Road Infrastructure Analysis with Reference to Traffic Stream Characteristics and Accidents: An Application of Benchmarking Based Safety Analysis and Sustainable Decision-Making
Abstract
:1. Introduction
2. Road Accident Risk Index and Benchmarking
3. Materials and Methods
3.1. Framework for Research Design
- Step 1.
- Study area selection and segmentation of motorway (M-2) on per km basis.
- Step 2.
- Selection of accident-prone segments naming them as decision-making units (DMUs).
- Step 3.
- Selection of variables for calculating traffic accident risk index.
- -
- Output variables were separated as number of accidents (NoA) and number of affected people (NoAP)-killed or injured.
- -
- Input variables were separated as volume/capacity, vehicles km travelled and vehicles hrs Travelled.
- Step 4.
- Application of DEA program using Lingo Software with the concept of minimizing accidents and killing/injuries and maximizing traffic exposure (V/C, VKT, VHT) to calculate composite traffic accident risk index.
- Step 5.
- Application of cross efficient methodology to calculate a unique value of the traffic accident risk index for each DMU and severity ranking.
- Step 6.
- Application of GIS mapping to produce a visual understanding of risk-prone locations on motorways.
- Step 7.
- Discussion about top ten risky motorway sections and decision-making.
3.2. Study Area
3.3. Data Description and Preparation
3.4. Accident Risk Analysis Using Data Envelopment Analysis
3.5. Cross Efficiency Calculation for Ranking
4. Results and Discussion
4.1. Risk Analysis and Calulations
4.2. Risky Segment Identification and Impact on Human Safety
4.3. Safety Management Financial Decision Making
4.4. Advantages and Limitations of Using the DEA Method
5. Limitations of the Study
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Stage | Variable | Description | N | Mean | SD | Min. | Q1 | Med. | Q3 | Max. |
---|---|---|---|---|---|---|---|---|---|---|
Output | NOA | No. of accidents | 99 | - | - | 1 | 1 | 1 | 2 | 14 |
NOAP | No. of affected persons: killed or injured | 99 | - | - | 1 | 2 | 4 | 7 | 162 | |
Input | V/C | Volume/capacity | 99 | 0.050 | 0.006 | 0.042 | 0.045 | 0.047 | 0.056 | 0.060 |
VKT | Vehicle kilometers traveled | 99 | 8562 | 1077 | 7301 | 7838 | 8092 | 9690 | 10375 | |
VHT | Vehicle hours traveled | 99 | 7486 | 4324 | 3877 | 4855 | 5509 | 7781 | 19421 | |
Note: N = number of road segments (Size-1 km each), SD = standard deviation, Q1 and Q3 = quartiles of data |
DMU | Rated DMU | ||||
---|---|---|---|---|---|
1 | 2 | 3 | …… | n | |
DMU1 | …… | ||||
DMU2 | …… | ||||
DMUn | …… | ||||
Mean | …… |
Seg.ID | Km Post | V/C | VKT | VHT | NOA | NOAP | DEA-Risk | RARI (CE-Risk) | Rank |
---|---|---|---|---|---|---|---|---|---|
Input I1 | Input I2 | Input I3 | Output O1 | Output O2 | |||||
538B | 229 | 0.047 | 8092 | 19,421 | 14 | 162 | 14.00 | 21.81 | 1 |
223A | 223 | 0.045 | 7838 | 15,676 | 10 | 30 | 10.91 | 13.00 | 2 |
229A | 229 | 0.045 | 7838 | 15,676 | 8 | 50 | 8.72 | 11.53 | 3 |
418B | 223 | 0.047 | 8092 | 19,421 | 8 | 24 | 8.00 | 9.74 | 4 |
589B | 246 | 0.044 | 7645 | 6116 | 5 | 11 | 6.73 | 7.60 | 5 |
224A | 224 | 0.045 | 7838 | 15,676 | 5 | 23 | 5.45 | 6.85 | 6 |
670B | 224 | 0.047 | 8092 | 19,421 | 5 | 25 | 5.00 | 6.48 | 7 |
609B | 286 | 0.056 | 9690 | 7268 | 5 | 12 | 5.35 | 6.11 | 8 |
533B | 253 | 0.044 | 7645 | 4059 | 3 | 18 | 4.08 | 5.56 | 9 |
432B | 239 | 0.047 | 8092 | 6474 | 4 | 4 | 5.07 | 5.51 | 10 |
255A | 255 | 0.042 | 7301 | 3877 | 3 | 7 | 4.27 | 5.18 | 11 |
699B | 254 | 0.044 | 7645 | 4059 | 3 | 9 | 4.08 | 5.06 | 12 |
234A | 234 | 0.045 | 7838 | 9406 | 3 | 8 | 3.69 | 4.22 | 13 |
694B | 234 | 0.047 | 8092 | 9710 | 3 | 10 | 3.56 | 4.18 | 14 |
225A | 224 | 0.045 | 7838 | 15,676 | 3 | 7 | 3.27 | 3.81 | 15 |
260A | 260 | 0.042 | 7301 | 3877 | 2 | 7 | 2.85 | 3.59 | 16 |
286A | 286 | 0.060 | 10,375 | 7781 | 3 | 9 | 3.00 | 3.50 | 17 |
241A | 241 | 0.045 | 7838 | 5879 | 2 | 9 | 2.65 | 3.24 | 18 |
661B | 211 | 0.049 | 8400 | 4500 | 2 | 9 | 2.45 | 3.22 | 19 |
244A | 244 | 0.042 | 7301 | 5476 | 2 | 4 | 2.84 | 3.20 | 20 |
- | - | - | - | - | - | - | - | - | - |
- | - | - | - | - | - | - | - | - | - |
453B | 230 | 0.047 | 8092 | 19,421 | 1 | 1 | 1.00 | 1.14 | 95 |
560B | 225 | 0.047 | 8092 | 19,421 | 1 | 1 | 1.00 | 1.14 | 96 |
293A | 293 | 0.060 | 10,375 | 7781 | 1 | 2 | 1.00 | 1.13 | 97 |
287A | 287 | 0.060 | 10,375 | 7781 | 1 | 2 | 1.00 | 1.13 | 98 |
288A | 288 | 0.060 | 10,375 | 7781 | 1 | 1 | 1.00 | 1.09 | 99 |
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Shah, S.A.R.; Ahmad, N. Road Infrastructure Analysis with Reference to Traffic Stream Characteristics and Accidents: An Application of Benchmarking Based Safety Analysis and Sustainable Decision-Making. Appl. Sci. 2019, 9, 2320. https://doi.org/10.3390/app9112320
Shah SAR, Ahmad N. Road Infrastructure Analysis with Reference to Traffic Stream Characteristics and Accidents: An Application of Benchmarking Based Safety Analysis and Sustainable Decision-Making. Applied Sciences. 2019; 9(11):2320. https://doi.org/10.3390/app9112320
Chicago/Turabian StyleShah, Syyed Adnan Raheel, and Naveed Ahmad. 2019. "Road Infrastructure Analysis with Reference to Traffic Stream Characteristics and Accidents: An Application of Benchmarking Based Safety Analysis and Sustainable Decision-Making" Applied Sciences 9, no. 11: 2320. https://doi.org/10.3390/app9112320
APA StyleShah, S. A. R., & Ahmad, N. (2019). Road Infrastructure Analysis with Reference to Traffic Stream Characteristics and Accidents: An Application of Benchmarking Based Safety Analysis and Sustainable Decision-Making. Applied Sciences, 9(11), 2320. https://doi.org/10.3390/app9112320