做好交通行业政务信息工作的研究

以山东省港航业政务信息工作为例，对如何做好交通行业政务信息工作进行研究。做好交通行业政务信息工作的关键是要根据交通行业的特点，构建交通政务信息网络；围绕港航行业管理内容，突出港航信息特色；同时也离不开领导的重视和支持。     

福州轨道交通建设安全管控平台的设计与应用

福州城市轨道交通建设规模不断增大，复杂的地质、环境条件以及众多工种施工的交叉作业和人员活动，给安全生产带来挑战。针对建设全过程安全管控的具体业务关系和需求分析，梳理和固化工作岗位、职责、流程、响应、整改等安全管理的事项和要求，强化对管控过程的追溯和考核管理。研究信息平台的业务需求、平台架构、网络拓扑、功能模块、数据库设计方法和实现思路，利用Ssm框架、Quartz框架、Encache技术以及SQLServer数据库技术，设计开发基于B/S模式的城市轨道交通安全管控信息系统，应用于在建的5条线路。信息平台的应用，可实现安全管理的标准化、科学化和精细化，极大地提高管理水平和工作效率。     

上海松江区现代有轨电车规划研究

现代有轨电车因其环保、舒适、高效、低造价、高品质等特点,在国际上得到广泛应用,我国多个城市也在规划和建设之中。但是,由于其在国内尚为一种全新的交通方式,没有标准或规范予以指导,规划及设计体系不完善,有些已建项目已呈现问题。文章结合上海松江区规划背景及交通特征,阐述有轨电车示范线T1、T2线的布局规划、建设的必要性及适应性,为即将开展有轨电车规划及建设的城市提供借鉴与参考。     

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.     

车辆牌照信息调查法在公路OD调查中的应用

分析停车问询调查法和车辆牌照信息调查法在公路OD调查中的优缺点,结合浙江省两次全省公路OD调查的实际经验,指出公路OD调查自动化和常态化的发展方向.     

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.     

铁路货物追踪系统的构建

铁路货物运输组织是铁路工作的一个核心内容,铁路货物追踪系统的设计与实现能够为货主提供更好、更快的服务,同时加快和保障铁路货运工作的组织。通过对铁路整车货物运输的流程分析,制定货物追踪系统的目标,分析系统功能。通过数据的采集和信息共享平台的建立,提出货物追踪系统的实现途径。