A planning tool for evaluating vehicles miles travelled and traffic safety forecasts of growth management scenarios: A case study of Baton Rouge and New Orleans

This study describes an adaptable planning tool that examines potential change in vehicle miles travelled (VMT) growth and corresponding traffic safety outcomes in two urbanized areas, Baton Rouge and New Orleans, based on built environment, economic and demographic variables. This model is employed to demonstrate one aspect of the potential benefits of growth management policy implementation aimed at curbing VMT growth, and to establish targets with which to measure the effectiveness of those policies through a forecasting approach. The primary objective of this research is to demonstrate the need to break with current trends in order to achieve future goals, and to identify specific policy targets for fuel prices, population density, and transit service within the two study regions. Models indicate based on medium growth scenarios, Baton Rouge will experience a 9 percent increase in VMTs and New Orleans will experience 10 percent growth. This translates to corresponding increases in crashes, injuries and fatalities. The paper provides forecasts for planners and engineers to consider an alternative future, based on desired goals to reduce VMTs and therefore improve safety outcomes. A constrained-forecast model shows a cap on VMTs and crash rates is achievable through policy that increases fuel prices, population density and annual transit passenger miles per capita at reasonable levels through a growth management approach.     

Dynamic demand estimation and prediction for traffic urban networks adopting new data sources

Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far.The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements.Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF).Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.     

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.     

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.     

对地铁信号系统列车运行速度制约因素的探讨

信号系统是保障地铁运行安全及提高运行效率的重要设备,列车超速防护功能是信号系统一个非常关键的功能。信号系统防护下的列车运行速度由车辆、线路、轨道、限界及信号系统等条件共同决定。由于相关专业对信号系统安全制动模型不了解,在实际工程中易造成"速度浪费"现象。从信号系统安全制动模型的原理出发,对车辆、线路、轨道、限界等专业对列车运行速度的制约因素进行分析,提出建议。     

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.     

基于地基回弹因素的盾构隧道管片上浮预测

针对软弱地层盾构隧道管片脱出盾尾后出现的上浮问题,分析盾构施工过程中管片上浮的诱因,得出软弱地层中管片的上浮主要是地层应力重分布产生的地基回弹力引起的。在此基础上建立隧道开挖动态模型,分析隧道上浮量变化规律以及地层特性对管片上浮量的影响规律,得出地层弹性模量和管片上浮量呈一定的指数关系,且弹性模量对管片上浮量的影响远大于地层黏聚力和内摩擦角。通过力平衡理论计算盾构开挖引起的地层回弹力、管片上浮量以及隧道上浮影响的地层高度范围,并通过对珠江狮子洋工程管片上浮量的计算与实测对比,证明该公式的合理性,为盾构隧道工程中考虑地基抗力引起的管片上浮量预测提供一种新的方法。     

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。     

假设检验在沥青混凝土路面厚度检验中的应用

对概率论中的假设检验的方法进行了介绍,并将此方法应用在沥青混凝土路面厚度单点合格率的检验中.     

龙坑枢纽立交方案设计

随着高速公路逐年增长,使得高速公路与高速公路相交的枢纽立交也越来越多.对龙坑枢纽立交方案比选设计进行了详细介绍.