Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.     

Studies on emergency evacuation management for maritime transportation

Marine transportation is a vital component of the world’s economy and transportation network. The number of people using passenger ships around the globe is increasing worldwide. Similar to other transportation systems, passenger safety is critical in maritime shipment. As emergency evacuation processes for ships are highly different from and more complicated than those for buildings and other vehicles, many researchers have published a vast range of documents related to this peculiar research area. However, there is a tangible lack of sufficient literature review studies that investigate marine emergency evacuation (MEE). That being the case, the potential of marine transportation and the effect of emergency evacuation operation on life safety have inspired the proposal of this study. This paper offers a review of the available literature on MEE modelling, analysis and planning during the period from 1973 to 2017 using a systematic approach. After reviewing relevant academic journals, peer-reviewed conference papers, and technical reports of agencies, relevant literature is analysed. In addition, the literature review is extended by means of proposing a framework methodology which considers different possible conditions and situations during MEE. Finally, insights for ship managers and policymakers are discussed and potential future research directions are identified.     

长大货物车适应THDS轴温探测技术的研究

针对不适应THDS轴温探测系统的长大货物车,分析论证了现车轴箱改进方案适应THDS轴温探测的不足,并提出了轴箱优化方案。经计算、试验和装车验证表明,新轴箱可行、有效、安全,满足THDS轴温探测要求,保证了长大货物车运输安全。     

Impact assessment of extreme weather events on transport networks: A data-driven approach

The present paper presents a data-driven method for assessing the resilience of the European passenger transport network during extreme weather events. The method aims to fill in the gap of current research efforts regarding the quantification of impacts attributed to climate change and the identification of substitutability opportunities between transport modes in case of extreme weather events (EWE). The proposed method consists of three steps concerning the probability estimation of an EWE occurring within a transportation network, the assessment of its impacts and the passengers’ flow shift between various transport modes. A mathematical formulation for the proposed data-driven method is provided and applied in an indicative European small-scale network, in order to assess the impacts of EWE on modal choice. Results are expressed in passenger differentiated flows and the paper concludes with future research steps and directions.     

基于客流需求的城市轨道交通动态时刻表优化模型

为使城市轨道交通列车运行时刻表更贴合客流需求,依据不断变化的客流需求确定每列车的发车时刻和停站时间,采用多目标优化方法构建以乘客出行时间费用和列车运行时间费用最小为目标、列车发车时刻和停站时间为决策变量的城市轨道交通动态时刻表优化模型,并采用粒子群算法求解。以广州地铁13号线为例进行验证,结果表明优化后的时刻表更满足客流需求,能有效地提高乘客出行效率,具有更好的动态适应性。     

考虑空车调配协调的货物列车开行方案优化研究

为实现空车调配与货物列车开行方案协调优化，结合基本运行图架构与车流径路，构建货运时空服务拓展网络。考虑配空与装卸取送、集编发等环节的时间接续要求，节点与区段不对流空车要求，以重车流全程运送与空车配送等广义总费用最少为目标，建立整数规划弧路模型。针对既有算法设计局限性，结合重车或空车配空的时间接续要求，提出将不同的 k 短路重车流方案与空车配空方案相关联的改进可行解构造方法，设计混合差分进化求解算法。实例研究表明，考虑空车调配进行重车、空车流组织协调优化，能够减少空车走行费用，及时满足装车需求，有效保证作业车流配合中转车流集结编组及时挂线，提高方案可实施性。     

重载货车摩擦缓冲器阻抗特性的数值模拟

为预测摩擦缓冲器的实际工作状态,从几何特征和作用原理的角度,建立详细的MT-2型缓冲器理论模型。首先,通过对缓冲器内部各摩擦元件的运动学和静力学分析,推导出缓冲器在准静态下的阻抗特性;其次,引入附加摩擦系数量化各摩擦元件之间动静态摩擦过渡时的黏滞补偿,并模拟出缓冲器在动态下的阻抗特性;最后,利用C80型货车冲击试验数据对该缓冲器的理论模型进行验证。验证结果表明:总体上,数值模拟和现场试验下的缓冲器示功曲线基本吻合,说明模型的正确性;局部上数值模拟中缓冲器从加载Ⅰ阶段过渡至加载Ⅱ阶段的突变现象在冲击试验中表现的并不明显,还有待进一步完善。     

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.