A dynamic speed guidance method at on-ramp merging areas of urban expressway considering driving styles

doi:10.1007/s42524-023-0285-x

Front. Eng

2024, Vol. 11

Issue (1) : 92-106 https://doi.org/10.1007/s42524-023-0285-x

A dynamic speed guidance method at on-ramp merging areas of urban expressway considering driving styles

Haoran LI¹, Yunpeng LU², Yaqiu LI³, Junyi ZHANG⁴(

)

¹. School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China; Suzhou Automotive Research Institute, Tsinghua University, Suzhou 215134, China
². School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China
³. School of Transportation, Southeast University, Nanjing 211189, China; Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi Hiroshima 739-8529, Japan
⁴. School of Transportation, Southeast University, Nanjing 211189, China

Download: PDF(6196 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Dynamic speed guidance for vehicles in on-ramp merging zones is instrumental in alleviating traffic congestion on urban expressways. To enhance compliance with recommended speeds, the development of a dynamic speed-guidance mechanism that accounts for heterogeneity in human driving styles is pivotal. Utilizing intelligent connected technologies that provide real-time vehicular data in these merging locales, this study proposes such a guidance system. Initially, we integrate a multi-agent consensus algorithm into a multi-vehicle framework operating on both the mainline and the ramp, thereby facilitating harmonized speed and spacing strategies. Subsequently, we conduct an analysis of the behavioral traits inherent to drivers of varied styles to refine speed planning in a more efficient and reliable manner. Lastly, we investigate a closed-loop feedback approach for speed guidance that incorporates the driver’s execution rate, thereby enabling dynamic recalibration of advised speeds and ensuring fluid vehicular integration into the mainline. Empirical results substantiate that a dynamic speed guidance system incorporating driving styles offers effective support for human drivers in seamless mainline merging.

Keywords driving styles speed guidance driving safety assistance on-ramp merging

Corresponding Author(s): Junyi ZHANG

Just Accepted Date: 24 January 2024 Online First Date: 27 February 2024 Issue Date: 13 March 2024

Cite this article:

Haoran LI,Yunpeng LU,Yaqiu LI, et al. A dynamic speed guidance method at on-ramp merging areas of urban expressway considering driving styles[J]. Front. Eng, 2024, 11(1): 92-106.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-023-0285-x
https://academic.hep.com.cn/fem/EN/Y2024/V11/I1/92

Fig.1 Framework of the speed guidance system.

Fig.2 Predecessor-leader following.

Fig.3 The position of the 9 vehicles at merging area.

Fig.4 The speed of the 9 vehicles at merging area.

Fig.5 The acceleration of the 9 vehicles at merging area.

Fig.6 The closed-loop feedback vehicle speed guidance system.

Fig.7 Speed tracking of the model based on trigonometric function.

Fig.8 Variable step size closed-loop feedback speed tracking.

Fig.9 Driving distance of vehicle tracking under two speed guidances.

Fig.10 Local magnification of driving distance.

Fig.11 The curve of closed-loop feedback speed with step size of 2 s.

Fig.12 The curve of closed-loop feedback speed with step size of 3 s.

Fig.13 The curve of closed-loop feedback speed with step size of 4 s.

Fig.14 Driving distance of vehicle tracking under fixed step size and variable step size.

Fig.15 Speed tracking error when K_p = 5.

Fig.16 Speed tracking error when K_p = 10.

Fig.17 Speed tracking error when K_p = 20.

Fig.18 Speed guidance results of different driving styles.

Fig.19 Driving distance results of different driving styles.

Fig.20 Local magnification of driving distance.

1	M BarthS MandavaK BoriboonsomsinH Xia (2011). Dynamic ECO-driving for arterial corridors. In: IEEE Forum on Integrated and Sustainable Transportation Systems. Vienna: IEEE, 182–188
2	W Chen, Y Liu, X Yang, Y Bai, Y Gao, P Li, (2015). Platoon-based speed control algorithm for eco-driving at signalized intersection. Transportation Research Record: Journal of the Transportation Research Board, 2489( 1): 29–38 https://doi.org/10.3141/2489-04
3	X Chen, (2019). The development trend and practical innovation of smart cities under the integration of new technologies. Frontiers of Engineering Management, 6( 4): 485–502 https://doi.org/10.1007/s42524-019-0057-9
4	Z Chen, K Liu, J Wang, T Yamamoto, (2022). H-ConvLSTM-based bagging learning approach for ride-hailing demand prediction considering imbalance problems and sparse uncertainty. Transportation Research Part C: Emerging Technologies, 140: 103709 https://doi.org/10.1016/j.trc.2022.103709
5	M Cheng, C Zhang, H Jin, Z Wang, X Yang, (2022). Adaptive coordinated variable speed limit between highway mainline and on-ramp with deep reinforcement learning. Journal of Advanced Transportation, 2435643 https://doi.org/10.1155/2022/2435643
6	Y Ci, L Wu, J Zhao, Y Sun, G Zhang, (2019). V2I-based car-following modeling and simulation of signalized intersection. Physica A, 525: 672–679 https://doi.org/10.1016/j.physa.2019.03.062
7	S Fang, L Yang, T Wang, S Jing, (2020). Trajectory planning method for mixed vehicles considering traffic stability and fuel consumption at the signalized intersection. Journal of Advanced Transportation, 1456207 https://doi.org/10.1155/2020/1456207
8	Z Gao, H J Huang, J Guo, L Yang, J Wu, (2023). Future urban transport management. Frontiers of Engineering Management, 10( 3): 534–539 https://doi.org/10.1007/s42524-023-0255-3
9	P G Gipps, (1986). A model for the structure of lane-changing decisions. Transportation Research Part B: Methodological, 20( 5): 403–414 https://doi.org/10.1016/0191-2615(86)90012-3
10	S Gong, L Du, (2018). Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles. Transportation Research Part B: Methodological, 116: 25–61 https://doi.org/10.1016/j.trb.2018.07.005
11	J Han, A Sciarretta, L L Ojeda, G de Nunzio, L Thibault, (2018). Safe- and eco-driving control for connected and automated electric vehicles using analytical state-constrained optimal solution. IEEE Transactions on Intelligent Vehicles, 3( 2): 163–172 https://doi.org/10.1109/TIV.2018.2804162
12	B HomChaudhuri, A Vahidi, P Pisu, (2017). Fast model predictive control-based fuel efficient control strategy for a group of connected vehicles in urban road conditions. IEEE Transactions on Control Systems Technology, 25( 2): 760–767 https://doi.org/10.1109/TCST.2016.2572603
13	K Huang, X Yang, Y Lu, C C Mi, P Kondlapudi, (2018). Ecological driving system for connected/automated vehicles using a two-stage control hierarchy. IEEE Transactions on Intelligent Transportation Systems, 19( 7): 2373–2384 https://doi.org/10.1109/TITS.2018.2813978
14	M Karimi, C Roncoli, C Alecsandru, M Papageorgiou, (2020). Cooperative merging control via trajectory optimization in mixed vehicular traffic. Transportation Research Part C: Emerging Technologies, 116: 102663 https://doi.org/10.1016/j.trc.2020.102663
15	D Kennedy, S P Philbin, (2019). Techno-economic analysis of the adoption of electric vehicles. Frontiers of Engineering Management, 6( 4): 538–550 https://doi.org/10.1007/s42524-019-0048-x
16	Z E A Kherroubi, S Aknine, R Bacha, (2022). Novel decision-making strategy for connected and autonomous vehicles in highway on-ramp merging. IEEE Transactions on Intelligent Transportation Systems, 23( 8): 12490–12502 https://doi.org/10.1109/TITS.2021.3114983
17	T Kim, H Y Jeong, (2014). A novel algorithm for crash detection under general road scenes using crash probabilities and an interactive multiple model particle filter. IEEE Transactions on Intelligent Transportation Systems, 15( 6): 2480–2490 https://doi.org/10.1109/TITS.2014.2320447
18	H Kimura, M Takahashi, K Nishiwaki, M Iezawa, (2022). Decision-making based on reinforcement learning and model predictive control considering space generation for highway on-ramp merging. IFAC-PapersOnLine, 55( 27): 241–246 https://doi.org/10.1016/j.ifacol.2022.10.519
19	A Ladino, M Wang, (2020). A dynamic game formulation for cooperative lane change strategies at highway merges. IFAC-PapersOnLine, 53( 2): 15059–15064 https://doi.org/10.1016/j.ifacol.2020.12.2026
20	X Liang, S I Guler, V V Gayah, (2018). Signal timing optimization with connected vehicle technology: Platooning to improve computational efficiency. Transportation Research Record: Journal of the Transportation Research Board, 2672( 18): 81–92 https://doi.org/10.1177/0361198118786842
21	X Y Lu, H S Tan, S E Shladover, J K Hedrick, (2004). Automated vehicle merging maneuver implementation for AHS. Vehicle System Dynamics, 41( 2): 85–107 https://doi.org/10.1076/vesd.41.2.85.26497
22	D MarinescuJ ČurnM BourocheV Cahill (2012). On-ramp traffic merging using cooperative intelligent vehicles: A slot-based approach. In: 15th International IEEE Conference on Intelligent Transportation Systems. Anchorage, AK: IEEE, 900–906
23	V Milanés, J Godoy, J Villagrá, J Pérez, (2011). Automated on-ramp merging system for congested traffic situations. IEEE Transactions on Intelligent Transportation Systems, 12( 2): 500–508 https://doi.org/10.1109/TITS.2010.2096812
24	C Miyajima, Y Nishiwaki, K Ozawa, T Wakita, K Itou, K Takeda, F Itakura, (2007). Driver modeling based on driving behavior and its evaluation in driver identification. Proceedings of the IEEE, 95( 2): 427–437 https://doi.org/10.1109/JPROC.2006.888405
25	S Moridpour, M Sarvi, G Rose, E Mazloumi, (2012). Lane-changing decision model for heavy vehicle drivers. Journal of Intelligent Transport Systems, 16( 1): 24–35 https://doi.org/10.1080/15472450.2012.639640
26	I A Ntousakis, I K Nikolos, M Papageorgiou, (2016). Optimal vehicle trajectory planning in the context of cooperative merging on highways. Transportation Research Part C: Emerging Technologies, 71: 464–488 https://doi.org/10.1016/j.trc.2016.08.007
27	J Rios-Torres, A A Malikopoulos, (2017). A survey on the coordination of connected and automated vehicles at intersections and merging at highway on-ramps. IEEE Transactions on Intelligent Transportation Systems, 18( 5): 1066–1077 https://doi.org/10.1109/TITS.2016.2600504
28	Y Shi, Z Yuan, H Yu, Y Guo, Y Qi, (2021). A graph-based optimal on-ramp merging of connected vehicles on the highway. Machines, 9( 11): 290 https://doi.org/10.3390/machines9110290
29	T Tang, H Huang, H Y Shang, (2017). An extended macro traffic flow model accounting for the driver’s bounded rationality and numerical tests. Physica A, 468: 322–333 https://doi.org/10.1016/j.physa.2016.10.092
30	A UnoT SakaguchiS Tsugawa (1999). A merging control algorithm based on inter-vehicle communication. In: Proceedings of 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems. Tokyo: IEEE, 783–787
31	W Wu, P K Li, Y Zhang, (2015). Modelling and simulation of vehicle speed guidance in connected vehicle environment. International Journal of Simulation Modelling, 14: 145–157 https://doi.org/10.2507/IJSIMM14(1)CO3
32	Q Xin, R Fu, W Yuan, Q Liu, S Yu, (2018). Predictive intelligent driver model for eco-driving using upcoming traffic signal information. Physica A, 508: 806–823 https://doi.org/10.1016/j.physa.2018.05.138
33	Q Xu, J Wang, B Wang, X Yan, (2020). Modeling and simulation of intersection quasi-moving block speed guidance based on connected vehicles. Journal of Intelligent and Connected Vehicles, 3( 2): 67–78 https://doi.org/10.1108/JICV-01-2020-0002
34	C Yu, Y Feng, H X Liu, W Ma, X Yang, (2018). Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections. Transportation Research Part B: Methodological, 112: 89–112 https://doi.org/10.1016/j.trb.2018.04.007
35	Y Zhang, P A Ioannou, (2017). Coordinated variable speed limit, ramp metering and lane change control of highway traffic. IFAC-PapersOnLine, 50( 1): 5307–5312 https://doi.org/10.1016/j.ifacol.2017.08.982
36	J Zhao, V L Knoop, M Wang, (2020). Two-dimensional vehicular movement modelling at intersections based on optimal control. Transportation Research Part B: Methodological, 138: 1–22 https://doi.org/10.1016/j.trb.2020.04.001
37	S Zhao, K Zhang, (2021). Online predictive connected and automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions. Transportation Research Part B: Methodological, 145: 80–117 https://doi.org/10.1016/j.trb.2020.12.009
38	W Zhao, D Ngoduy, S Shepherd, R Liu, M Papageorgiou, (2018). A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection. Transportation Research Part C: Emerging Technologies, 95: 802–821 https://doi.org/10.1016/j.trc.2018.05.025

Viewed

Full text

Abstract

Cited

Shared

Discussed