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.     

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.     

Insights into vehicle trajectories at the handling limits: analysing open data from race car drivers

Race car drivers can offer insights into vehicle control during extreme manoeuvres; however, little data from race teams is publicly available for analysis. The Revs Program at Stanford has built a collection of vehicle dynamics data acquired from vintage race cars during live racing events with the intent of making this database publicly available for future analysis. This paper discusses the data acquisition, post-processing, and storage methods used to generate the database. An analysis of available data quantifies the repeatability of professional race car driver performance by examining the statistical dispersion of their driven paths. Certain map features, such as sections with high path curvature, consistently corresponded to local minima in path dispersion, quantifying the qualitative concept that drivers anchor their racing lines at specific locations around the track. A case study explores how two professional drivers employ distinct driving styles to achieve similar lap times, supporting the idea that driving at the limits allows a family of solutions in terms of paths and speed that can be adapted based on specific spatial, temporal, or other constraints and objectives.     

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。     

DC01型电动列车牵引控制单元及其插件板微机检测装置

介绍了上海轨道交通DC01型电动列车牵引控制单元及其插件板微机检测装置的检测原理和软硬件结构。阐述了工控主机、电气控制箱等硬件设计,以及检测装置软件的主程序模块设计、插件板检测程序设计、静态程序设计等软硬件方面所需要解决的关键技术及其实现方法。该装置的研制运用提高了牵引控制单元系统及其插件板的检修质量和检修效率。     

高速磁浮车辆垂直动力学仿真与平稳性研究

通过建立高速磁浮车辆的动力学模型,研究输入线路激励后车辆的运行平稳性和舒适度。通过对高速磁浮车辆的悬挂参数分析研究并优化,来评价车辆在不同速度下的运行平稳性和舒适度。在这些研究分析的基础上,对现有上海磁浮车辆的悬挂参数进行更为合理的优化设计,以实现磁浮车辆良好的平稳性和舒适度。     

地铁列车中可燃物热释放速率的测定

介绍了目前常用的室内火灾模拟方法——区域模拟与场模拟,并对其各自的特点和局限性进行对比讨论;同时还介绍了基于质量损失速率的热释放速率测试方法,给出了部分实验数据和结果分析。火源热释放速率的大小对于火灾的温度分布及烟气流动的影响较大。