A combined assessment of air and noise pollution on the High Line,New York City

The High Line is an elevated public park in New York City, transformed from an unused freight rail line. Pedestrians walking through Manhattan’s West Side can walk either on the High Line or on a footpath below. Using Manhattan as a laboratory, this paper offers a combined assessment of noise and particulate matter pollution for its pedestrians. Noise and PM_2.5 levels were recorded simultaneously for two cases (i) pedestrians walking on a footpath alongside road traffic and (ii) pedestrians walking on the elevated High Line. Testing took places over three days in autumn 2014. Results were analysed to investigate if pedestrians using the High Line would have a lower pollution exposure to those using the footpath below. Results showed statistically significant differences between the upper and lower levels in exposure to both pollution types. In order to quantify the overall impact, results are expressed through a combined air–noise pollution index. This index indicates that the average reduction in PM_2.5 and noise pollution along the High Line compared to the footpath below is approximately 37%.     

新城区轨道交通线路规划关键问题研究

对国内外主城与新城衔接的新城区(郊区)轨道线特征及交通功能等因素进行总结分析，并结合武汉市及国内外典型城市新城区轨道交通规划建设经验，对新城区轨道交通线路规划关键问题做深入研究。提出对新城区轨道交通线路规划中与其他不同层次轨道线网功能及技术标准的衔接关系；与新城区发展应注重以轨道交通引领新城区协同发展，与城市空间结构高度契合关系；新城区轨道线路走向应沿预测主要客流走廊，串联新城区商业、交通枢纽等大型客流集散点，以缓解开通初期普遍面临客流量增长缓慢，客运强度偏低的问题。     

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。     

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。     

高速公路直线路段突起路标间隔设置方法

基于驾驶人的视觉惰性、视觉警示频率和感觉特性,分析了突起路标的视觉警示作用,确定了突起路标的闪现频率,计算与修正了突起路标的初始间隔。应用ADAMS/Car仿真软件模块,建立了道路、车辆和突起路标模型,将车型设置为大型车和小型车,大型车的车速设置为60、80、100km·h-1,小型车的车速设置为80、100、120km·h-1,大型车和小型车的方向盘转角均设置为1°、3°、5°。分别对提出的12m间隔与现行规范推荐的15m间隔各进行243次仿真,分析振动警示效果。仿真结果表明:在12m间隔下,平均碾压率为93.1%,小型车和大型车对应的平均警示率分别为41.7%和5.6%;在15m间隔下,平均碾压率为93.7%,小型车和大型车对应的平均警示率分别为33.3%和28.9%;12、15m间隔对小型车都具有较好的振动警示效果,15m间隔对大型车的振动警示效果更好。当高速公路夜间交通流较大或需要加大交通设施的夜间安全效果时,可选12m间隔提供良好的视觉连续性和视觉警示效果;考虑施工维护的方便性和经济性或者大型车比例较大时,可选择15m间隔。     

《深圳市综合交通体系规划((2013—2030)》编制探索——以更具竞争力、可持续的综合交通体系促进城市转型发展

通过回顾评价深圳市历次综合交通体系规划的得失,分析城市转型对于综合交通体系的挑战以及新形势下综合交通体系规划的定位。以《深圳市综合交通体系规划(2013—2030)》为例,对规划编制的内容和方法开展研究,具体包括:转型导向的交通发展目标与指标体系、一体化的综合交通体系组织、立足区域协作的整体交通战略、与组团结构相匹配的交通分区组织、与城市功能布局的协调互动、注重协同的交通管控政策,以及应对大数据的智能交通策略等。最后介绍规划编制组织特点并提出城市转型期综合交通体系规划的进一步思考。