Research on traffic congestion mechanism and countermeasures based on dynamic traffic assignment

Traffic congestion is widely distributed around a network. Generally, to analyze traffic congestion, static traffic capacity is adopted. But dynamic characteristics must be studied because congestion is a dynamic process. A Dynamic Traffic Assignment modeling fundamental combined with an urban congestion analysis method is studied in this paper. Three methods are based on congestion analysis, and the stochastic user optimal DTA models are especially considered. Correspondingly, a dynamic system optimal model is suggested for responding congestion countermeasures and an ideal user optimal model for predicted congestion countermeasure respectively.

A Temporal Domain Decomposition Algorithmic Scheme for Large-Scale Dynamic Traffic Assignment

With emergent interest of Simulation-Based Dynamic Traffic Assignment(SBDTA)in the field of transportation network modeling,deployment of SBDTA models for traffic operations and transportation planning have increased significantly in recent years.In parallel,research and development of innovative approaches of the SBDTA model have enhanced the quality of both the assignment component,i.e,improvement of convergence quality of the Dynamic User Equilibrium(DUE)problem,and the traffic simulation element.However,computational requirement remains to be one of the great challenges for DTA implementations on large-scale networks with a long analysis period.This paper presents a temporal decomposition scheme for large spatial-and temporal-scale dynamic traffic assignment,in which the entire analysis period is divided into Epochs.Vehicle assignment is performed sequentially in each Epoch,thus improving the model scalability and confining the peak run-time memory requirement regardless of the total analysis period.A proposed self-turning scheme adaptively searches for the run-time-optimal Epoch setting during iterations regardless of the characteristics of the modeled network.Extensive numerical experiments confirm the promising performance of the proposed algorithmic schemes.

Bus frequency optimization in a large-scale multi-modal transportation system:integrating 3D-MFD and dynamic traffic assignment

A properly designed public transport system is expected to improve traffic efficiency.A high-frequency bus service would decrease the waiting time for passengers,but the interaction between buses and cars might result in more serious congestion.On the other hand,a low-frequency bus service would increase the waiting time for passengers and would not reduce the use of private cars.It is important to strike a balance between high and low frequencies in order to minimize the total delays for all road users.It is critical to formulate the impacts of bus frequency on congestion dynamics and mode choices.However,as far as the authors know,most proposed bus frequency optimization formulations are based on static demand and the Bureau of Public Roads function,and do not properly consider the congestion dynamics and their impacts on mode choices.To fill this gap,this paper proposes a bi-level optimization model.A three-dimensional Macroscopic Fundamental Diagram based modeling approach is developed to capture the bi-modal congestion dynamics.A variational inequality model for the user equilibrium in mode choices is presented and solved using a double projection algorithm.A surrogate model-based algorithm is used to solve the bi-level programming problem.

Integration control for traffic corridors considering guidance information

The problem of designing integration traffic strategies for traffic corridors with the use of ramp metering, speed limit, and route guidance is considered in this paper. As an improvement to the previous work, the presented approach has the following five features： 1） modeling traffic flow to analyze traffic characteristics under the influence of variable speed limit, on-ramp metering and guidance information; 2） building a hierarchy model to realize the integration design of traffic control and route guidance in traffic corridors; 3） devising a multi-class analytical dynamic traffic assignment （DTA） model for traffic corridors, where not only the route choice process will be different for each user-class, but also the traffic flow operations are user-class specific because the travel time characteristic for each user-class is considered; 4） predicting route choice probabilities adaptively with real-time traffic conditions and route choice behaviors corresponding to variant users, rather than assuming as pre-determined; and 5） suggesting a numerical solution algorithm of the hierarchy model presented in this paper based on the modified algorithm of iterative optimization assignment （IOA）. Preliminary numerical test demonstrates the potential of the developed model and algorithm for integration corridor control.

Route choice models based on altering OD

The condition and physical sense of actual dynamic user optimum are explained by analyzing a simple road network route choice. To match the practical application requirements, assignment network and simulation network are classified account for varying flowing loading. Instantaneous dynamic user optimum model should be applied to the former and actual dynamic user optimum model the latter respectively. The two model’s feasibility is studied as well. Considering the application in ATMS, the model is mainly used to analyze the altering OD problem. Moreover, it adds the method of route adapting into the object function selection to appraise elastic trip strategy and set up real means of route inducement.

Day-to-Day Market Power and Efficiency in Tradable Mobility Credits

An active transportation and demand management framework focusing on tradable mobility credits(TMC)is integrated into an agent-based modeling and simulation(ABMS)platform.In this framework,it is conceived that an auction market within which mobility credits can be transferred between buyers and sellers is constructed in general.The idea of ABMS is extensively incorporated to mimic system users’daily route choices as well as market-related micro-economical decision making process under TMC circumstance.Users are able to form individual propensities towards available bid/ask choices by reinforcement learning principles.The integrated platform offers a brand new insight view of microscopic aspect of the daily operations of credit transfer market,which has hardly been obtained by prior analytical models.Day-to-day traffic dynamics and market dynamics can be captured.Besides,market MOEs,including convergence,stability,efficiency and relative market powers of buyers and sellers under different market policies are investigated.