TY  - JOUR
AU  - Nešić, Miladin
AU  - Lipovac, Krsto
AU  - Vujanić, Milan
AU  - Jovanović, Dragan
PY  - 2016
UR  - http://jakov.kpu.edu.rs/handle/123456789/734
AB  - This paper examines the possibility of applying the Subjective Black Spot Identification Method on state roads. A survey was conducted using interviews about 659 drivers' attitudes on the existence of Black Spots, on nine sections of state roads in the Republic of Serbia. A total of 124 locations were obtained which drivers believed were Perceived Dangerous Locations (PDLs). A set of hypotheses was defined in order to examine whether a particular PDL is a Black Spot and the test was carried out using the Bayesian Multiple Testing (BMT). Since an actual Black Spot has not been recognized as a PDL in the survey, which consequently is not subject to the BMT, new concept that includes: frequency of mishits in identifying real 'Black Spots' (RPM) and real 'non Black Spots' (RNM) and frequency of hits in identifying real 'Black Spots' (RPH) and real 'non Black Spots' (RNH) have been therefore introduced, enabling the inclusion of this outcome in the BMT. Optimisation methods have been proposed for the optimum threshold t selection with the minimization of the frequency of mishits (RPM and RNM) and maximization of the frequency of hits (RPH and RNH). Two operatively usable solutions have been offered here: if the consumption of resources and the effectiveness of spending of funds for identification are primarily low, then the best result is obtained using the optimisation with the minimization of the sum of mishits frequency. Then t = 24.7% (threshold of votes for selecting PDLs as Black Spots), and the ratio of correctly and wrongly selected Black Spots is 1:1.16. On the other hand, if the goal is to detect as many real Black Spots, regardless of the reduction in the effectiveness of spending of funds, then the optimisation with the equalizing of the frequencies of mishits gives the best results. In that case, t = 7.7%, and the ratio of correctly and wrongly selected Black Spots is 1:7.15.
PB  - Vilnius Gedinimas Technical University
T2  - Transport
T1  - Roadside public survey approach in black spot identification on rural roads: case study
VL  - 31
IS  - 2
SP  - 271
EP  - 281
DO  - 10.3846/16484142.2016.1193055
ER  - 

@article{
author = "Nešić, Miladin and Lipovac, Krsto and Vujanić, Milan and Jovanović, Dragan",
year = "2016",
abstract = "This paper examines the possibility of applying the Subjective Black Spot Identification Method on state roads. A survey was conducted using interviews about 659 drivers' attitudes on the existence of Black Spots, on nine sections of state roads in the Republic of Serbia. A total of 124 locations were obtained which drivers believed were Perceived Dangerous Locations (PDLs). A set of hypotheses was defined in order to examine whether a particular PDL is a Black Spot and the test was carried out using the Bayesian Multiple Testing (BMT). Since an actual Black Spot has not been recognized as a PDL in the survey, which consequently is not subject to the BMT, new concept that includes: frequency of mishits in identifying real 'Black Spots' (RPM) and real 'non Black Spots' (RNM) and frequency of hits in identifying real 'Black Spots' (RPH) and real 'non Black Spots' (RNH) have been therefore introduced, enabling the inclusion of this outcome in the BMT. Optimisation methods have been proposed for the optimum threshold t selection with the minimization of the frequency of mishits (RPM and RNM) and maximization of the frequency of hits (RPH and RNH). Two operatively usable solutions have been offered here: if the consumption of resources and the effectiveness of spending of funds for identification are primarily low, then the best result is obtained using the optimisation with the minimization of the sum of mishits frequency. Then t = 24.7% (threshold of votes for selecting PDLs as Black Spots), and the ratio of correctly and wrongly selected Black Spots is 1:1.16. On the other hand, if the goal is to detect as many real Black Spots, regardless of the reduction in the effectiveness of spending of funds, then the optimisation with the equalizing of the frequencies of mishits gives the best results. In that case, t = 7.7%, and the ratio of correctly and wrongly selected Black Spots is 1:7.15.",
publisher = "Vilnius Gedinimas Technical University",
journal = "Transport",
title = "Roadside public survey approach in black spot identification on rural roads: case study",
volume = "31",
number = "2",
pages = "271-281",
doi = "10.3846/16484142.2016.1193055"
}

Nešić, M., Lipovac, K., Vujanić, M.,& Jovanović, D.. (2016). Roadside public survey approach in black spot identification on rural roads: case study. in Transport
Vilnius Gedinimas Technical University., 31(2), 271-281.
https://doi.org/10.3846/16484142.2016.1193055

Nešić M, Lipovac K, Vujanić M, Jovanović D. Roadside public survey approach in black spot identification on rural roads: case study. in Transport. 2016;31(2):271-281.
doi:10.3846/16484142.2016.1193055 .

Nešić, Miladin, Lipovac, Krsto, Vujanić, Milan, Jovanović, Dragan, "Roadside public survey approach in black spot identification on rural roads: case study" in Transport, 31, no. 2 (2016):271-281,
https://doi.org/10.3846/16484142.2016.1193055 . .