Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SSRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.     

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.     

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。     

甘泉铁路区间通过能力研究

阐述甘泉铁路车站分布和运量发展情况及货运量增加后区间通过能力不足的问题,介绍甘泉铁路行车组织和区间通过能力计算方法,计算各区间的通过能力,结合近期货物运量500万t、700万t、1000万t三种情况,计算需求通过能力,并给出提高通过能力的具体方案,为下一步货运量的增加做准备。     

也论柴油轿车在国内市场的发展前景

研究了世界范围内柴油机在乘用车领域内的应用现状,阐述了柴油机作为车用动力的优势。根据国内的政策环境与市场环境,分析了排放法规、柴油品质对市场的影响。根据对柴油轿车的性能要求,包括振动和噪声、排放等,分析了更适合我国轿车柴油机发展的技术路线,并重点对若干关键技术和难点进行了剖析。柴油机技术进步和市场化应用表明,柴油轿车在我国具有广阔的应用前景,政府和企业应做好政策和技术上的准备。     

不锈钢地铁车辆侧墙焊缝缺陷率分析

由于非熔透型激光叠焊工艺的局限性,侧墙焊缝处会产生内外钢板局部未焊透的现象即焊缝缺陷.为研究这种焊缝在不同缺陷率下的拉剪强度及其对车体静强度的影响,结合某不锈钢地铁车辆激光焊车体,通过建立不同焊缝缺陷率的车体有限元模型,分析各个工况下不同焊缝缺陷率的车体侧墙焊缝剪应力的变化,以此确定在静载工况下不锈钢激光焊车体侧墙焊缝缺陷率的限值.     

深圳市货运节点功能重组与空间重构

当前在产业转移升级趋势明显、土地资源加剧紧张的状况下,如何将经济部门的物流产业及园区规划、运输部门的行业发展规划、城市规划部门的综合交通体系规划三者协调统一,是各大城市亟须解决的重点和难点问题。对此,《深圳市综合交通体系规划(2013—2030)》开展了以重新组织城市货运空间为重点的货运规划实践。首先对深圳市货运系统发展现状进行分析,阐述货运系统在用地、运输方式、场站布局、货运通道等方面存在的问题。其次,梳理深圳市货运交通规划面临的行政管理、规划编制和落实、规划方法适应性、物流用地空间等方面的挑战。最后,以物流功能集聚为出发点,拓展货运枢纽的功能和属性,提出枢纽分级、立体化布局、公共空间共享专营等规划方法与实施机制。     

深圳市交通需求管理政策的法治思考

深圳市制定和实施了若干小汽车交通需求管理政策,完善相关政策的法制保障成为政策实施的关键问题。首先结合深圳市交通需求管理实践经验,梳理小汽车增量调控、停车收费政策的制定与实施情况。指出交通需求管理政策的制定与实施面临经济杠杆存在法理争议、行政手段法律依据不足和程序合法性存疑等法治障碍。进而提出在国家层面构建交通管理法治体系、在地方层面完善法律法规以及规范交通需求管理政策出台程序等完善路径。     

基于时间约束的铁路空车调配系统可靠性分析

为弥补现有铁路空车调配系统缺乏空车调配可靠性研究的缺陷,在传统空车调配优化模型的基础上,本文提出了一种新的基于时间约束的空车调配可靠性分析方法。通过深入分析铁路空车调配过程,建立效益最大化目标函数,生成约束条件,以规定时间内的实际到达空车数与需求空车数之比计算出各需求站的空车调配可靠性,再利用各需求站可靠性均值求解空车调配系统的可靠性。最后,使用LINGO软件进行算例分析,论证了该方法的可行性和有效性。     

云南省公路货运站场现状及发展对策

根据对云南省公路货运站场功能、站场经营、服务对象、管理支持、客户关系、营销管理、质量管理、吞吐量、经济特性等方面的抽样调查,分析了云南省公路货运站场现状及存在的主要问题,提出了云南省公路货运站场的发展对策.