Appointment-based Analysis Of Urban Commuter Travel

Road congestion during the rush hours of urban commuting has seriously affected the daily life of residents and the sustainable development of the city.Commuters are the main participants of the morning and evening peak travel in the city,and their choice of travel behavior directly affects the traffic flow state on the road.Studying individual travel behavior is an important basis for exploring the characteristics of urban commuter traffic.With the development of real-time communication and other technologies,travel reservation,as a new and service-oriented means of transportation demand management,has been gradually discussed and explored in practice.In this thesis,a multi-agent-oriented simulation model framework is constructed for individual decision-making,and the travel behavior choices of commuting individuals and the corresponding road traffic conditions are studied.On the basis of the simulation framework,the reservation travel mechanism is set,and the action mechanism and implementation effect of reservation travel are studied.The research shows that reservation travel can effectively uniform the time-space distribution of traffic flow and improve travel efficiency.The research results can provide theoretical support for the formulation of relevant traffic management measures,and are of great significance to the sustainable development of cities.The main work of the thesis is as follows:(1)To sort out and summarize the domestic and foreign studies on commuting behavior,reservation and agent simulation.On this basis,this thesis puts forward the research problems and content,expounds the innovation points of this thesis,and gives the technical route of this thesis.(2)Build a simulation model framework for individual decision making.The travel simulation framework takes the agent as the basic unit and the commuting cost as the objective function to minimize commuting cost.The cycle and iterative optimization are formed by the individual generation module,the initial scheme generation module,and the traffic allocation and plan selection module,so as to achieve the system state of random user equilibrium.In the module of traffic assignment and plan selection,this thesis proposes a co-evolution algorithm combining genetic algorithm and simulated annealing,which can transform the traffic flow-oriented traffic assignment into the individual-oriented dynamic traffic flow assignment and realize the optimal choice of travel plan.(3)Modeling of urban commuting in the reservation scenario.This thesis introduces the concept of travel reservation and development of travel in urban commuter travel booking is analyzed the applicability of this specific scenario and consideration.Taking the dissipation process of queue in bottleneck congestion as an example,the mechanism of the action of the reservation mechanism is analyzed.On this basis,the simulation modeling of the reservation trip is carried out,and two important modules of road state discrimination and departure time selection in the reservation trip model are elaborated in detail.The detailed process of commuting reservation is designed,which lays a foundation for the subsequent simulation case experiments.(4)Simulation analysis of urban commuting.Morning rush to Jinan real road network of commuter traffic behavior simulation environment,simulation experiments of the single path and road network respectively,based on departure time,travel time,commuting costs and the analysis of the intersection delay and other indicators,commuter individual commuter travel choice behavior is studied,this paper is verified to travel simulation model framework is able to simulate the commuters travel decisions.On this basis,through the traffic simulation experiment under the reservation scenario,the comparison verifies that the reservation can guide the travel behavior,uniform traffic flow in time and space,and then alleviate the problem of mismatch between traffic supply and demand,which can be used as an effective control measure of urban traffic in the future.