Skip to main content
SpringerLink
Log in

Corridor capacity analysis with mesoscopic simulation: Erzincan province sample

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

Today, the existing traffic networks cannot cope-up with the increasing transportation demands and traffic volume and fail to meet the demands. This causes economic and social losses. Increasing transportation demands and traffic volume in Erzincan city center of Turkey cause the main arteries and junctions to be insufficient. In such problems, solutions are generally produced using parameters such as trip and delay durations and exhaust emissions. Another point to be considered is to ensure the improved intersections to work as a whole. In this study, the main arteries and the junctions on these in Erzincan city center were discussed. In this context, totally 14 intersections were tried to be improved. Suggestions were offered regarding delay time, travel time, queue lengths, NOx and CO2 emissions, and average speed values as the design criteria. Simulations were made in AIMSUN program for the current situation and four different scenarios. Analytical Hierarchy Process (AHP), which is one of the multi-criteria decision-making methods for the network was employed to determine which scenario was more appropriate. At the end of the study, decrease of up to 23% for travel time, 47% for delays, 48% for queue lengths, 11% for NOx, and 13% for CO2 and increase of up to 30% in average speed were obtained. According to AHP, Scenario 4 had the highest priority value among all alternatives. Moreover, the reliability of the results was ensured by supporting the mesoscopic simulation with AHP which is a multi-criteria decision-making method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13

Similar content being viewed by others

References

  1. GDH (General Directorate of Highways) (2018) Trafik Kazalari Ozeti. GDH, Ankara, Turkey (in Turkish)

    Google Scholar 

  2. Park B and Schneeberger J 2003Microscopic simulation model calibration and validation: case study of VISSIM simulation model for a coordinated actuated Signal system. Transp. Res. Rec. J. Transp. Res. Board. 1856, 185–192

  3. Siddharth S and Ramadurai G 2013 Calibration of VISSIM for Indian heterogeneous traffic conditions. Procedia-Soc. Behav. Sci. 104: 380–389

    Article  Google Scholar 

  4. Bayrak O U and Abret N E 2019 A multi-criteria decision for determining the appropriate junction design type: AHP approach with microsimulation. Feb-Fresenius Environ. Bull. 7183

  5. Jiang Z and Huang Y X 2009. Parametric calibration of speed–density relationships in mesoscopic traffic simulator with data mining. Inf. Sci. 179(12): 2002–2013

    Article  Google Scholar 

  6. Zlatkovic M, Zlatkovic S, Sullivan T, Bjornstad J and Shahandashti S K F 2019. Assessment of effects of street connectivity on traffic performance and sustainability within communities and neighborhoods through traffic simulation. Sustain. Cities Soc. 46: 101409

    Article  Google Scholar 

  7. Toledo T, Cats O, Burghout W and Koutsopoulos H N 2010. Mesoscopic simulation for transit operations. Transp. Res. Part C Emerg. Technol. 18(6): 896–908

    Article  Google Scholar 

  8. Jamshidnejad A, Papamichail I, Papageorgiou M and De Schutter B 2017. A mesoscopic integrated urban traffic flow-emission model. Transp. Res. Part C Emerg. Technol., 75: 45–83

    Article  Google Scholar 

  9. Celikoglu H B and Dell’Orco M 2007. Mesoscopic simulation of a dynamic link loading process. Transp. Res. Part C Emerg. Technol. 15(5): 329–344

    Article  Google Scholar 

  10. Dell’Orco M, Marinelli M and Silgu M A 2016. Bee colony optimization for innovative travel time estimation, based on a mesoscopic traffic assignment model. Transp. Res. Part C Emerg. Technol., 66: 48–60

    Article  Google Scholar 

  11. Saaty T L 1977 A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15(3): 234–281

    Article  MathSciNet  Google Scholar 

  12. Vaidya OS and Kumar S 2006 Analytic hierarchy process: An overview of applications. Eur. J. Oper. Res. 169(1): 1–29

    Article  MathSciNet  Google Scholar 

  13. Murat Y S, Arslan T, Cakici Z and Akçam C 2016. Analytical hierarchy process (AHP) based decision support system for urban intersections in transportation planning. In: Using Decision Support Systems for Transportation Planning Efficiency (pp. 203–222). IGI Global

  14. Rahimov K, Motamadnia A and Samadi S 2016. Technical and economic evaluation of Pinavia interchange in comparison with roundabout intersection by AIMSUN. Civ. Eng. J. 2(3): 102–112

    Article  Google Scholar 

  15. Bayata H F and Bayrak O Ü Yeni Yapılması Planlanan bir Kavşağın Mikro-Simülasyon ile Değerlendirilmesi. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 11(3): 550–559

  16. Zhang R and Yao E 2019. Mesoscopic model framework for estimating electric vehicles’ energy consumption. Sustain. Cities Soc. 47: 101478

    Article  Google Scholar 

  17. Xu Y, Song X, Weng Z and Tan G 2014. An entry time-based supply framework (ETSF) for mesoscopic traffic simulations. Simul. Modell. Pract. Theory 47: 182–195

    Article  Google Scholar 

  18. Bennett J and Betts B 2017, August). Aimsun saturation flow calibration for hybrid simulation: challenges and recommendations. In: Australian Institute of Traffic Planning and Management (AITPM) National Conference, 2017, Melbourne, Victoria, Australia

  19. Ciuffo B, Casas J, Montanino M, Perarnau J and Punzo V 2013. Gaussian process metamodels for sensitivity analysis of traffic simulation models: Case study of AIMSUN mesoscopic model. Transp. Res. Rec. 2390(1): 87–98

    Article  Google Scholar 

  20. Schenk M, Tolujew J and Reggelin T2009. Mesoscopic modeling and simulation of logistics networks. IFAC Proc. Vol. 42(4): 582–587

  21. Xu T, Moon D H and Baek S G 2012. A simulation study integrated with analytic hierarchy process (AHP) in an automotive manufacturing system. Simulation, 88(4): 450–463

    Article  Google Scholar 

  22. Alp S and Engin T Trafik Kazalarının Nedenleri ve Sonuçları Arasındaki İlişkinin Topsis ve AHP Yöntemleri Kullanılarak Analizi ve Değerlendirilmesi. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 10(19), 63

  23. Barić D, Pilko H and Strujić J (2016). An analytic hierarchy process model to evaluate road section design. Transport, 31(3), 312–321

    Article  Google Scholar 

  24. Demiriz A O 2019 Coridor Capacity Analysis with Mesoscopic Simulation: Erzincan Province Sample, Ataturk University and Graduate School of Natural and Applied Sciences, Master Thesis, Erzurum (in Turkish)

  25. Yu L, Yu L, Chen X and Guo J 2006 Calibration of VISSIM for bus rapid transit systems in Beijing using GPS data. J. Public Transp.. 9(3): 13

    Article  Google Scholar 

  26. Hellinga B R 1998 Requirements for the calibration of traffic simulation models. Proc. Can. Soc. Civ. Eng. 4: 211–222

  27. Dalaff C, Ebendt R, Erdmann J, Gurczik G and Touko L C 2013 Benchmarking SUMO Generated Traffic Simulation Results Based on GEH Statistic. In: 1st SUMO User Conference (p. 54)

  28. Alomari A H, Al-Deek H, Sandt A, Rogers J H and Hussain O 2016 Regional evaluation of bus rapid transit with and without transit signal priority. Transp. Res. Rec. J. Transp. Res. Board. 2554, 46–59

    Article  Google Scholar 

  29. Karakikes I, Spangler M and Margreiter M 2016 Motorway network simulation using bluetooth data. Transp. Telecommun. J. 17(3): 242–251

    Article  Google Scholar 

  30. Song G, Yu L and Zhang Y 2012 Applicability of traffic microsimulation models in vehicle emissions estimates: Case study of VISSIM. Transp. Res. Rec. J. Transp. Res. Board. 2270, 132–141

    Article  Google Scholar 

  31. Wu Y, Chen H and Zhu F 2019. DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles. Transp. Res. Part C Emerg. Technol., 103, 246–260

    Article  Google Scholar 

  32. Ceylan H, Başkan Ö, Ceylan H and Haldenbilen S 2011. Yaklaşık Hesaplama Metodu ile Sinyalize Kavşaklarda Gecikme Bileşenlerinin Matematiksel Çözümü. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 13(2): 279–288

    Google Scholar 

  33. Murat Y Ş 2006 Sinyalize kavşaklardaki taşıt gecikmelerinin bulanık mantık ile modellenmesi. İMO Teknik Dergi 3903(3916), 258

    Google Scholar 

  34. Triantaphyllou E and Mann S H 1995 Using the analytic hierarchy process for decision making in engineering applications: some challenges. Int. J. Ind. Eng. Appl. Pract. 2(1): 35–44

    Google Scholar 

  35. Saaty T 1980 The Analytic Hierarchy Process (New York: McGrawHill, 1980). MATH Google Scholar

  36. Saaty T L 1994 Fundamentals of decision making and priority theory with the analytic hierarchy process. Vol. VI. Universitas Pittsburgh. USA

    Google Scholar 

  37. Yu J, Wang L and Gong X 2013 Study on the status evaluation of urban road intersections traffic congestion base on AHP-TOPSIS modal. Procedia-Soc. Behav. Sci. 96: 609–616

    Article  Google Scholar 

  38. Effat H A and Hassan O A 2013 Designing and evaluation of three alternatives highway routes using the Analytical Hierarchy Process and the least-cost path analysis, application in Sinai Peninsula, Egypt. Egypt. J. Remote Sens. Space Sci. 16(2): 141–151

    Article  Google Scholar 

  39. Abdi, M R and Labib A W 2003 A design strategy for reconfigurable manufacturing systems (RMSs) using analytical hierarchical process (AHP): a case study. Int. J. Prod. Res. 41(10): 2273–2299

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, Erzincan Binali Yildirim University, Erzincan, Turkey

    Ahmet Oğuz Demİrİz & Halİm Ferİt Bayata

  2. Department of Civil Engineering, Faculty of Engineering, Atatürk University, Erzurum, Turkey

    Osman Ünsal Bayrak

Authors
  1. Ahmet Oğuz Demİrİz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Osman Ünsal Bayrak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Halİm Ferİt Bayata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmet Oğuz Demİrİz.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Demİrİz, A.O., Bayrak, O.Ü. & Bayata, H.F. Corridor capacity analysis with mesoscopic simulation: Erzincan province sample. Sādhanā 46, 10 (2021). https://doi.org/10.1007/s12046-020-01533-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-020-01533-9

Keywords

Search

Navigation