基于整体规划框架的区域综合货运规划建模方法研究

区域一体化快速发展背景下，如何系统谋划区域综合交通运输规划，使经济、土地/空间及交通与环境协调发展，是实现区域可持续发展亟需深入研究的课题。因此，本文统筹考虑经济、 土地/空间及交通和环境要素的动态交互关系，提出大区域综合货运整体规划模型的设计与开发方法。利用PECAS(Production, Exchange and Consumption Allocation System)理论框架分析生产者、消费者、交换商品、土地(空间)和运输方式之间的交互关系，并通过PECAS的集计经济流表设计模型结构，构建相应的宏观经济预测、社会经济活动空间分配、空间开发以及交通运输需求预测这4个模块，模拟区域社会经济活动增长及其空间分布与土地/空间开发及综合交通需求时变等特征之间的互动耦合关系。在社会经济发展目标、土地空间和环境等约束条件下，通过构建空间经济模型和综合交通一体化网络分配模型，实现面向多货品和多方式的综合货运整体规划建模方法，以辅助区域产业布局、土地利用与综合交通系统的整体规划。本文以长江经济带为研究案例，基于相关数据构建相应的大区域综合货运整体规划模型，分析评估2012—2035年模型预测结果。结果显示，预测得到的综合交通年平均日货运量拟合优度超过85%，分担率误差低于1%， 验证了本文建模方法的有效性。     

Development of distress condition index of asphalt pavements using LTPP data through structural equation modeling

Traditional pavement distress index such as the Pavement Condition Index (PCI) developed by U.S. Army Corps of Engineers determines coefficients of distresses based on subjective ratings. This study proposed an asphalt pavement distress condition index based on various types of distress data collected from the Long-Term Pavement Performance (LTPP) database through Structural Equation Modeling (SEM). The SEM method treated the overall distress index as a latent variable while various distresses were treated as endogenous and other influence factors such as age, layer thickness, material type, weather, environment and traffic, were exogenous observed variables. The SEM method modeled the contributions of various distresses as well as the influence of other factors on the overall pavement distress condition. Influences of age, layer thickness, material type, environment and traffic on the latent distress condition were in accordance with previous studies. Compared with previous attempts to model latent pavement condition index utilizing SEM method, more pavement condition measurements and influencing factors were included. Specifically, this study adopted the robust maximum likelihood estimator (MLR) to estimate parameters for non-normally distributed data and derived the explicit expression of latent variables with intercepts. A multiple regression prediction model was built to calculate an overall condition index utilizing those measured distress data. The established pavement distress index prediction model provided a rational estimation of weighting coefficients for each distress type. The prediction model showed that alligator cracking, longitudinal cracking in wheel path, non-wheel path longitudinal cracking, transverse cracking, block cracking, edge cracking, patch and bleeding were significant for the latent pavement distress index.     

Assessing public opinions of and interest in new vehicle technologies: An Austin perspective

Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever-evolving transportation system. Anticipating public acceptance and adoption of these technologies is important. A recent internet-based survey polled 347 Austinites to understand their opinions on smart-car technologies and strategies. Results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7253) appears to be much higher than that for adding partial (Level 3) automation ($3300) to their current vehicles.Ordered probit and other model specifications estimate the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding various automation technologies and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, who live in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.     

Comparison of road freight transport trends in Europe. Coupling and decoupling factors from an Input–Output structural decomposition analysis

Decoupling road freight transport from economic growth has been acknowledged by the European Union as a key means to improving sustainability. It is therefore important to identify both the coupling and decoupling drivers of road freight transport demand in order to determine possible factors that may contribute to reduce road transport in the future without curbing economic development. This research proposes an Input–Output (IO) structural decomposition analysis (SDA) to explain road freight transport in terms of a set of key factors that have strongly influenced road freight demand in recent decades in European countries—such as economic growth, economic structure and the evolution of road transport intensity (including improvements in both supply and transport systems). This methodological approach allows us to quantify and compare their contribution in different European countries to either increase or decrease road freight transport demand. The empirical basis for this analysis is a dataset of nine European countries which have IO tables and road transport data available from 2000 to 2007, comprising data on domestic production, imports and exports as well as tonne-kms for 11 types of commodity classes. The results show that, as a whole, aggregate road transport demand has grown—driven mainly by economic activity—but this growth has been strongly curbed in some countries by changes in road freight transport intensity and moderately by the dematerialization of the economy. International transport has been also proven to be a key factor driving road freight transport volumes. Moreover, the increased penetration of foreign operators in national haulage markets appears to have reinforced the final decoupling levels observed in some cases.     

The kinematic wave model with finite decelerations: A social force car-following model approximation

This paper derives a five-parameter social force car-following model that converges to the kinematic wave model with triangular fundamental diagram. Analytical solutions for vehicle trajectories are found for the lead-vehicle problem, which exhibit clockwise and counter-clockwise hysteresis depending on the model’s parameters and the lead vehicle trajectory. When coupled with a stochastic vehicle dynamics module, the model is able to reproduce periods and amplitudes of stop-and-go waves, as reported in the field. The model’s stability conditions are analysed and its trajectories are compared to real data.     

生产率降低损失计算中成本的模糊估测模型

讨论了在工程项目索赔中因生产率降低所造成损失的索赔问题。利用时间序列指平滑原理方法和模糊数学理论，给出了因生产率降低所造成损的计算中成本的模糊估测方法，     

2000吨川江油轮结构设计特点

本文介绍了2000吨川江及三峡库区油船标准船型结构设计的主要特点以及为降低船舶造价和营运成本所采取的措施。     

TransCAD在地区客运规划中的应用

结合新疆生产建设兵团客运规划的实际情况，研究了TransCAD在地区客运规划中的运用，详细介绍了在已知基年交通分布和经济指标的情况下，建立交通产生量与经济指标的回归模型，进而预测出交通产生量、交通分布和交通分配。     

Modeling travel behavior by the structural relationships between lifestyle,built environment and non-working trips

In the context of sustainable urban transport in developing countries, individuals’ travel behavior faces multiple factors which influence their mobility patterns. Recognizing these factors could be a favorable method to organize more regular and sustainable trip patterns. This study aims to identify the less well-known lifestyle along with more popular built environment as the main factors which shape travel behaviors. Employing data from 900 respondents of 22 urban areas in city of Shiraz, Iran, this paper explores travel behaviors as non-working trip frequencies by different modes. Results of structural equation model indicate a strong significant effect of individual’s lifestyle patterns on their non-working trips. However, built environment impact on travel behavior is small compared to lifestyle. Besides, other variables such as travel attitudes and socio-economic factors stay crucial in the mode choice selection. These findings indicate the necessity of regarding lifestyle orientations in travel studies as well as objective factors such as land use attributes.     

GIS-based transit trip allocation methods converting stop-level boarding and alighting trips into TAZ trips

ABSTRACT

This study introduces a framework to improve the utilization of new data sources such as automated vehicle location (AVL) and automated passenger counting (APC) systems in transit ridership forecasting models. The direct application of AVL/APC data to travel forecasting requires an important intermediary step that links stops and activities – boarding and alighting – to the actual locations (at the traffic analysis zone (TAZ) level) that generated/attracted these trips. GIS-based transit trip allocation methods are developed with a focus on considering the case when the access shed spans multiple TAZs. The proposed methods improve practical applicability with easily obtained data. The performance of the proposed allocation methods is further evaluated using transit on-board survey data. The results show that the methods can effectively handle various conditions, particularly for major activity generators. The average errors between observed data and the proposed method are about 8% for alighting trips and 18% for boarding trips.