Decouple transport CO2 emissions from China’s economic expansion: A temporal-spatial analysis

China’s transport industry is energy intensive and high-polluting. While with the surging urbanization and the development of service industry, China’s economic relies more and more on the transport sector. Therefore, exploring the relationship between transport energy-related carbon emission (TECE) and economic development is crucial to the realization of China’s “Post Paris” mitigation target. The paper carries out a decoupling research between TECE and Gross domestic product (GDP) at both national level and province level based on Logarithmic Mean Divisia Index (LMDI) decomposition analysis with the extended Kaya identity and Tapio decoupling model. The model quantifies eight factors’ effects on the relationship with focusing on external macro socio-economic related factors (i.e., spatial pattern, urbanization, per capita service industry output value, reciprocal of the service industry’s share of GDP, and demographic variable) successfully. The key conclusions are indicated as follows: (1) the national decoupling status was extensive coupling during 2004–2010 and then weak decoupling during 2010–2016. The progress can be attributed to the decline of energy intensity. (2) Per capita service output was always the prominent factor to promote carbon emissions growth in different time periods and provinces with inhibiting the advancement of decoupling process, followed by urbanization. (3) Scenario analysis shows that with the continuous growth of traffic demand and the promotion of urbanization, improving energy efficiency has become the key link to realize the decoupling between China’s TECE and its economy.     

Orchestration of driving simulator scenarios based on dynamic actor preparation and automated action planning

In driving simulation, a scenario includes definitions of the road environment, the traffic situation, simulated vehicles’ interactions with the participant’s vehicle and measurements that need to be collected. The scenarios need to be designed in such a way that the research questions to be studied can be answered, which commonly imply exposing the participant for a couple of predefined specific situations that has to be both realistic and repeatable. This article presents an integrated algorithm based on Dynamic Actor Preparation and Automated Action Planning to control autonomous simulated vehicles in the simulation in order to generate predefined situations. This algorithm is thus able to plan driving actions for autonomous vehicles based on specific tasks with relevant contextual information as well as handling longitudinal transportation of simulated vehicles based on the contextual information in an automated manner. The conducted experiment shows that the algorithm is able to guarantee repeatability under autonomous traffic flow. The presented algorithm can benefit not only the driving simulation community, but also relevant areas, such as autonomous vehicle and in-vehicle device design by providing them with an algorithm for target pursue and driving task accomplishment, which can be used to design a human-vehicle cooperation system in the coming era of autonomous driving.     

Use of ecological footprinting to explore alternative transport policy scenarios in an Irish city-region

The objective of this paper is to compare the ecological footprint for travel-commuting patterns for the residents of an Irish city-region, that is Limerick city-region, in 1996 and 2002. Scenario building, based on ecological footprint analysis, is used to estimate the impact of different policy choices for 2010. The optimal policy mix for sustainable travel is proposed and consists of a mix of reduced demand through travel demand measures, better spatial planning and technological improvements in fuel economy.     

智能运货车程序设计

实现运货车智能化重要的一环是程序设计。文章通过对智能运货车分模块编程,实现车辆的感知、预测、定位、路由寻经、规划、控制等功能。规划模块是核心模块,根据实际情况做出车道线保持、超车、停止、左转、右转、泊车等行为决策,分别编写各行为的执行程序,决策时调用相应的程序,实现了智能运货车的程序设计。仿真结果显示该设计的效果良好。     

高职《现代公司管理》课程教学方法探究

《现代公司管理》课程的教学方法改革要紧紧围绕人才培养目标来进行。改革课程教学方法应以重点培养学生自主学习的能力、踏实的实践能力、创造性的工作能力为核心，使培养的人才适应经济社会发展的需要。文章分析了目前《现代公司管理》课程教学中存在的问题，并提出多种《现代公司管理》课程教学方法，重点介绍了采用顺向迁移建构方法、案例教学法的加深、情景模拟教学方法、结合大学生创业进行教学和社会实践教学等方法，以期达到培养人才的目的。     

城市交通出行方式对能源与环境的影响

社会经济的快速增长使得人们的出行日益频繁,从而导致城市机动车保有量迅速增加和能源需求及其大气污染排放不断上升。本文以上海为例,应用LEAP模型,研究了在确保交通需求增长的前提下,发展不同的交通出行方式对能源需求和大气污染物排放的影响。结果表明,调整上海市交通发展模式,即有序发展私人交通、大力发展公共交通对于减少全市道路交通能源需求,减缓供应压力,降低大气污染排放具有重要意义。     

More gray hair—but for whom? Scenario-based simulations of elderly activity travel patterns in 2020

This paper reports the results of a scenario-based simulation study to explore mobility effects of an aging society in the Netherlands. Four accumulative behavioral scenario variants, embedded in an economic and demographic scenario are used to simulate possible future activity-travel patterns, using the Albatross system as the simulator. The variants account for likely differences in activity-travel behavior between elderly today and elderly in the future. Trends ongoing over the last decade in the Netherlands suggest that future elderly need to work longer, change their activity pattern with most growth occurring in the social/leisure activity category, will try to avoid morning peak hours by rescheduling their activities and may introduce more spatial diversity in terms of their residence location. Results show that these behavioral and spatial changes lead to a significant increase in travel demands as well as temporal, spatial and modal shifts in mobility patterns. We discuss possible policy implications of these predictions and evaluate the specific strength of activity-based models for studies of this kind.

Forecasting ridership for a metropolitan transit authority

The recent volatility in gasoline prices and the economic downturn have made the management of public transportation systems particularly challenging. Accurate forecasts of ridership are necessary for the planning and operation of transit services. In this paper, monthly ridership of the Metropolitan Tulsa Transit Authority is analyzed to identify the relevant factors that influence transit use. Alternative forecasting models are also developed and evaluated based on these factors—using regression analysis (with autoregressive error correction), neural networks, and ARIMA models—to predict transit ridership. It is found that a simple combination of these forecasting methodologies yields greater forecast accuracy than the individual models separately. Finally, a scenario analysis is conducted to assess the impact of transit policies on long-term ridership.     

Powertrain technologies and their impact on greenhouse gas emissions in key car markets

The role alternative car technologies may play in effectively tackling the problem of climate change is still highly uncertain. This paper aims at investigating possible impacts of car powertrain technologies on future energy demand and its corresponding greenhouse gas emissions until 2030. A system dynamics model covering nine car technologies in China, France, Germany, India, Japan and the United States was applied, with a focus on electric cars. Four main scenarios are constructed and sensitivity analysis undertaken. Greenhouse gas emissions from cars in the six countries are simulated to reach up to 2.6 gigatonnes in 2030 (a 13–32% increase between 2020 and 2030, depending on the scenario). The main conclusion from model-based policy analysis is that electric cars may have a positive contribution to emissions mitigation in the passenger road transport system. However, greenhouse gas emissions from cars arising from the combined effect of car manufacturing and scrappage and electricity generation processes are expected to grow more dramatically. As a result, actions that support both low-emission (re-)manufacturing and clean electricity generation are needed. These results complement accurate but static life cycle assessments and open the discussion for dynamic model assumptions.