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


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). Problems accompanying this change include childhood obesity, traffic congestion, air pollution, and pedestrian safety issues. (NHTSA 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) in 2005. The SRTS program is administered and guided by the Federal Highway Administration (FHWA) of the U.S. Department of Transportation (USDOT). The FHWA recommends that SRTS efforts in the United States 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 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 (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 and facilitates evaluation of walkability and bikeability conditions. Built on the GIS database, Internet GIS provides advanced online information services 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.


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, 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) based on four environmental attributes: dwelling density, connectivity (using road centerline 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) (Sarkar 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 1998), Level of Service (LOS) (Botma 1995), Bicycle Safety Index Rating (BSIR) (Davis 1987), roadway condition index (RCI), Bicyclist Stress Level (Sorton and Walsh 1994), Intersection Hazard Score (IHS) (Landis 1994), Bicycle Level of Service (BLOS) (Landis, et al. 1997), Bicycle Compatibility Index (BCI) (Harkey et al. 1998), intersection BLOS (Landis et al. 1997), Compatibility of Roads for Cyclists (CRC) (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.


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, and be flexible to adapt to future changes. Figure 1 is a logical schema 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, 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 geodatabase, special topological rules, such as street centerlines must not cross properties, may be applied to certain features as needed. 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.



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


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 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.



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 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 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., 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.


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).



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 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 + [f.sub.b] x [W.sub.b] + [f.sub.sw] x [W.sub.s]) + 0.253 ln ([Vol.sub.15]/L) + 0.0005 [SPD.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) ... (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 (TRB 1994). Scores of LOS are stratified 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 ). 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)

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 + 0.335LNS + 0.018 [SP.sub.85] + 0.006(ADT * SIG) + 0.238 COM ... (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 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.





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.


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Ruihong Huang is an associate professor in the Department of Geography, Planning, and Recreation at Northern Arizona University, 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

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

 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
 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
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Article Details
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Author:Huang, Ruihong; Hawley, Dawn
Publication:URISA Journal
Article Type:Report
Geographic Code:1USA
Date:Jan 1, 2009
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