Reducing CO2 emissions in temperature-controlled road transportation using the LDVRP model

Temperature-controlled transport is needed to maintain the quality of products such as fresh and frozen foods and pharmaceuticals. Road transportation is responsible for a considerable part of global emissions. Temperature-controlled transportation exhausts even more emissions than ambient temperature transport because of the extra fuel requirements for cooling and because of leakage of refrigerant. The transportation sector is under pressure to improve both its environmental and economic performance. To explore opportunities to reach this goal, the Load-Dependent Vehicle Routing Problem (LDVRP) model has been developed to optimize routing decisions taking into account fuel consumption and emissions related to the load of the vehicle. However, this model does not take refrigeration related emissions into account. We therefore propose an extension of the LDVRP model to optimize routing decisions and to account for refrigeration emissions in temperature-controlled transportation systems. This extended LDVRP model is applied in a case study in the Dutch frozen food industry. We show that taking the emissions caused by refrigeration in road transportation can result in different optimal routes and speeds compared with the LDVRP model and the standard Vehicle Routing Problem model. Moreover, taking the emissions caused by refrigeration into account improves the estimation of emissions related to temperature-controlled transportation. This model can help to reduce emissions of temperature-controlled road transportation.     

Integrating mesoscopic dynamic traffic assignment with agent-based travel behavior models for cumulative land development impact analysis

A number of approaches have been developed to evaluate the impact of land development on transportation infrastructure. While traditional approaches are either limited to static modeling of traffic performance or lack a strong travel behavior foundation, today’s advanced computational technology makes it feasible to model an individual traveler’s response to land development. This study integrates dynamic traffic assignment (DTA) with a positive agent-based microsimulation travel behavior model for cumulative land development impact studies. The integrated model not only enhances the behavioral implementation of DTA, but also captures traffic dynamics. It provides an advanced yet practical approach to understanding the impact of a single or series of land development projects on an individual driver’s behavior, as well as the aggregated impacts on the demand pattern and time-dependent traffic conditions. A simulation-based optimization (SBO) approach is proposed for the calibration of the modeling system. The SBO calibration approach enhances the transferability of this integrated model to other study areas. Using a case study that focuses on the cumulative land development impact along a congested corridor in Maryland, various regional and local travel behavior changes are discussed to show the capability of this tool for behavior side estimations and the corresponding traffic impacts.     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

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.     

城市大型医院周边道路交通问题剖析及改善对策——以深圳市为例

为了合理科学组织医院周边及内部交通,避免重现目前深圳大型医院周边普遍存在的交通拥堵、停车困难、进院车辆排队、的士乘坐不便等交通问题,以深圳市为例,分析现状大型医院周边存在的道路交通问题和产生原因,提出完善周边道路交通设施、大力发展公共交通、合理规划医院布局、建立科学有效的交通组织体系等改善对策.     

我国城市防洪排涝地下深隧规划设计与施工方法

为了建设符合中国国情的地下排水深隧,解决城市内涝问题,消除"城市看海"现象,采用调研分析的研究方法,通过调研国外城市排水深隧建设的成功案例,借鉴成熟经验并结合我国国情探索出我国城市排水深隧在规划设计、施工技术等方面的建设模式。主要结论如下:1)深隧规划必须坚持雨污分流原则,防涝标准应设为百年一遇;2)深隧层位应建于地下30~50 m的位置,隧道管线应布设在城市易涝区,并且将修建深隧、改造浅管和清淤河道衔接起来,形成一套完整的防洪排涝系统,让深隧的作用得以充分发挥,才能解决城市内涝问题;3)排水深隧要注重防水设计,防止雨水污染地下水;4)为提高深隧的经济效益,应将深隧与地下快速路的规划结合起来,功能上多样化。     

北京市商业区可达性研究

为建立商业区可达性计算方法,并为未来商业区的选址、设计提供参考,选取北京市较为典型的5个商业区为研究对象,对商业区的特点进行了分析。从理论上探讨了计算商业区可达性包含的4个因素：土地利用、交通系统、时间因素以及个体因素。借鉴潜能模型,提出了可达性理论模型,并给出具体的可达性计算公式。以北京市5个商业区中18个大中型商场为例,计算可达性以及各区域到达这些商业区的可达性分布。     

高职物流管理专业人才培养模式的改革实践

在深入研究高职教学规律和物流管理专业人才培养模式改革实践的基础上,提出在高职物流管理专业改革与建设的总体目标,从“工学交替”人才培养模式的构建策略、“面向工作过程系统化”的课程体系与教学内容改革、基于关键能力培养的“六步骤”法教学组织与实施模式、专兼结合的“双师结构”教学团队建设以及实习实训基地建设等方面,阐述了物流专业改革与建设的方案与措施。     

奥运与北京城市轨道交通规划建设

北京奥运会的召开促使北京社会经济发展进入了新阶段,奥运的高标准、高要求推动了城市基础设施的建设,特别是引领北京城市轨道交通建设进入了飞速发展时期。简要论述了奥运前和近期北京城市轨道交通建设规划概况,并阐述了可持续发展、与城市重大功能区发展一体化、交通一体化的轨道交通规划理念。介绍了奥运轨道交通线网覆盖范围,在此基础上提出了奥运交通圈层划分,并重点对奥运支线的线路运营组织方案做了分析。最后,指出要加强对奥运轨道交通车站周边交通衔接设施的使用,进一步扩大公共交通系统的服务范围。     

中美城市交通模型现况评估

交通需求模型预测未来一个区域内对交通系统的需求.这些模型是辅助交通官员与专家对重大交通项目投资和政策策略作出理性决定的重要工具.根据本刊2008年第1期对中国具有代表性的几个城市交通模型系统的示例介绍,对中国目前的交通模型研发、数据及应用进行总结,并比较了中美交通模型的差异.通过对现阶段中国交通模型建设和使用的深入了解,并结合已有研究和实际工作经验,对中关交通模型及其相关的工作进行系统性和前瞻性的讨论.