城市公交线网的一票制差异化票价策略优化

针对城市公交系统客流空间分布和线网运力配置不均衡问题,提出公交线路间差异化定价策略,通过价格杠杆调节客流需求分布. 针对我国普遍采用的一票制模式,在分析乘客广义出行费用的基础上,利用基于Logit 的线路间客流分配方法,并以总社会成本最低为目标,以满足输送能力和收费额度等为约束,建立了固定需求下的城市公交线网的差异化票价策略优化模型. 根据模型特点,设计了基于模拟退火算法的模型求解方法,综合优化各线路的票价水平和发车频率. 最后,将提出的模型和算法应用到典型公交线网上,算例结果表明,优化方法在较低的收费额度下即可实现较为明显的网络改善效果.     

变频器的合理选用

结合理论与实践,全面论述了变频器的合理选择与使用方面的有关问题,包括变频器容量与型式的选择,使用频率与起停时间的设置,以及和其它调速方法的结合.     

A nonlinear model reference adaptive inverse control algorithm with pre-compensator

In this paper, the reduced-order modeling （ROM） technology and its corresponding linear theory are expanded from the linear dynamic system to the nonlinear one, and H∞ control theory is employed in the frequency domain to design some nonlinear system＇ s pre-compensator in some special way. The adaptive model inverse control （AMIC）theory coping with nonlinear system is improved as well. Such is the model reference adaptive inverse control with pre-compensator （PCMRAIC）. The aim of that algorithm is to construct a strategy of control as a whole. As a practical example of the application, the nunlerical simulation has been given on matlab software packages. The numerical result is given. The proposed strategy realizes the linearization control of nonlinear dynamic system. And it carries out a good performance to deal with the nonlinear system.     

单线圈模式下交通参数估计研究

介绍了单线圈在交通参数测量中发挥的作用,阐述了单线圈模式下进行交通流参数估算,主要是交通速度估计的意义和目前采用的主要算法.在此基础上,利用交通流仿真软件PARAMICS并应用此软件搭建单线圈路网模型,通过它获得单线圈模式下交通速度估计的仿真数据,并对数据进行了分析.     

Time control point strategy coupled with transfer coordination in bus schedule design

A schedule consisting of an appropriate arrival time at each time control point can ensure reliable transport services. This paper develops a novel time control point strategy coupled with transfer coordination for solving a multi‐objective schedule design problem to improve schedule adherence and reduce intermodal transfer disutility. The problem is formulated using a robust mixed‐integer nonlinear programming model. The mixed‐integer nonlinear programming model is equivalently transformed into a robust mixed‐integer linear programming model, which is then approximated by a deterministic mixed‐integer linear programming model through Monte Carlo simulation. Thus, the optimal scheduled arrival time at each time control point can be precisely obtained using cplex . Numerical experiments based on three bus lines and the mass rapid transit system in Singapore are presented, and the results show that the schedule determined using the developed model is able to provide not only reliable bus service but also a smooth transfer experience for passengers. Copyright © 2016 John Wiley & Sons, Ltd.     

A robust optimization approach for dynamic traffic signal control with emission considerations

We consider an analytical signal control problem on a signalized network whose traffic flow dynamic is described by the Lighthill–Whitham–Richards (LWR) model (Lighthill and Whitham, 1955; Richards, 1956). This problem explicitly addresses traffic-derived emissions as constraints or objectives. We seek to tackle this problem using a mixed integer mathematical programming approach. Such class of problems, which we call LWR-Emission (LWR-E), has been analyzed before to certain extent. Since mixed integer programs are practically efficient to solve in many cases (Bertsimas et al., 2011b), the mere fact of having integer variables is not the most significant challenge to solving LWR-E problems; rather, it is the presence of the potentially nonlinear and nonconvex emission-related constraints/objectives that render the program computationally expensive.To address this computational challenge, we proposed a novel reformulation of the LWR-E problem as a mixed integer linear program (MILP). This approach relies on the existence of a statistically valid macroscopic relationship between the aggregate emission rate and the vehicle occupancy on the same link. This relationship is approximated with certain functional forms and the associated uncertainties are handled explicitly using robust optimization (RO) techniques. The RO allows emissions-related constraints and/or objectives to be reformulated as linear forms under mild conditions. To further reduce the computational cost, we employ a link-based LWR model to describe traffic dynamics with the benefit of fewer (integer) variables and less potential traffic holding. The proposed MILP explicitly captures vehicle spillback, avoids traffic holding, and simultaneously minimizes travel delay and addresses emission-related concerns.     

A dual control approach for repeated anticipatory traffic control with estimation of network flow sensitivity

This paper investigates a strategic signal control, which anticipates travelers' route choice response and determines signal timings to optimize network‐wide objectives. In general traffic assignment models are used for anticipating this route choice response. However, model‐reality mismatch usually brings suboptimal solutions to the real system. A repeated anticipatory control resolves the suboptimality and addresses the modeling error by learning from information on model bias. This paper extends the repeated control approach and focuses on the estimation of flow sensitivity as well as its influence on control, which is a crucial issue in implementation of model bias correction. The main objective of this paper is first to analyze the estimation error in the real flow derivative that is estimated from noisy measurements. A dual control method is then presented, improving both optimization objective function and derivative estimation during the control process. The proposed dual algorithm is tested on a simple network as well as on a midsize network. Numerical examples confirm the reliable performance of the new reality‐tracking control strategy and its ability to identify (local) optimal solutions on real traffic networks. Copyright © 2016 John Wiley & Sons, Ltd.     

A new generalized heterogeneous data model (GHDM) to jointly model mixed types of dependent variables

This paper formulates a generalized heterogeneous data model (GHDM) that jointly handles mixed types of dependent variables—including multiple nominal outcomes, multiple ordinal variables, and multiple count variables, as well as multiple continuous variables—by representing the covariance relationships among them through a reduced number of latent factors. Sufficiency conditions for identification of the GHDM parameters are presented. The maximum approximate composite marginal likelihood (MACML) method is proposed to estimate this jointly mixed model system. This estimation method provides computational time advantages since the dimensionality of integration in the likelihood function is independent of the number of latent factors. The study undertakes a simulation experiment within the virtual context of integrating residential location choice and travel behavior to evaluate the ability of the MACML approach to recover parameters. The simulation results show that the MACML approach effectively recovers underlying parameters, and also that ignoring the multi-dimensional nature of the relationship among mixed types of dependent variables can lead not only to inconsistent parameter estimation, but also have important implications for policy analysis.     

Robust identification of air traffic flow patterns in Metroplex terminal areas under demand uncertainty

Multi-Airport Systems (MAS), or Metroplexes, serve air traffic demand in cities with two or more airports. Due to the spatial proximity and operational interdependency of the airports, Metroplex airspaces are characterized by high complexity, and current system structures fail to provide satisfactory utilization of the available airspace resources. In order to support system-level design and management towards increased operational efficiency in such systems, an accurate depiction of major demand patterns is a prerequisite. This paper proposes a framework for the robust identification of significant air traffic flow patterns in Metroplex systems, which is aligned with the dynamic route service policy for the effective management of Metroplex operations. We first characterize deterministic demand through a spatio-temporal clustering algorithm that takes into account changes in the traffic flows over the planning horizon. Then, in order to handle uncertainties in the demand, a Distributionally Robust Optimization (DRO) approach is proposed, which takes into account demand variations and prediction errors in a robust way to ensure the reliability of the demand identification. The DRO-based approach is applied on pre-tactical (i.e. one-day planning) as well as operational levels (i.e. 2-h rolling horizon). The framework is applied to Time Based Flow Management (TBFM) data from the New York Metroplex. The framework and results are validated by Subject Matter Experts (SMEs).     

Maritime accident risk estimation for sea lanes based on a dynamic Bayesian network

ABSTRACT

The safety of maritime transportation has become increasingly important with the development of international economics and trade. This paper introduces a dynamic Bayesian network (DBN) model to facilitate the estimation of the dynamic emergency risk in sea lanes. The DBN model is a novel model that can efficiently represent and infer complex stochastic knowledge. To construct this model, available data, which were collected from emergency investigation reports by the International Maritime Organization (IMO), are employed in conjunction with expert knowledge to develop and demonstrate a BN; an evidence theory approach is applied to calculate the prior probability with the help of historical data; the conditional probability is learned by the expectation maximization (EM) algorithm; and the transition probability is obtained by a Markov model. Finally, the Viterbi algorithm is adopted to estimate emergency risk. The emergencies that occurred in the Indian Ocean from 2009 to 2018 were used as a case study for risk estimation. Sensitivity analysis was conducted to identify significant influential factors. The results show that the sea lane risk in the Indian Ocean fluctuates within a small range, presenting an overall downward trend over time. These findings provide a reference for maritime stakeholders to make proper decisions.