Advanced information feedback in intelligent traffic systems

Abstract

The optimal information feedback is very important to many socioeconomic systems like stock market and traffic systems aiming to make full use of resources. As to traffic flow, a reasonable real-time information feedback can improve the urban traffic condition by providing route guidance. In this paper, the influence of a feedback strategy named congestion coefficient feedback strategy is introduced, based on a two-route scenario in which dynamic information can be generated and displayed on the board to guide road users to make a choice. Simulation results adopting this optimal information feedback strategy have demonstrated high efficiency in controlling spatial distribution of traffic patterns compared with the other two information feedback strategies, i.e., travel time and mean velocity.

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

“…Different drivers departing from the same place could choose two different paths to get to the same destination which corresponds to the "one entrance and one exit" system. Thus the road condition in present work is closer to reality than some previous works (Wahle et al,2000;Lee et al, 2001;Wang et al, 2005). The rules at the exit of the two-route system are as follows: Fig.…”

“…( 6 ) where n i stands for vehicle number of the ith congestion cluster in which cars are close to each other without a gap between any two of them, and q denotes the total number of congestion clusters on one route. Every cluster is evaluated by a weight w,w h e r ew = 2a n do n ec a n check out that w > 2 leads to the similar results with w = 2 (Wang et al, 2005). WCCFS: Every time step, the traffic control center will receive data from the navigation system (GPS) like CCFS, and the work of the center is to compute the congestion coefficient of each road with a reasonable weighted function and display it on the information board.…”

“…First, for MVFS, we have mentioned that the NS model has a random brake scenario which causes the fragile stability of velocity, thus MVFS cannot completely reflect the real condition of routes. The other reason for the disadvantage of MVFS is that flux consists of two parts, mean velocity and vehicle density, but MVFS only grasps one part and lacks the other part of flux (Wang et al, 2005). Also, the one exit structure of the traffic system and the special velocity update mechanism for the vehicle closest to the exit can also cause oscillation as explained in the last paragraph.…”

“…PFS is based on CCFS, and the predicted congestion coefficient (C p )isdefinedas: which indicates that PFS uses future road condition (the value of congestion coefficient C at time t + ∆t) as feedback information. It has been proved that TTFS is the worst one which brings a lag effect to make it impossible to provide the road users with the real situation of each route (Lee et al, 2001); CCFS is more efficient than MVFS because the random brake mechanism of the Nagel-Schreckenberg (NS) model (Nagel & Schreckenberg, 1992) brings fragile stability of velocity (Wang et al, 2005); WCCFS is more efficient than CCFS for the reason that CCFS does not take the weights of different parts of the route into consideration (Dong et al, 2010b). 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).…”

“…instantaneous average velocity. Wang et al (2005) proposed a third type of information feedback (CCFS), i.e. instantaneous congestion coefficient which is defined as…”