DOI: 10.5937/jaes0-49458
This is an open access article distributed under the CC BY 4.0
Volume 22 article 1224 pages: 593-603
The Swedish Traffic Conflicts Technique (STCT) is a systematic approach used to examine traffic conflicts, specifically emphasising the correlation between severe conflicts and accidents. It uses safety indicators such as average speed, post-encroachment time, deceleration rate, time to collision, and traffic flow size to evaluate the gravity of interactions between pedestrian and motorised vehicles. The development of the TCT has been significant, with studies highlighting the impact of speeding, inattentiveness, inadequate following distance, signal violations, drowsiness, excessive alcohol consumption, and reckless driving on road safety. The Adaptive STCT for road traffic safety is a significant area of research and development, aiming to enhance understanding of the global implementation and efficacy of the Adaptive Swedish TCT in enhancing road traffic safety. The STCT has been applied in various countries, including Sao Carlos, Nanjing, Ho Chi Minh City, and Qatar, and has shown significant development in identifying hazardous manoeuvres at urban intersections, facilitating the adoption of safer designs and efficient risk management measures. Nevertheless, research on the STCT's implementation on rural roads is limited; it highlights the need for further investigation and implementation in rural environments due to varying road safety issues.
Our thanks and appreciation go to Assoc. Prof. Ghazwan Al-Haji, Associate Professor at Linkoping University as the Coordinator of ERASMUS+ CBHE PROJECT "ASIASAFE" funded by a grant from the European Union with contract number 618325-EPP-1-2020-1-SE-EPPKA2-CBHE-JP in collaboration with the Master of Civil Engineering Study Programme of Universitas Muhammadiyah Yogyakarta and the Institute for Research and Innovation (LRI) Universitas Muhammadiyah Yogyakarta.
1. Baraskar, D. G. (2022). Study on road traffic multimodal simulation,
comparative study on simulation software on intersection and its road safety
audit. International Journal of Engineering Applied Sciences and Technology.
2.
Jarašūnienė, A., &
Batarlienė, N. (2018). Analysis of road traffic safety increase using
intelligent transport systems in Lithuania. Vision Zero for Sustainable Road
Safety in Baltic Sea Region.
3.
Johnsson, C., Laureshyn, A.,
& Ceunynck, T. D. (2018). In search of surrogate safety indicators for
vulnerable road users: A review of surrogate safety indicators. Transport
Reviews, 38(6), 765-785. https://doi.org/10.1080/01441647.2018.1442888
4.
Campbell, R. E., & King, L.
E. (1970). Rural intersection investigation for the purpose of evaluating the
General Motors traffic-conflicts technique. Highway Research Board Special
Report (No. 107), 10.
5.
Sayed,
T., & Zein, S. R. (1999). Traffic conflict
standards for intersections. Transportation Planning and Technology, 22(4),
309-323. https://doi.org/10.1080/03081069908717634
6.
Tarko, A. P. (2020). Traffic
conflicts as crash surrogates.
7.
Chen,
W.-H., et al. (2021). Prediction of rear-end conflict
frequency using multiple-location traffic parameters. Accident Analysis
& Prevention. https://doi.org/10.1016/j.aap.2021.106007
8.
Gui-ping, X. (2006). The gray
cluster theory method in evaluation of traffic safety based on traffic conflict
technique at signal intersection. Journal of North China Institute of
Science and Technology.
9.
Naidu, M. B., & Chhabra, R.
S. (2018). Safety indicators for heterogeneous non-lane-based traffic: A case
study at Outer Ring Road-Delhi. Indian Journal of Science and Technology, 11(10).
https://doi.org/10.17485/ijst/2018/v11i10/103552
10.
Peng,
J. L., & Wang, D. (2011). Pre-warning information
system of traffic safety. Advanced Materials Research. https://doi.org/10.4028/www.scientific.net/amr.368-373.3320
11.
Zheng, L., Sayed, T., &
Mannering, F. L. (2020). Modeling traffic conflicts for use in road safety
analysis: A review of analytic methods and future directions. Analytic
Methods in Accident Research, 100142. https://doi.org/10.1016/j.amar.2020.100142
12.
Soares, L. C., do Prado, H. A.,
Balaniuk, R., Ferneda, E., & Bortoli, A. D. (2018). Caracterização de
acidentes rodoviários e as ações governamentais para a redução de mortes e
lesões no trânsito. Revista Transporte y Territorio. https://doi.org/10.34096/rtt.i19.5331
13.
Jasiūnienė, V., Pociūtė, G.,
Vaitkus, A., Ratkevičiūtė, K., & Pakalnis, A. (2018). Analysis and
evaluation of trapezoidal speed humps and their impact on the driver. Baltic
Journal of Road and Bridge Engineering. https://doi.org/10.7250/bjrbe.2018-13.404
14.
Vaitkus, A., et al. (2017).
Traffic calming measures: An evaluation of the effect on driving speed. Promet-Traffic
& Transportation. https://doi.org/10.7307/ptt.v29i3.2265
15.
Jakobowsky,
C., Siebert, F., Schießl, C., Junghans, M., Dotzauer, M., & Dotzauer, M.
(2021). Why so serious? - Comparing two traffic
conflict techniques for assessing encounters in shared space. Transactions
on Transport Sciences. https://doi.org/10.17605/osf.io/cztvn
16.
Brown, G. (1994). Traffic
conflicts for road user safety studies. Canadian Journal of Civil
Engineering. https://doi.org/10.1139/l94-001
17.
Kuang, Y., Qu, X., & Yan,
Y. (2017). Will higher traffic flow lead to more traffic conflicts? A crash
surrogate metric-based analysis. PLOS ONE. https://doi.org/10.1371/journal.pone.0182458
18.
Paul, M. (2019). Safety
assessment at unsignalized intersections using post-encroachment time's
threshold: A sustainable solution for developing countries. Lecture Notes in
Civil Engineering. https://doi.org/10.1007/978-981-13-7162-2_10
19.
Bonela,
S. R., & Kadali, B. R. (2022). Review of traffic
safety evaluation at T-intersections using surrogate safety measures in
developing countries context. IATSS Research. https://doi.org/10.1016/j.aap.2023.107380
20.
Mahmud, S. M. S., Ferreira, L.,
Hoque, M. S., & Tavassoli, A. (2017). Application of proximal surrogate
indicators for safety evaluation: A review of recent developments and research
needs. IATSS Research, 41, 153-163.
21.
Zheng, L., & Sayed, T.
(2019). Comparison of traffic conflict indicators for crash estimation using
peak over threshold approach. Transportation Research Record. https://doi.org/10.1177/0361198119841556
22.
Tak, S., Lee, D., Choi, S.,
& Yeo, H. (2017). Collision avoidance system with uni-directional
communication for mitigating the adverse effects on following vehicles. WIT
Transactions on the Built Environment. https://doi.org/10.2495/ut170361
23.
Kim, H. S., Hwang, Y., Yoon,
D., Choi, W., & Park, C. H. (2014). Driver workload characteristics
analysis using EEG data from an urban road. IEEE Transactions on Intelligent
Transportation Systems. https://doi.org/10.1109/tits.2014.2333750
24.
Yoshino, K., Oka, N., Yamamoto,
K., Takahashi, H., & Kato, T. (2013). Correlation of prefrontal cortical
activation with changing vehicle speeds in actual driving: A vector-based
functional near-infrared spectroscopy study. Frontiers in Human Neuroscience.
https://doi.org/10.3389/fnhum.2013.00895
25.
Bulla-Cruz, L. A., Laureshyn,
A., & Lyons, L. (2020). Event-based road safety assessment: A novel
approach towards risk microsimulation in roundabouts. Measurement. https://doi.org/10.1016/j.measurement.2020.108192
26. Hydén, C., & Linderholm, L. (1984). The Swedish
traffic-conflicts technique. In Traffic safety and the environment (pp.
149-158). https://doi.org/10.1007/978-3-642-82109-7_12
27.
Jian, L. (2010). Safety
evaluation of signalized intersections using traffic conflict technique. Journal
of Transport Information and Safety. https://doi.org/10.1016/j.jtis.2010.09.003
28.
Sheykhfard, A., Haghighi, F.,
Papadimitriou, E., & van Gelder, P. (2021). Analysis of the occurrence and
severity of vehicle-pedestrian conflicts in marked and unmarked crosswalks
through naturalistic driving study. Transportation Research Part F: Traffic
Psychology and Behaviour, 80, 64-82. https://doi.org/10.1016/j.trf.2020.11.008
29.
Ling-jun, W. (2009). Evaluation
of traffic safety at intersections based on traffic conflict technique. Journal
of Hefei University of Technology, 32(2), 54-59.
30.
Golob,
T. F., Recker, W. W., & Alvarez, V. M. (2004). Freeway
safety as a function of traffic flow. Accident Analysis & Prevention, 36(4),
589-597. https://doi.org/10.1016/j.aap.2003.09.006
31.
Xiao, X., & Wu, Z. (2015).
Research review of the impacts of adverse weather on traffic flow index. In Proceedings
of the 15th TRB Conference (pp. 1-12). https://doi.org/10.1061/9780784479292.288
32.
Hauer, E. (2014). The art of
regression modeling in road safety. Springer. https://doi.org/10.1007/978-3-319-12529-9
33.
Laureshyn, A., & Varhelyi,
A. (2018). The Swedish traffic conflict technique: Observer’s manual.
Swedish National Road and Transport Research Institute.
34.
Elvik, R., Høye, A., Vaa, T.,
& Sørensen, M. (2009). The handbook of road safety measures (2nd
ed.). Emerald Group Publishing. https://doi.org/10.1108/9781848552517
35.
Amoros, E., Martin, J.-L.,
& Laumon, B. (2006). Under-reporting of road crash casualties in France. Accident
Analysis & Prevention, 38(3), 627-635. https://doi.org/10.1016/j.aap.2005.11.006
36.
Alsop, J. C., & Langley, J.
D. (2001). Under-reporting of motor vehicle traffic crash victims in New
Zealand. Accident Analysis & Prevention, 33(1), 1-10. https://doi.org/10.1016/s0001-4575(00)00049-x
37.
Chin, H. C., & Quek, S. T.
(1997). Measurement of traffic conflicts. Safety Science, 27(3),
169-182. https://doi.org/10.1016/s0925-7535(97)00041-6
38.
Haque, M. M., Boyle, L. N.,
Essa, M., & Sayed, T. (2015). Simulated traffic conflicts: Do they
accurately represent field-measured conflicts? Transportation Research
Record, 2514(1), 36-44. https://doi.org/10.3141/2514-06
39.
Sun,
Z., Chen, Y., Wang, P., Fang, S., & Tang, B. (2021). Vision-based traffic conflict detection using trajectory learning
and prediction. IEEE Access, 9, 86521-86531. https://doi.org/10.1109/access.2021.3061266
40.
Cantisani, G., Moretti, L.,
& Barbosa, Y. D. A. (2019). Safety problems in urban cycling mobility: A
quantitative risk analysis at urban intersections. Safety, 5(1), 6. https://doi.org/10.3390/safety5010006
41.
Amare, V., & Smirnovs, J.
(2021). Road traffic safety analysis of different junction types on the state
roads. IOP Conference Series: Materials Science and Engineering, 1202(1),
012034. https://doi.org/10.1088/1757-899x/1202/1/012034
42.
Sarkar, D. R., Rao, K. R.,
& Chatterjee, N. (2023). A review of surrogate safety measures on road
safety at unsignalized intersections in developing countries. Accident
Analysis & Prevention, 195, 107380. https://doi.org/10.1016/j.aap.2023.107380
43.
Zhang, Y., Yao, D., Qiu, T. Z.,
& Peng, L. (2014). Scene-based pedestrian safety performance model in mixed
traffic situation. IET Intelligent Transport Systems, 8(3), 186-192. https://doi.org/10.1049/iet-its.2013.0012
44.
Young, W., Sobhani, A., Lenne,
M. G., & Sarvi, M. (2014). Simulation of safety: A review of the state of
the art in road safety simulation modelling. Accident Analysis &
Prevention, 62, 1-14. https://doi.org/10.1016/j.aap.2014.01.008
45.
Chaudhari, A., Gore, N.,
Arkatkar, S. S., Joshi, G., & Pulugurtha, S. S. (2020). Exploring
pedestrian surrogate safety measures by road geometry at midblock crosswalks: A
perspective under mixed traffic conditions. IATSS Research, 44(1),
12-24. https://doi.org/10.1016/j.iatssr.2020.06.001
46.
Zheng, L., Ismail, K., &
Meng, X. (2014). Traffic conflict techniques for road safety analysis: Open
questions and some insights. Canadian Journal of Civil Engineering, 41(6),
633-641. https://doi.org/10.1139/cjce-2014-0045
47.
Leledakis, A., Lindman, M.,
Östh, J., Wågström, L., Davidsson, J., & Jakobsson, L. (2021). A method for
predicting crash configurations using counterfactual simulations and real-world
data. Accident Analysis & Prevention, 155, 105932. https://doi.org/10.1016/j.aap.2020.105932
48.
Hu,
Y., Li, Y., Huang, H., Lee, J., Yuan, C., & Zou, G. (2021). A high-resolution trajectory data driven method for real-time
evaluation of traffic safety. Accident Analysis & Prevention, 155,
106503. https://doi.org/10.1016/j.aap.2021.106503
49.
Engstrom,
J., Liu, S.-Y., DinparastDjadid, M., & Simoiu, C. (2022). Modeling road user response timing in naturalistic settings: A
surprise-based framework. Accident Analysis & Prevention, 164,
107460. https://doi.org/10.1016/j.aap.2024.107460
50.
Meirelles, R. L. M. (2003).
Aplicação da técnica sueca de análise de conflitos de tráfego em cruzamentos
críticos da cidade de São Carlos. Universidade de São Paulo. https://doi.org/10.11606/d.18.2017.tde-10072017-160241
51.
Kocourek, J., & Padělek, T.
(2016). Application of the traffic conflict technique in the Czech Republic. In
Smart Cities Symposium Prague (SCSP) (pp. 1-7). https://doi.org/10.1109/scsp.2016.7501037
52.
Li,
S., Xiang, Q., Ma, Y., Gu, X., & Li, H. (2016). Crash
risk prediction modeling based on the traffic conflict technique and a
microscopic simulation for freeway interchange merging areas. International
Journal of Environmental Research and Public Health, 13(11), 1157. https://doi.org/10.3390/ijerph13111157
53.
Vuong, T. Q. (2017). Traffic
conflict technique development for traffic safety evaluation under mixed
traffic conditions of developing countries. Journal of Traffic and
Transportation Engineering, 4(2), 83-93. https://doi.org/10.17265/2328-2142/2017.04.004
54.
Cafiso, S., Graziano, A. D.,
& Pappalardo, G. (2017). In-vehicle stereo vision system for identification
of traffic conflicts between bus and pedestrian. Journal of Traffic and
Transportation Engineering. https://doi.org/10.1016/j.jtte.2016.05.007
55.
Meel,
I. P., Vesper, A., Borsos, A., & Koren, C. (2017). Evaluation
of the effects of auxiliary lanes on road traffic safety at downstream of
U-turns. Transportation Research Procedia. https://doi.org/10.1016/j.trpro.2017.05.186
56.
Ghanim, M. S., & Shaaban,
K. (2019). A case study for surrogate safety assessment model in predicting
real-life conflicts. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-018-3326-8
57.
Zheng, L., Sayed, T., &
Tageldin, A. (2018). Before-after safety analysis using extreme value theory: A
case of left-turn bay extension. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2018.09.023
58.
Zheng, L., & Sayed, T.
(2019). Application of extreme value theory for before-after road safety
analysis. Transportation Research Record. https://doi.org/10.1177/0361198119841555
59.
Kusumastutie,
N. S., & Rusmandani, P. (2019). A brief review:
Traffic conflict techniques and the challenges of the studies in Indonesia. MATEC
Web of Conferences. https://doi.org/10.1051/matecconf/201927003004
60.
Uzondu, C., Jamson, S., &
Lai, F. (2019). Investigating unsafe behaviours in traffic conflict situations:
An observational study in Nigeria. Journal of Traffic and Transportation
Engineering. https://doi.org/10.1016/j.jtte.2018.06.002
61.
Romanowska, A., Jamroz, K.,
& Olszewski, P. (2019). Review of methods for assessing traffic conditions
on basic motorway and expressway sections. Archives of Transport. https://doi.org/10.5604/01.3001.0014.0205
62.
Otero-Niño,
J. D. J., et al. (2019). Road safety assessment in
preferential bus lanes through field analysis and microsimulation of traffic
conflicts. Revista Facultad De Ingenieria-Universidad De Antioquia. https://doi.org/10.17533/udea.redin.n90a10
63.
Qi,
W., Wang, W., Shen, B., & Wu, J. (2020). A modified
post encroachment time model of urban road merging area based on lane-change
characteristics. IEEE Access. https://doi.org/10.1109/access.2020.2987959
64.
Kizawi, A. (2021). Analysis of
vehicle-pedestrian and bicyclist conflicts in Győr-Hungary using Swedish
conflict technique. Acta Technica Jaurinensis. https://doi.org/10.14513/actatechjaur.00605
65.
Formosa, N., Quddus, M.,
Papadoulis, A., & Timmis, A. (2022). Validating a traffic conflict
prediction technique for motorways using a simulation approach. Sensors.
https://doi.org/10.3390/s22020566
66.
Swanson,
J. M., et al. (2022). Observing pedestrian-vehicle
traffic conflicts in school zones to evaluate the effectiveness of road safety
interventions and reduce injuries in Ghana, Vietnam, and Mexico, 2019-2021. Journal
of Injury and Violence Research. https://doi.org/10.5249/jivr.v14i3.1710
67.
Xu, Z., & Chen, D. (2022).
Detection method for all types of traffic conflicts in work zones. Sustainability.
https://doi.org/10.3390/su142114159
68.
Latif, M. (2021). Inservice
road safety audit review of Toronto intersections. Ryerson University. https://doi.org/10.32920/ryerson.14649567
69.
Arun, A., Haque, M., Chung, E.,
Bhaskar, A., Washington, S., & Sayed, T. (2021). A systematic mapping
review of surrogate safety assessment using traffic conflict techniques. Accident
Analysis & Prevention. https://doi.org/10.1016/j.aap.2021.106016
70.
Suhadi,
I., & Rangkuti, N. M. (2019). Analisa tingkat
keselamatan lalu lintas pada persimpangan dengan metode traffic conflict
technique (TCT). Journal of Civil Engineering, Building and Transportation.
https://doi.org/10.31289/jcebt.v3i2.2701
71.
Yu, J., & Wang, L. (2015).
Traffic safety evaluation of urban road intersection based on grey clustering. International
Conference on Industrial Technology. https://doi.org/10.2991/icitmi-15.2015.69
72.
Yuan, Y., Yi, A., Wang, Y.,
& Chen, X. (2021). Research on technical model and method of urban road
traffic accident and traffic conflict based on artificial intelligence. 2021
International Conference on Aviation Safety and Information Technology. https://doi.org/10.1145/3510858.3511416
73.