Lane-level differences in traffic conditions on urban roads are becoming increasingly significant. To remedy this problem, this study proposes a method for the microscopic traffic simulation calibration problem that considers the complexity of traffic conditions on-road sections and the differences in operating states between lanes. A simulation model was established by collecting actual data. Calibration parameters were determined using sensitivity analysis. A calibration model was built to minimize the relative errors of the roadway efficiency and lane differential indicators. The values of these parameters were obtained using a genetic algorithm (GA). The calibration processes were automated using programming. To assess the reliability of the proposed method, we conducted five sets of comparative experiments focusing on two aspects: calibration methods and algorithm utilization. Results indicate that the proposed method significantly enhances simulation accuracy, particularly in lane-level traffic simulations. In comparison to approaches considering only section-level traffic characteristics and default application software parameters, the proposed method yielded reductions in errors by 3.7%, 5.8%, 6.6%, and 3.2% for simulating lane occupancy rate and cross-section flow. The proposed method demonstrated a simulation error of approximately 5%, while the artificial neural network method was about 7%, validating the effectiveness of the algorithms employed. It can play a crucial role in multilane traffic flow, intelligent driving tests, vehicle–road cooperation, and other related study areas.
BurgerMVanMHegyiA, et al. Considerations for model-based traffic control. Transp Res Part C Emerg Technol2013; 35: 1–19.
2.
LiLChenXZhangL. A global optimization algorithm for trajectory data-based car-following model calibration. Transp Res Part C Emerg Technol2016; 68: 311–332.
3.
LilyA. The highway capacity manual. 6th ed. Washington, DC: The National Academies press, 2016, p. 41.
4.
AlamMJPinchemelACaetanoM. Airport evacuation under panic conditions: a microsimulation modeling applied at Ottawa International Airport. Simulation2024; 99: 1213–1224.
5.
HuangZSmirnovaMZhuZ. Freeway work zone effects on vehicular fuel consumption explored by a four-lane model. Simulation2024; 100: 145–155.
6.
BenekohalRF. Procedure for validation of microscopic traffic flow simulation models. Trans Res Board1991; 1320: 190–202.
7.
CheuRLJinXNgKC, et al. Calibration of FRESIM for Singapore expressway using genetic algorithm. J Transp Eng1998; 124: 526–535.
8.
HellingaB. Requirements for the calibration of traffic simulation models. Can J Civ Eng1998; 1: 211–222.
9.
ParkBSchneebergerJ. Microscopic simulation model calibration and validation: case study of VISSIM simulation model for a coordinated actuated signal system. Transp Res Rec2003; 1856: 185–192.
10.
HollanderYLiuR. The principles of calibrating traffic microsimulation models. Transportation2008; 35: 347–362.
11.
Lopez-NeriERamirez-TrevinoALopez-MelladoE. A modeling framework for urban traffic systems microscopic simulation. Simul Model Pract Theory2010; 18: 1145–1161.
12.
DowlingRSkabardonisA. Traffic analysis toolbox volume III: guidelines for applying traffic microsimulation modeling software. Transp Res C Emerg Technol2016; 68: 311–332.
13.
SunJHuangYLiL, et al. Integrated simulation test platform for environment perception and planning decision of intelligent vehicle. J Syst Simul2020; 32: 236–246.
14.
VargaBDobaDTettamantiT. Optimizing vehicle dynamics co-simulation performance by introducing mesoscopic traffic simulation. Simul Model Pract Theory2023; 125: 102739.
15.
PottsIBRingertJFBauerKM, et al. Relationship of lane width to saturation flow rate on urban and suburban signalized intersection approaches. Transp Res Rec2007; 2027: 45–51.
16.
PanLHaoYWangS. Evaluating the effects of signal countdown timers on queue discharge characteristics at signalized intersections in China. Transp Res Rec2012; 2286: 39–48.
17.
SandoTMussaRN. Site characteristics affecting operation of triple left-turn lanes. Transp Res Rec2003; 1852: 55–62.
18.
TarawnehMSTarawnehTM. Effect on utilization of auxiliary through lanes of downstream right-turn volume. J Transp Eng2002; 128: 458–464.
19.
DaganzoCF. A behavioral theory of multi-lane traffic flow. Part I: long homogeneous freeway sections. Transp Res B Methodol2002; 36: 131–158.
20.
SonSCetinMKhattakA. Are some freeway lanes under-utilized? In: transportation research board annual meeting, Washington, DC, 23–27 January 2011, Paper no. 01336777, pp. 1–15. Washington, DC: TRB.
21.
CaoYYangZZuoZ. The effect of curb parking on road capacity and traffic safety. Eur Transp Res Rev2017; 9: 4.
22.
PapathanasopoulouVMarkouIAntoniouC. Online calibration for microscopic traffic simulation and dynamic multi-step prediction of traffic speed. Transp Res C Emerg Technol2016; 68: 144–159.
23.
ZhangJHounsellNShresthaB. Calibration of bus parameters in microsimulation traffic modelling. Transp Plan Technol2012; 35: 107–120.
24.
HuXYanM. Data assimilation for online model calibration in discrete event simulation. Simulation2024; 100(6): 529–544.
25.
HenclewoodDSuhWRodgersMO, et al. A calibration procedure for increasing the accuracy of microscopic traffic simulation models. Simulation2016; 93: 35–47.
26.
AzevedoCLCiuffoBCardosoJL, et al. Dealing with uncertainty in detailed calibration of traffic simulation models for safety assessment. Transp Res C Emerg Technol2015; 58: 395–412.
27.
TanWJAndelfingerPCaiW, et al. Spatial iterative coordination for parallel simulation-based optimization of large-scale traffic signal control. Simulation2024; 00: 1–18.
28.
PazAMolanoVMartinezE, et al. Calibration of traffic flow models using a memetic algorithm. Transp Res C Emerg Technol2015; 55: 432–443.
OmraniRKattanL. Concurrent estimation of origin-destination flows and calibration of microscopic traffic simulation parameters in a high-performance computing cluster. J Transp Eng A2018; 144: 04017068.
31.
ShahrakiHSAlecsandruCMaghsoudiR, et al. An efficient soft computing-based calibration method for microscopic simulation models. J Transp Saf Secur2018; 10: 367–386.
32.
TangLZhangDHanY, et al. Parallel-computing-based calibration for microscopic traffic simulation model. Transp Res Rec2024; 2678: 279–294.
33.
MaheshwaryPBhattacharyyaKMaitraB, et al. A methodology for calibration of traffic micro-simulator for urban heterogeneous traffic operations. J Traffic Transp Eng2020; 7: 507–519.
34.
GuoYSayedTZhengL, et al. An extreme value theory-based approach for calibration of microsimulation models for safety analysis. Simul Model Pract Theory2021; 106: 102172.
35.
Al-MsariHKotingSAhmedAN, et al. Review of driving-behaviour simulation: VISSIM and artificial intelligence approach. Heliyon2024; 10: 1–32.
36.
DongCYangKGuoJ, et al. Analysis and control of intelligent traffic signal system based on adaptive fuzzy neural network. In: The 5th international conference on transportation Information and Safety, Liverpool, 14–17 July 2019, pp.1–6. Piscataway, NJ: IEEE.
37.
NieHCuiNDuY, et al. An improved velocity forecasts method considering the speed and following distance of two vehicles. In: Proceedings of the 39th Chinese control conference, Shenyang, China, 27–29 July 2020, pp. 5517–5522. Piscataway, NJ: IEEE.
38.
RosasCJulaHLeeJ, et al. Neural network delay estimation with inclusion of heavy goods vehicular data. In: 2021 IEEE Green energy and smart systems conference (IGESSC), Long Beach, CA, 1–2 November 2021, pp. 1–6. Piscataway, NJ: IEEE.
39.
PatwaryAUZHuangWLoHK. Metamodel-based calibration of large-scale multimodal microscopic traffic simulation. Transp Res C2021; 124: 102859.
40.
BathaeeY. The artificial intelligence black box and the failure of intent and causation. Harv J L Tech2018; 31: 890–938.
41.
ArafatMNafisSRSadeghvaziriE, et al. A data-driven approach to calibrate microsimulation models based on the degree of saturation at signalized intersections. Transp Res Interdiscip Perspect2020; 8: 100231.
42.
GallelliVGuidoGVitaleA, et al. Effects of calibration process on the simulation of rear-end conflicts at roundabouts. J Traffic Transp Eng2019; 6: 175–184.
43.
StevanovicAStevanovicJSoJ, et al. Multi-criteria optimization of traffic signals: mobility, safety, and environment. Transp Res C2015; 55: 46–68.
44.
PaulMCharanVSoniV, et al. Calibration methodology of microsimulation model for unsignalized Intersection under heterogeneous traffic conditions. In: ASCE India conference, New Delhi, India, 12–14 December 2017, pp. 618–627. Baltimore,MD: ASCE.
45.
MengLHanWXKeS. Traffic conflict identification technology of vehicle intersection based on vehicle video trajectory extraction. Procedia Comput Sci2017; 109: 963–968.
46.
HoDTGroTliEISujitPB, et al. Optimization of wireless sensor network and UAV data acquisition. J Intell Robot Syst2015; 78: 159–179.
47.
HuangFLiuPYuH, et al. Identifying if VISSIM simulation model and SSAM provide reasonable estimates for field measured traffic conflicts at signalized intersections. Accid Anal Prev2013; 50: 1014–1024.
48.
OlstamJTapaniA. A review of guidelines for applying traffic simulation to level-of-service analysis. Procedia Soc Behav Sci2011; 16: 771–780.
49.
XuLLiLLiGZ, et al. Review of multilane traffic flow theory and application. J Chang’an Univ, Nat Sci Ed2020; 40: 78–90.
50.
CoonerSARanftSERathodYK, et al. Development of guidelines for triple left and dual right-turn lanes. Report, Texas Transportation Institute, Austin, TA, July2011.
51.
CongMWangL. Survey on the theory of meta-heuristic algorithms. High Technol Lett2003; 5: 105–110.
52.
JohnH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Cambridge: MIT Press1992; pp. 1–14.
53.
LiuYZuoBNiA, et al. Calibrating microscopic traffic simulators using machine learning and particle swarm optimization. Transp Lett2020; 20: 1–13.
54.
OtkovicIITollazziTSramlM. Calibration of microsimulation traffic model using neural network approach. Expert Syst Appl2013; 40: 5965–5974.
55.
HaleDKAntoniouCBrackstoneM, et al. Optimization-based assisted calibration of traffic simulation models. Transp Res C Emerg Technol2015; 55: 100–115.
56.
KaiWChunxiaYHuQ. An adaptive-learning-based genetic algorithm for collaborative scheduling of distributed operating rooms. Appl Soft Comput2022; 131: 109755.
57.
XiaoZLiuXXuJ, et al. Highly scalable parallel genetic algorithm on Sunway many-core processors. Future Gener Comput Syst2021; 114: 679–691.
