This study examines the influence of illuminance on car-following characteristics in mixed traffic flow. We selected an expressway tunnel with a large variation in illuminance and designed a real vehicle test to explore the influence of changes in tunnel illuminance on car-following characteristics. The load factor of vehicle recognition based on the critical speed of illuminance change is introduced to correct the delay time of adaptive cruise control vehicle recognition. The continuous cellular automata model was used to simulate a mixed traffic flow. The characteristics of the mixed traffic flow under the influence of illuminance are analyzed for traffic efficiency, stability, and safety. The results show that changes in illuminance cause vehicle speeds to diminish at tunnel entrances and exits while following distances increase. Inside the tunnel, however, vehicle speeds and following distances remain relatively stable. As connected autonomous vehicles (CAV) become more common, the maximum flow and average speed in expressway tunnels increase. In addition, the time to collision (TTC) value increases gradually, while the time exposed rear collision risk index (TERCRI) decreases gradually. When CAV penetration exceeds 80%, the speed-density curve is smoother, the vehicle motion state is close to a straight line on a certain slope, and the position of deceleration is less, which is close to the ideal state of stable driving. This study could have a positive impact in reducing the speed fluctuation of vehicles in expressway tunnels, improving the efficiency of traffic, and preventing rear-end crashes in mixed traffic flow environments.
YanY.DaiT.LiX.TangJ.GuoZ. Driving Risk Assessment Using Driving Behavior Data under Continuous Tunnel Environment. Traffic Injury Prevention, Vol. 20, No. 8, 2019, pp. 807–812
2.
MillerE. E.BoyleL. N.Driver Behavior in Road Tunnels: Association with Driver Stress and Performance. Transportation Research Record: Journal of the Transportation Research Board, 2015. 2518: 60–67.
3.
LecceseF.SalvadoriG.ÖnerM.KazanasmazT.Exploring the Impact of External Shading System on Cognitive Task Performance, Alertness and Visual Comfort in a Daylit Workplace Environment. Indoor and Built Environment, Vol. 29, No. 7, 2020, pp. 942–955.
4.
CaliendoC.De GuglielmoM. L.GuidaM.A Crash-Prediction Model for Road Tunnels. Accident Analysis & Prevention, Vol. 55, 2013, pp. 107–115.
5.
AssociationW. R.Human Factors and Road Tunnel Safety Regarding Users. World Road Association (PIARC), Paris, 2008.
6.
DaiY.BianY.WuY.ZhaoX.ZhangJ.How the Active Visual Guidance Device Affects Drivers on the Foggy Bridge: Safety Analysis Considering Emergency. Measurement, Vol. 217, 2023, p. 113027.
7.
YeungJ. S.WongY. D.Road Traffic Accidents in Singapore Expressway Tunnels. Tunnelling and Underground Space Technology, Vol. 38, 2013, pp. 534–541.
8.
YanG.WangM.YuL.DuanR.XiaP., editors. Effects of Ambient Pressure on Smoke Movement Patterns in Vertical Shafts in Tunnel Fires with Natural Ventilation Systems. In Building Simulation, Tsinghua University Press, Beijing, China, Vol. 13, 2020, 931–941.
9.
WangS.DuZ.JiaoF.ZhengH.NiY.Drivers’ Visual Load at Different Time Periods in Entrance and Exit Zones of Extra-Long Tunnel. Traffic Injury Prevention, Vol. 21, No. 8, 2020, pp. 539–544.
10.
PengL.WengJ.YangY.WenH.Impact of Light Environment on Driver’s Physiology and Psychology in Interior Zone of Long Tunnel. Frontiers in Public Health, Vol. 10, 2022, p. 842750.
11.
HeS.TähkämöL.MaksimainenM.LiangB.PanG.HalonenL.Effects Of Transient Adaptation On Drivers’ Visual Performance In Road Tunnel Lighting. Tunnelling and Underground Space Technology, Vol. 70, 2017, pp. 42–54.
12.
González-HernándezB.UsamiD. S.PrasolenkoO.BurkoD.GalkinA.LobashovO.PersiaL.The Driver’s Visual Perception Research to Analyze Pedestrian Safety at Twilight. Transportation Research Procedia, Vol. 45, 2020, pp. 827–834.
13.
WangY.WangL.WangC.ZhaoY.How Eye Movement and Driving Performance Vary Before, During, and After Entering a Long Expressway Tunnel: Considering the Differences of Novice and Experienced Drivers Under Daytime and Nighttime Conditions. SpringerPlus, Vol. 5, 2016, pp. 1–10.
14.
ZhangC.KangC.YeL.WengJ.HuangZ.WuK.The Influence of Dimming-Induced Luminance Change on Driving Safety in Tunnels. Heliyon, Vol. 9, No. 1, 2023, p. e12928.
15.
YanG.WangM.QinP.YanT.BaoY.WangX.Comparative Study on Drivers’ Eye Movement Characteristics and Psycho-Physiological Reactions at Tunnel Entrances in Plain and High-Altitude Areas: A Pilot Study. Tunnelling and Underground Space Technology, Vol. 122, 2022, p. 104370.
16.
QinG.DongL.QinL., editors. Driver’s Pupil Characteristics While Driving Through a Tunnel. Proc., Asia-Pacific Conference on Intelligent Medical 2018 & International Conference on Transportation and Traffic Engineering, 2018, Beijing, China, Association for Computing Machinery, New York, pp. 305–311.
17.
DuZ.WanH.WuC.PanX.Safety Evaluation of Highway Tunnel-Entrance Illuminance Transition Based on Eye-Pupil Changes. Promet-Traffic&Transportation, Vol. 26, No. 5, 2014, pp. 363–370.
18.
ZhigangD.ZhengZ.ZhengM.RanB.ZhaoX.Drivers’ Visual Comfort at Highway Tunnel Portals: A Quantitative Analysis Based on Visual Oscillation. Transportation Research Part D: Transport and Environment, Vol. 31, 2014, pp. 37–47.
19.
AvedisovS. S.BansalG.OroszG.Impacts of Connected Automated Vehicles on Freeway Traffic Patterns at Different Penetration Levels. IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 5, 2020, pp. 4305–4318.
20.
ZongF.WangM.TangM.LiX.ZengM.An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles. IEEE Access, Vol. 9, 2021, pp. 66241–66252.
21.
LiuW.ChenY.LiH.ZhangH.Quantitative Study on Road Traffic Environment Complexity Under Car-Following Condition. Sustainability, Vol. 14, No. 10, 2022, p. 6251.
22.
WangW. -J.MaM. -H.LiangS. -D.MaG. -Y.WangY. -S.Density Waves in an Improved Car-Following Model Under Intelligent Transportation System Environment. Modern Physics Letters B, Vol. 36, No. 15, 2022, p. 2250014.
23.
XinZ.XuJ.Analysis of a Car-Following Model with Driver Memory Effect. International Journal of Bifurcation and Chaos, Vol. 25, No. 4, 2015, p. 1550057.
24.
PengG. -H.JiaT. -T.KuangH.TanH. -L.ChenT.A Novel Car-Following Model by Sharing Cooperative Information Transmission Delayed Effect Under V2X Environment and its Additional Energy Consumption. Chinese Physics B, Vol. 31, No. 5, 2022, p. 058901.
25.
YuB.ZhouH.WangL.WangZ.CuiS.An Extended Two-Lane Car-Following Model Considering the Influence of Heterogeneous Speed Information on Drivers with Different Characteristics Under Honk Environment. Physica A: Statistical Mechanics and its Applications, Vol. 578, 2021, p. 126022.
26.
Jun-FangT.BinJ.Xin-GangL. L.Zi-YouG.A New Car-Following Model Considering Velocity Anticipation. Chinese Physics B, Vol. 19, No. 1, 2010, p. 010511.
27.
KakimotoY.Iryo-AsanoM.OrhanE.NakamuraH.A Study on the Impact Of AV-HDV Mixed Traffic On Flow Dynamics Of Single-Lane Motorway. Transportation Research Procedia, Vol. 34, 2018, pp. 219–226.
28.
WangX.LiuM.CiY.WuL.Effect of Front Two Adjacent Vehicles’ Velocity Information on Car-Following Model Construction and Stability Analysis. Physica A: Statistical Mechanics and its Applications, Vol. 607, 2022, p. 128196.
29.
KnospeW.SantenL.SchadschneiderA.SchreckenbergM.Towards a Realistic Microscopic Description Of Highway Traffic. Journal of Physics A: Mathematical and General, Vol. 33, No. 48, 2000, p. L477.
30.
YaoZ.DengH.ChenZ.HeX.AiY.WuY.Linear Internal Stability for Mixed Traffic Flow of CAVs with Different Automation Levels. Physica A: Statistical Mechanics and its Applications, Vol. 642, 2024, p. 129759.
31.
JiangY.ZhuF.GuQ.WuY.WenX.YaoZ.Influence of CAVs Platoon Characteristics on Fundamental Diagram of Mixed Traffic Flow. Physica A: Statistical Mechanics and its Applications, Vol. 624, 2023, p. 128906.
32.
LiR.SunS.WuY.HaoH.WenX.YaoZ.Fundamental Diagram of Mixed Traffic Flow Considering Time Lags, Platooning Intensity, and the Degradation of Connected Automated Vehicles. Physica A: Statistical Mechanics and its Applications, Vol. 627, 2023, p. 129130.
33.
LiuY.LinX.HeF.LiM., editors. Cellular Automata for Modeling Safety Issues in Mixed Traffic of Conventional and Autonomous Vehicles. In 18th COTA International Conference of Transportation Professionals, Beijing, China, American Society of Civil Engineers, Reston, Virginia, 2018, pp. 56–65.
34.
TalebpourA.MahmassaniH. S.Influence of Connected and Autonomous Vehicles on Traffic Flow Stability and Throughput. Transportation Research Part C: Emerging Technologies, Vol. 71, 2016, pp. 143–163.
35.
QinY.XiaoT.WangH.Optimization Strategy for Connected Automated Vehicles to Reduce Energy Consumption on Freeway in Rainy Weather. Energy, Vol. 296, 2024, p. 131204.
36.
GongB.WangF.LinC.WuD.Modeling HDV and CAV Mixed Traffic Flow on a Foggy Two-Lane Highway with Cellular Automata and Game Theory Model. Sustainability, Vol. 14, No. 10, 2022, p. 5899.
37.
FuC.LuZ.DingN.BaiW.Distance Headway-Based Safety Evaluation of Emerging Mixed Traffic Flow Under Snowy Weather. Physica A: Statistical Mechanics and its Applications, Vol. 642, 2024, p. 129792.
38.
WuD.PengR.LingX.Hybrid Characteristics of Heterogeneous Traffic Flow in Intelligent Network. Journal of Southwest Jiaotong University, Vol. 57, No. 4, 2022, pp. 761–768.
39.
ZhuZ.ZhouH.FuY.KuiZ.WangS.DengS.HouK.Cooperative Control Technology of Networked Autonomous Driving Vehicle Based on Delay Compensation. Journal of System Simulation, Vol. 31, No. 7, 2019, pp. 1448–1459.
40.
DuX.ZHANGM.Based on Fusing Optical Flow Method and Background Modeling for Lane Line Recognition. Journal of Chongqing University of Technology (Natural Science), Vol. 35, No. 3, 2021, pp. 29–35.
41.
WangX.WangC., editors. Vehicle Multi-target Detection in Foggy Scene Based on Foggy env-YOLO Algorithm. In 2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE), Beijing, China, 2022, IEEE, Piscataway, New Jersey, 451–456.
42.
YeldanO.A Stochastic Continuous Cellular Automata Traffic Model with Fuzzy Decision Rules. PhD thesis. Italy: Politecnico di Milano, Milan, 2012.
43.
MilanésV.ShladoverS. E.Modeling Cooperative and Autonomous Adaptive Cruise Control Dynamic Responses Using Experimental Data. Transportation Research Part C: Emerging Technologies, Vol. 48, 2014, pp. 285–300.
44.
GippsP. G.A Behavioural Car-Following Model for Computer Simulation. Transportation Research Part B: Methodological, Vol. 15, No. 2, 1981, pp. 105–111.
45.
ZhaoH. -T.ZhaoX.Jian-chengL.XinL. -Y.Cellular Automata Model for Urban Road Traffic Flow Considering Internet of Vehicles and Emergency Vehicles. Journal of Computational Science, Vol. 47, 2020, p. 101221.
46.
LiuN.LiangB.XiaoY.ZhangH.Driving Safety of Extra-Long Tunnel Based on Reaction Time Characteristics of Drivers. Science Technology and Engineering, Vol. 20, No. 26, 2020, pp. 10927–910933.
47.
ShladoverS. E.SuD.LuX. -Y.Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow. Transportation Research Record: Journal of the Transportation Research Board, 2012. 2324: 63–70.
48.
RahmanM. S.Abdel-AtyM.Longitudinal Safety Evaluation of Connected Vehicles’ Platooning on Expressways. Accident Analysis & Prevention, Vol. 117, 2018, pp. 381–391.
49.
QinY.WangH.Improving Traffic Safety Via Traffic Flow Optimization of Connected and Automated Vehicles. China Journal of Highway and Transport, Vol. 31, No. 4, 2018, pp. 202–210.
50.
MahmudS. S.FerreiraL.HoqueM. S.TavassoliA.Application of Proximal Surrogate Indicators for Safety Evaluation: A Review of Recent Developments and Research Needs. IATSS Research, Vol. 41, No. 4, 2017, pp. 153–163.
51.
OhC.ParkS.RitchieS. G.A Method for Identifying Rear-End Collision Risks Using Inductive Loop Detectors. Accident Analysis & Prevention, Vol. 38, No. 2, 2006, pp. 295–301.
52.
LiL.WangC.ZhangY.QuX.LiR.ChenZ.RanB.Microscopic State Evolution Model of Mixed Traffic Flow Based on Potential Field Theory. Physica A: Statistical Mechanics and its Applications, Vol. 607, 2022, p. 128185.
53.
YeL.YamamotoT.Evaluating the Impact of Connected and Autonomous Vehicles on Traffic Safety. Physica A: Statistical Mechanics and its Applications, Vol. 526, 2019, p. 121009.
54.
LiJ.ChenC.YangB.V2X Assisted Co-Design of Motion Planning and Control for Connected Automated Vehicle. IET Intelligent Transport Systems, Vol. 18, No 12, 2024, pp. 1–17.
55.
ChenC.SongY.WangY. D.HuX.LiuJ.A Machine Learning-Based Approach to Assess Impacts of Autonomous Vehicles on Pavement Roughness. Philosophical Transactions of the Royal Society A, Vol. 381, No. 2254, 2023, p. 20220176.
56.
BarbieriL.TedeschiniB. C.BrambillaM.NicoliM.Deep Learning-based Cooperative LiDAR Sensing for Improved Vehicle Positioning. IEEE Transactions on Signal Processing, Vol. 72, 2024, pp. 1666–1682.
57.
ZouJ.GuoX.WangF.A Study on Pavement Classification and Recognition Based on VGGNet-16 Transfer Learning. Electronics, Vol. 12, No. 15, 2023, p. 3370.
58.
LiuG.XuY.HeZ.RaoY.XiaJ.FanL.Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles. IEEE Access, Vol. 7, 2019, pp. 114487–114495.
59.
EchabH.KhalloukA.Ez-ZahraouyH.Effect of Connected and Autonomous Vehicles on Traffic Flow at a Bidirectional Road. International Journal of Modern Physics C, Vol. 33, No. 7, 2022, p. 2250085.
60.
WangZ.MiQ.XiangJ.ZhangZ.TangJ.An Evaluation of Lane Management Strategy for CAV Priority In Mixed Traffic. IET Intelligent Transport Systems, Vol. 18, No. 3, 2024, pp. 467–479.
