Multi-class dynamic network traffic flow propagation model with physical queues

Yanfeng LI, Jun LI

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Front. Eng ›› 2017, Vol. 4 ›› Issue (4) : 399-407. DOI: 10.15302/J-FEM-2017041
RESEARCH ARTICLE
RESEARCH ARTICLE

Multi-class dynamic network traffic flow propagation model with physical queues

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Abstract

This paper proposes an improved multi-class dynamic network traffic flow propagation model with a consideration of physical queues. Each link is divided into two areas: Free flow area and queue area. The vehicles of the same class are assumed to satisfy the first-in-first-out (FIFO) principle on the whole link, and the vehicles of the different classes also follow FIFO in the queue area but not in the free flow area. To characterize this phenomenon by numerical methods, the improved model is directly formulated in discrete time space. Numerical examples are developed to illustrate the unrealistic flows of the existing model and the performance of the improved model. This analysis can more realistically capture the traffic flow propagation, such as interactions between multi-class traffic flows, and the dynamic traffic interactions across multiple links.

Keywords

first-in-first-out (FIFO) / multi-class traffic / physical queues / traffic flow modeling

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Yanfeng LI, Jun LI. Multi-class dynamic network traffic flow propagation model with physical queues. Front. Eng, 2017, 4(4): 399‒407 https://doi.org/10.15302/J-FEM-2017041

References

[1]
Bliemer M (2007). Dynamic queuing and spillback in analytical multiclass dynamic network loading model. Transportation Research Record: Journal of the Transportation Research Board, 2029: 14–21
CrossRef Google scholar
[2]
Daganzo C F (1994). The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research Part B: Methodological, 28(4): 269–287
CrossRef Google scholar
[3]
Daganzo C F (1995). The cell transmission model, Part II: Network traffic. Transportation Research Part B: Methodological, 29(2): 79–93
CrossRef Google scholar
[4]
Huang H J, Lam W H K (2002). Modeling and solving the dynamic user equilibrium route and departure time choice problem in network with queues. Transportation Research Part B: Methodological, 36(3): 253–273
CrossRef Google scholar
[5]
Huang H J, Lam W H K (2003). A multi-class dynamic user equilibrium model for queuing networks with advanced traveler information systems. Journal of Mathematical Modelling and Algorithms, 2(4): 349–377
CrossRef Google scholar
[6]
Kuwahara M, Akamatsu T (2001). Dynamic user optimal assignment with physical queues for a many-to-many OD pattern. Transportation Research Part B: Methodological, 35(5): 461–479
CrossRef Google scholar
[7]
Leclercq L, Laval J A (2007). A multiclass car-following rule based on the LWR model. In: Appert-Rolland C, Chevoir F, Gondret P, Lassarre S, Lebacque J P, Schreckenberg M, eds. Traffic and Granular Flow ’07. Berlin: Springer, 735–753
[8]
Levin M W, Boyles S D (2016). A multi-class cell transmission model for shared human and autonomous vehicle roads. Transportation Research Part C, Emerging Technologies, 62: 103–116
CrossRef Google scholar
[9]
Lighthill M J, Whitham G B (1955). On kinematic waves. II. A theory of traffic flow on long crowded roads. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. The Royal Society, 229(1178): 317–345
[10]
Liu S, De Schutter B, Hellendoorn H (2013). Multi-class traffic flow and emission control for freeway networks. In: 16th International IEEE Conference on Intelligent Transportation Systems-(ITSC). IEEE, 2223–2228
[11]
Liu S, Hellendoorn H, De Schutter B (2017). Model predictive control for freeway networks based on multi-class traffic flow and emission models. IEEE Transactions on Intelligent Transportation Systems, 18(2): 306–320
CrossRef Google scholar
[12]
Lo  H, Ran B, Hongola B (1996). Multiclass dynamic traffic assignment model: Formulation and computational experiences. Transportation Research Record: Journal of the Transportation Research Board, 1537: 74–82
CrossRef Google scholar
[13]
Lo H K, Szeto W Y (2002a). A cell-based variational inequality formulation of the dynamic user optimal assignment problem. Transportation Research Part B: Methodological, 36(5): 421–443
CrossRef Google scholar
[14]
Lo H K, Szeto W Y (2002b). A cell-based dynamic traffic assignment model: formulation and properties. Mathematical and Computer Modelling, 35(7-8): 849–865
CrossRef Google scholar
[15]
Lo H K, Szeto W Y (2004). Modeling advanced traveler information services: Static versus dynamic paradigms. Transportation Research Part B: Methodological, 38(6): 495–515
CrossRef Google scholar
[16]
Logghe S, Immers L H (2008). Multi-class kinematic wave theory of traffic flow. Transportation Research Part B: Methodological, 42(6): 523–541
CrossRef Google scholar
[17]
Newell G F (1993). A simplified theory of kinematic waves in highway traffic, Part I: General theory; Part II: Queuing at freeway bottlenecks; Part III: Multi-destination flows. Transportation Research Part B: Methodological, 27(4): 281–314
CrossRef Google scholar
[18]
Nie X (2003). The study of dynamic user-equilibrium traffic assignment. Dissertation for the Doctoral Degree. Davis: University of California, 242–255
[19]
Ran B, Boyce D (1996). Modeling Dynamic Transportation Network: An Intelligent Transportation System Oriented Approach. New York: Springer
[20]
Richards P I (1956). Shock waves on the highway. Operations Research, 4(1): 42–51
CrossRef Google scholar
[21]
Szeto W Y, Jiang Y, Sumalee A (2011). A cell-based model for multi-class doubly stochastic dynamic traffic assignment. Computer-Aided Civil and Infrastructure Engineering, 26(8): 595–611
CrossRef Google scholar
[22]
Tuerprasert K, Aswakul C (2010). Multiclass cell transmission model for heterogeneous mobility in general topology of road network. Journal of Intelligent Transport Systems, 14(2): 68–82
CrossRef Google scholar
[23]
van Wageningen-Kessels F, Van Lint H, Hoogendoorn S, Vuik K (2010). Lagrangian formulation of multiclass kinematic wave model. Transportation Research Record: Journal of the Transportation Research Board, 2188: 29–36
CrossRef Google scholar
[24]
van Wageningen-Kessels F (2016). Framework to assess multiclass continuum traffic flow models. Transportation Research Record: Journal of the Transportation Research Board, 2553: 150–160
CrossRef Google scholar
[25]
Wong G C K, Wong S C (2002). A multi-class traffic flow model—An extension of LWR model with heterogeneous drivers. Transportation Research Part A, Policy and Practice, 36(9): 827–841
CrossRef Google scholar

Acknowledgements

This work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 71571150 and 71361006), the Humanities and Social Science Foundation of The Ministry of Education (Grant No. 14YJA630026), and the Fundamental Research Funds for the Central Universities (Grant No. 26815WCX03). The authors are grateful to the reviewer for his/her constructive comments.

RIGHTS & PERMISSIONS

2017 The Author(s) 2017. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)
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