Identifying Urban Road Black Spots with a Novel Method Based on the Firefly Clustering Algorithm and a Geographic Information System
<p>Comparison of different distance calculations.</p> "> Figure 2
<p>Contrast of search result of different distances.</p> "> Figure 3
<p>A pseudo-code of the Firefly Algorithm.</p> "> Figure 4
<p>Flowchart of the Firefly Clustering Algorithm.</p> "> Figure 5
<p>The area of study (images are from google map).</p> "> Figure 6
<p>The road traffic network.</p> "> Figure 7
<p>Set the origin–destination (OD) cost matrix parameter and output the spatial distance.</p> "> Figure 8
<p>The initial distribution of accident points.</p> "> Figure 9
<p>The clustering result with OD cost distance displayed on geographic information system (GIS).</p> "> Figure 10
<p>The clustering result with Euclidean distance displayed on GIS.</p> "> Figure 11
<p>Variation coefficient of the distance of the accident points.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Definition of Black Spot
2.2. Distance-Measure Impacts on the Identification of Black Spot
2.3. Firefly Clustering Algorithm to Identify the Black Spot
2.4. Study Area and Distance Calculation with GIS
3. Results
3.1. Firefly Clustering Algorithm and OD Cost Distance
3.2. Firefly Clustering Algorithm and Euclidean distance
3.3. Comparison Results between OD Cost Distance and Euclidean Distance
3.4. Further Analysis between OD Cost Distance and Euclidean Distance
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Blincoe, L.; Miller, T.R.; Zaloshnja, E. The economic and societal impact of motor vehicle crashes (Revised). Ann. Emerg. Med. 2015, 66, 194–196. [Google Scholar]
- Toroyan, T. Global status report on road safety. Inj. Prev. 2009, 15, 286. [Google Scholar] [CrossRef] [PubMed]
- Sorensen, M.; Elvik, R. Black Spot Management and Safety Analysis of Road Networks; Institute of Transport Economics: Washington DC, USA, 2007. [Google Scholar]
- Östen, J.; Wanvik, P.O.; Elvik, R. A new method for assessing the risk of accident associated with darkness. Accid. Anal. Prev. 2009, 41, 809–815. [Google Scholar]
- Erdogan, S. Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey. J. Saf. Res. 2009, 40, 341–351. [Google Scholar] [CrossRef]
- Weber, D.C. Accident rate potential: An application of multiple regression analysis of a poisson process. J. Am. Stat. Assoc. 1971, 66, 285–288. [Google Scholar] [CrossRef]
- Sugiyanto, G. The cost of traffic accident and equivalent accident number in developing countries (case study in Indonesia). ARPN J. Eng. Appl. Sci. 2017, 12, 389–397. [Google Scholar]
- Pei, Y.L. Improvement in the quality control method to distinguish the black spot of the road. J. Harbin Inst. Technol. 2006, 38, 97–100. [Google Scholar]
- Erdogan, S.; Yilmaz, I.; Baybura, T.; Gullu, M. Geographical information systems aided traffic accident analysis system case study: City of afyonkarahisar. Accid. Anal. Prev. 2008, 40, 174–181. [Google Scholar] [CrossRef]
- Joshua, S.C.; Nicholas, J.G. Estimating truck accident rate and involvements using linear and poisson regression models. Transp. Plan. Technol. 1990, 15, 41–58. [Google Scholar] [CrossRef]
- Liu, Y.T. A fuzzy-based model for macroscopic evaluation of road traffic safety. China J. Highw. Transp. 1995, 8, 169–175. [Google Scholar]
- Deublein, M.; Schubert, M.; Adey, B.T. Prediction of road accidents: A bayesian hierarchical approach. Accid. Anal. Prev. 2013, 51, 274–291. [Google Scholar] [CrossRef] [PubMed]
- Chong, M.; Ajith, A.; Marcin, P. Traffic accident analysis using machine learning paradigms. Informatica 2005, 29, 89–98. [Google Scholar]
- Samuel, C.; Keren, N.; Shelley, M. Frequency analysis of hazardous material transportation incidents as a function of distance from origin to incident location. J. Loss Prev. Process Ind. 2009, 22, 783–790. [Google Scholar] [CrossRef] [Green Version]
- Kowtanapanich, W.; Tanaboriboon, Y.; Chadbunchachai, W. Applying public participation approach to black spot identification process. IATSS Res. 2006, 30, 73–85. [Google Scholar] [CrossRef] [Green Version]
- Department of Transport and Regional Services (DOTARS). National Black Spot Program. 2003. Available online: http://www.dotars.gov.au/transprog/road/blackspot/index.htm (accessed on 8 July 2014).
- Yang, X.S. Firefly algorithms for multimodal optimization in Stochastic Algorithms Foundations and Applications. Springer 2009, 5792, 169–178. [Google Scholar]
- Yang, X.S. Firefly algorithm stochastic test functions and design optimization. Int. J. Bio-Inspired Comput. 2010, 2, 78–84. [Google Scholar] [CrossRef]
- Tyler, J. Glow-Worms. Available online: http://website.lineone.net/galaxypix/Tylerbookpt1.html (accessed on 11 March 1994).
- Yang, X.S. Nature Inspired Metaheuristic Algorithms; Luniver Press: London, UK, 2008. [Google Scholar]
- Karaboga, D.; Ozturk, C. A novel cluster approach: Artificial bee colony (ABC) algorithm. Appl. Soft Comput. 2010, 11, 652–657. [Google Scholar] [CrossRef]
- Marinakis, Y.; Marinaki, M.; Doumpos, M.; Matsatsinis, N.; Zopounidis, C. A hybrid stochastic genetic-GRASP algorithm for clustering analysis. Oper. Res. Int. J. 2008, 8, 33–46. [Google Scholar] [CrossRef]
- Jain, A.K.; Murty, M.N.; Flynn, P.J. Data clustering: A review. ACM Comput. Surv. 1999, 31, 264–323. [Google Scholar] [CrossRef]
- Falco, I.D.; Cioppa, A.D.; Tarantino, E. Facing classification problems with particle swarm optimization. Appl. Soft Comput. 2007, 7, 652–658. [Google Scholar] [CrossRef]
- Miller, H.J. Potential contributions of spatial analysis to geographic information systems for transportation (GIS-T). Geogr. Anal. 1999, 31, 373–399. [Google Scholar] [CrossRef]
Method | Principle | Advantages | Disadvantages | Suitable Conditions |
---|---|---|---|---|
Accident frequency method | Identify and sort accidents according to accident frequency. | Considers the length and traffic use of a road section. | Does not consider the regression effect of accidents. | Is suitable for road sections or intersections where conditions are similar and traffic is not heavy [4]. |
Matrix analysis method | Identify an accident according to the accident number and accident frequency. | Evaluation result is accurate and flexible. | Identification criteria is subjective. | Is suitable for road sections or intersections where conditions are similar and traffic is not heavy [5]. |
Accident rate method | Identify the accident based on the accident rate. | Considers many accident factors. | Needs a lot of accident data and neglects randomness of accidents. | Is suitable for describing regional accident conditions [6]. |
Equivalent accidents number method | Identify the accident according to the equivalent accident number. | Considers many accident factors. | Needs a lot of accident data and it is difficult to use to determine the weight value. | Is suitable for urban roads or highways with similar conditions [7]. |
Quality control method | Identify the accident according to a set threshold. | Considers the traffic conditions and its evaluation result is accurate | Requires a lot of traffic data and classification work. | Applies to road sections with low traffic flows [8]. |
Cumulative frequency method | Identify the accident according to accident number and accident rate per kilometer | Uses a lot of basic traffic data. | Does not take into account the conditions of an accident. | Applies to roads with widely varying accident conditions [9]. |
Regression analysis method | Considers a lot of factors of accident. | Considers different factors of accidents. | There are high requirements for the model parameters and basic data. | Applies to the regional accident quantification [10]. |
Fuzzy evaluation method | Considers a lot of factors of accident. | Its mathematical model is simple and suitable for multi-level problems. | Index weight is subjective. | Widely used in many conditions [11]. |
Expert experience method | Identify the accident according to accident number. | Can estimate the result quickly and easily. | Is too subjective. | Applies to roads that lack basic data [12]. |
BP neural network | Considers a lot of factors of accident. | Can evaluate the accident comprehensively | Indicator is not directly related to the accident. | Applies to the highway [13]. |
Time | Traffic Fatality | Road Name | Accident Location |
---|---|---|---|
2014-03-03 10:30 | 0 | North industrial road | North industrial road and Happiness Square intersection |
2014-02-05 21:50 | 0 | Kaiyuan Road | No. 66 east gate, north industrial road, Kaiyuan Road |
2014-02-13 22:18 | 1 | Provincial Highway 102 | 19.7 km of Provincial Highway 102 |
2014-02-11 19:45 | 0 | Lintang Road | 100 meters east of the intersection of Lintang Road and Jiaxuan Road |
2014-01-11 21:20 | 0 | Wenquan Road | Wenquan Road, about 30 meters west of union college |
2014-05-21 23:15 | 0 | Provincial Highway 102 | Gengchen gas station on Provincial highway 102 |
2014-03-09 20:00 | 0 | Wenliang Road | 1 km north of Lujia intersection |
Euclidean Distance | OD Cost Distance | Location Record |
---|---|---|
1 | — | Chunxiu Road section |
2 | 1 | Chunxiu and Feiyue Intersection |
3 | 2 | Chunxiu and Feiyue Intersection |
4 | 3 | Chunxiu and Feiyue Intersection |
5 | — | Chunxiu Road section |
6 | — | Chunxiu Road section |
Euclidean Distance | OD Cost Distance | Location Record |
---|---|---|
1 | 1 | Chunbo and Feiyue Intersection |
2 | 2 | Chunbo and Feiyue Intersection |
3 | 3 | Chunbo and Feiyue Intersection |
4 | 4 | Chunbo and Feiyue Intersection |
5 | 5 | Chunbo and Feiyue Intersection |
6 | — | Feiyue Road section |
Euclidean Distance | OD Cost Distance | Location Record |
---|---|---|
1 | — | Century Avenue section |
2 | 1 | Chunhui and Century Avenue Intersection |
3 | 2 | Chunhui and Century Avenue Intersection |
4 | 3 | Chunhui and Century Avenue Intersection |
5 | — | Chunhui Road section |
6 | 4 | Chunhui and Century Avenue Intersection |
7 | — | Century Avenue section |
Distance (m) | Euclidean Distance Clustering Center I | Distance (m) | OD cost Distance Clustering Center II | Difference (m) | % |
---|---|---|---|---|---|
Accident point 1 | 135.3 | — | 160.6 | 25.3 | 18.70% |
Accident point 2 | 58.6 | Accident point 1 | 80.7 | 22.1 | 37.71% |
Accident point 3 | 43.5 | Accident point 2 | 66.4 | 22.9 | 52.64% |
Accident point 4 | 90.3 | Accident point 3 | 123.2 | 32.9 | 36.43% |
Accident point 5 | 116.2 | — | 156.3 | 40.1 | 34.51% |
Accident point 6 | 146.5 | — | 182.8 | 36.3 | 24.78% |
Distance (m) | Euclidean Distance Clustering Center I | Distance (m) | OD Cost Distance Clustering Center II | Difference (m) | % |
---|---|---|---|---|---|
Accident point 1 | 108.6 | Accident point 1 | 127.5 | 18.9 | 17.40% |
Accident point 2 | 83.5 | Accident point 2 | 90.1 | 6.6 | 7.90% |
Accident point 3 | 38.3 | Accident point 3 | 58.9 | 20.6 | 53.79% |
Accident point 4 | 85.1 | Accident point 4 | 98.6 | 13.5 | 15.86% |
Accident point 5 | 123.5 | Accident point 5 | 136.3 | 12.8 | 10.36% |
Accident point 6 | 143.4 | — | 162.6 | 19.2 | 13.39% |
Distance (m) | Euclidean Distance Clustering Center I | Distance (m) | OD Cost Distance Clustering Center II | Difference (m) | % |
---|---|---|---|---|---|
Accident point 1 | 134.8 | — | 161.5 | 26.7 | 19.81% |
Accident point 2 | 58.6 | Accident point 1 | 70.2 | 11.6 | 19.80% |
Accident point 3 | 38 | Accident point 2 | 49.8 | 11.8 | 31.05% |
Accident point 4 | 94.3 | Accident point 3 | 112.8 | 18.5 | 19.62% |
Accident point 5 | 137.9 | — | 154.6 | 16.7 | 12.11% |
Accident point 6 | 102.6 | Accident point 4 | 125.5 | 22.9 | 22.32% |
Accident point 7 | 144.6 | — | 178.9 | 34.3 | 23.72% |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yuan, T.; Zeng, X.; Shi, T. Identifying Urban Road Black Spots with a Novel Method Based on the Firefly Clustering Algorithm and a Geographic Information System. Sustainability 2020, 12, 2091. https://doi.org/10.3390/su12052091
Yuan T, Zeng X, Shi T. Identifying Urban Road Black Spots with a Novel Method Based on the Firefly Clustering Algorithm and a Geographic Information System. Sustainability. 2020; 12(5):2091. https://doi.org/10.3390/su12052091
Chicago/Turabian StyleYuan, Tengfei, Xiaoqing Zeng, and Tongguang Shi. 2020. "Identifying Urban Road Black Spots with a Novel Method Based on the Firefly Clustering Algorithm and a Geographic Information System" Sustainability 12, no. 5: 2091. https://doi.org/10.3390/su12052091