Influence of traffic flow of on-ramps on the mainline speed on freeways

Authors

DOI:

https://doi.org/10.61089/aot2024.b2h1zb09

Keywords:

freeway safety, on-ramp influence, travel average speed, VISSIM simulation, logistic model

Abstract

Vehicles entering from on-ramps can increase the speed dispersion of the mainline and induce frequent changing lanes or acceleration and deceleration behaviors. These complex traffic behaviors interfere with traffic on the mainline and thus result in congestion and safety issues. Reasonable management and control of ramps, especially on-ramps, has been proven to be an effective solution for traffic congestion caused by ramp traffic flow. Understanding the influence of traffic flow of on-ramps on the average speed of the freeway mainline is useful for creating effective ramp management strategies. In this study, field tests were employed to gather traffic flow data on some typical basic freeway interchanges in China. As it is difficult to obtain the required traffic conditions only through field tests, the VISSIM traffic simulation model was also utilized. The same set of field data was used in VISSIM and the driver behavior model parameters CC0 (standstill distance between vehicles) and CC1 (time headway) were calibrated based on the sensitivity analysis to truly reflect the actual traffic conditions. The simulation program was executed with the calibrated parameters and various on-ramp traffic volumes to supplement the traffic data. The gathered traffic data sets from field tests and simulations were classified into four groups based on the various on-ramp traffic flow patterns (free-flow, reasonably free-flow, unstable flow, and congested flow condition). The influence of on-ramp traffic flow on the mainline average speed is discussed for each group. The results showed that the average travel speed of the mainline is significantly affected by the v/C ratio of the on-ramp, as the v/C ratio of the entrance ramp increases, the average travel speed of the mainline significantly decreases. Additionally, the four-parameter logistic model was developed to model the mainline average speed changes with different mainline v/C ratios under various on-ramp traffic flow patterns. The results demonstrate that the model fits the data well. The findings of this study can provide reference information for the implementation of ramp management strategies.

References

Belletti, F., Haziza, D., Gomes, G., & Bayen, A. (2018). Expert Level Control of Ramp Metering Based on Multi-task Deep Reinforcement Learning. IEEE Transactions on Intelligent Transportation Systems, 19, 1198-1207. http://dx.doi.org/10.1109/TITS.2017.2725912.

Bhatt, K., & Shah, J. (2022). An Impact of Gap Acceptance on Road Safety: A Critical Systematic Review. Journal of Sustainable Development of Transport and Logistics, 7(1), 6–22. https://doi.org/10.14254/jsdtl.2022.7-1.1.

Budzynski, M., Tubis, A., & Rydlewski, M. (2021). Preliminary Safety Assessment of Polish Interchanges. Archives of Transport, 58(2), 99-113. https://doi.org/10.5604/01.3001.0014.8969.

Chandra, S., Mehar, A., & Velmurugan, S. (2016). Effect of Traffic Composition on Capacity of Multilane Highways. KSCE Journal of Civil Engineering, 20, 2033-2040. https://doi.org/10.1007/s12205-015-0479-9.

Chen, W., & Bokka, S. (2005) Stochastic Modeling of Nonlinear Epidemiology. Journal of Theoretical Biology, 234(4), 455-470. https://doi.org/10.1016/j.jtbi.2004.11.033.

Cheng, M., Zhang, C., Jin, H., Wang, Z., & Yang, X. (2022). Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement Learning. Journal of Advanced Transportation, 2022(2435643), 1-16. https://doi.org/10.1155/2022/2435643.

Daamen, W., Loot, M., & Hoogendoorn, S. (2010). Empirical Analysis of Merging Behavior at Freeway On-ramp. Transportation Research Record Journal of the Transportation Research Board, 2188(2188), 108-118. https://doi.org/10.3141/2188-12.

Diedrich, G., Santen, L., Schadschneider, A., & Zittartz, J. (2000). Effects of On- and Off-Ramps in Cellular Automata Models for Traffic Flow. International Journal of Modern Physics C, 11(02), 335-3450. https://doi.org/10.1142/S0129183100000316.

Evans, L., Elefteriadou, L., & Gautam, N. (2001). Probability of Breakdown at Freeway Merges Using Markov Chains. Transportation Research Part B: Methodological, 35(3), 237-254. https://doi.org/10.1016/S0191-2615(99)00049-1.

Golob, F., Recker, W., & Alvarez, M. (2004) Safety Aspects of Freeway Weaving Sections. Transportation Research Part A Policy & Practice, 38(1), 35-51. http://dx.doi.org/10.1016/j.tra.2003.08.001.

Hu, J., Li, F., Han, B., & Yao, J. (2017). Analysis of the Influence on Expressway Safety of Ramps. Archives of Transport, 43(3), 43-51. https://doi.org/10.5604/01.3001.0010.4226.

Lertworawanich, P., & Elefteriadou, L. (2003). A Methodology for Estimating Capacity at Ramp Weaves Based on Gap Acceptance and Linear Optimization. Transportation Research Part B, 37(5), 459-483. https://doi.org/10.1016/S0191-2615(02)00024-3.

Li, G., & Cheng, J. (2019). Exploring the Effects of Traffic Density on Merging Behavior. IEEE Access, 7, 51608-51619. https://doi.org/10.1109/ACCESS.2019.2911302.

Liang, X., Lu, Q., Lin, P., & Xu, J. (2016). Freeway Ramp Metering using Fuzzy Logic and Genetic Proportional Plus Integral Control. International Journal of Control and Automation, 9(11), 61-76. http://dx.doi.org/ 10.14257/ijca.2016.9.11.06.

Liu, X., Xu, J., Li, M., Wei, L., & Ru, H. (2019). General-Logistic-Based Speed-Density Relationship Model Incorporating the Effect of Heavy Vehicles. Mathematical Problems in Engineering. 2019(6039846), 1-10. https://doi.org/10.1155/2019/6039846.

Lyu, N., Wen, J., & Hao, W. (2022). Lane-Level Regional Risk Prediction of Mainline at Freeway Diverge Area. International Journal of Environmental Research and Public Health, 19(10), 5867. https://doi.org/10.3390/ijerph19105867.

Manual, H.C., (2000). Highway Capacity Manual, Transportation Research Board, Washington, DC, fourth edition Edition, ISBN 0-309-06681-6.

Marchetti, C., Meyer, P., & Ausubel, J. (1996). Human population dynamics revisited with the logistic model: how much can be modeled and predicted? Technological Forecasting & Social Change, 52(1), 1–30. https://doi.org/10.1007/BF02438865.

Mccartt, A., Northrup, V., & Retting, R. (2004). Types and Characteristics of Ramp-Related Motor Vehicle Crashes on Urban Interstate Roadways in Northern Virginia. Journal of Safety Research, 35(1), 107-114. http://dx.doi.org/10.1016/j.jsr.2003.09.019.

Mhirech, A., Ez-Zahraouy, H., & Ismaili, A. (2011). The Effect of On- and Off-Ramps Positions on the Traffic Flow Behavior, Canadian Journal of Physics, 87(2), 105-110. https://doi.org/10.48550/arXiv.physics/0612255.

Mohamed, A., Ci, Y., & Tan, Y. (2020). A Novel Methodology for Estimating the Capacity and Level of Service for The New Mega Elliptical Roundabout Intersection. Journal of Advanced Transportation, 8467152, 1-18. https://doi.org/10.1155/2020/8467152.

Proctor, M. (2010). Recovery Rates of Chlorophyll-Fluorescence Parameters in Desiccation-Tolerant Plants: Fitted Logistic Curves as A Versatile and Robust Source of Comparative Data. Plant Growth Regulation, 62(3):233-240. https://doi.org/10.1007/s10725-010-9456-y.

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, 8, 72835-72846. https://doi.org/10.1109/ACCESS.2020.2987959.

Qu, H., Chen, W., Niu, M., & Li, X. (2016). Forecasting Realized Volatility in Electricity Markets Using Logistic Smooth Transition Heterogeneous Autoregressive Models. Energy Economics, 54, 68-76. https://doi.org/10.1016/j.eneco.2015.12.001.

Qu, X., Yang, Y., Liu, Z., & Jin, S. (2014) Potential Crash Risks of Expressway On-Ramps and Off-Ramps: A Case Study in Beijing, China. Safety Science, 70, 58-62. https://doi.org/10.1016/j.ssci.2014.04.016.

Shen, J., Li, W., Qiu, F., & Zheng, S. (2015). Capacity of Freeway Merge Areas with Different On-Ramp Traffic Flow. Promet-Traffic-Traffico, 27(3), 227-35. https://doi.org/10.7307/ptt.v27i3.1566.

Srikanth, S., & Mehar, A. (2022). Development of Lane Change Models Through Microscopic Simulation under Mixed Traffic. Jurnal Teknologi, 84(4), 21-27. https://doi.org/10.11113/jurnalteknologi.v84.18074.

Sun, J., Zhao, L., & Zhang, H. (2014). Mechanism of Early-onset Breakdown at Shanghai’s Expressway On-ramp Bottlenecks. Transportation Research Record Journal of the Transportation Research Board, 2421, 64-73. https://doi.org10.3141/2421-08.

Tang, T., Li, J., Yang, S., & Shang, H. (2015). Effects of On-Ramp on the Fuel Consumption of the Vehicles on the Main Road under Car-Following Model. Physica A: Statistical Mechanics and its Applications, 419, 293-300. http://dx.doi.org/10.1016/j.physa.2014.10.051.

Wang, D., Fu, F., Luo, X., Jin, S., & Ma, D. (2016). Travel Time Estimation Method for Urban Road Based on Traffic Stream Directions. Transportmetrica A: Transport Science, 12, 479-503. https://doi.org/10.1080/23249935.2016.1151964.

Wang, H., Li, J., Chen, Q., & Ni, D. (2011). Logistic Modeling of the Equilibrium Speed-Density Relationship. Transportation Research Part A, 45(6), 554–566. https://doi.org/10.1016/j.tra.2011.03.010.

Wang, Y., & Cheu, R. (2013) Safety Impacts of Auxiliary Lanes at Isolated Freeway On-Ramp Junctions. Journal of Transportation Safety & Security, 5(4), 327-343. http://dx.doi.org/10.1080/19439962.2012.761661.

Wang, Y., Wang, L., Yu, X., & Guo, J. (2023). Capacity Drop at Freeway Ramp Merges with Its Replication in Macroscopic and Microscopic Traffic Simulations: A Tutorial Report. Sustainability, 2023(15), 2050. https://doi.org/10.3390/su15032050.

Xu, D., Rouphail, M., Aghdashi, B., Ahmed, I., & Elefteriadou, L. (2020) Modeling Framework for Capacity Analysis of Freeway Segments: Application to Ramp Weaves. Transportation Research Record Journal of the Transportation Research Board, 2674(1), 148-159. https://doi.org/10.1177/0361198119900157.

Yang, B., Liu, P., Chan, C., Xu, C., & Guo, Y. (2019). Identifying the Crash Characteristics on Freeway Segments Based on Different Ramp Influence Areas. Traffic Injury Prevention, 20(4), 1-6. https://doi.org/ 10.1080/15389588.2019.1588965.

Ye, Y., Liu, Y., Liang, X., & Dong, C. (2013). Freeway Ramp Metering Based on Cell Transmission Model and Single Neuron. Applied Mechanics and Materials, 423-426, 2877-2881. http://dx.doi.org/10.4028/www.scientific.net/AMM.423-426.2877.

Yu, X., Xu, W., & Alam, F. (2015). Optimal Coordination of Ramp Metering via Iterative Dynamic Programming. International Journal of Intelligent Transportation Systems Research, 13(3), 203-218. http://dx.doi.org/10.1007/s13177-014-0096-x.

Zhang, W., Wei, S., Wang, C., & Qiu, M. (2023). Asymmetric Behaviour and Traffic Flow Characteristics of Expressway Merging Area in China. Promet - Traffic&Transportation, 35(1), 12–26. https://doi.org/10.7307/ptt.v35i1.4200.

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Published

2024-03-13

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Original articles

How to Cite

Ru, H., Luan, J., Ding, Q., & Xu, J. (2024). Influence of traffic flow of on-ramps on the mainline speed on freeways. Archives of Transport, 69(1), 59-73. https://doi.org/10.61089/aot2024.b2h1zb09

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