基于公交IC卡和GPS数据的乘客上下车站点研究

文章通过对公交IC卡和GPS数据的分析,给出了利用IC卡和GPS数据推导乘客上下车站点的判别方法。通过充分挖掘两类公交数据中的信息,考虑乘客刷卡滞后的情况,采用相邻站点进站时间与刷卡时间匹配的方法识别上车站点;同时考虑到同行人员代刷卡行为,改进了下车站点的判断机理;最后结合成都市公交IC卡和GPS数据进行实例分析,结果表明所采用的判别方法上车站点识别率较高,下车站点的判断结果符合实际情况,具有较高的准确性。     

公交惯常出行与随机出行的时空分布差异

差异化和精细化的公交服务要求城市管理者和公交运营者加深对公交出行变异性的认识。本文将公交惯常出行定义为乘客个体多次在同一时段或空间乘坐公交的行为，随机出行的定义与之相反。利用厦门市30天连续的常规公交数据，使用DBSCAN算法对出行天数在10天以上的高频乘客进行划分，用核心点来表征惯常出行，用非核心点来表征随机出行。结果表明有78.70%的出行在时间上具有稳定性，有72.32%的出行在空间上具有稳定性。研究结论凸显了公交高频乘客也具有随机出行，且随机出行的统计特征和时空分布与惯常出行有较大差异。研究结论可以应用于个体时空模式挖掘、乘客人群划分、定制公交线路制定、公交差异化服务设置等方面。     

基于一卡通数据的公交换乘行为分析方法

本文为了完善乘客的公交出行链,设计了一种基于一卡通数据的公交换乘行为分析方法。文章首先分析公交换乘行为的时间和空间影响因素,然后设计以公交出行记录为基础的公交换乘行为识别流程,通过实例推断出研究时间段内所有对象的公交换乘行为,最后以公交换乘系数为依据判断城市公交直达性的优劣。     

基于GPS数据的公交线路校核:以厦门市为例

实际运营中,公交车辆服务的线路编号调整频率高,而在车辆信息表中存在更新不及时及只增加不删除的情况,极大地影响了结合GPS数据匹配上车站点方法的准确性。本文提出了一种基于公交GPS到离站数据的线路校核方法,首先根据距离最近原则为每条公交GPS到离站数据匹配上车站点,并对途经该上车站点的线路号计数一次;然后按照车辆编号对GPS到离站数据进行分组,分别统计各组中,线路号计数值最大的线路号,以此作为该车辆编号的校核线路;最后根据GPS数据点、原始线路、校核线路进行抽样人工复核,验证校核的准确度。案例数据表明,校核有效率高达98%,并将人工检查的比例降至20%以下,在保证准确度的同时提高了处理效率。     

单线路半自动公交编组与调度模型

基于半自动公交能够实现多辆公交单元协同行驶的特点，本文提出半自动公交编组与调度模型。考虑编组公交动态运行特性、乘客特性等因素，以公交公司运营成本与乘客出行时间成本为优化目标，优化发车间隔和编组规模，并采用改进的遗传算法进行求解。最后基于成都市116路公交线路对本文提出的模型进行评价，结果表明：与非编组模式相比本策略能降低35.9%的乘客出行成本与20.0%的运营成本，证明了本文提出的半自动公交编组模型的有效性。     

基于CUBE的公共自行车衔接换乘影响研究

为改善城市公交预测模型的预测效果,提高其对交通方案评价的可靠性,分析了CUBE软件PT模块的运行机理,考虑西安市公共自行车的大规模使用和城市交通模型PT模块中未包含公共自行车出行方式的不足,在原有的PT模块基础上增加了公共自行车接驳和换乘模式,提出了一种包含公交、公共自行车、步行共三种出行方式的改进的公交预测模式。以西安市高新区BRT线路规划为例,对改进的公交预测模式进行了应用。改进前后的预测数据对比分析结果显示:添加公共自行车模块后,3条BRT线路连接小区的数量分别增加11个、12个、18个,与常规公交的换乘量分别增加7.5%、8.1%、9.3%,全天客运量分别增加9.2%、10.2%、13.6%,准确反应了公共自行车在改善公交接驳方面的作用。     

基于广义有序Logit模型的公交运行效能提升

为了更精准的评价公交乘客满意度，本文综合考虑乘客社会经济属性、公交服务主观感知指标以及公交服务客观指标对乘客满意度的影响，建立了更加客观全面的公交乘客满意度评价机制。在此基础上，结合广义有序Logit模型的拟合结果，研究各指标是否对乘客满意度有影响，并引入边际效应概念，实现了定量化描述指标调整带来的公交乘客满意度的提升。将该满意度评价方法应用到宜春中心城区公交满意度分析中，甄别出制约公交乘客满意度的瓶颈因素，指明了满意度提升优化方向，对于线网组织优化具有积极意义。     

快速公交系统站台停靠时间模型研究

快速公交系统停靠站台停车延误是影响快速公交运行车速的关键因素之一,因此构建快速公交系统站台停靠时间模型是提升快速公交服务水平的基础理论研究。本文选取盐城BRT-1号线的起始站、中途站、客流离散站等三类站点为研究对象,综合运用数理统计法与数据挖掘法,构建快速公交系统站台停靠时间模型,并对该模型的合理性进行了检验。研究表明:盐城市BRT-1号线三类站台的快速公交车辆停靠时间与上下车乘客人数呈线性关系,即快速公交车辆停靠时间与上下车乘客人数的检验参数R2均大于0.8。     

基于AVL数据的公交到达率阈值估计方法

公交进站排队是影响公交服务可靠性的主要原因之一。为提高公交运营可靠性，提出以不同排队长度的互补累积概率作为公交站点的服务水平指标，基于大量公交AVL数据，探究公交到达率与互补累积排队概率之间的函数关系，计算不同服务水平下站点的到达率阈值。研究发现到达率与不同排队长度下的互补累积概率的散点图均呈“S”型分布，针对该特点提出以Logistic模型来描述两者之间的函数关系，并以成都市5条公交走廊沿线的19个站点为例对其拟合结果进行分析。结果表明，模型在19个站点均有较好的拟合优度，因此认为公交到达率与互补累积排队概率符合Logistic函数关系。该研究结果为不同公交站点合理到达率阈值的选取提供了基于数据驱动的分析方法。     

夜班公交路线服务评价——以银川市为例

在"优先发展公共交通战略"的背景下,夜班公交不只是超一线城市才有,很多大中城市已经开始结合自身情况,开启了服务大众为核心的夜班公交线路,夜班公交已经逐渐发展成为已成为人们夜间出行不可缺少的交通方式,充分满足了大众对夜间出行对稳定、环保、安全的出行需求。夜班公交线路近年来不断开设和体系不断健全,但是很少学者研究夜班公交路线服务评价体系。本文将提出基于多种出行方式的夜班公交评价模型来解决这一问题,结合银川市一周交通量数据,运用GIS对银川交通发生量进行可视化分析,对夜班公交线路的合理评价,最后给出优化建议。     

An efficient realization of deep learning for traffic data imputation

Traffic data provide the basis for both research and applications in transportation control, management, and evaluation, but real-world traffic data collected from loop detectors or other sensors often contain corrupted or missing data points which need to be imputed for traffic analysis. For this end, here we propose a deep learning model named denoising stacked autoencoders for traffic data imputation. We tested and evaluated the model performance with consideration of both temporal and spatial factors. Through these experiments and evaluation results, we developed an algorithm for efficient realization of deep learning for traffic data imputation by training the model hierarchically using the full set of data from all vehicle detector stations. Using data provided by Caltrans PeMS, we have shown that the mean absolute error of the proposed realization is under 10 veh/5-min, a better performance compared with other popular models: the history model, ARIMA model and BP neural network model. We further investigated why the deep leaning model works well for traffic data imputation by visualizing the features extracted by the first hidden layer. Clearly, this work has demonstrated the effectiveness as well as efficiency of deep learning in the field of traffic data imputation and analysis.     

Rationalizing Reliable Imputation Durations of Genetically Designed Time Delay Neural Network and Locally Weighted Regression Models

Abstract

Estimating missing values is known as data imputation. Previous research has shown that genetic algorithms (GAs) designed locally weighted regression (LWR) and time delay neural network (TDNN) models can generate more accurate hourly volume imputations for a period of 12 successive hours than traditional methods used by highway agencies. It would be interesting and important to further refine the models for imputing larger missing intervals. Therefore, a large number of genetically designed LWR and TDNN models are developed in this study and used to impute up to a week-long missing interval (168 hours) for sample traffic counts obtained from various groups of roads in Alberta, Canada. It is found that road type and functional class have considerable influences on reliable imputations. The reliable imputation durations range from 4–5 days for traffic counts with most unstable patterns to over 10 days for those with most stable patterns. The study results clearly show that calibrated GA-designed models can provide reliable imputations for missing data with ‘block patterns’, and demonstrate their further potentials in traffic data programs.     

数字运政大数据管理平台功能架构浅析

针对云南省道路运输行业管理系统较多、系统独立运行、融合复用不够等问题,本文对运政管理过程中的道路运输行业信息资源整合及数字化安全监管应用进行了浅析,提出以统一门户整合各类业务应用系统,以业务中台融合管理应用,以数据中台打造基于大数据融合支撑应用的数字运政平台功能架构研究,有效支撑道路运输的数字化建设。     

How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications

ABSTRACT

The information-rich vessel movement data provided by the Automatic Identification System (AIS) has gained much popularity over the past decade, during which the employment of satellite-based receivers has enabled wide coverage and improved data quality. The application of AIS data has developed from simply navigation-oriented research to now include trade flow estimation, emission accounting, and vessel performance monitoring. The AIS now provides high frequency, real-time positioning and sailing patterns for almost the whole world's commercial fleet, and therefore, in combination with supplementary databases and analyses, AIS data has arguably kickstarted the era of digitisation in the shipping industry. In this study, we conduct a comprehensive review of the literature regarding AIS applications by dividing it into three development stages, namely, basic application, extended application, and advanced application. Each stage contains two to three application fields, and in total we identified seven application fields, including (1) AIS data mining, (2) navigation safety, (3) ship behaviour analysis, (4) environmental evaluation, (5) trade analysis, (6) ship and port performance, and (7) Arctic shipping. We found that the original application of AIS data to navigation safety has, with the improvement of data accessibility, evolved into diverse applications in various directions. Moreover, we summarised the major methodologies in the literature into four categories, these being (1) data processing and mining, (2) index measurement, (3) causality analysis, and (4) operational research. Undoubtedly, the applications of AIS data will be further expanded in the foreseeable future. This will not only provide a more comprehensive understanding of voyage performance and allow researchers to examine shipping market dynamics from the micro level, but also the abundance of AIS data may also open up the rather opaque aspect of how shipping companies release information to external authorities, including the International Maritime Organization, port states, scientists and researchers. It is expected that more multi-disciplinary AIS studies will emerge in the coming years. We believe that this study will shed further light on the future development of AIS studies.     

How to assess sustainability of suppliers in volume discount context? A new data envelopment analysis approach

In this paper, we reformulate conventional data envelopment analysis (DEA) models and propose a novel method for evaluating sustainability of suppliers in the presence of interval volume discount offers, fuzzy data, and ordinal data. To this end, we convert all data into interval data. To convert fuzzy data into interval data, we use nearest weighted interval approximation by applying weighting function and we convert each ordinal data into interval one. Then, using enhanced Russell model, interval efficiencies are obtained. After that, using preference degree approach, we rank suppliers. Finally, a case study is presented to illustrate our proposed approach.     

Strava Metro data for bicycle monitoring: a literature review

ABSTRACT

Monitoring bicycle trips is no longer limited to traditional sources, such as travel surveys and counts. Strava, a popular fitness tracker, continuously collects human movement trajectories, and its commercial data service, Strava Metro, has enriched bicycle research opportunities over the last five years. Accrued knowledge from colleagues who have already utilised Strava Metro data can be valuable for those seeking expanded monitoring options. To convey such knowledge, this paper synthesises a data overview, extensive literature review on how the data have been applied to deal with drivers’ bicycle-related issues, and implications for future work. The review results indicate that Strava Metro data have the potential—although finite—to be used to identify various travel patterns, estimate travel demand, analyse route choice, control for exposure in crash models, and assess air pollution exposure. However, several challenges, such as the under-representativeness of the general population, bias towards and away from certain groups, and lack of demographic and trip details at the individual level, prevent researchers from depending entirely on the new data source. Cross-use with other sources and validation of reliability with official data could enhance the potentiality.     

Trip generation modeling using data collected in single and repeated cross‐sectional surveys

The majority of US metropolitan regions still use the four‐step urban transportation modeling system to develop their travel forecasts. Trip generation, the first step of this system, has as objective of predicting the expected total travel demand in a region. The commonly used methods in planning practice for predicting this expected total travel demand typically use only the most recent cross‐sectional data available from a study region for model development, which ties the resulting travel‐forecast model to the economic environment prevailing at the time of data collection. Applying such models to generate forecasts of travel in economic environments significantly different from those embodied in the estimated model parameters could result in greater errors than would otherwise be the case. To address the aforementioned problem, this paper proposes the development of trip generation models estimated on multiple independent cross‐sectional datasets collected in the same urban region but at different times representing different economic environments. Data used in the research were collected in cross‐sectional household travel behavior surveys undertaken in the Greater Toronto Area, Canada in 1986, 1996, 2001, and 2006. The results lead to the conclusion that well‐specified models, estimated on pooled multiple cross‐sectional datasets, yield travel predictions in the base and horizon years, respectively, that have smaller error compared with corresponding travel predictions generated with single cross‐sectional models. Copyright © 2012 John Wiley & Sons, Ltd.     

Estimation of a route choice model for urban public transport using smart card data

This paper describes a logit model of route choice for urban public transport and explains how the archived data from a smart card-based fare payment system can be used for the choice set generation and model estimation. It demonstrates the feasibility and simplicity of applying a trip-chaining method to infer passenger journeys from smart card transactions data. Not only origins and destinations of passenger journeys can be inferred but also the interchanges between the segments of a linked journey can be recognised. The attributes of the corresponding routes, such as in-vehicle travel time, transfer walking time and to get from alighting stop to trip destination, the need to change, and the time headway of the first transportation line, can be determined by the combination of smart card data with other data sources, such as a street map and timetable. The smart card data represent a large volume of revealed preference data that allows travellers' behaviour to be modelled with higher accuracy than by using traditional survey data. A multinomial route choice model is proposed and estimated by the maximum likelihood method, using urban public transport in ?ilina, the Slovak Republic, as a case study     

Implausible OECD motor vehicle travel data

To assess the fuel efficiency of motor vehicles in a given country, an estimate of kilometers traveled is required. Examination of kilometers per liter among countries contributing data to OECD revealed implausible results for several. Also kilometers per vehicle were anomalous. The kilometers per vehicle based on a stratified random sample of U.S. travel varied substantially from the numbers reported by OECD during 2000–2014. OECD motor vehicle travel data are unusable.