Does transport help people to gain employment? A systematic review and meta-analysis of the empirical evidence

ABSTRACT

The role of transport in providing access to employment has received considerable attention. Since transport policies may be motivated by assumed effects on employment probability outcomes, it is important to establish the nature of the relationship between transport and employment outcomes. While the majority of the empirical evidence suggests a positive association, it is not conclusive or consistent and often shows mixed results. To address this confusion, our study has systematically reviewed this evidence base and synthesised it through meta-analysis. We first identified 93 studies that quantitatively assessed the impact of transport on employment outcomes. By systematically merging the empirical evidence, this study establishes a positive association between transport and employment outcomes, with varying effects for four identified categories of transport measures (or combinations thereof): car ownership, public transport access, commute times, and job accessibility levels. This positive association persists in studies that control for endogeneity between transport and employment, but a larger evidence base is needed to establish a more robust relationship, in particular for cities and smaller (rural) areas outside the US-context and with regard to public transport. We then selected 20 methodologically comparable studies for inclusion in the meta-analysis. Our meta-regression models clearly demonstrate that car ownership significantly increases individual employment probabilities, in particular among welfare recipients. Young drivers benefit from access to household cars when these are not in use by their parents, and they are more sensitive to the time and cost implications of longer commutes. While our systematic review suggests that better access to public transport and higher levels of job accessibility increases employment probabilities, meta-regression analysis requires more consistent transport measures. The findings in this study are important for policymakers in that they imply that job seekers may benefit from public policies targeted at improving their access to public transport, in particular for people without access to cars and in areas with fewer job opportunities.     

基于云平台的城市轨道交通能源管理系统

阐述了基于云平台的城市轨道交通能源管理系统架构和建设方式。该系统利用云计算的高效和弹性来部署建设,采用线网-车站二层管理模式,降低了线网和线路服务器等的硬件部署成本和维护成本。通过将能源管理系统内部使用的业务应用转换为基于云平台的能源管理应用软件,能源管理系统可获取云平台提供的共享数据,并将经过业务处理后的数据存储到云平台中,以服务于云平台上其他业务,实现了数据资源的逻辑统一和高效利用。     

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.     

广西交通科技资源共享平台建设探讨

文章针对广西交通科技项目管理系统现状,分析了建设交通科技资源共享平台的必要性,阐述了交通科技资源共享平台建设的关键技术、创新点及主要内容,指出该共享平台的建设可改善和提高交通运输科技项目质量及监管水平。     

高职院校公共《计算机基础》课程实践教学研究

对高职院校公共《计算机基础》实践教学效果及其存在的问题进行剖析,针对这些问题所采取的应对措施进行了探索和研究。提出加强实践教学改革的方案和具体方法,使高职院校的计算机基础教育能更好地与市场接轨。     

乾元公司铁路专用线场站模式探讨

目前,传统的铁路专用线建设模式是铁路专用线自接轨站接轨,通过区间正线引入厂区的建设模式,这种模式的区间正线受地形条件、厂区位置和沿线规划影响较大,这些影响将不可避免地导致铁路专用线建设工程投资的增加,如能根据运输性质考虑缩短区间正线长度,在接轨站旁边设装车作业站,将大大减少专用线工程投资.以乾元公司铁路专用线建设模式为例来研究铁路专用线场站模式,就如何在接轨站旁边设装车作业站,实现接轨站设备与铁路专用线共用进行探讨.     

大断面异形盾构隧道弯矩传递系数原型加载试验研究

由于异形盾构隧道特殊的结构断面型式,管片设计暂无相关规范可循,故基于原型三环管片力学加载试验对异形盾构管片环向接头弯矩传递系数进行研究,研究结果表明:1)随埋深增加,异形盾构管片结构整体刚度提升,但由于各接头刚度与相邻管片结构刚度比随埋深增加变化规律不一致,异形盾构管片接头弯矩传递能力呈部分减弱部分增强的现象;2)同埋深条件下,随着侧压力系数的增加,除右拱腰处接头外,其余接头弯矩传递能力随着接头刚度与相邻管片结构刚度比的增大而逐渐增强;3)随着埋深增加,各接头弯矩传递能力对侧压力系数的敏感程度逐渐减弱;4)极限破坏后,异形盾构管片内外弧面裂缝的分布规律证明了明显的弯矩传递现象。     

大型活动交通影响评价方法

大型活动吸引和产生的突增交通量会造成周边地区的交通拥挤。通过比较大型活动与大型商业建设项目的交通影响评价特点和分析大型活动交通管理的影响因素,分别针对“提供一次性服务的大型活动”和“提供长效服务的大型活动”,设计了交通需求的预测方法。以交叉口服务水平分析方法为基础,提出一种了大型活动的交通影响评价方法。以长春市南岭体育场举办的“中国足球甲A联赛”为对象,运用Vissim微观交通仿真软件对所提出的方法进行了初步验证。结果表明,在大型活动条件下,所提出的方法能够确定需要进行交通管制的空间范围。