Effects of prediction feedback in multi-route intelligent traffic systems

Abstract

We first study the influence of an efficient feedback strategy named prediction feedback strategy (PFS) based on a multi-route scenario in which dynamic information can be generated and displayed on the board to guide road users to make a choice. In this scenario, our model incorporates the effects of adaptability into the cellular automaton models of traffic flow. Simulation results adopting this optimal information feedback strategy have demonstrated high efficiency in controlling spatial distribution of traffic patterns compared with the other three information feedback strategies, i.e., vehicle number and flux. At the end of this paper, we also discuss in what situation PFS will become invalid in multi-route systems.

Application of Cellular Automaton Model to Advanced Information Feedback in Intelligent Transportation Systems

“…We also show the results about average flux versus weight factor (k) by adopting WCCFS, average flux versus position of pillar (point T) by adopting CAFS, and average flux versus prediction time (T p )b ya d o p t i n gP F S .T h e s e results indicate that PFS has more advantages than the other five strategies in the two-route system with only one entrance and one exit. However, as we stress before that PFS is not easy to realize and will be invalid when the transportation system is multi-route (Dong et al, 2010d). The numerical simulations demonstrate that the weight factor k (WCCFS), the position of point T (CAFS) and the prediction time T p (PFS) play very important roles in improving the road conditions.…”

“…Here, we use the result of median rounding ⌊n m ⌋ of the ith congestion cluster to represent its position. Furthermore, in order to provide road users with better guidance, Dong et al (2009a;2009b;2010a;2010d) proposed another two types of information feedback strategies named corresponding angle feedback strategy (CAFS) and prediction feedback strategy (PFS), respectively. The corresponding angle coefficient is defined as…”

“…However, WCCFS is still not the best one due to the fact that the weight function F(n m ) does not contain any information related to the length of the congestion cluster while the corresponding angle θ takes both the length and route location of each cluster into account (Dong & Ma, 2010a). Though the road capacity adopting PFS is the best one among these feedback strategies (Dong et al, 2009a;Dong et al, 2009b;Dong et al, 2010d), the validity of PFS depends on the length of the route when the traffic system is multi-route (Dong et al, 2010d). Also, PFS is not easy to realize since it needs predicted road data, which will be discussed in detail in the following paragraphs.…”

Route Guidance Strategy for Heterogeneous Traffic Flow in an Asymmetric Two-Route Scenario

“…TTFS, MVFS and CCFS were three typical information feedback strategies. Subsequently, many improved strategies were proposed based on the above three kinds of feedback strategies, for example, prediction feedback strategy [4], corresponding angle feedback strategy [5], weighted vehicle density feedback strategy (WVDFS) [6], mean velocity difference feedback strategy and congestion coefficient difference feedback strategy [7], vacancy length feedback strategy [8], flux feedback strategy including time flux feedback and space flux feedback [9], and exponential function feedback strategy [10], tour-time feedback strategy [11]. In addition, Zhao et al [12] introduced a bounded rational threshold into existing feedback strategies to improve their efficiency in terms of their capacity, oscillation, and deviations from the equilibrium.…”

An Intelligent Traffic Flow Control System Based on Radio Frequency Identification and Wireless Sensor Networks

“…Such an improvement in transportation system operations can lead to improvement in the safety and efficiency of today's transportation network. Because the international transport sector pays closer attention to and provides fuller cooperation in the promotion of ITS, the further development of the potential of a dynamic signal control model per second basis [1][2][3][4][5] for close integration into developing telecommunication and control technologies to produce more intelligent traffic control timing signals in real-time has become a key issue in long-term resource investment. The belief is that this trend will continue well into the future.…”