基于数据融合的公交客流规模测算方法

公交客流规模测算往往存在调查成本受限和准确度要求较高的矛盾.提出基于公交IC卡历史数据与人工补充调查数据的数据融合测算方法,以准确推算公交客流规模.首先根据公交线路的基本属性,采用聚类分析方法划分线路类型,从每一类中选择具有代表性的线路.基于IC卡数据分析公交客流时变特征,运用有序样本聚类Fisher算法将线路小时刷卡量进行聚类分析.划分刷卡量相似时段,进而采用优化方法确定调查抽样率,确定相应的调查车辆进行人工补充调查,最终经过数据融合计算获得公交客流规模.基于上海市某辖区IC卡数据进行案例分析,测算得到三类公交线路的日均客流量.     

Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data

Assessing sustainability of supply chains is a critical and increasingly complex problem. In recent years sustainability has received more attention in supply chain management (SCM) literature with triple bottom lines including social, environmental, and economic factors. Conventional data envelopment analysis (DEA) models consider decision making units (DMUs) as black boxes that consume a set of inputs to produce a set of outputs and do not take into consideration internal interactions of DMUs. Two-stage DEA models deal with such DMUs. However, existing two-stage DEA models are applicable only in technologies characterized by positive inputs/outputs. This paper aims to build and present a new two-stage DEA model considering negative input-intermediate-output data. Some numerical examples along with some theorems and properties are given to show capability of proposed method. The proposed ideas are used in a case study where 29 Iranian supply chains producing equipment of expendable medical devices are evaluated in terms of sustainability.     

Predicting market potential and environmental benefits of deploying electric taxis in Nanjing,China

This paper investigates the market potential and environmental benefits of replacing internal combustion engine (ICE) vehicles with battery electric vehicles (BEVs) in the taxi fleet in Nanjing, China. Vehicle trajectory data collected by onboard global positioning system (GPS) units are used to study the travel patterns of taxis. The impacts of charger power, charging infrastructure coverage, and taxi apps on the feasibility of electric taxis are quantified, considering taxi drivers’ recharging behavior and operating activities. It is found that (1) depending on the charger power and coverage, 19% (with AC Level 2 chargers and 20% charger network coverage) to 56% (with DC chargers and 100% charger network coverage) of the ICE vehicles can be replaced by electric taxis without driving pattern changes; (2) by using taxi apps to find nearby passengers and charging stations, drivers could utilize the empty cruising time to charge the battery, which may increase the acceptance of BEVs by up to 82.6% compared to the scenario without taxi apps; and (3) tailpipe emissions in urban areas could be significantly reduced with taxi electrification: a mixed taxi fleet with 46% compressed-natural-gas-powered (CNG) and 54% electricity-powered vehicles can reduce the tailpipe emissions by 48% in comparison with the fleet of 100% CNG taxis.     

Modeling time-of-day car use behavior: A Bayesian network approach

In this research, a Bayesian network (BN) approach is proposed to model the car use behavior of drivers by time of day and to analyze its relationship with driver and car characteristics. The proposed BN model can be categorized as a tree-augmented naive (TAN) Bayesian network. A latent class variable is included in this model to describe the unobserved heterogeneity of drivers. Both the structure and the parameters are learned from the dataset, which is extracted from GPS data collected in Toyota City, Japan. Based on inferences and evidence sensitivity analysis using the estimated TAN model, the effects of each single observed characteristic on car use measures are tested and found to be significant. The features of each category of the latent class are also analyzed. By testing the effect of each car use measure on every other measure, it is found that the correlations between car use measures are significant and should be considered in modeling car use behavior.     

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.     

Generating lane-based intersection maps from crowdsourcing big trace data

Lane-based road information plays a critical role in transportation systems, a lane-based intersection map is the most important component in a detailed road map of the transportation infrastructure. Researchers have developed various algorithms to detect the spatial layout of intersections based on sensor data such as high-definition images/videos, laser point cloud data, and GPS traces, which can recognize intersections and road segments; however, most approaches do not automatically generate Lane-based Intersection Maps (LIMs). The objective of our study is to generate LIMs automatically from crowdsourced big trace data using a multi-hierarchy feature extraction strategy. The LIM automatic generation method proposed in this paper consists of the initial recognition of road intersections, intersection layout detection, and lane-based intersection map-generation. The initial recognition process identifies intersection and non-intersection areas using spatial clustering algorithms based on the similarity of angle and distance. The intersection layout is composed of exit and entry points, obtained by combining trajectory integration algorithms and turn rules at road intersections. The LIM generation step is finally derived from the intersection layout detection results and lane-based road information, based on geometric matching algorithms. The effectiveness of our proposed LIM generation method is demonstrated using crowdsourced vehicle traces. Additional comparisons and analysis are also conducted to confirm recognition results. Experiments show that the proposed method saves time and facilitates LIM refinement from crowdsourced traces more efficiently than methods based on other types of sensor data.     

Sustainability and interactivity between cities and ports: a two-stage data envelopment analysis (DEA) approach

Ports and cities are intrinsically linked. Port city, which serves as a link between the local and global economy, is an integration of both urban and port systems. Constrained by different regional structures, port cities have developed various formulations over time. Using a two-stage data envelopment analysis (DEA) approach, this paper proposes a new method for measuring the sustainable development of different port city systems. While the previous empirical studies often separate the port system and the urban system, this paper assesses the two systems in an integrated way. Moreover, this paper develops a systematic methodology for measurement of sustainability. Twenty world-leading container port cities are selected for this study using contemporary data. The DEA results display their relative differences in levels of sustainable development, which reveal the effectiveness of the prevailing policies in them. Eventually, recommendations are drawn for other port cities by referring to the best practices.     

客货滚装码头车辆定位系统需求分析和技术实现

针对某客货滚装码头中车辆运输的智能化调度需求,进行车辆位置智能感知的研究,采用物联网感知和计算机视觉技术,得出一种基于视频监控的车辆定位系统方案。该系统能够实时监控码头内进出港车辆,跟踪和记录其路径,支持越界报警,为智能化调度和安全监控提供基础数据。     

基于移动导航数据的信号配时反推

在交通管理和评价时,信号配时对监测评价路口运行状态,评价路口配时方案至关重要.但是,大范围的实时信号配时方案的获取尚缺乏简明有效的途径.本文提出两种基于移动导航数据计算固定配时路口信号配时的方法.第一种方法是在不考虑驾驶员驾驶行为差异性时,得到路口红灯和车均延误的关系模型,从而计算某相位的红灯时长.另外一种方法是基于车辆通过停止线的时间,结合本文提出的上升梯度法,得到某阶段红灯时长.本文通过实际的路口案例计算,将预测结果和已知路口的信号配时比较,表明此方法计算得到的红绿灯时长准确度较高,为后续进行路口运行状态和通行能力研究提供了数据支持.     

基于GPS与图像融合的智能车辆高精度定位算法

车辆自定位是实现智能车辆环境感知的核心问题之一.全球定位系统(Global Positioning System,GPS)定位误差通常在10 m左右,不能满足智能车辆的定位需求;惯性导航系统成本较高,不适于智能车辆的推广.本文在视觉地图基础上,提出一种基于GPS与图像融合的智能车辆定位算法.该算法以计算当前位置距离视觉地图中最近一个数据采集点的位姿为目标,首先运用GPS信息进行初定位,在视觉地图中选取若干采集点作为初步候选,其次运用Oriented FAST and Rotated BRIEF(ORB)全局特征进行特征匹配,得到一个候选定位结果,最后通过待检测图像中的局部特征点与候选定位结果中的三维局部特征点建立透视n点模型(Perspective-n-Point,Pn P),得到车辆当前的位姿,并以此对候选定位结果进行修正,得到最终定位结果.实验在长为5 km的路段中进行,并在不同天气及不同智能车辆平台测试.经验证,平均定位精度为11.6 cm,最大定位误差为37 cm,同时对不同天气具有较强鲁棒性.该算法满足了智能车定位需求,且大幅降低了高精度定位成本.