Using GIS Machine Learning Technique to Analysis Road Safety

near Parks

Yan Duan

Urban and Regional Planning Department, University at Buffalo-State University of New York, Buffalo, New York, U.S.A.

Keywords: Park Accessibility, Road Safety, Level of Traffic Stress, Machine Learning, Non-Motorized Transportation.

Abstract: Parks are essential components of the city fabric, providing recreation and relaxation places to residents. 0.25-

mile is a good walking distance. People within the park’s 0.25-mile service area are more likely to use non-

motorized transportation to travel to the park. In complex traffic settings, bicycles and pedestrians are

frequently at a higher risk of being involved in crashes. Thus, exploring road safety near parks is significant

for cyclists and pedestrians. This study aims to analyze and measure spatial relationships between traffic crash

clusters and park entrances to explore factors that influence traffic crashes near park entrances. The result

suggests that road traffic level of stress and roadway design and park parking locations are both factors to

impact the traffic conflicts near park entrances. To guarantee and improve safe non-motorized transportation

to parks, park planners and designers should consider alternative park accessibility and connectivity for

cyclists and walkers. This research aims to find factors that influence active transportation safety around parks.

The result of this paper will contribute to creating a safe walking and bicycle environment around parks.

1 INTRODUCTION

Parks provide a critical opportunity to strengthen the

fabric of a community, weaving together the social

and cultural landscape with the built and natural

environment. Research uncovered evidence of a

beneficial relationship between urban parks and

emotions, exercise, and attention (Kondo et al 2018).

As consequently, the COVID-19 epidemic has raised

the demand for active transportation, such as cycling

and walking, it provides an affordable, healthy, and

pollution-free daily option for transportation. In the

post-COVID Era, parks and public green nature

places have been gaining attractiveness as a

destination for active travel activities. Ensuring

accessibility and safety near parks and green spaces

is crucial. Also, the demand for active transportation

options like walking and cycling has grown within the

past ten years (Hasani et al 2019).

In urban places,

pedestrians and cyclists are frequently at a higher risk

of being involved in crashes due to complex traffic

situations. Plenty of research reveals that people

value safety beyond all else when choosing a means

of transportation. Although they love to ride, cyclists

feel that their commute is more dangerous and

vulnerable than that of automobiles, which is a major

impediment to riding and walking (Ferreira et al

2022). In urban settings, walking is a vital kind of

active transportation. Due to their vulnerability in

complex traffic conditions, people walking,

motorcycle riders, and pedal cyclists face a higher

chance of collisions in metropolitan areas than the

vehicle users.

Road safety is influenced by a multitude of factors,

including drivers, environmental conditions, and

vehicle specifications (Boggs et al 2020). Curb cuts

and junctions can have an impact on how frequently

and how badly cars collide with other cars, with

people, and with the environment. The type,

frequency, and severity of collisions are subsequently

impacted by these conflicts (Huang et al 2018).

Previous studies have explored park accessibility and

walkability through a built environment design

perspective, such as bike and pedestrian infrastructure,

parking space, transit stops, etc.

This study aims to explore park accessibility from

a traffic safety perspective. This paper examines

relationships between traffic crash location and park

entrance roadway level of traffic stress to explore

potential factors related to the crash locations near

parks. Traffic levels of stress can be a tool for planning

level analysis. The result can guide park designers to

think about design guidelines for park safety.

394

Duan, Y.

Using GIS Machine Learning Technique to Analysis Road Safety near Parks.

DOI: 10.5220/0012888000004547

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Data Science and Engineering (ICDSE 2024), pages 394-399

ISBN: 978-989-758-690-3

2 DATA AND STUDY AREA

2.1 Data Sources

Data used in our study areas are from multiple sources.

The table below illustrates the data set and sources.

Table 1 below explains the data and sources used in

this research. All data was downloaded as ArcGIS shp.

files. The crash data used in this study was from the

Georgia Department of Transportation between 2018

and 2022. A total of 152,517 accidents were collected

throughout the five-year range. The crash data includes

the location of all types of crashes, the roadway

alignment, surface conditions, weather conditions,

lighting conditions, and level of severity. Road

Centerline includes roadway speed limits, road names,

and road classification. The county boundary is the

exterior boundary of Gwinnett County. County Park

includes 53 developed parks in Gwinnett County.

Table 1: Research Data Source.

Data Source

Road

centerline

Atlanta Regional Commission Open Data

https://opendata.atlantaregional.com/

Traffic

Accident

Georgia Department of Transportation

https://gdot.aashtowaresafety.net/crash-data#/

County

boundary

Gwinnett County GIS Data Resource

https://gcgis-gwinnettcountyga.hub.arcgis.com/

County Park

Gwinnett County Online GIS Data

https://gcgis-gwinnettcountyga.hub.arcgis.com/

2.2 Study Area

The geographic scope of this research is within the

Gwinnett County Boundary. Fig. 1 shows the

Gwinnett County boundary, municipalities within the

County boundary, County Parks, major roads, and

non-residential land use.

3 METHODOLOGY

3.1 Data Process

This section presents the methodological component

of this study. A total of three methods are used in this

research. (1) GIS-mapping of the crash location by

using the hotspot cluster method. (2) Coding roadway

traffic level of stress based on the decision tree

method in GIS. (3) GIS-mapping road network for

visualizing park's 0.25 mi walking area.

3.1.1 GIS-Mapping Crash Hot Spot Analysis

(Getis-Ord 𝑮

𝒊

∗

) (Spatial Statistics)

The Hot Spot Analysis is a tool for calculation of the

Getis-Ord 𝐺



∗

statistic (pronounced G-i-star). The

resulting p-values and z-scores can show the spatial

clustering of characteristics with high or low values.

The Getis-Ord local statistic is given as:

𝑮

𝒊

∗

∑



,













∑



,











∑



,







∑



,













(1)

Figure 1: Study Area-Gwinnett County (Picture credit: Original).

Using GIS Machine Learning Technique to Analysis Road Safety near Parks

395

Where 𝑥



is the attribute value for feather 𝑗, 𝑤

,

is the spatial weight between feature 𝑖 and 𝑗 , 𝑛 i s

equal to the total number of features, a n d 𝑋



a n d 𝑆

can be calculated through the following formulas (2)

and (3):

𝑿



∑











(2)

𝑺=



∑













−



𝑋







(3)

The 𝐺



∗

statistic is a 𝓏 -socre so no further

calculations are required.

3.1.2 Level of Traffic Stress Method

An indicator named the Level of Traffic Stress (LTS)

differentiates a road network's features according to

how challenging it can be for walkers and bikers

(Huertas et al 2020).

When testing the perceived level of travel comfort,

Level of Traffic Stress is a useful measure for people

walking or bicycling along a given roadway.

Supporting efforts to develop safe and connected

networks of transportation facilities, LTS analysis can

identify streets that work well and areas in need of

improvement. Moving beyond minimum design

criteria, LTS helps planners, engineers, and advocates

understand the interrelated factors that either

encourage or discourage walking and bicycling.

Bicycle Level of Traffic Stress (BLTS) can be

used as a measurement for performance and safe with

respect to bicyclist. Pedestrian Level of Traffic Stress

(PLTS) is used to people who are neither riding on a

bicycle nor in a car. LTS is applicable regardless of

the presence or absence of a bike lane or sidewalk,

and it may be evaluated for both proposed and current

conditions.

The LTS's simplicity, which is based on a clear

decision tree approach in most of its implementations,

is one of its greatest advantages. However, because

there are so many segment-level variables required

for the categorization, LTS can be difficult to use and

comprehend (Harvey et al 2019). This research only

coded LTS for roadways near park entrances that have

a high traffic crash density cluster.

Table 2 below shows a matrix that determines

Bicycle Level of Traffic Stress and Pedestrian Level

of Traffic Stress for mixed traffic (no designated bike

lane, whether it has a shoulder or not). This table

shows a total of 4 levels of traffic stress, ranging from

1 to 4. There are 3 variables used for determining the

level of traffic stress: Traffic through lanes, annual

average daily traffic (AADT), and traffic speed (mph).

The degree of traffic stress is positively correlated

with all three variables. It suggests higher AADT,

greater velocity limits, and more lanes along roads

will all end up in higher levels of traffic stress.

3.2 Analysis of Roadway Network

Systems

Anyone may utilize network analysis to find service

areas surrounding a specific location on a network. A

network service area is a shed. A shed that contains

all accessible streets—that is, roadways that are

within a given impedance—is referred to as a network

service area. For instance, the 0.25-mile service area

for a park entrance includes all the streets that can be

reached within 0.25 miles (0.5 minutes walking) from

that park entrance. By doing this analysis, it can seen

that the 0.25-mile distance that people are most likely

to walk to parks. The average distance that an

American will walk before deciding to drive is 400

meters, or 0.25 miles or five minutes on foot. In US

park accessibility studies, a quarter mile (0.4 km) was

chosen as a typical threshold distance (Cutts et al

2009). In GIS, the study uses a network analysis tool

to create a 0.25-mile walking area for each park.

People who live or work within the 0.25-mile walking

area are more likely to use walking or bicycling to

access the park. These people have a higher risk of

being exposed to traffic crashes.

Table 2: Bicycle Level of Traffic Stress and Pedestrian Level of Traffic Stress Criteria.

Lanes AADT ≤ 20 mph 25 mph 30mph 35mph 40mph 45mph 50+ mph

1 thru lane

per direction

(or 1 lane

one-way

street)

0-750 1 1 3 4 4 4 4

751-1500 1 2 3 4 4 4 4

1501-3000 2 2 3 4 4 4 4

3000+ 2 3 3 4 4 4 4

2 thru lanes

per direction

0-7000 3 3 3 4 4 4 4

>7000 3 3 4 4 4 4 4

3+ thru lanes

er direction

Any ADT 4 4 4 4 4 4 4

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396

Figure 2: 2018-2022 Traffic Crash Point Map (Picture credit: Original).

Figure 3: 2018-2022 Traffic Crash Density Heat Map (Picture credit: Original).

Table 3: Parks have traffic crash clusters near their entrance.

Park Name Area (AC) Park Classification Street Name Bike

Lane

Creek Par

154.6 Communit

Par

Ozora R

Best Friend Par

43.1 Community Par

Jimmy Carter Blv

Club Drive Par

25.5 S

ecial Nei

hborhood Par

Club D

Duncan Creek Par

114.3 Community Par

Braselton Hwy Yes

Freeman's Mill Par

11.9 S

ecial Nei

hborhood Par

Alcov

Jones Bridge Par

30.0 Community Par

Jones Bridge R

Lilburn Activit

Buildin

2.1 Activit

Buildin

s Hillcrest R

Luck

Shoals Par

70.0 Communit

Par

Britt R

Yes

Mountain Park Par

43.4 Community Par

Five Forks Trickum R

Peachtree Rid

e Par

153.8 Communit

Par

Suwanee Creek R

Using GIS Machine Learning Technique to Analysis Road Safety near Parks

397

Pinckneyville Community

Cente

14.4 Community Recreation Centers Peachtree Industrial

Blv

Yes

Shorty Howell Par

66.5 Community Par

Pleasant Hill R

Yes

Sim

sonwood Par

222.4 O

en S

ace Par

Jones Brid

e Ci

South Gwinnett Par

22.9 Community Par

McGee R

Yellow River Post Office 5.1 Cultural Resource Park Five Forks Trickum Rd No

4 RESULT

4.1 County-Wide Level Mapping

Result Analysis

Fig. 2 shows county-wide crash locations. Fig. 3 is a

crash heatmap; it shows crash clusters. When some of

the crash cluster areas are within a park's 0.25-mile

walking shed, they bring safety risk to active

transportation.

4.2 Park Level Mapping Result

Analysis

This research finally selected 15 parks where traffic

crash clusters fall exactly on its main entrance. Then,

this paper selected the roadways that are next to park

entrances. The level of traffic stress results shows

most of the selected roadways are in traffic levels of

stress 4. This brings risk to pedestrians and cyclists

who travel to the 15 parks. In terms of the rest of the

county parks, there are traffic crash clusters within the

0.25-mile walking area of all county parks. Except for

those 15 parks that have higher traffic risk, all county

parks have potential traffic risk for cyclists and

walkers. Fig. 4 shows one example of the map result.

Figure 4: Bay Creek Park Entrance and Traffic Cluster Spot

(Picture credit: Original).

Table 3 shows detailed information on the fifteen

(15) parks that have high traffic risk for cyclists and

pedestrians. Most of them are community parks,

those park sizes are 25 acres or more. Community

Park typically is designed to serve an area within a ½

mile to over a 3-mile radius. Most of them do not

have separate infrastructure for active transportation.

In summary, based on the spatial analysis above,

there is a strong traffic crash impact near the park

entrance. And there is a correlation between the road

level of traffic stress and the density of crashes. A

higher and larger traffic crash density is on a higher

roadway level of traffic stress.

For deeper investigation, 226 traffic crashes that

occurred at the park entrance between 2018 and 2022

were gathered. Table 4 shows the crash detailed

information on the traffic crash near parks’ entrances.

It shows most of the crashes happened in clear

weather, daylight, and dry weather conditions. Most

of the injuries are not severe. The result may indicate

that under good weather conditions during the

daytime, people may be less careful when driving.

There is no data showing fatal crashes near park

entrances. There are, however, high levels of traffic

stress, such as high travel speed, multiple travel lanes,

and large traffic volumes that cause incidents when

turning into park entrance. To improve park

accessibility and safety for walkers and cyclers, there

should be more regulations around park entrances,

such as speed control, active and motorized

transportation entrance separation, control signals,

etc (Jerrett et al 2016 & Francis et al 2012).

Table 4: Detailed Information of 226 Traffic Crashes that

Near Park Entrance.

Surface Number

Dry 116

Wet 39

Water 3

Ice/Forst 1

Slush 1

Weather Number

Clear 138

Cloudy 53

Rain 33

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Snow 1

Sleet 1

Injury Number

No Injury 130

Possible Injury /

Complaint

Suspected Minor/Visible

Injury

Unknown 3

Light Number

Daylight 154

Dark-Lighted 31

Dark-Not-Lighted 21

Dawn 5

Dusk 5

5 CONCLUSION

This research uses GIS machine learning techniques

to explore traffic crashes and park accessibility and

safety for active transportation. Heatmap, street

network analysis, and roadway level of traffic stress

are three mythologies used for spatial analysis. This

study explores the connection between crash risk and

the quantity of traffic stress on the roads. A total of 53

developed county parks were used as research targets

and a total number of 152,517 traffic crashes

collected from 2018 to 2022 were used in this analysis.

The traffic heatmap shows a total of 15 park entrances

having traffic crash clusters. Roadways that are next

to those park entrances were analyzed in GIS for

traffic level of stress. The result shows that 12

roadways are in traffic stress level of 4, which is

categorized as the highest level of traffic stress. 2

roadways are in traffic stress level 3, which is

categorized as the second highest level. Simpson

Wood Park is an open space park where pedestrians

and bicycles rarely go, with few facilities and

amenities in the park, so people don’t consider that

park entrance roadway level. In planning park

entrances, a level of roadway traffic stress should be

considered to lessen the risk of crashes. Park entrance

should avoid being opened at roadways that are in

traffic stress level 4 or 3. Roadways that are in high

functional classification, with large number of

through lanes or having high speed limits will

negatively impact active transportation safety near

their park entrances, especially for parks that have

many facilities and amenities. A good number of

facilities and amenities will attract many visitors, and

this can increase traffic volume. Thus, to guarantee

park travel safety, within a park 0.25-mile walking

distance, regulations such as park zone speed control,

non-motorized and motorized entrance separation,

and traffic volume control should be considered when

designing a park. To guarantee that active travelers

receive the proper degree of care and access, laws

strategy, and planning efforts could be implemented

to address safe approaches to parks.

An additional aspect of the physical environment

that may promote a sense of community is public

areas, such parks, and plazas, which allow for chance

of interactions between neighbors. while offering

opportunities for people to access recreation and

nature. Walking and cycling to public places are not

only for improving residents’ physical and mental

health, but also saves energy to improve

environmental sustainability.

REFERENCES

M. Kondo, J. McKeon, C. Branas, International journal of

environmental research and public health, 15(3), 445

(2018).

M. Hasani, A. Jahangiri, IN. Sener, S. Munira, JM. Owens,

B. Appleyard, S. Ryan, SM. Turner, M. Ghanipoor,

Journal of advanced transportation, (2019).

M.C. Ferreira, P.D. Costa, D. Abrantes, J. Hora, S. Felicio,

M. Coimbra, TG. Dias, Transportation research part

F: traffic psychology and behaviour, 91:136-163

(2022).

A.M. Boggs, B. Wali, A.J. Khattak, Accident Analysis &

Prevention, 135:105354 (2020).

Y. Huang, X. Wang, D. Patton, Journal of transport

geography, 69:221-233 (2018).

J.A. Huertas, A. Palacio, M. Botero, GA. Carvajal, T. van

Laake, D. Higuera-Mendieta, SA. Cabrales, LA.

Guzman, OL. Sarmiento, AL. Medaglia,

Transportation Research Part D: Transport and

Environment, 85:102420 (2020).

C. Harvey, K. Fang, DA. Rodriguez, Mineta Transportation

Institute Publications (2019).

B.B. Cutts, K.J. Darby, C.G. Boone, A. Brewis, Social

science & medicine, 69(9):1314-1322 (2009).

M. Jerrett, J.G. Su, K.E. MacLeod, C. Hanning, D.

Houston, J. Wolch, Environmental research 151:742-

755 (2016).

J. Francis, B. Giles-Corti, L.Wood, M. Knuiman, Journal of

environmental psychology 32(4): 401-409 (2012).

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