With the use of ArcView GIS and Network Analyst new station coverage areas were developed to encompass the surrounding urban areas. Tualatin Valley Fire and Rescue has the used a GIS to determine the placement of its Jaws of Life equipment. This is done by geocoding or plotting each incident, which the fire department responded to, on a map. After the incidents are displayed hot sports, or areas of high activity, become evident. With this information the fire department can store or deploy resources closer to the areas of high activity. Tualatin Valley Fire and Rescue, after receiving this information, decided to store its Jaws of Life equipment near entrances to several freeways. These entrances had higher occurrences of high speed accidents (Amdahl, 2001a). 2.3 NETWORK MODELING WITHIN A GIS Linear modeling is the way in which street networks are represented and modeled in a GIS (see Figure 2.5). A network is comprised of two main components, edges and junctions.

21 10 Figure 2.4. Tualatin Valley Fire and Rescue response map. Source: Amdahl, G. (2001a). Tualatin valley fire and rescue. Disaster response: GIS for public safety, Figure 2.5. Dynamic segmentation. Source: Goodchild, M. F. (1998). Geographic information systems and disaggregate transportation modeling. Geographical Systems, 5(1-2),

22 11 Examples of edges are streets, transmission lines and pipes. Examples of junctions include street intersections, fuses, and service taps. In a network edges connect at junctions and entire networks are built. This is the way in which real world streets are modeled within a GIS (Goodchild 1998; Zeiler, 1999). These entrances had higher occurrences of high speed accidents (Amdahl, 2001b). Dynamic segmentation is the process of adding a structure representing two types of discrete entities, zero dimensional and one dimensional. These entities are known as network points and network segments. This data, stored in table form, is then displayed in graphical form on a map. The process of dynamic segmentation requires two things from the data. Each event in an event table must include a location along the linear feature. Each linear feature must have a unique identifier and measurement system (Goodchild, 1998). Dynamic segmentation works by displaying each feature in a table on a map. This can be useful for many applications including this study. This is commonly used in GIS-T applications with the segments representing transportation routes. The way in which dynamic segmentation can be applied to GIS is through the use of polylines. Each polyline represents a street segment in the real world. Each street segment has different attributes which can change based on the goal of the modeling. Within this study, depending on the variable being modeled, each street segment will be given a different value in order to predict the travel time between two points. GIS street networks are not homogeneous features. Many variations and anomalies exist between street segments within a single street network. Speed limits change form one street segment to the next, construction impacts travel speed and number of traffic lanes causes bottlenecks for vehicle travel. GIS network modeling has advanced to account for many of these variables in recent years. This is done by using a technique called linear referencing. Linear referencing views street network attributes as linear events occurring within the street network. The variation of each attribute is linked to specific location along the linear street network. Changes are measured from one linear feature, or street segment, along the entire street network (Thill, 2000). This process will be used throughout this study as street segment attributes change from one segment to the next.

23 12 CHAPTER 3 METHODOLOGY The objective of this study is to determine the impact variables have within a street network model for emergency response. Impact is defined as either making the model more or less accurate. The goal is to make a street network model more accurate in predicting response times to incidents. This is done by modifying the time it takes for a vehicle to traverse a segment, adding time for each turn a vehicle takes or determining the impact precipitation has on the model. The flow chart below depicts the steps necessary to test the variables in a street network model in order to predict actual response times (see Figure 3.1). Figure 3.1. Methodological flowchart.

24 13 A model which predicts response times will be created using the closest facility algorithm in the Network Analyst extension of ArcGIS The model uses an impedance value to calculate the time it takes to travel from one location to another. The base impedance value takes the length of a street segment, in feet, and divides it by the speed limit, which is converted to feet per minute. The impedance value will change based on the variable which is being modeled. For example if it is determined that on slopes of six percent an extra 10 seconds is added the impedance value will reflect this reduction. The goal is to determine the impact each variable will have. When this is complete the model can be compensated based on the impact of each variable. Actual incident response times, collected by each fire station annually, will be used to compare against model times. The response time accuracy varies based upon the method used for collection. Each station has a different method for determining travel time. Methods vary depending on a variety of factors and will be discussed in a future section along with accuracy. The model has been run for fire stations within three cities described in section 3.2. It has been verified against actual response times using mean sum of squared residuals. This type of regression compares multiple explanatory variables against a single dependent variable (Wooldridge, 2008). The necessary steps to complete this study are as follows (1) Select appropriate data variables which, when put in a street network model, have an impact on emergency response times, (2) Prepare the data in order to complete the analysis, (3) Model the data in each study area, (4) Check the results of the model against the actual travel times using correlation analysis, (5) Validate results, (6) Remodel with new variable. 3.1 STUDY AREAS There are three study areas in this study. Each has been chosen based on access to data, and geographic location. The geographic location is important because it allows for certain variables such as percentage slope and weather delays to be modeled. The first area chosen in our study is Wilson, North Carolina (see Figure 3.2). This city is located in the eastern central part of the state. It is 100 feet above mean sea level. The average annual precipitation is 47.1 inches (State Climate Office of North Carolina, 2009). The total population in Wilson is 48,000. This city has five fire stations which I will conduct the

25 14 Figure 3.2. Wilson, North Carolina. analysis with. They are displayed as fire engines in the map below. The reason this city was chosen is because of the data availability and climate. (City-Data.com, 2009a). The second location chose for this study is Aurora, Colorado (see Figure 3.3). The reason this city was chosen is because it has moderate elevation change throughout the city allowing for slope calculation. This city also receives a diverse variety of weather conditions. Aurora, Colorado is located in the northeast portion of the state. The elevation is 5,471 feet above mean sea level. The average annual precipitation Aurora, Colorado receives is 15.6 inches (National Weather Service, 2009). The population in Aurora is 319,000. There are 10 fire stations which will be used in the analysis. The stations designated in red are double fire stations. The stations designated in gray are single fire stations. The designation of single or double, for the fire stations, does not have an impact on this research study. The reason this city was chosen was because of data availability, diverse climate and varied topography (City-Data.com, 2009b).

26 15 Figure 3.3. Aurora, Colorado. The final location chosen in this study is Elgin, Illinois (see Figure 3.4). This location receives diverse weather similar to Aurora and is located near the city of Chicago. Elgin is located in the northeast corner of the state. The elevation is 745 feet above mean sea level. The average precipitation is inches per year. The population in Elgin is 106,000. The reason this city was chosen to be part of this study is because of data availability and the most diverse climate of the three cities chosen (City-Data.com, 2009c). 3.2 GIS TOOLS AND STATISTICAL PACKAGES The GIS tools which will be used in this study are ArcGIS Desktop and the Network Analyst Extension within this software. ArcGIS Desktop, formally known as ArcView, was announced on April 24, This release marked the first time a single system or software package could be used for geographic data creation. In previous versions of ArcView 3.x this was not possible. The core component of ArcGIS 8.x Desktop was ArcView. This software package included ArcMap, ArcCatalog and ArcToolbox. ArcMap is used for creating and editing maps and data within the mapping environment. ArcCatalog

27 16 Figure 3.4. Elgin, Illinois. is used for organizing, managing and storing geographic and tubular data. ArcToolbox contains tools used for manipulation and examination of geographic data. The release of ArcGIS 9.0 included the release of the Network Analyst extension. This extension is used for a variety of tasks including defining a service area, closest facility analysis and origin destination analysis (Environmental Systems Research Institute, 2009). Table 3.1 is a list of all the GIS tools used in this study. 3.3 DATA The model was created from and tested against pre existing data and data which will be gathered from a variety of sources. The existing data includes the geography of each study area. The geography consists of the streets layer, the fire stations layer, incidents layer and elevation layer. The streets layer contains information about the street network including addresses, street names, speed limits and segment lengths. Figure 3.5 is an example of this feature layer showing various fields within this layer including the speed limit (SPEED), segment length (Shape_Length) and time estimated to traverse each segment (Minutes). The Minutes field in the table below is the estimated time without any variable input in the model.

28 17 Table 3.1. GIS Tools Table Tools Function Vendor ArcMap Display Data ESRI ArcCatalogue Organize Data ESRI ArcToolbox Contains tools used in analysis ESRI Network Analyst Extension Contains the closest facility tool ESRI ModelBuilder Used to create the model for slope adjustment ESRI Python Create the model which extrapolates weather events Open Source Figure 3.5. Base streets layer. Source: Elgin Fire Department, Illinois.

29 18 The fire stations layer includes the names, numbers and locations of each fire station within a city. The incidents layer, which is the layer in which the model will be compared to, contains all information about particular incidents within a given year. The most important field in this layer is the travel time field. This field includes the actual time it took for the vehicle to travel from the station to each incident. Figure 3.6 is an example of the incident table. It shows the travel time which is displayed in minutes. Figure 3.6. Incident table. Source: Aurora Fire Department, Colorado. The final data layer which will be used is the elevation layer. This layer will be in the form of a digital elevation model. For this layer raster data is necessary because the model which calculates the slope of the street network requires it. This data will be gathered from various sources including the fire stations and the United States Geological Survey. Below is a table which includes all of the data layers for this particular study. Table 3.2 is a list of all the data layers used this study. For access to the data in this study approval must be granted by the fire chief in each fire district. A request for access to the data was given and the approval to use data within this study was given by the Fire Chief for each fire district. The request was done via and the approval was given via .

30 19 Table 3.2. Data Layers Name Type Size Format Provider Wilson Base Streets Layer Polyline Feature Class 10.1 MB Personal Geodatabase Wilson Fire Department, The Omega Group Aurora Base Streets Layer Polyline Feature Class 26.3 MB Personal Geodatabase Aurora Fire Department, The Omega Group Elgin Base Streets Layer Polyline Feature Class 21.7 MB Personal Geodatabase Elgin Fire Department, The Omega Group Wilson Network Network Dataset 3 MB Personal N/A Dataset Layer Geodatabase Aurora Network Network Dataset 4 MB Personal N/A Dataset Layer Geodatabase Elgin Network Dataset Network Dataset 2 MB Personal N/A Layer Geodatabase Wilson Incidents Layer Point Feature Class Personal Geodatabase Wilson Fire Department, The Omega Group Aurora Apparatus Layer Point Feature Class Personal Geodatabase Aurora Fire Department, The Omega Group Elgin Incidents Layer Point Feature Class Personal Geodatabase Wilson Fire Department, The Omega Group Wilson Elevation Digital Elevation Raster Image United States Geological Survey Layer Model Aurora Elevation Digital Elevation Raster Image United States Geological Survey Layer Model Elgin Elevation Layer Digital Elevation Model Raster Image United States Geological Survey 3.4 MODEL DESIGN As I have stated above the model will be created in the Network Analyst extension of ArcGIS This model will add variables in the hope of influencing the outcome. Figure 3.7 is a diagram of the model which will be used to predict the travel time from the station to the incident.

31 20 Figure 3.7. Street network model diagram. The first layer in the model depicted above is the base streets feature layer. This is the foundation for analysis within a street network and is described in detail above. This layer is also where the slope and weather adjustment will be made. The next feature layer is the Network Dataset. This layer is the base streets layer transformed into a Network. This is done so analysis can be conducted in Network Analyst. The network dataset is created to complete network analysis in network analyst. ESRI has developed the network dataset to represent detailed network models such as transportation networks. The network dataset is comprised of simple features such as points and lines. It can also be created in either a shapefile or geodatabase environment. Within this study we will be using the geodatabase environment (Environmental Systems Research Institute, 2009). The third layer in the model is the closest facility tool in Network Analyst. This tool is used to find the closest facility from a list of points to another list of points. For example there could be a list of fire stations and a separate list of incidents. The closest facility could be computed from the stations to the incidents. In this study the fire stations are the origins and the incidents are the destinations. The closest facility layer contains four components, the facilities layer, the incidents layer, the routes layer and the barriers layer (see Figure 3.8). The facilities layer, in this study, includes all the locations which are used as starting points for travel. The facilities in this study are all the fire stations in each city. The routes layer is only populated after the closest facility problem is solved. The routes layer consists of each

32 21 Figure 3.8. Closest facility tool in network analyst. Source: Aurora Fire Department, Colorado. route to each incident. This layer is populated with the time the model predicted to travel from the station to the incident. The next layer is the barrier layer. This layer is used to include barriers in the street network within the analysis. For example is a street is blocked for construction a barrier would be placed in this location. When the analysis is complete the route would be directed around the barrier. The incidents layer includes all incidents the fire department responded to. This is the most important layer in the closest facility tool. Adjusting this layer is critical to achieving accurate model results. When the closest facility tool is populated with incidents to find routes to it is important these incidents are queried according to the fire station which responded to them. This is done by selecting only the records which apply to a given response scenario. The first field which must be queried is the travel time field. This field described in minutes the time it took from the vehicle to travel from the station to the incident. This field has values ranging from zero (time not recorded) to hundreds of minutes (bad records). The time which The Omega Group has decided is valid, is any record over 30 seconds and under 10 minutes. This eliminates all records which were not recorded properly.

33 22 The next field which must be queried is the incident type. This eliminates all incidents which the fire engine did not respond to. (Environmental Systems Research Institute, 2009). When the closest facility layer is populated with the required information the analysis is ready to begin. The route is solved in Network Analyst and the output is polyline file which includes the predicted travel time from station to incident. The output is the final element of the model and is used when comparing the actual travel time to the predicted travel time Turn Penalties Travel time within a street network is difficult to predict. It is affected by variables which are impossible to predict and also variables which are possible to predict. This study will be using variables which are either constant or have significant data which is able to be modeled. The foundation for tuning a street network is creating penalties for making turns. When the most accurate set of turn penalties is found other variables can be added to the model. An example of a turn penalty is the addition of a certain number of seconds for every left turn the vehicle makes in the route from the fire station to the incident. Turn penalties can be changed depending on the type or size of the vehicle turning. The turn penalties being used in this study are derived from the article titled Slopes, Sharp Turns and Speed by Mike Price. In this article turn penalties are introduced in a street network. These penalties range from slight to very high. The values for each range in penalties have been tested with actual vehicles. Turn penalties are broken into 4 categories each representing a turn direction. Each turn direction is represented by a range of degrees in a circle. For a right turn the angle must be greater than 30 degrees and less than 150 degrees. For a left turn the angle must be greater than 210 degrees and less than 330 degrees. For a U turn the angle must be greater than 150 degrees and less than 210 degrees. It is also possible to add a delay, in each intersection, if the vehicle is not making a turn. This is known as a straight through penalty. Straight through penalties have an angle greater than 330 degrees and less than 30 degrees. Turn penalties are included in the model when the network dataset is created. When the network attributes are defined the impedance field is defined. This is the field in which the model calculation is based. When this field is created it is possible to define turn penalties.

34 23 Turn penalties can be defined either manually or with a script. The Visual Basic script in Figure 3.9 is an example of setting turn penalties. Figure 3.9. Turn penalty script. The top line of the script defines TurnTime as the extra time, in minutes, to be added on the turn. The second line of the script defines a as the angle of the turn being executed. In the example of a right turn the script is telling the model that for every right turn the time is increased by 5/60th of a minute. A second way for turn penalties to be created is manually. This is done during the same time period as with a script Slope Adjustment The slope of a street segment impacts the time it takes a vehicle to travel along the street segment. For locations with diverse topography travel time can be severely impacted because of the increased time it takes a vehicle to climb a steep slope. If the model which predicts emergency response is going to be accurate the increased travel time due to street slope must be accounted for. According to the Encyclopedia Britannica, slope is the Numerical measure of a line s inclination relative to the horizontal (slope, 2009). In the context of a street segment this means the change in elevation above sea level from the start to the end point. The slope value is represented as a percent. In the article titled Slopes, Sharp Turns and Speed (Price, 2008) the time addition for the slope percentage is given in Figure 3.10.

35 24 Figure Slope time multiplier table (Price, 2008). This data has been tested with a variety of vehicles for accuracy. In this table the base time is considered to have a zero percent slope. This means the elevation from the start of the street segment is the same of the elevation at the end of the street segment. As the slope of the street segment increases the time it takes to cross the segment is increased. If the street slope is five percent the time is increased by a quarter. Calculating the slope percentage of a street segment is a simple operation requiring only two data layers, the elevation layer and the streets layer. As stated above the elevation layer is a raster layer which is available free of charge from the USGS. The streets layer is provided by the respective fire department and The Omega Group. A model (see Figure 3.11) has been created which calculates the time increase, due to streets slope, for the entire street network. The first process in this model involves interpolating the street feature class using the interpolate shape tool. This process gives the street segment a three dimensional aspect or Z value. The Z value represents the elevation field in the feature class. The Z value is extracted from the elevation raster input in the model. The next step in calculating the slope is to add the coordinate fields required in the slope calculation. The fields required are the start and end fields for the X, Y and Z coordinates. This is done with the add field tool in the

36 25 Figure Slope calculation model. model. The next step involves calculating the geometry for each of these fields. For the X and Y coordinate this can be done either manually or programmatically. In this above model a Visual Basic script calculates both fields. The Z fields must be calculated programmatically. This can be done a variety of ways depending on the source of the elevation data. This study will be using the code from the article Using 9.3 Functionality and Scripts by Mike Price. Thiscode extracts the z values from an interpolated shape (streets feature class) using Visual Basic for Applications (see Figure 3.12). Figure Z value extraction. Source: Price, M. (2008). Slopes, Sharp Turns and Speed. ArcUser, When the above code is run, the extracted values are input into the start and end Z fields in the streets feature class. The final step in calculating the slope adjustment involves the addition and calculation of the slope specific fields in the feature class. The first field is the slope field. This field is calculated using a combination of the Pythagorean Theorem and rise over run slope formula. The value of the slope field is both negative (downhill) and

37 26 positive (uphill). These values must be converted to positive numbers. This is done using the abs function in Visual Basic. When the values have been converted to positive numbers they are stored in a new field. The next calculation is the slope adjustment calculation. This takes into account the increase time it takes to travel up steep slopes. This value is stored in a new field. The final calculation involves taking the field of values accounted for increased slope and multiplying them by the base time field. The base time field is the speed limit for each street segment multiplied by the length of each street segment. The final output is a field of travel time values, in minutes, for each street segment. The values in this field have been edited to account for slope percentage Weather Conditions Weather conditions cause significant delays in emergency response. If incidents, which occurred on days with inclement weather, are extracted from the model accuracy should improve. Weather days can be defined many ways, in the context of this study they are defined as days in which travel has been impacted. The data for this variable has been acquired from the national weather service for each region. A table will be created for each day of the year in which the incidents occurred. The date range will vary depending the when the incidents occurred. Each day which has received measureable precipitation is added to the table. According to Jim Purpura, Meteorologist in charge for the San Diego office of the National Weather Service, measureable precipitation is defined as any precipitation over 1/100 of an inch (Jim Purpura, Personal Communication, November 4, 2009). The days which receive measureable precipitation are separated into three categories, light, moderate and heavy. Light precipitation is defined as precipitation amount between.01 and.039 of an inch. Moderate precipitation is defined as precipitation between.039 and.16 of an inch. Heavy precipitation is defined as precipitation over.16 of an inch. Days which received trace amounts of precipitation are not added to the table. A new field is added which converts the precipitation amount being measured, either light moderate or heavy, to one and any day with no precipitation to zero. When the weather table has been populated a new field is added which is identical to a field in the incidents feature class. For this study the field is the Alarm_Date field. This allows for a join operation to be performed in ArcMap. The

38 27 weather table is joined to the incidents layer in arc map using a script. The script also filters any rows with the value one. This represents days which received measureable precipitation. When the weather days have been extracted the analysis is ready to begin. Figure 3.13 is an example of a table in which the amount of precipitation for each day is added. Figure Incidents layer with precipitation data. Source: The Omega Group. The second example is of a table when the days which received precipitation are removed from the model (see Figure 3.14). These days are designated with a zero in each field. The zero stands for the amount of precipitation which fell at the given location. If precipitation had fallen there would be a one. Figure Incidents layer with precipitation data excluded. Source: The Omega Group.

39 MEAN SUM OF SQUARED RESIDUALS The model output and the actual travel time will be evaluated using the mean sum of squared residuals. This statistical technique compares the discrepancy between data and an estimation model. This technique is perfect for comparing the model travel times to the actual travel times in this study.

40 29 CHAPTER 4 MODEL CREATION To complete the research questions and objectives detailed above, a street network model which computes travel time between two points was created using historical events and the results with the inclusion of each added network variable were analyzed. This model included the variables of turn penalties, slope adjustment and weather delays in the analysis. Analyses of time recording methods, for each fire station, were also analyzed. Both the creation of the street network model and the analysis of recording time methods were completed to answer the following three questions. Would the variability of recording emergency response time methods impact street network model results? Is model accuracy improved with the addition of the dynamic weather variable? What is the relationship between dynamic and static variables within a street network model? Within this chapter the creation of the model is presented. This process has 5 key steps. The key steps to the creation of the street network model include the following, (1) Gathering the data for the base map features, (2) Gathering the data for each street network variable, this includes turn penalties, Digital Elevation Models (DEM) and weather data, (3) Preparing the base map data for use in the model, (4) Preparing the variable data for use in the model, (5) Testing the model with the base map data and each of the data variables. Each of these topics will be covered in the following section. 4.1 BASE MAP DATA The base map data is used to prepare the map for analysis and also for storing real world data which will be used in the analysis. The base map features include the base streets feature class, the fire stations feature class and the incidents feature class. All three feature classes are provided by The Omega Group who has also given permission to use the data. The base streets feature class contains attributes which pertain to the streets in this area. These attributes include both the posted speed limit for each street segment (street sections between intersections) and the length, in feet, of each street segment. The base streets feature

41 30 class contains every street segment for the entire study area. This feature class forms the basis for the model construction and subsequent analysis. If this layer is not accurate the entire study cannot be completed. For this study the layer accuracy depends how well the segment length and segment speed field correlate with reality. The fire stations feature class is a layer which details the location and attributes of every fire station in the study area. The fire stations were the starting point for each model run. The closest facility tool requires both a start and an end point. Without this layer there would be no starting point. This layer is also critical because the travel time field in the incident feature class used these points as a starting location. Therefore the model must use the same starting location if the results are going to be compared. The final base map layer in this study was the incidents feature class. This is the most important base map layer. The reason for this is because the real world travel times have been recorded in this feature class. The real world times are recorded in minutes and are stored in a field in this feature class. These are the times in which the model was compared against. Figure 4.1 shows streets, fire stations and incidents. Figure 4.1. Street centerline, fire stations and incidents layer. 4.2 VARIABLE DATA In order to complete the analysis in this study data for each street network variable data must be gathered. This data came from sources such as The Omega Group, the United

42 31 States Geological Survey (USGS), Environmental Systems Research Institute (ESRI) and from each fire department in this research study. The first variable was gathered was for turn penalties within a street network. This data was gathered from a study done by Mike Price titled Slopes, Sharp Turns and Speed (Price, 2008). In this article he tested actual turn times within an intersection for small, medium and large vehicles. The results of this study were applied to the model, in this study, to determine if the addition of turn penalties actually improves street network model response time. In the Price article there are five levels of penalties, none, slight, moderate, high and very high. Each level adds more time for making a certain directional turn. For this study I created 5 Visual Basic (VB) scripts which add the necessary amount of time for making a directional turn (see Table 4.1). Table 4.1. Turn Penalties Turn Rule R-Turn Penalty L-Turn Penalty U-Turn Penalty Straight Penalty No Penalty Slight Penalty Moderate Penalty High Penalty Very High Penalty The second variable in this study which data was need was the slope variable. The slope of the street network was based upon the elevation change each street segment. The greater the percentage in elevation change the longer the vehicle takes to traverse said street segment. A street segment is a section of a street network between two intersections. The elevation data was obtained from the USGS as a DEM with 7 meter resolution. The DEM is selected from a map of the entire United States. An area is manually selected around each study area. The DEM files ranged from less than one to four megabytes depending on the land area. Figure 4.2 is an example of a USGS DEM.

43 32 Figure 4.2. Digital elevation model of Elgin, Illinois. The final variable in this study which needs data for analysis was the weather variable. For this variable daily weather records, for an entire year, are needed for each study area. This data included every day which received precipitation greater than 1/100 th of an inch. This data also included both rain and snow events. This data was acquired from the National Weather Service and State Climate office of North Carolina. The data was in Adobe Acrobat PDF format when it was received for this study. 4.3 PREPARING BASE MAP DATA The data, in its raw form, cannot be used for analysis. It must be formatted for use in the model. To prepare the base map data I first checked the streets feature class. It is important for this feature class to have accurate seed limit data. The speed limit data for each segment is the foundation for this research data. The second step in preparing the base map data is to create a Network Dataset, from the street centerline, in the Network Analyst Extension of ArcGIS Desktop. A network dataset is a representation of a network, in points and lines. In the context of this study the network dataset will represent a street network. A

44 33 Network Dataset is required for analysis within the Network Analyst extension of ArcGIS. It can be created to use shapefiles or feature classes in a geodatabase. This study used the geodatabase environment. The network dataset also contains the script for turn penalties in intersections. In order to create a network dataset a street file must be contained in a feature dataset within a geodatabase. There is an option to create a new network dataset. If this option is selected a wizard appears with directs the user through a series of steps required for the successful creation of a network dataset. The final step in preparing the base map data was to query the incidents in which each fire station responded. The incidents feature class contains every incident in which any fire station in the department responded. I separated the incidents based on which station responded to each incident. The result was a number of feature classes, depending on the number of fire stations in the study area, containing incident data which said station responded to. Both the Fire Stations layer and Incidents layer were added to a new ArcMap document, one for each study area (see Figure 4.3). This is the document which would be used for analysis in all three study areas. Figure 4.3. Elgin, Illinois ArcMap document. 4.4 PREPARING VARIABLE DATA The variable data must be prepared for before it is used in the model. The preparation of the data is done in ArcGIS using ArcCatalog, Modelbuilder and Python Scripting.

45 Turn Penalties The variable data in this study was formatted for analysis within Network Analyst in ArcGIS. Within the Network Dataset there is the option to add turn penalties. This can be done manually or automated with a script. As mentioned above I chose to automate this process with a script. I created 5 scripts, one for each time penalty, and loaded the script into the network dataset. There are five feature datasets, within the geodatabase. Each feature dataset contains the network dataset with a different time penalty (see Figure 4.4). Figure 4.4. Geodatabase Data Hierarchy. Each of the five scripts, which contain turn penalties, is input during the creation of the network dataset. The option to create turn penalties is located in the Evaluators section under the Default Values tab. Turn penalties can be either a constant value or a Visual Basic script. If turn penalties are set to a constant value every directional turn will incur the same time penalty. While this option may be easier to set setting turn penalties with a script creates more accurate results. When the option of using a VB script is chosen a dialogue box is open which allows the user to either create a script or upload a previously created script (see Figure 4.5). Five scripts were created in a text file before creating the network dataset. When the script is chosen for upload the network dataset can be completed and will contain a set amount of time for each directional turn (see Figure 4.6). This includes time for each turn direction, right, left, straight through and U turn. One important aspect of importing the turn script is changing the type from constant value to VB Script. If the VB Script option is not chosen the turn penalties will not work correctly.

46 35 Figure 4.5. Evaluators dialogue box. Figure 4.6. Visual Basic script dialogue Box.

47 Slope Adjustment The calculation of slope on a street network is time consuming and requires many steps before completion. In order to save time a model was created in ArcGIS using the Modelbuilder tool. This model contained every step required to compute the slope and travel time penalty of each street segment. The original streets feature class was converted to a 3D feature class using the Interpolate Shape tool and the DEM for elevation data. When the streets are able to read Z or elevation values they can utilize ArcGIS 9.3 geometry calculation functions which calculate Z values. The first field which was added was the base time field. This was calculated by dividing the speed limit by the segment length. This gives a rough estimate of how long it will take a vehicle to traverse each segment. Four fields were then added and VB code was used to calculate the start and end X and Y coordinates of each street segment. Both the X and Y coordinates are required for a correct slope calculation. Two fields were then added for both the start and end point of each segments Z value. Each of these fields were calculated using VB code which can only be used in ArcGIS 9.3. This code utilizes the geometry function and attaches the elevation values from the DEM to each segments start and end point. This code can only be utilized if the streets are converted to read three dimensions. At this point in the model each street segment has an X, Y and Z value and is ready for the slope calculation. The slope calculation simply calculates the rise over the run of each street segment. It is computed with simple VB code (see Figure 4.7). Figure 4.7. Slope calculation code. This code is stating that if the start X coordinate is equal to the end X coordinate the slope will be equal to zero. This occurs in the case of cul-de-sacs within the street network. If this code was not included in the model there would be an error each time the model is run. The second portion of the code is calculating the rise over the run with the X, Y and Z coordinates. The output of this code is both negative and positive numbers representing the percentage of the street slope. Another field is added and calculated to obtain the absolute value for the slope percentage. A field was then added and calculated to account for the

48 37 slope adjustment. A final field was added which divided the slope adjustment by the base time field. The final output of the slope adjustment model was a new field in the streets feature class with time added depending on the slope of the street segment Weather Calculation A script was created which extracted incident records based upon the amount of precipitation received. This script was created using a combination of VB and Python code. The script first selects the table in which the weather information is saved. This is a Microsoft Excel spreadsheet which has formatted weather data. The data in this spreadsheet must be formatted correctly or the script will not work. There are two fields in this table, the Alarm Date field, and the Weather field. The alarm date field was used as a join field to the incident dataset. The weather field details if precipitation, of a certain amount, occurred on each day. The next step in this script joins the weather data with the incident data; this is done using a join operation. In order to join the weather data to the incident data the incident data must be saved in layer (.lyr) format. The final step in this script saves the newly joined layer data as a featureclass. The output featureclass from this script is used in the model for the weather calculation. 4.5 TESTING THE MODEL Before the analysis could take place the model needed to be tested and any errors fixed. The first step in this process was assembling the model with each variable. This includes creating an ArcMap document and populating it with the necessary layers as well as enabling the Network Analyst extension. The layers which were added were the network datasets, for both slope and turn penalties, the incidents, including and excluding weather events, and the fire stations layer. When the layers were added to the map and the Network Analyst extension had been enabled, a new Closest Facilities layer was created. This layer was created to use the network dataset with no slope adjustment or turn penalties. The fire stations and incidents layer were added next. An important step when adding the incidents into the closest facility layer is to make sure the display name is the travel time. This was the travel times will be in the layer when the model is run. In order to run the model the solve button on the network analyst toolbar must be pressed (see Figure 4.8).

49 38 Figure 4.8. Network analyst toolbar. When this button is pressed the model calculates the travel time between each of the incidents and the selected fire station. The output is a table with the model projected travel times for each incident the fire station responded to for the entire year. The results were analyzed, from the test run, before conducting the analysis within each study area. The flow of the model is similar to the diagram in Figure 4.9. Figure 4.9. Complete GIS model for predicting travel time with the included variables.

50 39 CHAPTER 5 MODEL EXECUTION AND RESULTS The street network model was tested for each of the variables in the model. This included both the variables alone and joined with other variables. The model was also run for each station in the three study areas. The model results for each study area have been presented independently from one another. The results of each model run were recorded and analyzed in Microsoft Excel. Microsoft Excel was used because it is compatible with output formats in ArcGIS and also because it is simple to execute statistical functions with. The results of each model output were then compiled into a graph would prove whether the variables improved the model and not. 5.1 IMPORTING THE RESULTS When the execution of the model is completed, a new shapefile was created in the closest facility layer. This shapefile was called the routes layer. One of the attributes for this table was the predicted travel time from the station to each incident. In order to complete the analysis the actual recorded travel times must be appended to this table. This was done by opening the attribute table of the routes layer and adding a new field. This was a double format field because it would be storing numbers. When the new field was created the field calculator was used to trim the name field which contained the travel times (see Figure 5.1). The routes table was exported as a text file after each run of the model (See Figure 5.2). The two columns of importance within the text file are the Total_Minu and Travel_Tim column. The Total_Minu column is the model estimate of the travel time from the fire station to the incident. The Travel_Tim column is the actual travel time from the fire station to the incident. The text file was then imported into Microsoft Excel for analysis. In Microsoft Excel the text files were imported using the import data tool. When using this tool the fields are delimited by the comma which separates them. This is important because if they are not delimited this way they will not import correctly, they will be imported into a single field rendering them useless. Once the import is complete the extra

51 40 Figure 5.1. Travel time calculation. fields are removed and the analysis can begin. In Microsoft Excel the data analysis tool package is enabled. When this package is opened the t:-test: Paired Two Sample for Means tool is selected. This tool will give the correlation coefficient as well as the t-test value for each run of the model. 5.2 WILSON, NORTH CAROLINA RESULTS Wilson, North Carolina was tested as the first study area. Each model was input in the model and the results analyzed. The results were also analyzed for all variables combined. The variables were combined to determine if they affected each other. They were also combined to determine what effect they had on the model thus fulfilling the final research question. The results and analysis will be presented in this section. The model was first test without a variable input; this is known as the base result. This is the result which the variable results are evaluated against. The base result is the time, in seconds, for an

52 41 Figure 5.2. The routes layer. emergency response vehicle to travel from a fire station to an incident. The hypothesis of the model is that the variable input will decrease the mean sum of squared residuals thus increasing in accuracy. In the graph below the mean sum of squared residual results are compared with the input of each variable. The results from each station are independent from one another and do not have an effect on each other Wilson Turn Penalty Results The first variable input in the model was turn penalties. Turn penalties are a set amount of time added to each turn direction a vehicle makes when responding to an incident. The time added is different for each turn and is based upon the article Slopes, Sharp Turns and Speed by Mike Price. The penalty for a right turn is 3 seconds, a left turn is 5 seconds, and straight through is one second. When turn penalties were input in the model the accuracy was increased by an average of 19.26% over the base result. The greatest increase was from station three with a 29.13% increase in accuracy. The only decrease in accuracy came from station two with a 3.46% decrease in accuracy. The addition of turn penalties to this model increased accuracy substantially.

53 Wilson Slope Adjustment Results The second and final static variable applied to the model was the slope adjustment. The slope adjustment is the amount of added time it takes for emergency vehicles to traverse steep street slopes. The time addition was calculated in a study using actual emergency vehicles. The results of that study were presented in the article by Mike Price titled Slopes, Sharp Turns and Speed. The model was run for each station and the improvement was similar to the addition of turn penalties. The average increase for all stations was 19.52%. The greatest increase came from station three with a 29.13% increase. The only decrease came from station two with a 3.67% decrease. The final variable and only dynamic variable tested was the weather impact variable Wilson Weather Impact Results The weather impact variable is classified into three categories depending on the intensity of precipitation. The three categories in this study are light precipitation, moderate precipitation and heavy precipitation. The goal is to determine which category, if any, have an impact on emergency response. In the first category every day in which measureable precipitation, or precipitation over.01 of an inch, was recorded were extracted. The model was run and the results compared. The average increase for all stations was 22.74% over the base result. The greatest increase came from station three with a 31.97% increase in accuracy. No station decreased in accuracy and the station to increase the least was station two with a.48% increase. The second category, the effect moderate precipitation has on emergency response, was tested the same way the first category was. This category excluded all days in which moderate or heavy precipitation occurred. The average increase was 24.28% for all stations. The greatest increase came from station three with a 33.33% increase. The lowest percentage of increase came from station two with a 2.47% increase. The final category tested in the model was heavy precipitation. All days in which only heavy precipitation occurred were excluded from the model. The average increase with this run of the model for all stations was 22.01%. The greatest increase came from station three with a 31.33% increase. Station two had the lowest percentage of increase with only a.46% increase.

54 Wilson Compound Variable Results After each variable had been tested individually both the static and dynamic variables were combined and tested for each fire station. This was done in order to determine the relationship between the variables and determine if when combined the variables created a more accurate model. This also tested the impact of each variable on the other variables. The moderate precipitation variable was used because this variable had the greatest impact on the model of any of the weather impact variables. The hypothesis was, when combined each variable would cause the model to become more accurate. The average increase for all stations with all variables was 25.04%. This is greater than any single variable model input. The greatest increase came from station three with a 33.99% increase. The smallest increase came from station two with a 3.44% increase. In Figure 5.3 the lower numbers represent greater model accuracy. Figure 5.3. Wilson, North Carolina results graph. 5.3 AURORA, COLORADO RESULTS The second locations tested were the fire stations in Aurora Colorado. As mentioned above there are 10 fire stations within Aurora Colorado all of which were tested with both the static and dynamic variables. Aurora Colorado has diverse weather and topography which makes this city an ideal candidate for testing our variables. As with Wilson, North Carolina the base time was calculated using only the street segment length and speed limit. This time

55 44 was compared with each variable time to determine if each variable made the model more accurate Aurora Turn Penalty Results The first variable tested in the Aurora street network was turn penalties. Turn penalties are extra time added for directional turns within an intersection. Each station was tested independently and the results will be compared to the base times. The average increase was only 16.68% for all stations. The greatest increase came from station eight with a 21.13% increase. Station two had the smallest increase with only 8.20%. The impact of turn penalties on this study area were very similar to the first study area, Wilson, North Carolina Aurora Slope Adjustment Results The slope adjustment was the second variable tested in the Aurora street network. The slope adjustment added additional time for vehicles traversing steep street slopes. The city of Aurora is mostly flat but has areas with steep inclines. The average difference when applying the slope adjustment was only 1.29%. The greatest increase came from station six with a 6.30% increase. The greatest decrease came from station two with a 6.48% decrease. The results from this study area contrast sharply with Wilson, North Carolina. The slope adjustment has much less impact in Aurora Aurora Weather Impact Results Aurora Colorado receives a wide variety of precipitation each year, which makes it ideal for testing weather impact on emergency response results. Each day which received over.01 of an inch of precipitation was extracted from the model and the model was run. The average increase for all stations was 19.09%. The greatest improvement was at station eight with a 23.50% improvement. The smallest improvement came from station three with 10.96% improvement. The model was then test with only the moderate and heavy precipitation added. Moderate precipitation includes all days where more than.029 inches fall. With the moderate and heavy precipitation not included the model had an average improvement of 21.41%. This was the greatest average improvement for the weather impact variable of the

56 45 three tested. The greatest single improvement came from station six with an 26.9% improvement. The least improvement came from station three with a 12.74% improvement. The final aspect of the weather impact variable to be tested was the inclusion of both light and moderate precipitation and the exclusion of the heavy precipitation. The average improvement over all stations, with the light and moderate precipitation extracted, was 19.84%. This is slightly less than with both moderate and heavy precipitation subtracted. The greatest increase came from station six with a 25.44% increase. The smallest increase came from station three with only a 10.99% increase. With the addition of the weather impact variable every variable improved prediction accuracy Aurora Compound Variable Results As in Wilson each variable was tested individually then combined and tested in conjunction with each other. This tested the impact of each variable on one another and also the relationship between each variable. When the variables are joined together and tested the average difference among all stations is just over one half of a percent more accurate. The average increase for all stations was 21.19%. The greatest increase came from station six with a 27.63% increase. The smallest increase came from station three with a 13.61% increase. Figure 5.4 displays the results from Aurora, Colorado. 5.4 ELGIN, ILLINOIS RESULTS Elgin, Illinois was the final region to be tested with the model. There are 6 stations in this study area all of which will be included in the study. As with the first two study areas the base time was calculated using only segment length and speed limits. The base time will be compared with the model after each variable has been input Elgin Turn Penalty Results Street network turn penalties were the first variable tested in the Elgin study area. The results of the turn penalty variable are compared to the base results. The average improvement for all stations was only 2.81%. This was much lower than either of the other two study areas. The greatest improvement came from station one with a 9.37% improvement. The greatest decrease came from station three with a 4.86% decrease. The

57 46 Figure 5.4. Aurora, Colorado results graph. increase in accuracy with the addition of this variable is far less than the other two study areas Elgin Slope Adjustment Results The second variable tested was the slope adjustment. The slope adjustment is the additional time added for vehicles traversing steep street slopes. The city of Elgin has consistently flat topography so it is no surprise the differences are small. The average increase along all stations was 3.56%. The greatest increase came from station a 19.24% increase. The greatest decrease came from station five with a decrease. As with the turn penalty results the increase was much smaller in this study area Elgin Weather Impact Results The final variable tested in this study area was the weather impact results. This study area was tested with the same three precipitation classifications as the other two. The first classification is the extraction of all incidents which occurred on day with.01 inches of precipitation or more. The results for the extraction all precipitation days show an average increase of 2.2%. The greatest increase came from station three with an increase of 6.7%. The smallest increase came from station two with a 1.1% increase.

58 47 With only the moderate and heavy precipitation subtracted the model showed the most improvement. The average improvement over all stations was 3.1%. The greatest improvement, at 11.3% came from station three. The least improvement came from station two with 3.3%. The final classification tested was with only the heavy precipitation subtracted from the model. The average improvement was 3.1% over the entire area. The greatest improvement came from station three with an 11.3% improvement. The least improvement came from station two with only 2.6% improvement Elgin Compound Variable Results Elgin was test using all variables in conjunction similar to both Wilson and Aurora. This tested the relationship between both the static and dynamic variables and how they impact each other. The average difference for the entire study area was 4.86% increase. Station five in Elgin had the greatest increase of any station within the three study areas with a 17.28% increase. The greatest decrease came from station three with a 4.93% decrease. Figure 5.5 displays the results from Elgin, Illinois. Figure 5.5. Elgin, Illinois results graph. 5.5 TRAVEL TIME PREDICTION MODEL ACCURACY The performance of the travel time prediction model was tested using both a histogram and scatter plot for one fire station in each study area. The fire station had no variable input when both the scatter plot and histogram were created. For the Wilson study

59 48 area the scatter plot had a low correlation for actual data and model predicted data (see Figure 5.6). The correlation coefficient was only The histogram for the Wilson study area showed the model ran slower than the actual response times (see Figure 5.7). There are a variety of reasons which may have caused this. This station may have poor time recording methods. Figure 5.6. Wilson, North Carolina model preformance scatter plot. Figure 5.7. Wilson, North Carolina model preformance histogram.

60 49 The correlation in Aurora, Colorado was much better than in Wilson. The correlation coefficient was.4335 which is substantially higher than Wilson. This is evident in the graph below by the cluster distance of the data points. In Aurora the points are clustered more closely together. The linear progression of the data is easily visible in Aurora (see Figure 5.8). Figure 5.8. Aurora, Colorado model preformance scatter plot. The histogram in Aurora resembles a bell curve more than the other two stations (see Figure 5.9). The center of the graph is at zero, which means it describes the data very well. If the graph is centered below zero the model is running quicker than actual response times. If the model is centered above zero the model is running slower than actual response times. Figure 5.9. Aurora, Colorado model preformance histrogram.

61 50 The scatter plot in Elgin has a linear trend similar to Aurora (see Figure 5.10). The correlation coefficient is.3959 which is in between Wilson and Aurora. Figure Elgin, Illinois model preformance scatter plot. The histogram for Elgin closely resembles a bell curve (see Figure 5.11). The greatest frequency is centered at zero, which is what we hope to see when creating a model. The model was running slightly fast which is why the graph has greater frequencies with the negative bin numbers. Figure Elgin, Illinois model preformance histogram.

62 5.6 TRAVEL TIME RECORDING METHODS The method of recording the travel time varies from station to station and region to region. The travel time is defined as the time it takes the emergency response vehicle to travel from the fire station to the incident. Within this study this time is used as a comparison to the predicted model travel time. The closer the two time correlate the more accurate the model is. The accuracy of the recorded travel time depends on the methods used to obtain it. Travel time recording methods range from radio check in to automatic air brake timing Wilson, North Carolina Time Recording Methods Wilson, North Carolina uses a manual method to record travel times. There is a timer in the vehicle which is started as the engine leaves the station and stopped when the engine arrives at the incident. This system is standard with many agencies across the country and is prone to human error. For example if the button is not pressed or is pressed late the time will not be accurate Aurora, Colorado Time Recording Methods The fire department in Aurora, Colorado uses a sophisticated time recording program called CADTimes. CADTIMES is a Web-based dashboard that allows police, fire and EMS agencies to view information about their current events in real time. Many agencies view critical information about events in progress each day using this tool. It can also be used to view and share information during a large event involving multiple agencies. A number of agencies take advantage of CADTIMES by displaying the current alarm information on large screens in their fire stations. Aurora uses this software program in conjunction with the air brakes on each of their engines. When the air brakes are released the time begins and when the air brakes are enabled the clock stops Elgin, Illinois Time Recording Methods Elgin, Illinois uses two different methods depending on station. The first method is similar to the method Wilson, North Carolina uses. There is a timer within the fire engine which is pressed when the engine leaves the station and pressed again when the engine 51

63 52 arrives at the incident. This method records the time it took for the engine to travel from the station to the incident location. The second method uses communication operators within the command center to record the times. When the engine leaves the station a cal is placed over the radio and the timer is started. When the engine arrives at the station a second radio call is placed and the timer is stopped.

64 53 CHAPTER 6 SUMMARY The outcome of this research illustrates the effects both static and dynamic variables have on emergency response time. Although many variables effect emergency response time this study has shown the impact of turn penalties, slope adjustment and weather conditions. This study has also shown the importance of location to determining the effect each variable has on a street network model. The static variable of turn penalties showed decent improvement in accuracy over the base model result for the majority of the fire stations in each area. Many incidents the fire station responded to did not require the vehicle to make many turns. This could be the reason why turn penalties had little effect for certain stations. The slope adjustment had little effect in all areas except Wilson, North Carolina. This could be because both Aurora and Elgin were in topographically flat areas. In an area with greater topographic diversity the impact of this variable would have been greater. The dynamic weather variable showed greater improvement in the street network model than either static variable. Each of the three study areas had diverse weather conditions which provided a good testing environment for this model variable. The GIS street network model testing s also helped us answer the three research questions addressed in chapter one: Question 1: How does variability of recording emergency response time methods impact street network model results? The variability of recording time methods shows no discernable impact on the accuracy of emergency response times. Although the manual time recording methods, used by both Wilson and Elgin, seem to be less accurate they do not impact the accuracy of the travel time any more than Aurora. The average differences between each variable are roughly the same for each study area. The main reason for this is due to the lack of variance of recording times. The difference between the manual recording methods and automated recording methods is only a few seconds on average. The variance of only a few seconds was not enough to have a large impact on the accuracy of each model. The greater impact was the location of the fire station within the street network. The fire stations located along

65 54 the outside of the study area generally had lower model accuracy than the fire stations located in the central portion of the street network. The reason for this is because stations along the outside of the street network have limited options for traveling to a specific location. If one of the main streets is blocked for some reason a secondary route will add minutes to the travel time. Question 2: How can model accuracy be improved with the addition of dynamic weather variables? Weather conditions are a force which is in constant change at any given time. Modeling this change is essential to improving the accuracy of an emergency response time prediction model. A model has been created which filters days that have received precipitation out of the model. This model has been run to filter out incidents in three different tiers, light, moderate and heavy precipitation. Each tier has been evaluated to determine the overall impact on the accuracy of the model. The dynamic weather variable in this study has significantly improved travel time prediction accuracy for all three tiers. The tier with the greatest impact is the moderate precipitation. There was not one study area which posted an average decrease from the base result when the dynamic weather variable was applied. There was also not a single fire station which decreased in accuracy, within any study area, once the dynamic weather variable was applied. Question 3: What is the relationship between dynamic and static variables within a street network model? The initial hypothesis concerning the interaction between static and dynamic variables was that the combination of variables would increase the accuracy of the model more than any single variables addition. This proved to be true within every study area. The combination of the static and dynamic variables had the greatest effect on accuracy when the model was run. The combination of all variables improved accuracy more than any single variable. For each fire station the compound variable increased accuracy over every individual variable. The conclusion from this outcome is that when applied together the variables work together to increase model accuracy more than when each variable is applied individually. In conclusion this research has set up the possibility for modeling other variables which impact emergency response. Traffic impact is a dynamic variable which could be

66 55 modeled in conjunction with weather impact. Traffic impact is one of the most important variables when modeling emergency response. When this is combined with weather impact the accuracy may be drastically improved. Another step this research could take would be to incorporate both traffic and weather impact in a real-time model environment. If a real-time model is created, which includes both dynamic variables, emergency responders will be able to adjust their response route while responding to an incident. This will saves lives and protect property from damage. The model created in this study could be improved by gathering more accurate data. The digital elevation model used in this study had only 7 meter accuracy. If the resolution of this data was improved the accuracy the model, after the slope adjustment had been applied, would have improved. If the accuracy of the weather data was improved, the model accuracy would have improved as well. This includes getting daily weather information for each fire station in a study area as well as the entire street network. This research has been used at The Omega Group to improve their FireView Server product. With this research they are able to complete network routing using the selected variables in this study. This gives the fire departments more accuracy when computing best route scenarios for their emergency response vehicles.

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69 58 APPENDIX A PYTHON CODE FOR WEATHER EXTRACTION

70 59 #Imports import os, sys, arcgisscripting gp = arcgisscripting.create(9.3) gp.overwriteoutput = 1 #Add Toolbox gp.toolbox = "analysis" gp.toolbox = "management" gp.toolbox = "conversion" try: #Variables gp.workspace = gp.getparameterastext(0) weather = gp.getparameterastext(1) field1 = gp.getparameterastext(2) apparatus = gp.getparameterastext(3) field2 = gp.getparameterastext(4) temptbl = "temptbl" finaloutput = gp.getparameterastext(5) #Query all "non" weather events gp.tableselect(weather, temptbl, 'Weather = 0') #Join to Apparatus Data gp.addjoin(apparatus, field1, temptbl, field2) #Layer to Feature Class gp.featureclasstofeatureclass(apparatus, finaloutput, "Apparatus_Weather", 'Weather = 0') except:

71 60 gp.addmessage(gp.getmessages(2)) print gp.getmessages(2

72 61 APPENDIX B MODELBUILDER MODEL FOR SLOPE ADJUSTMENT

73 Figure B.1. Modelbuilder model for slope. 62