Interactive travel choices and traffic forecast in a doubly dynamical system with user inertia and information provision

This study models the joint evolution (over calendar time) of travelers’ departure time and mode choices, and the resulting traffic dynamics in a bi-modal transportation system. Specifically, we consider that, when adjusting their departure time and mode choices, travelers can learn from their past travel experiences as well as the traffic forecasts offered by the smart transport information provider/agency. At the same time, the transport agency can learn from historical data in updating traffic forecast from day to day. In other words, this study explicitly models and analyzes the dynamic interactions between transport users and traffic information provider. Besides, the impact of user inertia is taken into account in modeling the traffic dynamics. When exploring the convergence of the proposed model to the dynamic bi-modal commuting equilibrium, we find that appropriate traffic forecast can help the system converge to the user equilibrium. It is also found that user inertia might slow down the convergence speed of the day-to-day evolution model. Extensive sensitivity analysis is conducted to account for the impacts of inaccurate parameters adopted by the transport agency.     

轨道安全监测中传感器优化配置研究

为了研究轨道结构安全监测中传感器的合理配置方法,确保城市轨道交通安全运营,通过建立桥上交叉渡线道岔模型和钢轨应变传递误差最小的目标函数,提出传感器测点选择依据和监测方案评价标准,保证监测方案的经济性、合理性。结论表明:模型数据曲线可以指导监测系统的测点位置选择;随着传感器测点数量增加,监测误差逐渐减小,但在误差比率的"拐点"后增加单位个数传感器带来的收益降低;通过建立误差最小准则的目标函数,结合误差指标可以评价和优化传感器布设方案;钢轨位移、附加力等指标的监测方案也可以通过本文方法确定。     

A traffic-condition-based route guidance strategy for a single destination road network

Most of existing route guidance strategies achieves user optimal equilibrium by comparing travel time. Measuring travel time, however, might be uneasy on an urban road network. To contend with the issue, the paper mainly considers easily obtained inflow and outflow of a link and road capacity as input, and proposes a route guidance strategy for a single destination road network based on the determination of free-flow or congested conditions on alternative routes. An extended strategy for a complex network and a feedback approximation for avoiding forecast are further explored. Weaknesses of the strategy are also explicitly analyzed. To test the strategy, simulation investigations are conducted on two networks with multiple parallel routes. The results indicate that the strategy is able to provide stable splitting rates and to approximate user optimal equilibrium in different conditions, in particular when traffic demand is high. This strategy has potential to be applied in an urban road network due to its simplicity and easily obtained input data. The strategy is also applicable for single destination if some alternatives and similar routes are available.     

Cost scaling based successive approximation algorithm for the traffic assignment problem

This paper presents a cost scaling based successive approximation algorithm, called ε-BA (ε-optimal bush algorithm), to solve the user equilibrium traffic assignment problem by successively refining ε-optimal flows. As ε reduces to zero, the user equilibrium solution is reached. The proposed method is a variant of bush-based algorithms, and also a variant of the min-mean cycle algorithm to solve the min-cost flow by successive approximation. In ε-BA, the restricted master problem, implying traffic equilibration restricted on a bush, is solved to ε-optimality by cost scaling before bush reconstruction. We show that ε-BA can reduce the number of flow operations substantially in contrast to Dial’s Algorithm B, as the former operates flows on a set of deliberately selected cycles whose mean values are sufficiently small. Further, the bushes can be constructed effectively even if the restricted master problem is not solved to a high level of convergence, by leveraging the ε-optimality condition. As a result, the algorithm can solve a highly precise solution with faster convergence on large-scale networks compared to our implementation of Dial’s Algorithm B.     

基于灰色残差GM（1，1）模型的道路交通量预测的研究

道路交通体系是一个多因素、多层次、多目标的复杂系统。其中交通量信息系统具有明显的层次复杂性,结构关系的模糊性,动态变化的随机性,指标数据的不完全和不确定性。由于技术方法、人为因素、自然环境变化的影响,造成各种数据误差、短缺甚至虚假现象,系统的作用机制不明确,系统的状态、结构、边界关系难以精确描述,属于典型的灰色系统。在作量化、模型化、实体化研究时,能作为反映系统主要动态特征的数据是很少的。由于环境对系统的干扰,系统信息中原始数据序列往往呈现离乱情况,离乱数列即为灰色数列或称灰色过程,灰色理论利用那些较少的或不确切的表示系统行为特征的原始数据序列作生成变换后建立微分方程,对灰色过程建立的模型称为灰色模型（Greymodel）,简称GM模型。本文从理论上介绍了GM（1,1）模型和灰色残差GM（1,1）模型建立的一般过程,然后将其应用于交通量预测的实际例子中。预测结果表明,该方法是可行的。     

关于高速公路交通安全设施的设置

交通安全设施对减轻事故的严重度,排除各种纵、横向干扰,提高道路服务水平,提供视线诱导,增强道路景观等起着重要的作用,属于道路的基础设施.     

一级公路交叉口设计中的交通安全问题及对策

一级干线公路由于开口较多,不可避免地存在较多的交通安全问题.文章以湖北荆监一级公路为例,针对交叉口设计中的交通安全典型问题,提出了改善方法及相关对策.     

福州市交通安全与智能交通系统探讨

智能交通系统是一个高科技集成系统,它综合运用各种高新技术于整个交通管理系统之中,可以系统、全面、高效地提高交通运输的安全性.文章阐述了智能交通系统在交通安全中的作用及在福州市的应用情况,指出了福州市发展智能交通的方向,以提高福州市的交通安全管理水平.     

集聚经济视角下轨道交通投资对城市系统均衡的影响

随着城镇化进程加快,城市规模增加所带来的集聚效应和交通拥堵等负外部性之间的矛盾越来越突出,优化城市交通基础设施建设是缓解该矛盾的重要举措。本文同时考虑了集聚经济和交通拥挤两种外部性,构建并求解了一个两区域城市空间结构模型,分三种情况探究了轨道交通的投资引入和道路容量的优化对城市规模和城市空间结构均衡的影响。结果表明,轨道交通的投资引入能够增加城市人口规模和社会福利,使城市结构更加紧凑。但是,随着集聚经济水平的提高,城市规划者应逐步完善公交配套设施,以降低公交拥挤成本,提高公交出行分担率;还应制定相应的福利政策,避免由于个体效用下降而导致人口流出。     

Exploring the issue of integrating logistics and traffic control in urban areas

This paper proposes an optimization framework for urban transportation networks’ (re-)design which explicitly takes into account the specific decision-making processes of ordinary users and logistic operators. Ordinary users are typically commuters whose travels consist of well-defined pairs of origin and destination points, while logistic operators make deliveries at multiple locations. Obviously, these two user classes have different objectives and scopes of action. These differences are seldom considered in traffic research since most models aggregate the flow demand in OD matrices and use assignment models to predict the response of all users as if the dynamics of their optimization processes were of the same nature. This work demonstrates that better results can be achieved if the particular features of each user class are included in the models. It potentially improves the estimation of the responses and allows managers to shape their control measures to address specific user needs.