Urban traffic networks comprise a combination of various links. These networks are complicated as they have numerous intersections, meaning that using an analytical approach or parametric models to estimate driving speeds on arterial or highway roads results in low accuracy. In this study, a model is developed to estimate the link speed using speed data collected by a probe vehicle driven across different urban traffic links, which have interrupted flows. We discover multimodal distributions of travel speeds in each link’s probe vehicle data and use them to separate the vehicle groups and calculate the mean speed of links. This strategy makes it possible to obtain more detailed data, which are used to determine the traffic state and increase the accuracy of the model. The developed nonlinear model, suitable for low correlations of consecutive links’ speed data, is built on a recurrent neural network. Moreover, this study merges three machine-learning techniques to apply low correlations between link properties and speed states. The model developed lowered the mean absolute error by 35.9% on average when compared with the long short-term memory with raw data: 46.8% for the slow state, 55.7% for the state change, and 48.0% for the sudden change over 10 km/h.
Get full access to this article
View all access options for this article.
References
1.
VlahogianniE. I.KarlaftisM. G.GoliasJ. C.Short-Term Traffic Forecasting: Where We Are and Where We’re Going. Transportation Research Part C: Emerging Technologies, Vol. 43, No. 1.February2018, 2014, pp. 3–19. https://doi.org/10.1016/j.trc.2014.01.005.
2.
NewellG. F.A Simplified Theory of Kinematic Waves in Highway Traffic, Part I: General Theory. Transportation Research Part B: Methodological, Vol. 27, No. 4, 1993, pp. 281–287. https://doi.org/10.1016/0191-2615(93)90039-D.
3.
DaganzoC. F.The Cell Transmission Model: A Dynamic Representation of Highway Traffic Consistent with the Hydrodynamic Theory. Transportation Research Part B: Methodological, Vol. 28, No. 4, 1994, pp. 269–287. https://doi.org/10.1016/0191-2615(94)90002-7.
4.
PanT. L.SumaleeA.ZhongR. X.Indra-payoongN.Short-Term Traffic State Prediction Based on Temporal–Spatial Correlation. IEEE Transactions on Intelligent Transportation Systems, Vol. 14, No. 3, 2013, pp. 1242–1254. https://doi.org/10.1109/TITS.2013.2258916.
5.
GomesG.HorowitzR.Optimal Freeway Ramp Metering Using the Asymmetric Cell Transmission Model. Transportation Research Part C: Emerging Technologies, Vol. 14, No. 4, 2006, pp. 244–262. https://doi.org/10.1016/j.trc.2006.08.001.
6.
MuñozL.SunX.HorowitzR.AlvarezL.Traffic Density Estimation with the Cell Transmission Model. Proc., of the 2003 American Control Conference, Denver, CO, No. 5, 2003, pp. 3750–3755.
7.
ChienS. I.-J.KuchipudiC. M.Dynamic Travel Time Prediction with Real-Time and Historic Data. Journal of Transportation Engineering, Vol. 129, No. 6, 2003, pp. 608–616. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(608).
8.
Van LintJ. W. C.Online Learning Solutions for Freeway Travel Time Prediction. IEEE Transactions on Intelligent Transportation Systems, Vol. 9, No. 1, 2008, pp. 38–47.
9.
WangY.PapageorgiouM.MessmerA.RENAISSANCE – A Unified Macroscopic Model-Based Approach to Real-Time Freeway Network Traffic Surveillance. Transportation Research Part C: Emerging Technologies, Vol. 14, No. 3, 2006, pp. 190–212. https://doi.org/10.1016/J.TRC.2006.06.001.
10.
ChenH.Grant-MullerS.Use of Sequential Learning for Short-Term Traffic Flow Forecasting. Transportation Research Part C: Emerging Technologies, Vol. 9, No. 5, 2001, pp. 319–336. https://doi.org/10.1016/S0968-090X(00)00039-5
11.
YuG.HuJ.ZhangC.ZhuangL.SongJ.Short-Term Traffic Flow Forecasting Based on Markov Chain Model. Proc., IEEE IV2003 Intelligent Vehicles Symposium (Cat. No.03TH8683), Columbus, OH, 2003.
12.
HintonG. E.OsinderoS.TehY.-W.A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, Vol. 18, No. 7, 2006, pp. 1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527.
13.
ParkD.RilettL. R.Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks. Transportation Research Record: Journal of the Transportation Research Board, 1998. 1617: 163–170. https://doi.org/10.3141/1617-23
14.
ParkD.RilettL. R.HanG.Spectral Basis Neural Networks for Real-Time Travel Time Forecasting. Journal of Transportation Engineering, Vol. 125, No. 6, 1999, pp. 515–523. https://doi.org/10.1061/(ASCE)0733-947X(1999)125:6(515).
15.
IshakS.KothaP.AlecsandruC.Optimization of Dynamic Neural Network Performance for Short-Term Traffic Prediction. Transportation Research Record: Journal of the Transportation Research Board, 2003. 1836: 45–56.
16.
LingrasP.MountfordP.Time Delay Neural Networks Designed Using Genetic Algorithms for Short Term Inter-City Traffic Forecasting. In Engineering of Intelligent Systems (MonostoriL.VánczaJ.AliM., eds.), IEA/AIE 2001. Lecture Notes in Computer Science, Vol. 2070, Springer, Berlin, Heidelberg, pp. 290–299.
17.
FuscoG.ColombaroniC.ComelliL.IsaenkoN.Short-Term Traffic Predictions on Large Urban Traffic Networks: Applications of Network-Based Machine Learning Models and Dynamic Traffic Assignment Models. Proc., 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Budapest, Hungary, 2015.
18.
MaX.TaoZ.WangY.YuH.WangY.Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data. Transportation Research Part C: Emerging Technologies, Vol. 54, 2015, pp. 187–197. https://doi.org/10.1016/J.TRC.2015.03.014.
19.
FuR.ZhangZ.LiL.Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction. Proc., 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, China, 2016.
20.
ZhangD.KabukaM. R.Combining Weather Condition Data to Predict Traffic Flow: A GRU-Based Deep Learning Approach. IET Intelligent Transport Systems, Vol. 12, No. 7, 2018, pp. 578–585. https://doi.org/10.1049/iet-its.2017.0313.
21.
LeeE. H.KhoS.-Y.KimD.-K.ChoS.-H.Travel Time Prediction Using Gated Recurrent Unit and Spatio-Temporal Algorithm. Proceedings of the Institution of Civil Engineers - Municipal Engineer, Vol. 174, No. 2, 2020, pp. 1–9. https://doi.org/10.1680/jmuen.20.00004.
22.
KhanZ.KhanS. M.DeyK.ChowdhuryM.Development and Evaluation of Recurrent Neural Network-Based Models for Hourly Traffic Volume and Annual Average Daily Traffic Prediction. Transportation Research Record: Journal of the Transportation Research Board, 2019. 2673: 489–503.
23.
SunP.BoukercheA.TaoY.SSGRU: A Novel Hybrid Stacked GRU-Based Traffic Volume Prediction Approach in a Road Network. Computer Communications, Vol. 160, 2020, pp. 502–511. https://doi.org/10.1016/j.comcom.2020.06.028.
24.
DaiG.MaC.XuX.Short-Term Traffic Flow Prediction Method for Urban Road Sections Based on Space-Time Analysis and GRU. IEEE Access, Vol. 7, 2019, pp. 143025–143035. https://doi.org/10.1109/ACCESS.2019.2941280.
25.
Van PhuC. N.FarhiN.Estimation of Urban Traffic State with Probe Vehicles. IEEE Transactions on Intelligent Transportation Systems, Vol. 22, No. 5, 2020, pp. 1–12. https://doi.org/10.1109/tits.2020.2975120.
26.
ChenM.ChienS. I. J.Dynamic Freeway Travel-Time Prediction with Probe Vehicle Data: Link Based Versus Path Based. Transportation Research Record: Journal of the Transportation Research Board, 2001. 1768: 157–161.
27.
ZhengF.Van ZuylenH.Urban Link Travel Time Estimation Based on Sparse Probe Vehicle Data. Transportation Research Part C: Emerging Technologies, Vol. 31, 2013, pp. 145–157. https://doi.org/10.1016/j.trc.2012.04.007.
28.
KimE. J.ParkH. C.KhoS. Y.KimD. K.A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed. Journal of Advanced Transportation, Vol. 2019, 2019, pp. 1–12. https://doi.org/10.1155/2019/3958127.
29.
ParkH.-C.KangS.KhoS.-Y.KimD.-K.Investigation of Effects of Inherent Variation and Spatiotemporal Dependency on Urban Travel-Speed Prediction. Journal of Transportation Engineering, Part A: Systems, Vol. 146, No. 5, 2020, p. 04020027. https://doi.org/10.1061/jtepbs.0000341.
30.
ZhaoY.ZhengJ.WongW.WangX.MengY.LiuH. X.Various Methods for Queue Length and Traffic Volume Estimation Using Probe Vehicle Trajectories. Transportation Research Part C: Emerging Technologies, Vol. 107, 2019, pp. 70–91. https://doi.org/10.1016/j.trc.2019.07.008.
31.
AljamalM. A.AbdelghaffarH. M.RakhaH. A.Kalman Filter-Based Vehicle Count Estimation Approach Using Probe Data: A Multi-Lane Road Case Study. Proc., IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, IEEE, 2019.
32.
IqbalMd. S.ChoudhuryC. F.WangP.GonzálezM. C.Development of Origin–Destination Matrices Using Mobile Phone Call Data. Transportation Research Part C: Emerging Technologies, Vol. 40, 2014, pp. 63–74. https://doi.org/10.1016/J.TRC.2014.01.002.
33.
de FabritiisC.RagonaR.ValentiG.Traffic Estimation and Prediction Based on Real Time Floating Car Data. Proc., 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China, 2008.
34.
SeoT.KusakabeT.AsakuraY.Estimation of Flow and Density Using Probe Vehicles With Spacing Measurement Equipment. Transportation Research Part C: Emerging Technologies, Vol. 53, 2015, pp. 134–150. https://doi.org/10.1016/j.trc.2015.01.033.
YangF.YunM.-P.YangX.-G.Travel Time Distribution under Interrupted Flow and Application to Travel Time Reliability. Transportation Research Record: Journal of the Transportation Research Board, 2014. 2466: 114–124.
37.
WenT.-H.ChinW.-C.-B.LaiP.-C.Link Structure Analysis of Urban Street Networks for Delineating Traffic Impact Areas. In Advances in Complex Societal, Environmental and Engineered Systems (NemicheM.EssaaidiM., eds.), Nonlinear Systems and Complexity, Springer, Cham, 2017, Vol. 18, pp. 203–220.
38.
WuY. H.HungM. C.Non-Connective Linear Cartograms for Mapping Traffic Conditions. Cartographic Perspectives, No. 65, 2010, pp. 33–50. https://doi.org/10.14714/CP65.128.
39.
HassounM. H.Fundamentals of Artificial Neural Networks. MIT Press, Cambridge, MA, 1995.
40.
SchäferA. M.ZimmermannH. G.Recurrent Neural Networks Are Universal Approximators. In Artificial Neural Networks–ICANN 2006 (KolliasS. D.StafylopatisA.DuchW.OjaE., eds.), ICANN 2006. Lecture Notes in Computer Science No. 4131 LNCS-I, Springer, Berlin, Heidelberg, 2006, pp. 632–640.
