The Influence Of Route-choice Methods With Feedback Information On Traffic Flows

With the development of road construction and the increasing of vehicles in China,traffic problems are more and more severe.In order to ease the traffic congestion,most cities solve the traffic problem by continuously expanding the road network scale.With the advent of big data and 5G communication era,intelligent transportation emerges.As its core component,various traffic information evaluations and route-choice decisions have been paid more and more attention.In this paper,the traffic model is simulated with a lattice with a 1-to-2（one lane to two routes）fork point.Real-time particle densities are taken as information feedback.Based on the traffic density and combined with road capacity,different route selection schemes are proposed.The effect of information feedback in traffic networks is studied.（1）Traffic flows are simulated via cellular automaton.Each vehicle is supposed as a hard-core“particle”.Informed particles（IPs）and uninformed particles（UPs）coexist.Two scenarios,i.e.,route-choice decisions non-considering and considering road capacity,are considered,respectively.We find that IPs gain larger benefits than UPs,i.e.,the mean travel time for IPs is shorter than that of UPs.The benefit gap between IPs and UPs reduces when the disequilibrium degree of system becomes small.The phase state of traffic flows can be quantified by the benefit gap.Therefore,the effect of information feedback in traffic networks can be understood from the view of rewards of route-choice decisions.（2）Three types of informed particles and two types of uninformed particles are considered.They adopt different route-choice rules.With a macroscopic approach,the stability conditions of systems are theoretically analyzed,which separates long,intermediate and short travel time phases.The Monte Carlo simulation results certify theoretical predictions.The relative benefits of route-choice rules are compared by average travel times of different types of particles,which depends on system’s phases.These results are useful for traffic optimization on traffic networks.（3）A route-choice rule is proposed on parameter k,which is related to road capacities.The conditions of the user equilibrium and the system optimal are analyzed.With the cellular automaton and the Monte Carlo method,based on the mean travel time and the mean traffic flow quantity,the parameters k_ue（the user equilibrium）and k_so（the system optimal）are numerically obtained.