The traffic network and the communication network play a more and more important role in modern life. Almost all people use these two networks everyday. Thus the governments around the world increase the investment in their infrastructure. However, increasing the infrastructure alone, such as broadening the road and the bandwidth, can not meet the growing demand. More importantly, we must focus on optimizing management and making the best use of existing infrastructure in order to improve the performance of these two networks. The key to improving the efficiency is to design effective path strategies. In this paper, we first give a brief introduction of the similarities and the differences between the traffic network and the communication network. Then we review the route choice strategies used to guide the vehicle and path strategies used to guide the pedestrian in the transportation network, and the routing strategies in the communication network. By comparison, we find that the strategies based on the global information are better than those based on the local information. However, due to the factor of the network size, the strategies based on the local information are more suitable for the communication network, and the strategies based on the global information are more suitable for the transportation network.
CHEN Bokui
,
JIANG Yanqun
,
YAN Dengcheng
,
WANG Binghong
. Path strategies in transportation networkand communication network[J]. Science & Technology Review, 2017
, 35(14)
: 23
-26
.
DOI: 10.3981/j.issn.1000-7857.2017.14.002
[1] Wahle J, Bazzan A L C, Klügl F, et al. Decision dynamics in a traffic scenario[J]. Physica A:Statistical Mechanics and its Applications, 2000, 287(3/4):669-681.
[2] Lee K, Hui P, Wang B H, et al. Effects of announcing global informa tion in a two-route traffic flow model[J]. Journal of the Physical Society of Japan, 2001, 70(12):3507-3510.
[3] Wang W X, Wang B H, Zheng W C, et al. Advanced information feed back in intelligent traffic systems[J]. Physical Review E Statistical Non linear & Soft Matter Physics, 2005, 72(2):066702.
[4] Dong C F, Ma X, Wang G W, et al. Prediction feedback in intelligent traffic systems[J]. Physica A:Statistical Mechanics and its Applica tions, 2009, 388(21):4651-4657.
[5] Chen B, Dong C, Liu Y, et al. Real-time information feedback based on a sharp decay weighted function[J]. Computer Physics Communica tions, 2012, 183(10):2081-2088.
[6] Chen B, Xie Y, Tong W, et al. A comprehensive study of advanced in formation feedbacks in real-time intelligent traffic systems[J]. Physica A:Statistical Mechanics and its Applications, 2012, 391(8):2730-2739.
[7] Li M, Ding Z J, Jiang R, et al. Traffic flow in a Manhattan-like urban system[J]. Journal of Statistical Mechanics Theory & Experiment, 2011, 12(12):849-976.
[8] Knoop V L, Hoogendoorn S P, Van J W C. Routing strategies based on macroscopic fundamental diagram[J]. Transportation Research Record, 2012, 2315:1-10.
[9] Løvås G G. Modeling and simulation of pedestrian traffic flow[J]. Trans portation Research Part B:Methodological, 1994, 28(6):429-443.
[10] Cheung C Y, Lam W H K. Pedestrian route choices between escalator and stairway in MTR stations[J]. Journal of Transportation Engineering, 1998, 124(3):277-285.
[11] Hoogendoorn S P, Bovy P H L. Pedestrian route-choice and activity scheduling theory and models[J]. Transportation Research Part B:Meth odological, 2004, 38(2):169-190.
[12] Huang L, Wong S C, Zhang M P, et al. Revisiting Hughes' dynamic continuum model for pedestrian flow and the development of an effi cient solution algorithm[J]. Transportation Research Part B:Methodolog ical, 2009, 43(1):127-141.
[13] Jiang Y Q, Guo R Y, Tian F B, et al. Macroscopic modeling of pedes trian flow based on a second-order predictive dynamic model[J]. Ap plied Mathematical Modelling, 2016, 40(23):9806-9820.
[14] Jiang Y Q, Wong S C, Ho H W, et al. A dynamic traffic assignment model for a continuum transportation system[J]. Transportation Re search Part B:Methodological, 2011, 45(2):343-363.
[15] Jiang Y Q, Wong S C, Zhang P, et al. Numerical simulation of a con tinuum model for bi-directional pedestrian flow[J]. Applied Mathemat ics and Computation, 2012, 218(10):6135-6143.
[16] Jiang Y Q, Zhang P, Wong S C, et al. A higher-order macroscopic model for pedestrian flows[J]. Physica A:Statistical Mechanics and its Applications, 2010, 389(21):4623-4635.
[17] Wang W X, Wang B H, Yin C Y, et al. Traffic dynamics based on lo cal routing protocol on a scale-free network[J]. Physical Review E:Statistical Nonlinear & Soft Matter Physics, 2006, 73(2):026111.
[18] Wang W X, Yin C Y, Yan G, et al. Integrating local static and dynam ic information for routing traffic[J]. Physical Review E:Statistical Non linear & Soft Matter Physics, 2006, 74(1):16101.
[19] Ling X, Hu M B, Jiang R, et al. Pheromone routing protocol on a scale-free network[J]. Physical Review E:Statistical Nonlinear & Soft Matter Physics, 2009, 80(6):66110.
[20] Yan G, Zhou T, Hu B, et al. Efficient routing on complex networks[J]. Physical Review E:Statistical Nonlinear & Soft Matter Physics, 2006, 73(4):46108.
[21] Ling X, Hu M B, Jiang R, et al. Global dynamic routing for scale-free networks[J]. Physical Review E:Statistical Nonlinear & Soft Matter Physics, 2010, 81(1):16113.
[22] Lin B C, Chen B K, Gao Y C, et al. Advanced algorithms for local routing strategy on complex networks[J]. PloS One, 2016, 11(7):e0156756.