An Analysis of the Effectiveness of Mitigation Measures at Roadkill Hotspots in South Korea

Abstract

Collisions between wildlife and vehicles or roadkill remain a persistent issue. This poses a significant threat to the safety of both wildlife and drivers. The lack of systematically managed roadkill records poses challenges for nationwide research and comprehensive assessment in South Korea. Since 2018, the Ministry of Environment (MOE), Ministry of Land, Infrastructure and Transport (MOLIT), and National Institute of Ecology (NIE) in South Korea have been implementing roadkill investigations and management. The areas selected for roadkill mitigation measures were determined through hotspot analysis based on nationwide roadkill data collected using the Korean Roadkill Observation System (KROS), an integrated online platform. In this study, the top 50 roadkill hotspots were selected, and appropriate mitigation measures, including wildlife fences, warning signs, and speed enforcement cameras, were implemented. A total of 190.6 km of wildlife fences, 75 warning signs, and 27 speed enforcement cameras were installed. The results of these implementations revealed an average reduction in roadkill incidents of 80.2%. Subsequently, we compared and analyzed roadkill incidents before and after these mitigation measures were implemented. The comparative analysis based on hotspot grades showed that areas with lower grades had relatively lower reductions in roadkill incidents. Moreover, the study showed that the presence of multiple mitigation measures in a single area did not significantly differ from the effects of a single mitigation measure. This research will contribute to an enhanced understanding of roadkill mitigation measures and aid in preventing wildlife accidents on the road.

1. Introduction

South Korea has achieved remarkable levels of technological development and economic growth owing to high development pressures, urbanization, and industrialization. However, these outcomes have raised concerns regarding threats to natural ecosystems and biodiversity [1,2,3]. With a total road length of 112,977 km, South Korea has witnessed an average extension of over 1000 km of roads within a five-year period [4]. The construction of roads at a rate exceeding 1 km/km² of land has inevitably led to escalating conflicts between humans and wildlife on roads [5].

Roadkill, which refers to collisions between wildlife and vehicles, occurs continuously [6,7,8]. Currently, roadkill is considered a major contributor to vertebrate animal mortality [9,10,11]. This pressure can lead to a decline in natural population numbers, disrupt gene flow, and ultimately result in significant genetic issues and increases the risk of extinction [3,10,11]. In South Korea, 36,883 roadkill incidents were confirmed on expressways managed by the Korea Expressway Corporation between 2004 and 2019 [12]. The Korean water deer (Hydropotes inermis argyropus) is the species most affected by roadkill incidents, and is involved in an extrapolated 60,000 roadkill incidents annually in South Korea [13]. Roadkill poses a serious threat not only to wildlife but also to the safety of drivers [14,15]. However, road management agencies vary depending on the type of road, and there is no comprehensive record of roadkill incidents. Because of these constraints, the scope of roadkill research has been limited, making it difficult to assess the overall status of roadkill incidents in South Korea.

To address these challenges, the Ministry of Environment (MOE) and Ministry of Land, Infrastructure and Transport (MOLIT) jointly established the “Guideline of roadkill survey and management” in May 2018 [16]. The MOLIT is responsible for the overall planning and coordination of measures to reduce animal roadkill incidents, whereas the National Institute of Ecology (NIE) conducts field surveys, categorizes high-incidence areas, investigates causes, suggests mitigation strategies, and reports the findings to the MOE. The MOE collaborates with MOLIT to formulate mitigation measures based on the results of animal roadkill accident investigations. In addition, a unified online platform/application was established to collect nationwide roadkill information, enabling statistical analysis and scientific research to inform the development of effective mitigation strategies [16].

The analysis of roadkill hotspots is crucial for identifying effective mitigation measures through accurate location identification and on-site investigations [17,18]. Roadkill hotspots are defined as specific road segments where roadkill incidents exhibit spatial clustering rather than random distribution [19]. Research on spatial clusters of point events, such as roadkill incidents, have been conducted by researchers in the field of spatial statistics, primarily focusing on topics such as disease epidemiology and crime hotspots [20,21]. Various methods have been used to analyze the clustering of point-based roadkill data, including K-function analysis, Getis-Ord Gi* analysis, and kernel density estimation analysis [22,23,24,25]. Kernel density estimation is widely used in different fields owing to its intuitive and visually appealing representation [26,27].

The aim of this study was to propose a new system for roadkill mitigation measures and analyze the effectiveness of these measures based on data collected from the Korean Roadkill Observation System (KORS) [16]. The top 50 hotspots were selected, and after thorough on-site investigations, we implemented roadkill mitigation measures. Subsequently, we compared and analyzed roadkill incidents involving mammals before (in 2019) and after (in 2021) the implementation of these mitigation measures to assess their effectiveness.

2. Materials and Methods

2.1. Collection of Roadkill Data

Roadkill data were collected into the NIE’s the Korean Roadkill Observation System (KROS). The surveyors used a mobile application specifically designed for roadkill surveys to submit photos and location information on wildlife incidents. NIE reviews and approves the submitted data in KORS after verifying the species identification and location information. From the nationwide roadkill data collected through the KROS in 2019, 21,397 incidents were recorded (

Table 1. The status of roadkill incidents in 2019.

No.	Common Name	Scientific Name	Roadkill Incidents
1	Korean water deer	Hydropotes inermis	11,638
2	Raccoon dog	Nyctereutes procyonoides	1731
3	Siberian roe deer	Capreolus pygargus	557
4	Wild boar	Sus scrofa	315
5	Leopard cat	Prionailurus bengalensis	239
6	Siberian weasel	Mustela sibirica	156
7	Asian badger	Meles leucurus	141
8	Korean hare	Lepus coreanus	85
9	Eurasian otter	Lutra lutra	78
10	Red squirrel	Sciurus vulgaris	19
11	Siberian chipmunk	Eutamias sibiricus	17
12	Goat	Capra hircus	6
13	Nutria	Myocastor coypus	4
14	European fallow deer	Dama dama	3
15	Yellow-throated marten	Martes flavigula	3
16	Brown rat	Rattus norvegicus	2
17	Ussuri mole	Mogera robusta	1
18	Striped field mouse	Apodemus agrarius	1
19	Siberian Flying Squirrel	Pteromys volans aluco	1
20	-	unidentified (mammals)	215
21	Dog	Canis lupus familiaris *	1016
22	Cat	Felis catus *	4661
23	-	Birds, amphibians, reptiles, etc. *	508

* Excluded from the hotspot analysis in this study.

). Among these, 15,212 involved wild mammalian species (excluding dogs, cats, birds, amphibians, and reptiles) that were used for roadkill hotspot analysis.

2.2. Analysis of Roadkill Hotspots

We used the kernel density estimation method, which provides intuitive and visual representations, to analyze roadkill hotspots. To perform hotspot analysis, we utilized the location information of 15,212 roadkill incidents of wild mammals collected from January to December 2019. Kernel density analysis was conducted using the ‘kernel density tool’ in ArcGIS (version 10.5), with a Gaussian function applied within a 500 m radius. The kernel values obtained from the kernel density analysis were divided into 10 grades (kernel values ranging from 0 to 109.04), classifying each region according to the roadkill intensity. Among them, the top five grades (kernel values ranging from 18.8 to 109.04) were selected, identifying 50 roadkill hotspots nationwide. They were as follows: grade 1, which had the highest kernel values and included 16 areas (32%), followed by grade 2 with 14 areas (28%), grade 3 with seven areas (14%), grade 4 with eight areas (16%), and grade 5 with the lowest kernel values, which included five areas (10%) (

Table 2. The range of kernel values for each roadkill hotspot grade.

Grade	Kernel Values	Number of Area (%)
1	70.13–109.04	16 (32%)
2	50.03–70.13	14 (28%)
3	36.35–50.03	7 (14%)
4	26.51–36.35	8 (16%)
5	18.81–26.51	5 (10%)

).

To clip the kernel analysis results to the road shape, we used QGIS (version 3.16), and the road data were obtained from the National Traffic Information Center of the Ministry of Land, Infrastructure and Transport, using standard node links and road segment data (Figure 1a). On each road, the grade values determined through kernel values were represented in five levels, from red to yellow, (Figure 1b). Consequently, 50 roadkill hotspots were identified through kernel density estimation (Figure 1c). The 50 areas selected through roadkill hotspot analysis were plotted on a map. This map, illustrating administrative regions, road types, and roadkill grades, was made available as part of the public service on the EcoBank website (https://www.nie-ecobank.kr/spceinfo/main.do (accessed on 8 November 2023) (Figure 1).

2.3. Proposal of Roadkill Mitigation Measures

Mitigation measures for roadkill hotspots were suggested based on on-site investigations, applying the “Categorization of Roadkill Occurrence Characteristics and Criteria for Mitigation Techniques for Conservation and Restoration of Ecological Networks” from the NIEs 2018 research project on the conservation and restoration of ecological axes. Basic information, including the address, hotspot grade, hotspot length, distance to the nearest neighboring hotspot, road type, and speed limits applicable on these roads, was collected for the 50 roadkill hotspots (

Table 3. Road information for 50 roadkill hotspots on Korean national roads.

Hotspot No.	Province	Address	Hotspot Grade	Hotspot Length (km)	Distance to the Nearest Hotspot (km)	Speed Limit (km/h)
1	Gangwon-do	Seongsan-myeon, Gangneung-si	5	0.3	87.84	80
2	Gangwon-do	Yeongwol-eup, Yeongwol-gun	5	0.6	57.03	80
3	Gangwon-do	Hoengseong-eup, Hoengseong-gun	5	0.2	57.03	60
4	Gyeonggi-do	Juksan-myeon, Anseong-si	1	1.5	9.2	70
5	Gyeonggi-do	Samjuk-myeon, Anseong-si	3	0.7	9.2	70
6	Gyeonggi-do	Yangseong-myeon, Anseong-si	3	0.6	3.01	80
7	Gyeonggi-do	Yangseong-myeon, Anseong-si	2	0.7	3.01	80
8	Gyeonggi-do	Idong-eup, Cheoin-gu, Yongin-si	2	0.7	4.25	80
9	Gyeonggi-do	Majang-myeon, Icheon-si	2	0.6	26.33	70
10	Gyeonggi-do	Poseung-eup, Pyeongtaek-si	2	0.7	24.66	70
11	Gyeongsangnam-do	Sadeung-myeon, Geoje-si	5	1.0	38.28	70
12	Gyeongsangnam-do	Sangni-myeon, Goseong-gun	4	0.6	38.28	80
13	Gyeongsangbuk-do	Sandong-myeon, Gumi-si	4	0.8	6.47	80
14	Gyeongsangbuk-do	Jangcheon-myeon, Gumi-si	4	2.2	6.47	80
15	Gyeongsangbuk-do	Ian-myeon, Sangju-si	3	1.6	3.14	50
16	Gyeongsangbuk-do	Gonggeom-myeon, Sangju-si	2	0.7	3.14	80
17	Gyeongsangbuk-do	Oeseo-myeon, Sangju-si	2	1.7	4.25	80
18	Gyeongsangbuk-do	Jangsu-myeon, Yeongju-si	3	1.2	6.97	80
19	Gyeongsangbuk-do	Anjeong-myeon, Yeongju-si	4	1.4	10.78	80
20	Gyeongsangbuk-do	Gamcheon-myeon, Yecheon-gun	4	1.3	6.97	80
21	Jeollanam-do	Namyang-myeon, Goheung-gun	4	0.6	96.09	80
22	Jeollanam-do	Seji-myeon, Naju-si	5	0.5	86.20	80
23	Jeollabuk-do	Haseo-myeon, Buan-gun	4	0.6	67.08	80
24	Jeollabuk-do	Ingye-myeon, Sunchang-gun	4	0.6	36.21	80
25	Jeollabuk-do	Gwanchon-myeon, Imsil-gun	3	0.6	21.74	80
26	Jeollabuk-do	Osu-myeon, Imsil-gun	3	1.1	21.74	80
27	Sejong-si	Jeonui-myeon,	1	2.3	8.03	80
28	Chungcheongnam-do	Gyeryong-myeon, Gongju-si	2	1.0	22.15	70
29	Chungcheongnam-do	Jeongan-myeon, Gongju-si	1	1.0	7.30	70
30	Chungcheongnam-do	Gwangdeok-myeon, Cheonan-si	2	1.5	7.30	80
31	Chungcheongnam-do	Gwangseok-myeon, Nonsan-si	1	1.8	22.15	70
32	Chungcheongnam-do	Pangyo-myeon, Seocheon-gun	3	1.0	44.02	70
33	Chungcheongnam-do	Sinpyeong-myeon, Dangjin-si	1	1.1	24.66	70
34	Chungcheongnam-do	Unsan-myeon, Seosan-si	1	1.9	26.31	80
35	Chungcheongnam-do	Sinchang-myeon, Asan-si	1	2.2	3.61	80
36	Chungcheongnam-do	Chosa-dong, Asan-si	1	1.2	3.61	80
37	Chungcheongnam-do	Songak-myeon, Asan-si	1	2.0	14.71	60
38	Chungcheongnam-do	Sinam-myeon, Yesan-gun	1	0.8	14.25	80
39	Chungcheongnam-do	Oga-myeon, Yesan-gun	1	0.8	14.39	60
40	Chungcheongnam-do	Deoksan-myeon, Yesan-gun	2	1.2	14.39	70
41	Chungcheongnam-do	Mokcheon-eup, Dongnam-gu, Cheonan-si	2	1.3	23.65	70
42	Chungcheongnam-do	Taean-eup, Taean-gun	1	1.5	29.63	70
43	Chungcheongbuk-do	Chilseong-myeon, Goesan-gun	2	0.9	45.80	80
44	Chungcheongbuk-do	Simcheon-myeon, Yeongdong-gun	2	0.9	4.34	60
45	Chungcheongbuk-do	Iwon-myeon, Okcheon-gun	1	0.8	4.34	60
46	Chungcheongbuk-do	Annae-myeon, Okcheon-gun	1	0.7	12.96	70
47	Chungcheongbuk-do	Gunbuk-myeon, Okcheon-gun	2	0.7	11.97	80
48	Chungcheongbuk-do	Dongi-myeon, Okcheon-gun	2	0.6	6.74	60
49	Chungcheongbuk-do	Iwol-myeon, Jincheon-gun	1	0.7	8.76	80
50	Chungcheongbuk-do	Munbaek-myeon, Jincheon-gun	1	0.7	8.76	80

). Using this information, we installed suitable mitigation facilities for each current road condition, including speed enforcement cameras, wildlife fences, and warning signs. Additionally, in the installation of wildlife fences, cost and time savings were achieved by integrating them with existing structures, such as rockfall prevention fences and noise barriers, which effectively prevent wildlife from entering the road.

2.4. Analysis of Mitigation Effectiveness

The effectiveness of the mitigation measures was evaluated by comparing the pre-installation (2019) and post-installation (2021) data collected though the KROS for roadkill hotspots. Confidence intervals (CI) were calculated using an online calculator (https://sample-size.net/confidence-interval-proportion, accessed on 8 November 2023). To analyze the reduction rate of roadkill by hotspot grade and mitigation measure type, we compared the reduction rates before and after the implementation of mitigation measures for each hotspot grade. The difference in proportions was evaluated using the G-test, with calculations conducted using an online calculator (https://elem.com/~btilly/effective-ab-testing/g-test-calculator.html, accessed on 8 November 2023).

3. Results and Discussion

3.1. Development of the New Scheme for the Roadkill Mitigation Strategy

The “Guideline of Roadkill Survey and Management” allows the National Institute of Ecology (NIE) to conduct roadkill field surveys, categorize high-incidence areas, investigate causes, and propose mitigation strategies. To protect wildlife from roadkill incidents in South Korea, we propose a new scheme for reducing roadkill (Figure 2). The roadkill mitigation strategy consists of five steps (Figure 2). Step 1: Roadkill information was collected using the KROS. Surveyors uploaded photos and location information to the system using an application in the field. The integration of previously scattered data collection became possible through the KROS, enabling unified data management. Step 2: The NIE, as an affiliated agency of the MOE, used the collected data to perform hotspot analysis by applying a Gaussian function with a 500 m radius using kernel density estimation. This analysis of roadkill hotspots provides fundamental data for identifying priority locations that require effective mitigation measures, and for conducting field surveys to establish mitigation strategies. Step 3: The identified roadkill hotspot areas underwent detailed field investigations to select appropriate mitigation measures. During these on-site assessments of roadkill hotspot areas, the surrounding environment was surveyed, and the necessary measures were determined. These measures included wildlife fences, warning signs, and speed enforcement cameras. Step 4: Based on the results of on-site investigations, mitigation measures such as wildlife fences, warning signs, and speed cameras were selected. Step 5: Continuous monitoring of roadkill incidents and comparative analyses before and after the implementation of mitigation measures were necessary to assess their effectiveness. These findings allow for the evaluation of the effectiveness of various mitigation methods and suggest additional improvement measures if these measures are found to be less effective.

3.2. Proposal for the Roadkill Mitigation Measures for Roadkill Hotspots

To propose mitigation measures through on-site investigations, field surveys were conducted for the 50 locations selected through roadkill hotspot analysis. The results of the on-site investigations that assessed the surrounding landscape, ecological characteristics of wildlife, and road conditions led to the proposal of effective mitigation measures. These measures include: (1) Speed enforcement cameras (Figure 3a) installed on small roads where frequent roadkill incidents involving large species occur and effective measures such as wildlife fences are difficult to implement. Surveillance cameras were used to control the vehicle speeds. (2) Wildlife fences (Figure 3b) primarily used on large roads with speed limits exceeding 60 km/h, where it is difficult for drivers to avoid roadkill incidents. These fences are installed to prevent wildlife from entering the road. (3) Wildlife warning signs (Figure 3c), which are suitable and practical solutions for the respective locations (Figure 3), installed where drivers can effectively avoid roadkill incidents. These signs increase driver awareness. Mitigation measures to reduce roadkill incidents resulting from collisions between wildlife and vehicles can be approached from two perspectives. First, physical barriers can be installed to prevent wildlife from entering roads; wildlife fences are widely used because of their cost-effectiveness [22,28,29,30]. The second approach involves controlling vehicle speed by using wildlife caution signs to raise driver awareness [30]. Signs have the advantage of being easy to install and cost-effective compared with the other types of infrastructure. However, their effectiveness may be reduced in areas with high-speed traffic, owing to reduced visibility. Speed enforcement cameras are considered the most effective means of controlling vehicle speed and are expected to significantly reduce roadkill incidents through speed reduction [31].

3.3. Results of Roadkill Mitigation Measure Implementation

Roadkill data showed an increase with 21,397 incidents in 2019 and 37,261 incidents in 2021, which is a rise of 15,864 incidents, indicating an overall increase in nationwide roadkill data collection. From 2019 to 2021, the average daily traffic volume by road type showed the following values: for expressways, it was 49,281, 48,225, and 51,004; for national highways, it was 13,185, 13,093, and 13,173; and for local roads, it was 6124, 6190, and 6216 [32]. The ANOVA test results for the annual traffic volume in eight (highway, national road) or nine (local road) provinces nationwide showed no significant differences in the yearly traffic volume for highways (F = 0.0258, df = 2, 21, p = 0.9745), national roads (F = 0.0005, df = 2, 21, p = 0.9995), and local roads (F = 0.9468, df = 2, 26, p = 0.4020). The difference in the number of provinces is because there are no highways and national roads in Jeju province. These results indicated that the reduction in roadkill is not correlated with traffic volume. Mitigation measures were implemented in the top 50 hotspots, and after implementation, the number of roadkill incidents decreased from 1197 to 237, resulting in an average reduction rate of 80.2%, CI = 77.8–82.4% (

Table 4. Mitigation measures and reduction rates for 50 roadkill hotspots.

Hotspot Grade	Hotspot No.	Mitigation Measures		Roadkill Incidents
		Type *	Length and Quantity	2019	2021	Reduction Rate, % (CI)
1	50	F, W, S	8.6 km, 4, 4	19	6	68.4 (43.5–87.4)
1	49	F, W, S	8.6 km, 4, 4	21	0	100.0 (83.9–100.0)
1	46	F	1.7 km	22	0	100.0 (84.6–100.0)
1	45	W	2	22	2	90.9 (70.8–98.9)
1	42	F, S	1.7 km, 1	43	2	95.3 (84.2–99.4)
1	39	F, W	1.5 km, 2	22	3	86.4 (65.1–97.1)
1	38	F	7.0 km	29	1	96.6 (82.2–99.9)
1	37	W	4	42	6	85.7 (71.5–94.6)
1	36	F	9.8 km	39	2	94.9 (82.7–99.4)
1	35	F		45	9	80.0 (65.4–90.4)
1	34	F	4.4 km	47	6	87.2 (74.3–95.2)
1	33	F	3.5 km	29	1	96.6 (82.2–99.9)
1	31	F, S	4.0 km, 4	56	11	80.4 (67.6–89.8)
1	27	F	4.4 km	47	25	46.8 (32.1–61.9)
1	4	W	4	22	4	81.8 (59.7–94.8)
1	29	F, S	4.6 km, 4	38	9	76.3 (59.8–88.6)
2	30	F, S		33	7	78.8 (61.1–91.0)
2	48	W	2	14	1	92.9 (66.1–99.8)
2	47	F	6.4 km	18	3	83.3 (58.6–96.4)
2	44	F	1.9 km	22	0	100.0 (84.6–100.0)
2	43	F	3.5 km	21	4	81.0 (58.1–94.6)
2	41	F	5.0 km	33	0	100.0 (89.4–100.0)
2	40	W	2	26	5	80.8 (60.7–93.5)
2	28	F, W	3.4 km, 1	25	9	64.0 (42.5–82.0)
2	17	F	11.0 km	30	1	96.7 (82.8–99.9)
2	10	W	6	15	1	93.3 (68.1–99.8)
2	9	F, W	2.3 km, 2	14	0	100.0 (76.8–100.0)
2	8	F, W	7.1 km, 6	15	8	46.7 (21.3–73.4)
2	7	F	3.2 km	14	9	35.7 (12.8–64.9)
2	16	F	12.4 km	28	0	100.0 (87.7–100.0)
3	15	F		32	0	100.0 (89.1–100.0)
3	32	F	4.0 km	21	6	71.4 (0.5–88.7)
3	26	W	4	30	26	13.3 (0.0–30.7)
3	25	W	2	11	1	90.9 (58.7–99.8)
3	18	F	8.0 km	34	7	79.4 (62.1–91.3)
3	6	W, S	6, 4	14	0	100.0 (76.8–100.0)
3	5	W	10	11	4	63.6 (30.8–89.1)
4	24	F	1.3 km	11	2	81.8 (48.2–97.7)
4	23	F	5.0 km	11	0	100.0 (71.5–100.0)
4	21	F	6.4 km	13	1	92.3 (64.0–99.8)
4	20	F, W	10.1 km, 2	23	12	47.8 (26.8–0.7)
4	19	F, W	9.4 km, 4	20	2	90.0 (68.3–98.8)
4	14	F	18.0 km	33	5	84.8 (68.1–94.9)
4	13	F		15	3	80.0 (51.9–95.7)
4	12	F	6.4 km	10	1	90.0 (55.5–99.8)
5	22	W	4	11	2	81.8 (48.2–97.7)
5	11	S	4	16	15	6.3 (0.2–30.2)
5	3	F	6.0 km	7	5	28.6 (3.7–71.0)
5	2	S	2	13	9	30.8 (9.1–61.4)
5	1	W	4	10	1	90.0 (55.5–99.8)
Total				1197	237	80.2 (77.8–82.4)

* F: wildlife fence, W: warning sign, S: speed enforcement camera.

). Mitigation measures included wildlife fences (F), warning signs (W), and speed enforcement cameras (S). A total of 190.6 km of fences, 75 warning signs, and 27 speed enforcement cameras were installed (Table 4). Among the roadkill hotspots, numbers 35 and 36, 29 and 30, and 15 and 16 were in close proximity, and shared mitigation measures were implemented for these pairs (Table 4).

3.4. Effectiveness of Mitigation Measures

The roadkill reduction rates by hotspot grade were as follows in descending order: grade 2 84.4%, CI = 79.9–88.3%; grade 1 84.0%, CI = 80.6–87.0%; grade 4 80.9%, CI = 73.3–87.1%; grade 3 71.2%, CI = 63.4–78.3%; grade 5 43.7%, CI = 30.7–57.6% (

Table 5. Number of roadkill incidents and reduction rates by different hotspot grade.

Hotspot Grade	No. of Areas	Roadkill Incidents		Reduction Rate, % (CI)
		2019	2021
2	14	308	48	84.4 (79.9–88.3)
1	16	543	87	84.0 (80.6–87.0)
4	8	136	26	80.9 (73.3–87.1)
3	7	153	44	71.2 (63.4–78.3)
5	5	57	32	43.7 (30.7–57.6)
Total	50	1197	237	80.2 (77.8–82.4)

). The reduction rates of grade 2 and grade 1 showed significant differences from grade 3 (G = 9.9046, p < 0.01; G = 11.0032, p < 0.01, respectively), and grade 5 exhibited significant differences from all other grades (p < 0.001). There were no significant differences among the other grades.

Among the areas where two or more mitigation measures were implemented, the combination of warning signs and speed enforcement cameras (W, S) achieved a 100%, CI = 76.8–100.0% reduction rate, indicating the highest level of effectiveness (

Table 6. Number of roadkill incidents and reduction rates for different mitigation measures.

Mitigation Measures *	No. of Areas	Roadkill Incidents		Reduction Rate, % (CI)
		2019	2021
W, S	1	14	0	100.0 (76.8–100.0)
F	24	611	91	85.1 (82.0–87.8)
F, W, S	2	40	6	85.0 (70.2–94.3)
F, S	4	170	29	82.9 (76.4–88.3)
W	11	214	53	75.2 (68.9–80.9)
F, W	6	119	34	71.4 (62.4–79.3)
S	2	29	24	17.2 (5.9–35.8)
Total	50	1197	237	80.2 (77.8–82.4)

* F: wildlife fence, W: warning sign, S: speed enforcement camera.

). The areas where wildlife fences, warning signs, and speed enforcement cameras (F, W, S) were installed together exhibited the second-highest roadkill reduction rate of 85.0%, CI = 70.2–94.3% (Table 6). Wildlife fences and speed enforcement cameras (F, S) achieved a reduction rate of 82.9%, CI = 76.4–88.3%, and wildlife fences and warning signs (F, W) resulted in a 71.4%, CI = 62.4–97.3% reduction (Table 6). The combination with the highest reduction rate, warning signs and speed enforcement cameras (W, S), showed a significant difference from the combination with the lowest reduction rate, wildlife fences and warning signs (F, W) (G = 5.4309, p < 0.05). However, there were no significant differences between the combination of warning signs and speed enforcement cameras (W, S) and other mitigation measure combinations, including wildlife fences, warning signs, and speed enforcement cameras (F, W, S) (G = 1.3278, p = 0.25), as well as wildlife fences and speed enforcement cameras (F, S) (G = 2.2919, p = 0.13).

In areas where a single mitigation measure was implemented for roadkill hotspots, wildlife fences (F) demonstrated the highest reduction rate of 85.1%, CI = 82.0–87.8%. Warning signs (W) installed in 11 areas showed a reduction rate of 75.2%, CI = 68.9–80%. Areas where speed enforcement cameras (S) were installed showed a lower reduction rate of 17.2%, CI = 5.9–35.8% (Table 5). F and W (G = 0.9006, p = 0.3426) did not show a significant difference, while F and S (G = 117.993, p < 0.001) and W and S (G = 88.3556, p < 0.001) showed significant differences.

The roadkill reduction rate due to the installation of wildlife fences was 85.1%, which was similar to the 86% calculated based on overseas research cases [30]. The wildlife fences installed in this study had an average length of 5956.3 m ± 656.0 (SE), which was shorter than the confirmed average fence length in other research cases, 11,406.8 m ± 4666.4 (SE) [30]. However, they were considered effective. The roadkill reduction rate due to the installation of warning signs was 75.2%, which was higher than the approximately 60% observed in other studies [33]. In this study, 61% of the drivers recognized the warning signs and exhibited changes in their driving behavior [33]. It was also found that the most effective distance between the actual animal presence and the warning sign was about 100 m [33]. Applying these findings to domestic research appears to be necessary.

These results suggest that warning signs that promote driver awareness are more effective than legally binding speed enforcement cameras. However, considering the limited number of locations with speed cameras (two areas), further monitoring and validation are required.

4. Conclusions

In this study, using roadkill data from South Korea, we selected 50 roadkill accident hotspots and implemented appropriate mitigation measures, including wildlife fences, warning signs, and speed cameras. The analysis of the effectiveness of roadkill mitigation measures showed that wildlife roadkill decreased by 80.2%, CI = 77.8–82.4%. This demonstrates the effectiveness of roadkill mitigation measures in reducing collisions between vehicles and animals on the road. Particularly, when used individually, wildlife fences and warning signs showed high roadkill reduction rates. In the roadkill data for 2021, similar to 2019, species such as H. inermis, N. procyonoides, C. pygargus, S. scrofa, P. bengalensis, M. sibiricaand, and M. leucurus were predominantly observed. This is likely because small-sized mammals may not be accurately recorded in the data. Similarly, roadkill data for amphibians and reptiles are not being accurately collected, emphasizing the need for additional case studies in this field. These studies will provide crucial foundational information for the development of more specific roadkill mitigation strategies and will contribute to reducing wildlife accidents in the future.