Printer Friendly
The Free Library
23,403,340 articles and books


A data model and internet GIS framework for safe routes to school.

BACKGROUND

In 1969, approximately half of all students walked or bicycled to schools. But now, less than 15 percent of children do so; more than half of the students arrive at schools by private automobiles (FHWA FHWA Federal Highway Administration (US DoT) ). Problems accompanying this change include childhood obesity, traffic congestion The condition of a network when there is not enough bandwidth to support the current traffic load.

congestion - When the offered load of a data communication path exceeds the capacity.
, air pollution, and pedestrian safety issues. (NHTSA NHTSA National Highway Traffic Safety Administration (US government)  2004, Frank et al. 2005, Lopez et al. 2006, Hurvitz 2005, Crawford 2006, McMillan 2005, 2007). To address these issues, the Congress passed federal legislation to establish a National Safe Routes to School Program (SRTS SRTS Safe Routes to School (also seen as SR2S)
SRTS Synchronous Residual Time Stamp (ATM Forum)
SRTS Secondary Request to Send (ITU-T)
SRTS Service Request Tracking System
) in 2005. The SRTS program is administered and guided by the Federal Highway Administration The Federal Highway Administration (FHWA) is a division of the United States Department of Transportation that specializes in highway transportation. The agency's major activities are grouped into two "programs," The Federal-aid Highway Program and the Federal Lands Highway  (FHWA) of the U.S. Department of Transportation (USDOT USDOT United States Department of Transportation ). The FHWA recommends that SRTS efforts in the United States United States, officially United States of America, republic (2005 est. pop. 295,734,000), 3,539,227 sq mi (9,166,598 sq km), North America. The United States is the world's third largest country in population and the fourth largest country in area.  incorporate, directly or indirectly, the five components, often referred to as the five Es: engineering, education, enforcement, encouragement, and evaluation.

Information about walking and bicycling facility conditions of neighborhoods around schools is key to the implementation of the five Es. For example, urban planners and public health authorities need the information to assess neighborhood walking and bicycling safety conditions, transportation engineers need the information for roadway and intersection improvement, law enforcement officers need the information to respond to unsafe factors, law makers need the information to initiate new policies, parents need the information to understand their neighborhood safety and security conditions, and children also may need the information to guide their walking and bicycling activities.

Walking and bicycling safety data collection and assessment have been conducted by various interested parties such as urban planners, transportation engineers, and public health administrators. A significant trend in such data collection is to provide environment attribute information to planners and to evaluate new environmental and policy initiatives (Sallis et al. 1998, Ewing et al. 2003, Frank and Engelke 2001, Leslie et al. 2007). For example, Schlossberg et al. (2006) use street networks around transit stops and schools to quantitatively analyze local walkability and provide useful planning and evaluation tools for transportation planners interested in enhancing the local walkable environment. However, a good deal of existing pedestrian safety data collection activities are orientated to an adult walking environment (McMillan 2007, Schlossberg et al. 2007). For instance, Leslie et al. (2007) measure features of the built environment that may influence adults' physical activities and develop indexes of walkability at the local level. GIS technology has been used in some data collection activities to obtain spatial measures of urban form, transportation facilities, and resource accessibility (Schlossberg et al. 2007, Leslie et al. 2007).

Transportation engineers focus on individual transportation facilities at restricted locations. For example, a transportation project targeted at improving a specific street intersection or a segment of sidewalk surface may collect data in the geometry, traffic flow, pedestrians, and accidents at the construction site before and after the implementation of engineering measurements. Walking and bicycling safety checklists often are used for such project-specific data collection.

While walking and bicycling safety data collection is a common practice for urban planning urban planning: see city planning.
urban planning

Programs pursued as a means of improving the urban environment and achieving certain social and economic objectives.
 and transportation engineering projects, similar activities dedicated to SRTS are rarely seen in literature. Because most of the current data collection practices are not school-trip oriented, direct participants of SRTS programs, including children, parents, and schools, are not involved, and their concerns are not reflected. To date, there are no standards or specifications to guide comprehensive data collection for SRTS. Given that SRTS is a widely embracing public participating effort involving participants from a wide range of areas, including schools, parents, children, planners, engineers, public health organizations, and law enforcement institutions, keeping everybody informed is essential to the success of an SRTS program.

An Internet (or Web-based) geographic information system geographic information system (GIS)

Computerized system that relates and displays data collected from a geographic entity in the form of a map. The ability of GIS to overlay existing data with new information and display it in colour on a computer screen is used primarily to
 (GIS) has the potential to satisfy the broad information needs for SRTS. This paper presents a data model for a GIS database and a framework for Internet GIS applications that satisfy SRTS data collection, evaluation, analysis, and distribution. An SRTS database can support convenient storage of diversified walking and bicycling safety measures safety measures,
n.pl actions (e.g., use of glasses, face masks) taken to protect patients and office personnel from such known hazards as particles and aerosols from high-speed rotary instruments, mercury vapor, radiation exposure, anesthetic and
 and facilitates evaluation of walkability and bikeability conditions. Built on the GIS database, Internet GIS provides advanced online information services See Information Systems.  such as collection and dissemination of walking and bicycling safety data as well as safe route planning. It also provides a means of communication between different parties involved in an SRTS project. An Internet GIS, therefore, can serve as a platform on which every party can play a role in SRTS.

WALKABILITY AND BIKEABILITY INDICATORS

Supposedly, good urban form can lead to a reduction of total transportation costs and automobile usage, resulting in more livable communities (The Victoria Transportation Policy Institute 2007). McMillan (2005, 2007) maintains that urban form is a primary factor affecting children's travel behavior to school. Schlossberg et al. (2006) not only believe that urban form is a factor that affects students' transportation modes but also suggest that it can help predict school travel modes. Furthermore, Schlossberg (2007) proposed a series of urban form measures based on TIGER files in a GIS. These urban form measures fall into three categories containing a total of 13 measures: quality (e.g., minor road density, minor/major road ratio), proximity (e.g., pedestrian catchment area catchment area or drainage basin, area drained by a stream or other body of water. The limits of a given catchment area are the heights of land—often called drainage divides, or watersheds—separating it from neighboring drainage , impeded pedestrian catchment area), and connectivity (e.g., intersection density, dead-end density). In studying general walkability of local communities, Leslie et al. (2007) propose a walkability index of Census Collection District (CCD CCD
 in full charge-coupled device

Semiconductor device in which the individual semiconductor components are connected so that the electrical charge at the output of one device provides the input to the next device.
) based on four environmental attributes: dwelling density, connectivity (using road centerline cen·ter·line  
n.
1. A line that bisects something into equal parts.

2. A painted line running along the center of a road or highway that divides it into two sections for traffic moving in opposite directions, or, in the case of
 and intersection data), land-use accessibility and diversity of uses (entropy of land-use mix), and net area retail (shopping centers). They also argue the importance of objective measures of walkability factors in urban areas. McMillan (2007), however, pays more attention to perceptual aspects of urban forms and safety by surveying caregivers for their perceptions of a number of variables, including neighborhood safety, traffic safety, household transportation options, sociocultural norms, attitudes, and sociodemographics. Although land use was regarded an important factor of neighborhood walkability in the study of Leslie et al., it is excluded from considerations for school trips by other researchers because the school is the only destination (McMillan 2007, Schlossberg 2007).

Transportation engineers are more interested in safety conditions of transportation facilities, especially roadways and intersections, and they have proposed a host of indexes for walking and bicycling safety. Examples of these indexes include Pedestrian Level of Service (PLOS PLoS Public Library of Science
PLOS Parent Looking Over Shoulder
PLOS Proposed Level of Service
PLOS Primary Logistics Oriented School
) (Sarkar Sarkar could mean:
  • Government in Urdu/Persian/Hindi. Colloquially in India, it is a Metonymy for the incumbent government. The Persian wordSarkar is derived from two words; 'Sar' meaning Head and 'Kar' meaning Work.
 1993, Dixon 1995, Gallin 2001, Chu and Baltes 2001, Balts and Chu 2002), measure of pedestrian environments (Khisty 1994), pedestrian environment factor model (1000 Friends of Oregon 1993), pedestrian potential index and deficiency index (Portland Pedestrian Master Plan, City of Portland
This article is about the passenger train City of Portland; for cities around the world, see the disambiguation page Portland.
The City of Portland
 1998), Level of Service (LOS) (Botma 1995), Bicycle Safety Bicycle safety is the use of practices designed to reduce risk associated with cycling. Some of this subject matter is hotly debated: for example, the discussions as to whether bicycle helmets or cyclepaths really deliver improved safety.  Index Rating (BSIR BSIR British Society of Interventional Radiology ) (Davis 1987), roadway condition index (RCI RCI Royal Caribbean International
RCI Radio Canada International
RCI Rehabilitation Council of India
RCI Residential Communities Initiative
RCI Roof Consultants Institute
RCI Remote Control Interface
RCI Residential, Commercial, Industrial
), Bicyclist Stress Level (Sorton and Walsh 1994), Intersection Hazard Score (IHS IHS

(I.H.S.) first three letters of Greek spelling of Jesus; also taken as acronym of Iesus Hominum Salvator ‘Jesus, Savior of Mankind.’ [Christian Symbolism: Brewer Dictionary, 480]

See : Christ



IHS
) (Landis 1994), Bicycle Level of Service (BLOS BLOS Beyond Line-Of-Sight (over 600 Miles)
BLOS Bicycle Level of Service (roadway bike friendliness measure)
BLOS Branch If Lower or Same
) (Landis, et al. 1997), Bicycle Compatibility Index (BCI BCI Bat Conservation International
BCI Brain-Computer Interface
BCI Business Continuity Institute
BCI Business Cycle Indicators
BCI Banco de Credito e Inversiones (Chilean bank)
BCI Bell Canada International
) (Harkey et al. 1998), intersection BLOS (Landis et al. 1997), Compatibility of Roads for Cyclists (CRC (Cyclical Redundancy Checking) An error checking technique used to ensure the accuracy of transmitting digital data. The transmitted messages are divided into predetermined lengths which, used as dividends, are divided by a fixed divisor. ) (Noel et al. 2003). Some of these indexes focus on roadways and others emphasize intersections. Indexes usually are calculated as the weighted sum of a number of objective or subjective safety factors:

I - [n.summation over (i=0)] [w.sub.i] [x.sub.i] ... (1)

where I is walkability or bikeability index, [x.sub.i] is the measure of the i-th safety factor, and [w.sub.i] is the weight of the i-th factor. A factor usually is measured on a scale of 0 to 4 or 5. For example, Khisty (1994) proposed seven qualitative performance measures of pedestrian environments: attractiveness, comfort, convenience, safety, security, system coherence, and system continuity. Each measure is scored on the scale from 0 to 5, depending on the level of satisfaction, and the relative importance of each measure was determined from survey responses. Gallin (2001) determined the pedestrian LOS by scoring and weighting a total of 11 factors. Each factor is scored 0 to 4 and the weights range from 2 to 5. For example, the "path width" factor is scored as 0 if no pedestrian path is present, 1 if the path width is 0 to 1 meter, and up to a maximum of 4 if the path width is more than 2 meters. Table 1 summarizes commonly identified factors for all the walkability and bikeability indexes reviewed previously.

To accommodate the walking and bicycling safety factors shown in Table 1 and to develop a GIS that satisfies information demands from all parties involved in an SRTS project, a comprehensive GIS data model is needed to facilitate storage of walking and bicycling safety measures and computation of walkability and bikeability indexes. The following section presents a data model that satisfies these needs.

GIS DATA MODEL

A data model is a blueprint of a database. A good data model should support convenient storage of all necessary data, minimize redundancy, facilitate information retrieval information retrieval

Recovery of information, especially in a database stored in a computer. Two main approaches are matching words in the query against the database index (keyword searching) and traversing the database using hypertext or hypermedia links.
, and be flexible to adapt to future changes. Figure 1 is a logical schema A Logical Schema is a data model of a specific problem domain that is in terms of a particular data management technology. Without being specific to a particular database management product, it is in terms of either (for example, in 2007) relational tables and columns,  of a GIS data model that supports walking SRTS data storage and facilitates walkability and bikeability assessment.

This data model describes the structure of a GIS database that facilitates both spatial and nonspatial data storage for SRTS. The spatial data Data that is represented as 2D or 3D images. A geographic information system (GIS) is one of the primary applications of spatial data (land maps). See spatial analysis, spatial resolution and GIS glossary. , enclosed in the dashed-line box in Figure 1, consists of base feature classes including street centerlines, census data, vegetation coverage, properties, land use, photo points, etc. The spatial data set forms the basis for walking and bicycling safety data acquisition and storage. Except for photo points, most of the feature classes shown in Figure 1 are public data and, therefore, available from the local government. If this data model is implemented in an ESRI (Environmental Systems Research Institute, Inc., Redlands, CA, www.esri.com) The world's leading developer of geographic information systems (GIS) software, including programs that plot ZIP codes and addresses, demographic information and detailed, color-coded data.  geodatabase, special topological rules, such as street centerlines must not cross properties, may be applied to certain features as needed as needed prn. See prn order. . Region-based walkability/bikeability indexes, such as proximity, connectivity, as well as social and environmental indexes, can be derived from feature classes of the spatial data set.

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

The street centerline feature class makes up the backbone of the database because roadway-based and intersection-based walking and bicycling safety measures are related to it in this data model. This feature class contains attributes, such as segment length, speed limit, and CFCC CFCC Central Florida Community College
CFCC Cape Fear Community College (Wilmington, North Carolina)
CFCC Census Feature Class Code
CFCC Center for Families, Children & the Courts
CFCC Continuous Fiber Ceramic Composite
, which are available in TIGER line files. Properties that are pertinent to walking and bicycling safety are stored in a related table named "Streets," which contains fields including number of lanes (Lanes), Average Daily Traffic (ADT (Asynchronous Data Transfer) A transmission technique used in ISDN PBXs that dynamically allocates bandwidth. See also abstract data type.

ADT - abstract data type
 for traffic volume), speed limit (Speed in mph), the left and right outer lane widths (OLWL and OLWR in feet), percentage of street segment for left-side and right-side on-street parking (OSPL OSPL Overall Sound Pressure Level  and OSPR OSPR Office of Spill Prevention & Response (California Department of Fish and Game)
OSPR Optical Shared Protection Ring
OSPR Own Station Position Report
), whether it is a one-way street (One way: 0 = no, 1 = yes), and the existence of a median (Median: 0 = no, 1 = yes). A subjective measure of comfort (Comfort) is used in the "Streets" table as a comprehensive measure of perceptual safety and amenity factors. The "Comfort" is scored 0 through 4 by which 0 represents the lowest level of comfort and 4 the highest level. A basic network topology See topology.  can be established based on the street centerline and intersection feature classes to support network analyses based on shortest-path algorithms.

Walking and bicycling safety measures can be recorded along roadways and at intersections. Roadway safety measures, based on sidewalks and bike lanes, are stored in table "Side Lane"--a combination of sidewalk and bike lane bike lane ncarril m de bicicleta; carril m bici

bike lane bike npiste f cyclable

bike lane 
. Fields of this table include a primary key ID, a foreign key Street ID referring to the street centerline, the percentages of starting and ending points along a street segment (Start pct, End pct), right side/left side of a street segment based on the physical direction of the street segment in GIS (Side), the type of lane (0 = sidewalk, 1 = bike lane), the width (in feet), surface condition (0 to 4), and the type and width of buffer zone buffer zone
n.
A neutral area between hostile or belligerent forces that serves to prevent conflict.

Noun 1. buffer zone
 (e.g., none, paint, curb, plants, street furniture zone) that separates this lane from vehicle lanes (see Figure 2).

[FIGURE 3 OMITTED]

Measures of intersection safety are recorded in the crosswalk table. A crosswalk is related to an intersection point and a street segment to cross in this data model. Safety measures for crosswalks include the length, width, the greater curb radius, traffic control method (uncontrolled, stop sign, traffic lights, push button, guarded), existence of safety islands (0 = no, 1 = yes), and paint quality (0-4).

Based on this data model, a Microsoft Access database is developed to satisfy the needs for walking and bicycling data storage and for the development of safety indexes for regions, roadways, and locations. A field walkability and bikeability audit program for GPS-enabled portable computing devices is developed to assist field data collection (shown in Figure 3).

A simple edge-node network topology is enabled by the relationship between street centerlines and intersections in this data model. Because roadway walkability and bikeability indexes are associated with street centerlines and intersections, the best path that minimizes risks can be resolved by a shortest-path algorithm. It should be noted that establishing a high resolution network based on sidewalks and crosswalks is extremely difficult. Sidewalks often are discontinuous discontinuous /dis·con·tin·u·ous/ (dis?kon-tin´u-us)
1. interrupted; intermittent; marked by breaks.

2. discrete; separate.

3. lacking logical order or coherence.
 and crosswalks are incomplete. If a sidewalk is missing or discontinuous, the pedestrian has to walk on the street alternately, so that excessive nodes have to be added to the network to connect every broken sidewalk segment to street segments. More importantly, unlike driving, walking cannot be restrained to specific lanes. Pedestrians, especially children, can randomly cross open streets while walking in a residential area.

[FIGURE 4 OMITTED]

A FRAMEWORK FOR INTERNET GIS

An SRTS project is a collaborative effort of many parties from both government and the public. Accurate and timely information about walking and biking conditions in the neighborhood around schools can be used by various parties to promote safely walking or biking by children. For example, it can help schools plan the safest routes for walking and bicycling; it can allow administrators to monitor student walking and biking activities; it can inform authorities of emerging unsafe factors and help them make decisions in response to walking environment changes; and it may encourage parents to let their children walk or bike to school. Based on the data model discussed in a previous section, a framework of Web-based GIS is proposed for data collection, analysis, and information dissemination (see Figure 4). This Web-based GIS can serve as a platform for safe routes to school projects, in which every involved party can play a role.

This framework adopts a client-server Internet GIS architecture. The clients consist of all SRTS-involved parties who use Web browsers to access information services provided by the GIS server. The GIS server consists of three GIS-functional modules and four Web portals. GIS modules include a walkability/bikeability assessment module, a network analysis module, and a Web mapping module. A Web portal is a site that provides a single function via a Web page or site. Web portals in this Web-based GIS are used for online data entry and communication, which include a field data entry portal, a walking/biking activity monitoring portal, a walking/biking safety concern reporting portal, and a public opinion surveying portal. This Internet GIS framework adopts a thin-client architecture so that all data processing and map creation are performed by the server and a client can simply use a Web browser The program that serves as your front end to the Web on the Internet. In order to view a site, you type its address (URL) into the browser's Location field; for example, www.computerlanguage.com, and the home page of that site is downloaded to you.  to manipulate and view data. The following paragraphs explain the structure and functions of each module or portal.

Walkability/Bikeability Assessment Module

This module assesses the walking and bicycling safety conditions of neighborhoods, roadways, and intersections. Various walkability and bikeability indicators discussed previously can be computed based on safety measures associated with various transportation facilities in the database. It should be noted that with the help of the public opinion survey portal, perceptual safety and security indexes of transportation facilities and neighborhood environment can be obtained. These perceptual indexes then can be used to determine coefficients or relative weights of various walkability/bikeability measures. Moreover, with the perceptual safety or security indexes, regression models can be established for pedestrian and bicyclist LOS indexes (Landis et al. 1997, 2001). Assessment results, in turn, can be stored in the database and published online in map or tabular format.

Network Analysis Module

Based on roadway and intersection walkability or bikeability measures in the database, this module performs the following tasks using path-finding algorithms that minimize total risks:

* Identifies walkable/bikeable areas,

* Finds the best route between any location and a school,

* Plans the best walking school bus routes and stop locations given student home locations, and

* Plans school bus routes and locates stops given student home locations.

Overall, this module can attract a wide audience. For example, it can help parents and children find the best route to school. It may encourage more parents to select walking or bicycling as children's school trip mode. It also can assist the school to plan school bus routes and stops, and aid Parent-Teacher Associations (PTAs) to organize walking school buses or other walking/bicycling activities. Furthermore, it can be used by multimodal planners for traffic analysis and alternative development.

Web Mapping Module

A Web mapping module is an essential part of the system that creates maps dynamically on user requests and delivers the maps online. Examples of Web mapping include:

* General Web maps for interactive information query,

* Walkable or bikeable area map for school trips,

* Pedestrian or bicyclist roadway safety maps,

* Pedestrian or bicyclist intersection safety maps, and

* Best walking/bicycling path maps.

All these maps are interactive so that they can be zoomed, panned, and queried by online users.

Field Data Entry Portal

This portal facilitates online updating of walking and bicycling safety data collected by the field auditing instruments shown in Figure 3. Data collected by field auditing instruments are encoded in XML XML
 in full Extensible Markup Language.

Markup language developed to be a simplified and more structural version of SGML. It incorporates features of HTML (e.g., hypertext linking), but is designed to overcome some of HTML's limitations.
 documents that then are uploaded to the central GIS database through this portal by users with administrative privileges.

Walking/Biking Monitoring Portal

This portal of the Web-based GIS allows students to periodically log their walking and biking activities. Student walking and biking activities then can be queried and displayed in maps for specified time periods. The module not only can enable school authorities to obtain timely information of walking and biking activities of students, but also can be used by organizations such as PTAs to organize walking and bicycling competition programs.

Public Opinion Surveying Portal

The wonder of a Web-based GIS is its public accessibility. This portal provides various online surveys (e.g., http://zenith.geog.nau.edu/GIS/srts/survey.html). An important survey is to collect road safety or comfort level indexes to determine weights for walking and biking safety measures or criteria. Experts and residents can be invited to participate in the survey. Safety or security concerns of parents about the walking and bicycling environment may be collected by another survey. Public opinion also may be collected from online discussion areas in this portal to provide additional information to SRTS project personnel.

Safety Concern Reporting Portal

This portal provides an unsafe or unsecure factor reporting mechanism for the public to report unexpected unsafe conditions. Upon receiving a case, the system administrator is responsible for updating the information in the GIS database after verifying the reported cases.

IMPLEMENTATION

The data model and framework have been implemented in an experimental online information service for SRTS for the Sechrist Elementary School in Flagstaff, Arizona (see Figure 5).

[FIGURE 5 OMITTED]

[FIGURE 6 OMITTED]

The Sechrist Elementary School is located on the east side of Arizona State Highway 180 (Fort Valley Road), a high-traffic-volume road with 15,197 vehicles per day (FMPO 2003 Annual Traffic Volume Report), and is surrounded by hillslopes on three sides. Students of the school are mainly from three neighborhoods: the Coconino Estates neighborhood across Highway 180 to the south and west, Mount Elden neighborhood to the southeast, and Cheshire to the northwest (as shown in Figure 6).

A glance at the map in Figure 6 finds that the location of the school is not friendly for walking. First, it is not located inside any of the neighborhoods. The closest neighborhood is Coconino Estates located across the state highway. Moreover, although the Mount Elden neighborhood is within one-mile direct distance from the school, the entire neighborhood is out of the one-mile walking distance (see Figure 5) because of poor street connectivity. Furthermore, the Cheshire neighborhood is completely beyond a one-mile direct distance to school. Fortunately, a new bikeway bike·way  
n.
A bicycle lane or path.
 connecting the Cheshire and the school has been planned for the near future and is expected to improve the bikeability of that neighborhood. A database was created and an Internet GIS was developed in this research. The following sections demonstrate capabilities for walkability and walking safe evaluation as well as safe routes planning supported by the Internet GIS.

Pedestrian Catchment Area (PCA) Ratio and Intersection Density

A PCA is the walkable area within a network given an origin or destination location. This area can be derived from service area analysis with a GIS. A PCA ratio is the ratio of a PCA to a theoretical walkable area in a homogeneous space (a circle). Schlossberg (2007) suggests a PCA ratio of 0.5 to 0.6 for a walkable environment, and indicates that a ratio below 0.3 would reflect an inaccessible environment for walking. With a PCA ratio of 0.26, this school district is virtually unwalkable. This inaccessibility is because of the valley bottom location on one hand and the low street connectivity of the urban area on the other hand. Connectivity can be measured by intersection density. Schlossberg (2007) suggests that an intersection density of less than 100 per square mile indicates an unwalkable neighborhood. The Sechrist School district has a very low intersection density of 68 per square mile.

The Mount Elden neighborhood is connected to the network only at its southwest corner. Although most of this neighborhood is within one-mile direct distance from the school, it is totally out of the one-mile walking area (see Figure 5). If a walking link is established between the northwestern corner of the neighborhood and the Fort Valley road, the neighborhood would become mostly walkable and the PCA ratio can be increased to 0.32. Supported by network analysis and walkability assessment modules, alternative planning scenarios can be developed by the GIS.

Pedestrian Level of Service (PLOS)

To demonstrate the capability for roadway walkability assessment, the system calculates the pedestrian level of service (PLOS) for every street segment using the following formula proposed by Landis et al. (2001):

PLOS = -1.2021 ln ([W.sub.ol] + [W.sub.1] + [f.sub.p] x OSP (Online Service Provider) See online service.

OSP - Optical Signal Processor
 + [f.sub.b] x [W.sub.b] + [f.sub.sw] x [W.sub.s]) + 0.253 ln ([Vol.sub.15]/L) + 0.0005 [SPD (Serial Presence Detect) The method used by DIMM memory modules to communicate their capacity and features to the computer. Data such as manufacturer, size, speed, voltage and row and column addresses are stored in an EEPROM chip on the module. .sup.2] + 5.3876 ... (2)

where [W.sub.ol] represents outer lane width (feet), [W.sub.1] is width of shoulder or bike lane (feet), OSP is percent of segment with on-street parking, [W.sub.b] is buffer zone width (feet), Ws is sidewalk width (feet), L is total number of through lanes, SPD is average running speed of motor vehicle traffic (mi/hr), and [Vol.sub.15] is average traffic during a 15-minute period. In addition, [f.sub.p] (= 0.20), [f.sub.b] (= 5.37), and [f.sub.sw] (= 60.3 [W.sub.s]) are effect coefficients of their corresponding variables. Equation (2) measures three categories of walking safety factors: the lateral separation, traffic volume, and traffic speed. Coefficients of these factors were established based on step-wise regression analyses of real-time observations in walking events.

Most of the variables in Equation (2) are directly available from the GIS database, except the 15-minute traffic volume ([Vol.sub.1]5). However, this variable can be derived from the average daily traffic (ADT) by the following formula (Barsotti 2002):

Vol15 = (ADT * D * Kd) / (4 * PHF PHF Public Health Foundation
PHF Paired Helical Filaments
PHF Pakistan Hockey Federation
PHF Paul Harris Fellow
PHF Potentially Hazardous Food
PHF Peak Hour Factor (highway capacity, civil engineering)
PHF Psychiatric Health Facility
) ... (3)

where D (= 0.565) is directional factor, Kd (= 1/11) is peak to daily factor, and PHF (= 0.92) is peak hour factor. Values of these factors are available in the Highway Capacity Manual The Highway Capacity Manual (HCM) is publication of the Transportation Research Board (TRB) in the United States. It contains concepts, guidelines, and computational procedures for computing the capacity and quality of service of various highway facilities, including freeways,  (TRB TRB Transportation Research Board
TRB Technical Review Board
TRB Teacher Registration Board
TRB Test Review Board
TRB Total Relationship Balance
TRB Tap-Rack-Bang (shooting procedure)
TRB Theodore Roosevelt Building
 1994). Scores of LOS are stratified stratified /strat·i·fied/ (strat´i-fid) formed or arranged in layers.

strat·i·fied
adj.
Arranged in the form of layers or strata.
 into six classes labeled by letters as shown in Table 2 (Landis et al. 2001).

Vehicle count data are available for a number of locations in the neighborhoods for years 2000 to 2003 (2003 Annual Traffic Volume Report of the City of Flagstaff Flagstaff, city (1990 pop. 45,857), seat of Coconino co., N Ariz., near the San Francisco Peaks; inc. 1894. Lumbering, ranching, and a lively tourist trade thrive in the region, where many ruined pueblos, numerous state parks, several lakes, and large pine forests  ). For unmeasured residential streets, an ADT of 2,000 vehicles per day is assumed in calculating PLOS. Figure 7 is a snapshot of the interactive online roadway PLOS map. Roadway safety measures and PLOS values can be identified in the online GIS.

Pedestrian Intersection Safety Index (PISI PISI Pine Siskin (bird species)
PISI Port-Injection Spark-Ignition (engine) 
)

Intersection safety for pedestrians can be assessed by the Pedestrian Intersection Safety Index (PISI) of Federal Highway Administration (FHWA 2006):

PISI = 2.372 - 1.867SIG - 1.807STP STP or standard temperature and pressure, standard conditions for measurement of the properties of matter. The standard temperature is the freezing point of pure water, 0°C; or 273.15°K;.  + 0.335LNS LNS L2TP Network Server (terminates L2TP tunnels & provides PPP and network termination)
LNS Laboratory for Neutron Scattering
LNS Laboratori Nazionali del Sud (Italy)
LNS Logarithmic Number System
 + 0.018 [SP.sub.85] + 0.006(ADT * SIG) + 0.238 COM (1) (Computer Output Microfilm) Creating microfilm or microfiche from the computer. A COM machine receives print-image output from the computer either online or via tape or disk and creates a film image of each page.  ... (4)

where SIG is a binary variable for traffic signal-controlled crossing (0 = no, 1 = yes), STP is a binary variable for stop sign-controlled crossing (0 = no, 1 = yes), LNS represents total number of through lands on street being crossed, [SPD.sub.85] is the 85th percentile speed of street being crossed (mph), which may be estimated as the posted speed limit plus four to eight miles per hour (Fitzpatrick et al. 2003), ADT is the average daily traffic count in thousands, and COM is a binary variable for predominant commercial land use (0 = no, 1 = yes).

This GIS computes PISI for each crosswalk at an intersection and attributes the average of all crosswalks PISI to the intersection. Figure 8 is a snapshot of the PISI map. Crosswalk properties and intersection ISI ISI International Sensitivity Index, see there  values can be interactively identified by this Web-based GIS.

Network Analysis

Roadway and intersection walking and bicycling safety indexes can be incorporated into transportation networks to support safe path analysis in GIS. This is illustrated in Figure 9, in which the safest path from a student's home to the school was found with turn-by-turn trip directions. Network analysis also can be performed to find the best path given multiple locations for origins or destinations such as in organizing a walking school bus.

[FIGURE 7 OMITTED]

[FIGURE 8 OMITTED]

[FIGURE 9 OMITTED]

CONCLUSION

This paper presents a GIS data model and an Internet GIS framework for an SRTS information service. The data model can be used to guide the development of GIS databases for walking and bicycling safety data storage, retrieval, and analyses. It also provides a framework to guide data collection for SRTS projects. An Internet GIS is a Web-based application that provides online GIS services to allow the public as well as multiple agencies to seek SRTS-related information. The Internet GIS framework proposed in this paper consists of three GIS functional modules and four Web portals. The walkability/bikeability assessment module computes various walking and bicycling safety indexes at neighborhood, roadway, and intersection levels, while the network analysis module performs safe routes planning based on safety indexes. The Web mapping module presents query and analysis results in interactive maps and other various formats. The four Web portals expand online data communication to include field data uploading, online surveys, walking/bicycling safety concern reporting, and trip logging. The proposed system is flexible enough to incorporate data ranging from engineering standards to user perceptions. An Internet GIS based on this data model and framework can provide a public participation platform in which every SRTS-involved party, including children, parents, teachers, urban planners, transportation engineers, and law enforcement officers, can play a role.

References

1,000 Friends of Oregon. 1993. Making the land use--transportation--air quality connection. The Pedestrian Environment, Volume 4A.

Baltes, M. R., and X. Chu. 2002. Pedestrian level of service for mid-block street crossings. Transportation Research Record 1818.

Barsotti, E. 2002. Bicycle level of service application, http://www.co.kane.il.us/DOT/COM/Bicycle/FINAL/xK.pdf, accessed July 26, 2008.

Botma, H. 2005. Method to determine level of service for bicycle paths and pedestrian-bicycle paths. Transportation Research Record 1502.

Chu, X., and M. R. Baltes. 2001. Pedestrian mid-block crossing difficulty. Final report. Tampa, FL: National Center for Transit Research, University of South Florida


    [
.

City of Portland, Office of Transportation, OR. 1998. Identifying priorities for pedestrian improvements. Portland pedestrian master plan, Chapter 4, http://www.trans.ci.portland.or.us/Plans/PedestrianmasterPlan/Chapter4.pdf, accessed September 29, 2004.

Crawford A. P. 2006. Alert--how getting to school safely can fight childhood obesity. Vegetarian Times 339:60-60.

Davis, J. 1987. Bicycle safety evaluation. Chattanooga, TN: Auburn University, City of Chattanooga, and Chattanooga-Hamilton County Regional Planning Commission.

Dixon, L. 1995. Adopting corridor-specific performance measures for bicycle and pedestrian level of service. Transportation Planning 22(2).

Ewing, R., T. Schmid, R. Killingsworth, and S. Raudenbush, 2003. Relationship between urban sprawl and physical activity, obesity, and morbidity. American Journal of Health Promotion 18(1): 47-57.

FHWA. 2008. Http://safety.fhwa.dot.gov/saferoutes/, accessed May 30, 2008.

FHWA (USDOT). 2006. Pedestrian and bicyclist intersection safety indices--final report, http://www.tfhrc.gov/safety/pedbike/pubs/06125/06125.pdf, accessed November 27, 2007.

FHWA (USDOT). 2007. Pedestrian and bicyclist intersection safety indices--user guide, http://www.tfhrc.gov/safety/pedbike/pubs/06130/06130.pdf, accessed July 21, 2008.

Fitzpatrick, K., P. Carlson, M. A. Brewer, M. D. Wooldridge, and S. P. Miaou. 2003. NCHRP NCHRP National Cooperative Highway Research Program  Report 504: design speed, operating speed, and posted speed practices. Texas Transportation Institute The Texas Transportation Institute (TTI) is the largest transportation research agency in the United States. Created in 1950, primarily in response to the needs of the Texas Highway Department (now the Texas Department of Transportation), TTI has since broadened its focus to , http://trb.org/publications/nchrp/nchrp_rpt_504. pdf, accessed August 3, 2008.

Frank, L., and P. Engelke. 2001. The built environment and human activity patterns: exploring the impacts of urban form on public health. Journal of Planning Literature 16(2): 202-18.

Frank, L. et al. 2005. Linking objectively measured physical activity with objectively measured urban form: findings from SMARTRAQ. American Journal of Preventive Medicine preventive medicine, branch of medicine dealing with the prevention of disease and the maintenance of good health practices. Until recently preventive medicine was largely the domain of the U.S.  28(2): 117-25.

Gallin, N. 2001. Quantifying pedestrian friendliness. Road & Transport Research 10(1).

Harkey, D. L., D. W. Reinfurt, M. Knuiman, J. R. Stewart, and A. Sorton. 1998. Development of the bicycle compatibility index: a level of service concept. Report No. FHWA-RD-98-072. McLean, VA: Federal Highway Administration.

Hurvitz, P., 2005. The geography of obesity: mapping and modeling in King County, http://depts.washington.edu/uwecor/ docs/kcopi_hurvitz.pdf, accessed September 8, 2007.

Khisty, C. J. 1994. Evaluation of pedestrian facilities: beyond the level-of-service concept. Transportation Research Record 1438.

Landis, B. W. 1994. Bicycle interaction hazard score: a theoretical model. Transportation Research Record 1438.

Landis, B. W., V. R. Vattikuti, and M. T. Brannick. 1997. Real-time human perceptions: toward a bicycle level of service. Transportation Research Record 1578: 119-26.

Landis, B. W., V. R. Vattikuti, R. M. Ottenberg, D. S. McLeod, and M. Guttenplan. 2001. Modeling the roadside walking environment: a pedestrian level of service. Transportation Research Record 1773.

Leslie, E., B. Saelens, L. Frank, N. Owen, A. Bauman, N. Coffee, and G. Hugo. 2005. Residents' perceptions of walkability attributes in objectively different neighborhoods: a pilot study. Health & Place 11: 227-36.

Leslie, E., N. Coffee, L. Frank, N. Owen, A. Bauman, and G. Hugo. 2007. Walkability of local communities: using geographic information systems to objectively assess relevant environmental attributes. Health & Place 13: 111-22.

Lopez, R. P., and H. P. Hynes. 2006. Obesity, physical activity, and the urban environment: public health research needs. Environmental Health 5: 25.

McMillan, T. E. 2005. Urban form and a child's trip to school: the current literature and a framework. Journal of Planning Literature 19(4): 440-56.

McMillan, T. E. 2007. The relative influence of urban form on a child's travel mode to school. Transportation Research Part A 41: 69-79.

National Highway Traffic Safety Administration (NHTSA). 2004. Safe routes to school: practice and promise. DOTHS809-742, http://www.nhtsa.dot.gov/people/injury /pedbimot/ bike/Safe-Routes-2004/, accessed September 13, 2007.

Noel, N., C. Leclerc, and M. Lee-Gosselin. 2003. CRC index: compatibility of roads for cyclists in rural and urban fringe areas. Presented at the 82nd Annual Meeting of the Transportation Research Board, Washington, D.C., January 2003.

Sallis, J., A. Bauman, and M. Pratt, 1998. Environmental and policy interventions to promote physical activity. American Journal of Preventive Medicine 15(4): 379-97.

Sarkar, S. 1993. Determination of service levels for pedestrians, with European examples. Transportation Research Record 1405.

Schlossberg, M. 2007. From TIGER to audit instruments: using GIS-based street data to measure neighborhood walkability. Transportation Research Record 1982: 48-56.

Schlossberg, M., J. Greene, P. P. Phillips, B. Johnsan, and R. Parker. 2006. School trips: effects of urban form and distance on travel mode. Journal of the American Planning Association 72(3): 337-46.

Sorton, A., and T. Walsh. 1994. Bicycle stress level as a tool to evaluate urban and suburban bicycle compatibility. Transportation Research Record 1438.

Transportation Research Board (TRB). 1994. Highway capacity manual. Special Report 209, Updated Third Ed., Washington, D.C.

Victoria Transport Policy Institute. 2007. Walkability improvements: strategies to make walking convenient, safe and pleasant. Http://www.vtpi.org/tdm/tdm92.htm, accessed October 5, 2007.

Ruihong Huang is an associate professor in the Department of Geography, Planning, and Recreation at Northern Arizona University Northern Arizona University (NAU) is a public university in Flagstaff, Arizona in the United States.

As of Fall 2007, the university has 21,352 students, 13,989 of these are situated in the main Flagstaff campus<ref name="Enrollment" />.
, Flagstaff. His teaching and research interests include GIS data modeling, GIS for transportation, Internet GIS, spatial data mining, and urban spatial analysis.

Corresponding Address:

Department of Geography, Planning, and Recreation

Northern Arizona University, Box 15016

Flagstaff, AZ 86011-5016

(Phone) (928) 523-8219

(Fax) (928) 523-2275

Ruihong.Huang@nau.edu

Dawn Hawley is a professor in the Department of Geography, Planning, and Recreation at Northern Arizona University, Flagstaff. Her areas of interest in teaching and research include urban environments, public participation, resource and public policies, and GIS.
Table 1. Summary of Walking and Bicycling Environment Factors

Dimension       Environmental Measure

Regional        Quality (street classification analysis):
                Minor roads (mi)
                Major roads (mi)
                Minor road density (street miles per area)
                Minor-major road ratio

                Proximity (pedestrian catchment area):
                Pedestrian catchment area (ratio)
                Impeded pedestrian catchment area (ratio)
                Distance to school
                Route directness (ratio of the straight-line
                distance from home to school to the network
                distance from home to school)

                Connectivity (intersection analysis):
                Intersection density
                Dead-end density
                Intersection/dead-end ratio
                Impedance-based intersection density
                Impedance-based dead-end density
                Impeded intersection/dead-end ratio
                Change in intersection/dead-end ratio

                Environmental/social:
                Population density (by census tract)
                Dwelling density (by CCD)
                Block size
                Land-use mix
                Commercial density
                Accessibility to opportunities
                Accessibility to transit
                Attractiveness (e.g., tree cover)
                Physical barriers (e.g., slope)
                Crime rate

Roadway         Sidewalk presence
                Sidewalk width
                Sidewalk continuity
                Sidewalk quality (pavement condition)
                Outside lane width
                Shoulder or bike lane width
                On-street parking (percentage of road
                segment)
                Planting strip (yes/no)
                Attractiveness (favoring environmental
                factors such as landscape)
                Eyes on the street (security)
                Street lighting
                Geometric measures (curves)
                Terrain (maximum slope of segment)
                Motor vehicle volume
                Motor vehicle speed (limit)
                Number of through lanes
                Number of commercial driveways
                Crash records

Intersection    Crosswalk presence
                Crosswalk width
                Crosswalk length
                Width of the outside through lane
                Traffic control (no/stop sign/signal/pedestrian
                signal/push button)
                Median islands (presence)
                One way (yes/no)
                Traffic volume
                Vehicle speed
                Roadway width
                Crash records
                Number of lanes
                Curb radii
                On-street parking (yes/no)
                Right-turn-on-red allowance
                Surrounding development type
                Sight distance

Table 2. Categories of LOS Scores

LOS  Score

A    [less than or equal to] 1.5
B    > 1.5 and = 2.5
C    > 2.5 and = 3.5
D    > 3.5 and = 4.5
E    > 4.5 and = 5.5
F    > 5.5
COPYRIGHT 2009 Urban and Regional Information Systems Association (URISA)
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2009 Gale, Cengage Learning. All rights reserved.

 Reader Opinion

Title:

Comment:



 

Article Details
Printer friendly Cite/link Email Feedback
Author:Huang, Ruihong; Hawley, Dawn
Publication:URISA Journal
Article Type:Report
Geographic Code:1USA
Date:Jan 1, 2009
Words:5862
Previous Article:St. Kitts land resource analysis.
Next Article:Modernizing the register of deeds in Dane County, Wisconsin.
Topics:



Related Articles
GIS touted as world class.
Integrated mobile GIS and wireless Internet Map Servers for environmental monitoring and management.
GIS--an invaluable tool for oil & gas.
Bringing eco-tourism to the masses: interaction through GIS and the world wide web.
Badger army ammunition plant applied GIS for environmental remediation and restoration.
Montgomery Township Web-GIS solution.
The BioWatch Tool: GIS-enabled Sensor Siting.
Interoperable internet mapping--an open source approach.
GIS as an enterprise municipal system.
Complexities in GIS construction and spatial knowledge production in Dane County, Wisconsin.

Terms of use | Copyright © 2014 Farlex, Inc. | Feedback | For webmasters