Identification of communities in urban mobility networks using multi-layer graphs of network traffic

This paper proposes a novel approach to identify the pockets of activity or the community structure in a city network using multi-layer graphs that represent the movement of disparate entities (i.e. private cars, buses and passengers) in the network. First, we process the trip data corresponding to each entity through a Voronoi segmentation procedure which provides a natural null model to compare multiple layers in a real world network. Second, given nodes that represent Voronoi cells and link weights that define the strength of connection between them, we apply a community detection algorithm and partition the network into smaller areas independently at each layer. The partitioning algorithm returns geographically well connected regions in all layers and reveal significant characteristics underlying the spatial structure of our city. Third, we test an algorithm that reveals the unified community structure of multi-layer networks, which are combinations of single-layer networks coupled through links between each node in one network layer to itself in other layers. This approach allows us to directly compare the resulting communities in multiple layers where connection types are categorically different.     

Soft Radial Basis Cellular Neural Network (SRB-CNN) based robust low-cost truck detection using a single presence detection sensor

This paper does present a comprehensive concept for a robust and reliable truck detection involving solely one single presence sensor (e.g. an inductive loop, but also any other presence sensor) at a signalized traffic junction. Hereby, two operations modes are distinguished: (a) during green traffic light phases, and (b) a much challenging case, during red traffic light phases. First, it is shown how difficult the underlying classification task is, this mainly due to strongly overlapped classes, which cannot be easily separated by simple hyper-planes. Then, a novel soft radial basis cellular neural/nonlinear network (SRB-CNN) based concept is developed, validated and extensively benchmarked with a selection of the best representatives of the current related state-of-the-art classification concepts (namely the following: support vector machines with radial basis function, artificial neural network, naive Bayes, and decision trees). For benchmarking purposes, all selected competing classifiers do use the same features and the superiority of the novel CNN based classifier is thereby underscored, as it strongly outperforms the other ones. This novel SRB-CNN based concept does satisfactorily fulfill the hard industrial requirements regarding robustness, low-cost, high processing speed, low memory consumption, and the capability to be deployed in low cost embedded systems.     

腹地-口岸可达性格局演化及其空间溢出效应分析

为探索一带一路影响下腹地-口岸可达性演化特征及其空间溢出效应，构建腹地-口岸可达性测算模型，运用泰尔指数和空间马尔科夫链分析其空间差异特征及溢出效应。结果表明：腹地-口岸可达性高值区沿主要运输通道延伸并呈以经济实力较强的城市和城市群为中心的群状集聚现象。腹地-口岸可达性逐年优化，高值区向一带一路沿线扩散趋势明显；腹地-口岸可达性空间差异呈现以2013年为界先减少后增加的V型变化特征，东部和西部可达性空间非均衡程度大于中部和东北部；高可达性城市具有显著的正向空间溢出效应，低可达性城市与高可达性城市相邻时其类型向上转移的概率大于与中、低可达性城市相邻概率，且一带一路之后，类型向上转 移概率高于一带一路之前；腹地-口岸可达性形成以最短旅行时间为主导、路网密度为支撑的时空演化格局。     

铁路道岔防跳结构优化建议

为提升铁路道岔防跳结构的可维护性、完善检测方法和标准,提出以下优化方案:(1)改变防跳顶铁顶头截面形状,减小修配面积;(2)改变防跳限位装置螺栓头部厚度,选择更高强度的材质,取消开口销安装孔;(3)增加防跳间隔铁安装面内侧避让空间。通过对比分析或受力仿真分析,确认优化方案的可行性,提升道岔防跳结构零部件的维修便利性的同时,增强其防跳功能,提高行车安全性。最后,给出防跳间隙设置的建议,并提出防跳卡铁的检测方法,为铁路道岔防跳结构设计、检测提供参考。     

地铁车辆段轮对在线检测设备对出入段能力的影响分析

轮对在线检测设备在车辆段咽喉区的设置方式不同,引起信号转换轨位置的变化,从而影响列车出入段能力。为研究设备的合理设置方式,减少对列车出入段能力的影响,通过分解列车出段作业过程,计算出理想条件下的列车出段时间,再以几种典型的设备设置方式为例,分别计算出段作业时间差,对比分析后提出,在同等条件下,设备的最优位置应设于咽喉区第一付道岔和信号转换段之间,同时应尽量避免将设备设置在咽喉区第一付道岔内的岔线区。     

高铁对城市经济空间结构影响的研究综述

高铁是区域快速综合交通网的主骨架，作为城市间高效快捷的出行方式，为提升通道客运能力和缓解既有铁路货运压力起到重要作用。本文从高铁对城市可达性、要素市场及空间结构的影响等3方面，综述了国内外高铁对城市经济空间结构影响研究的主要成果，归纳总结了共性结论和存在的分歧。通过分析高铁对城市可达性变化的程度和可达性变化在不同区域之间是否平衡，揭示高铁(网络)对城市间出行需求的直接影响。城市可达性的改善进一步重塑生产要素的区域空间分布，加速资本和人口流动，促进产业发展和就业改善，并对土地和住房市场产生深远影响。市场各种要素的相互作用给城市宏观经济发展，特别是高铁站区空间重构带来重大机遇。系统评估高铁对城市经济空间结构的影响，能够为管理部门高铁投资决策、规划建设及影响评估等提供参考。最后，结合理论研究与实践经验，并基于新时代背景展望了有待开展的若干重点研究方向：一是将城市群视作新的利益集合体，研究高铁投资的经济效益、收益分配和高铁开通后对城市群经济发展和要素空间分布的影响；二是探究高铁通勤行为对城市资本、人口和土地等要素市场的影响，剖析车站选址和票价变化等影响高铁通勤的关键性因素；三是考虑城市间高铁竞争与合作博弈行为和客货混跑模式对城市经济空间结构影响的新变化；四是运用大数据和机器学习等新兴技术分析高铁对城市经济微观层面的影响。     

An inference engine for smartphones to preprocess data and detect stationary and transportation modes

A smartphone can be utilized as a cost-effective device for the purposes of intelligent transportation system. To detect the movement and the stationary statuses in the motorized and non-motorized modes, this study develops a new inference engine, including two sets of rules. The first sets of rules are defined by the related thresholds on the features of smartphone sensors while the second sets are extracted from the human knowledge to improve the results of the first rules. The experimental results reveal that by utilizing Inertial Measurement Unit (IMU) sensors in the proposed inference engine, it is possible to save 40% energy in comparison with the previous research. Moreover, this engine increases the accuracy of the motorized mode detection to 95.2% and determines the stationary states in motorized mode with 97.1% accuracy.     

Assessing carbon emissions from road transport through traffic flow estimators

Carbon emissions from road transport are one of the main issues related to modern transport planning. To address them adequately, the acquisition of reliable data about traffic flow is an essential prerequisite. However, the large quantity and the heterogeneity of available information often cause problems; missing or incomplete data are one of the most critical aspects. This paper discusses how technology handles imperfect information in order to obtain more accurate quantification of CO₂ emissions. First, an analysis of single estimators and combination models is provided, highlighting their main characteristics. Then, the TANINO model (Tool for the Analysis of Non-conservative Carbon Emissions In TraNspOrt) is presented, jointly developed at the University of Seville and at the IUAV University of Venice. It consists of two different modules: the first is a combination model that optimizes the results of three traffic flow single estimators, while the second is a macro-model of carbon evaluation, which takes into account road infrastructure, vehicle type and traffic conditions. TANINO is then tested to calculate CO₂ emissions along the ring road of the Spanish city of Seville, showing its more efficient performance, compared to the single estimators normally adopted for such aims. Transport planning can benefit from the adequate knowledge of traffic flows and related CO₂ emissions, since it allows a more reliable monitoring of the progresses granted by specific carbon policies.     

基于时间卷积自编码网络的实时交通事件自动检测方法

为了提高道路异常交通事件检测效率并降低误报率,提出了一种基于时间卷积自编码网络的实时交通事件自动检测方法。首先设计了基于波动相似性度量的交通模式搜索算法用来筛选具有相同交通规律的样本数据;并构造了交通流模式矩阵作为网络模型输入,以避免样本不均衡与单一样本数据随机性对交通模式学习的干扰;同时设计了新的时间卷积自编码网络对交通模式特征进行无监督提取并对未来交通参数进行合理预测;为了降低交通流参数随机波动性带来的事件判别的干扰,设计了异常状态评估方法,通过对模型预测误差分布的学习,结合当前检测数据给出最终的事件判定结果。采用美国西雅图I90公路与I405公路2015年全年的交通流检测数据与历史事故数据进行实证研究,并与6种典型交通事件检测算法进行性能对比。研究结果表明:基于时间卷积自编码网络的实时交通事件自动检测算法具有较高的检测率、较低的误报率以及更快的平均检测时间;综合各种交通运行情况下,可接受误检率分别为5%、10%时,平均检测率可分别达到93%、98%;同时算法能够自适应学习交通状态的动态变化,对不同交通运行环境具有较强适应性与稳定性。