A Training Framework for Understanding Conflict in the Coastal Zone

The coastal zone is attractive for human settlement because it tends to be resource rich, providing a good location to generate incomes and livelihoods. Equally, however, it is a complex and powerful natural environment, which means that the coastal zone often becomes a focus of conflict with multiple users competing for limited space and resources. Understanding the causes of, and determining solutions to, the ensuing problems requires inputs from a range of analyses across sectors and disciplines. However, different disciplinary and sectoral approaches frequently use different investigative methods, language, and means of presenting results. This article presents a framework that has been developed in collaboration with Indian institutions over the past three years in an effort to address some of these fundamental problems of analysis and translation into policy action. Key components addressed by the framework are strengthening the capacity to practice, knowing how to proceed and how to find out what needs to be known using experience-based problem solving.     

Modelling parking choices considering user heterogeneity

We examine car driver’s behaviour when choosing a parking place; the alternatives available are free on-street parking, paid on-street parking and parking in an underground multi-storey car park. A mixed logit model, allowing for correlation between random taste parameters and estimated using stated choice data, is used to infer values of time, both when looking for a parking space and for accessing the final destination. Apart from the cost of parking, we found that vehicle age was a key variable when choosing where to park in Spain. We also found that the perception of the parking charge was fairly heterogeneous, depending both on the drivers’ income levels and whether or not they were local residents. Our results can be generalised for personalised policy making related with parking demand management.     

Boundedly rational user equilibria (BRUE): Mathematical formulation and solution sets

Boundedly rational user equilibria (BRUE) represent traffic flow distribution patterns where travellers can take any route whose travel cost is within an ‘indifference band’ of the shortest path cost. Those traffic flow patterns satisfying the above condition constitute a set, named the BRUE solution set. It is important to obtain all the BRUE flow patterns, because it can help predict the variation of the link flow pattern in a traffic network under the boundedly rational behavior assumption. However, the methodology of constructing the BRUE set has been lacking in the established literature. This paper fills the gap by constructing the BRUE solution set on traffic networks with fixed demands. After defining ε-BRUE, where ε is the indifference band for the perceived travel cost, we formulate the ε-BRUE problem as a nonlinear complementarity problem (NCP), so that a BRUE solution can be obtained by solving a BRUE–NCP formulation. To obtain the BRUE solution set encompassing all BRUE flow patterns, we propose a methodology of generating acceptable path set which may be utilized under the boundedly rational behavior assumption. We show that with the increase of the indifference band, the acceptable path set that contains boundedly rational equilibrium flows will be augmented, and the critical values of indifference band to augment these path sets can be identified by solving a family of mathematical programs with equilibrium constraints (MPEC) sequentially. The BRUE solution set can then be obtained by assigning all traffic demands to the acceptable path set. Various numerical examples are given to illustrate our findings.     

The California Coastal Act and Ports: The Unintended Environmental Justice Implications of Preserving California's Coastline

Consistent with the policies set forth in the Coastal Zone Management Act, the California Coastal Act seeks to balance the utilization and conservation of coastal resources, taking into account the social and economic needs of the citizens of California. One way the statute pursues this balance is through its provisions for ports. These provisions have functioned as a type of smart growth planning for ports, encouraging densification of existing port districts and possibly averting maritime commercial and industrial sprawl along the California coast. One unintended consequence of the encouraged consolidation of port activities, when combined with the rapid growth in international trade over the last four decades, has been disproportionally large environmental and health impacts on low-income and minority communities surrounding ports. This article examines how the Port of Long Beach's conformance with the California Coastal Act has resulted in ongoing environmental justice concerns. It discusses approaches employed by the Port of Long Beach to reduce environmental justice concerns resulting from significant and unavoidable environmental impacts and offers suggestions for how to address this issue.     

回声测深仪计量检定装置的研究

为研究浅水回声测深仪计量检定标准装置,以完成量值传递,文章介绍了2种不同的检定装置,可根据具体环境条件选择水槽比对方式和模拟器方式对回声测深仪进行检定,并对回声测深仪的基本原理、工作流程、2种检定装置的构造、测试结果和优缺点进行了简要的阐述。     

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting.     

就地水泥冷再生施工技术在路面大中修养护中的应用

结合国道202线吉林省磐石段路面维修工程,对冷再生施工工艺及其性能指标进行了探讨,为就地冷再生技术在吉林省的推广应用提供参考经验。工程实践证明,旧路面就地冷再生技术具有成本低、工期短、节约材料、不损坏路基、消除污染保护环境、交通安全等特点,对于节约自然资源、保护生态环境具有极大的意义。     

大跨连续刚构桥施工线形控制技术

通过对大跨径预应力混凝土连续刚构梁桥施工过程中结构的线形测试,经过合理结构分析计算,研究其施工过程中的线形变化规律,并根据现场实测数据和采用有效控制方法,给出合理的立模标高,使大桥顺利合龙。     

大坡度引水隧道无轨运输出渣方式的应用

辽西北供水工程11#支洞，断面小、坡度大，出渣运输方式是决定工期的关键控制因素。采用无轨运输出渣方式，存在以下问题：隧道纵坡坡度较大，运输渣车爬坡困难、存在严重的安全隐患；断面小，自卸车在陡坡上运行和洞内调头困难等。对此采取扩大隧道开挖断面、设置避车洞，改装车辆、确保车辆制动的可靠性；加强安全管理措施等。实践证明，采用无轨运输方式出渣具有节约工期、经济效益显著等特点，可根据实际工况加以选用。     

Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

In today’s world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s personal-fleet evolution.Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings.Twenty five-year simulations of Austin’s household vehicle fleet suggest that, under all scenarios modeled, Austin’s vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet’s VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO₂ emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO₂ emissions by 30% (relative to trend).Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year.In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.