Evaluation of the Accuracy and Automation of Travel Time and Delay Data Collection Methods

Travel time and delay are among the most important measures for gauging a transportation system’s performance. To address the growing problem of congestion in the US, transportation planning legislation mandated the monitoring and analysis of system performance and produced a renewed interest in travel time and delay studies. The use of traditional sensors installed on major roads (e.g. inductive loops) for collecting data is necessary but not sufficient because of their limited coverage and expensive costs for setting up and maintaining the required infrastructure. The GPS-based techniques employed by the University of Delaware have evolved into an automated system, which provides more realistic experience of a traffic flow throughout the road links. However, human error and the weaknesses of using GPS devices in urban settings still have the potential to create inaccuracies. By simultaneously collecting data using three different techniques, the accuracy of the GPS positioning data and the resulting travel time and delay values could be objectively compared for automation and statistically compared for accuracy. It was found that the new technique provided the greatest automation requiring minimal attention of the data collectors and automatically processing the data sets. The data samples were statistically analyzed by using a combination of parametric and nonparametric statistical tests. This analysis greatly favored the GeoStats GPS method over the rest methods.

Multidimensional Visualization of Bikeshare Travel Patterns Using a Visual Data Mining Technique: Data Cubes

In order to explore the travel characteristics and space-time distribution of different groups of bikeshare users,an online analytical processing(OLAP)tool called data cube was used for treating and displaying multi-dimensional data.We extended and modified the traditionally threedimensional data cube into four dimensions,which are space,date,time,and user,each with a user-specified hierarchy,and took transaction numbers and travel time as two quantitative measures.The results suggest that there are two obvious transaction peaks during the morning and afternoon rush hours on weekdays,while the volume at weekends has an approximate even distribution.Bad weather condition significantly restricts the bikeshare usage.Besides,seamless smartcard users generally take a longer trip than exclusive smartcard users;and non-native users ride faster than native users.These findings not only support the applicability and efficiency of data cube in the field of visualizing massive smartcard data,but also raise equity concerns among bikeshare users with different demographic backgrounds.

Exploring the Evolution of Passenger Flow and Travel Time Reliability with the Expanding Process of Metro System Using Smartcard Data

Metro system has experienced the global rapid rise over the past decades. However,few studies have paid attention to the evolution in system usage with the network expanding. The paper's main objectives are to analyze passenger flow characteristics and evaluate travel time reliability for the Nanjing Metro network by visualizing the smart card data of April 2014,April 2015 and April 2016. We performed visualization techniques and comparative analyses to examine the changes in system usage between before and after the system expansion. Specifically,workdays,holidays and weekends were specially segmented for analysis.Results showed that workdays had obvious morning and evening peak hours due to daily commuting,while no obvious peak hours existed in weekends and holidays and the daily traffic was evenly distributed. Besides,some metro stations had a serious directional imbalance,especially during the morning and evening peak hours of workdays. Serious unreliability occurred in morning peaks on workdays and the reliability of new lines was relatively low,meanwhile,new stations had negative effects on exiting stations in terms of reliability. Monitoring the evolution of system usage over years enables the identification of system performance and can serve as an input for improving the metro system quality.

Using Data Mining to Find Patterns in Ant Colony Algorithm Solutions to the Travelling Salesman Problem

Travelling Salesman Problem(TSP) is a classical optimization problem and it is one of a class of NP-Problem.The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm(ACA) performing a searching operation and to develop a rule set searcher which approximates the ACA′s searcher.An attribute-oriented induction methodology was used to explore the relationship between an operations′ sequence and its attributes and a set of rules has been developed.At the end of this paper,the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.

Data analytics approach for travel time reliability pattern analysis and prediction

Travel time reliability(TTR)is an important measure which has been widely used to represent the traffic conditions on freeways.The objective of this study is to develop a systematic approach to analyzing TTR on roadway segments along a corridor.A case study is conducted to illustrate the TTR patterns using vehicle probe data collected on a freeway corridor in Charlotte,North Carolina.A number of influential factors are considered when analyzing TTR,which include,but are not limited to,time of day,day of week,year,and segment location.A time series model is developed and used to predict the TTR.Numerical results clearly indicate the uniqueness of TTR patterns under each case and under different days of week and weather conditions.The research results can provide insightful and objective information on the traffic conditions along freeway segments,and the developed data-driven models can be used to objectively predict the future TTRs,and thus to help transportation planners make informed decisions.

Floating Car Data Based Nonparametric Regression Model for Short-Term Travel Speed Prediction

A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series, collected from the floating car data （FCD） system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.

政府旅游数据开放的评价指标体系构建与组态分析:基于21个省级行政区的数据

在信息时代和智慧旅游背景下,政府主动对外开放内部的旅游数据,有利于充分释放旅游数据资源的商业价值与社会价值。使用变异系数法与熵权法进行组合赋权,构建了政府旅游数据开放评价指标体系,计算21个省级行政区的具体得分,并采用模糊集定性比较方法进行高评价值与低评价值条件组态分析。结果表明:指标体系包含4项一级指标、17项二级指标与51项三级指标,可以对当前省级行政区的旅游数据开放绩效进行有效评价;利用层、数据层是权重较高的一级指标,法律政策效率与内容、平台关系等是权重较高的二级指标;高评价值组态为综合发展型、数据辅助应用型,低评价值组态为政策与利用短板型、平台与数据问题型。研究结果可以为旅游数据开放评价相关学术研究与管理实践提供参考。

考虑交通信号和交通密度的城市路段个体行程时间建模

为探究城市路段中交通密度与交通信号对个体车辆行程时间的影响规律,提高城市道路行程时间的预测精度,基于车牌识别数据与信号机配时数据等多种融合数据,提出了由自由流行程时间、密度延误与随机误差项组成的行程时间关系函数,并在该函数基础上构建了考虑个体驾驶习惯的行程时间预测模型。首先,基于道路限速条件、车辆进入路段时的下游交叉口信号状态构建自由流行程时间函数;其次,提出了一种通过密度阈值判断车辆能否在当前信号周期通过下游交叉口的方法,并基于个体车辆进入路段时的路段密度计算出车辆所需等待信号周期个数及等待前方排队疏散所需时间,从而构建密度延误函数;然后,将个体实际行程时间与自由流行程时间、密度延误的差值作为随机误差项,通过高斯混合模型拟合随机误差项的概率分布;最后,选用安徽省宣城市多个路段作为案例,分析各路段行程时间函数各部分的具体表现,对该行程时间预测模型进行验证。结果表明:行程时间预测模型的MAPE,MAE,RMSE分别为6%~12%,5~15,13~35,在准确率方面优于其他算法,是一种有效的城市路段个体行程时间预测方法。

Impacts of Bus Lane on Bus Travel Time Reliability:a Case Study in Shenzhen

The aim of the paper is to evaluate the impacts of bus lane on bus travel time reliability.The data used are the Geographic Positioning System(GPS) data of two bus lines running parallel streets in Shenzhen,China,one of which is a bus lane and the other is a regular lane.Two linear regression models are developed to evaluate the influence of bus lane which has a separated right of way.Other factors including running direction,day of week,time of day,dwell time,and delay at the start point are also considered in the model.Without published time tables,coefficient of variance(CV) of travel time is employed to explore the impacts of bus lane.The results indicate that bus lane can save 22.0% of travel time,reduce 11.5% of the CV of travel time,and decrease the variance of headway by 17.4%.The analysis on bus travel time reliability could help operators and drivers improve the quality of transit service.It also sheds light on how to assess the effectiveness of bus lane for transit planners and service operators.

Causal Groupoid Symmetries and Big Data

The big problem of Big Data is the lack of a machine learning process that scales and finds meaningful features. Humans fill in for the insufficient automation, but the complexity of the tasks outpaces the human mind’s capacity to comprehend the data. Heuristic partition methods may help but still need humans to adjust the parameters. The same problems exist in many other disciplines and technologies that depend on Big Data or Machine Learning. Proposed here is a fractal groupoid-theoretical method that recursively partitions the problem and requires no heuristics or human intervention. It takes two steps. First, make explicit the fundamental causal nature of information in the physical world by encoding it as a causal set. Second, construct a functor F: C C′ on the category of causal sets that morphs causal set C into smaller causal set C′ by partitioning C into a set of invariant groupoid-theoretical blocks. Repeating the construction, there arises a sequence of progressively smaller causal sets C, C′, C″, … The sequence defines a fractal hierarchy of features, with the features being invariant and hence endowed with a physical meaning, and the hierarchy being scale-free and hence ensuring proper scaling at all granularities. Fractals exist in nature nearly everywhere and at all physical scales, and invariants have long been known to be meaningful to us. The theory is also of interest for NP-hard combinatorial problems that can be expressed as a causal set, such as the Traveling Salesman problem. The recursive groupoid partition promoted by functor F works against their combinatorial complexity and appears to allow a low-order polynomial solution. A true test of this property requires special hardware, not yet available. However, as a proof of concept, a suite of sequential, non-heuristic algorithms were developed and used to solve a real-world 120-city problem of TSP on a personal computer. The results are reported.

基于手机信令数据的城市居民动态OD矩阵提取方法

现有的城市居民出行调查周期较长,交通小区划分粒度粗糙,导致调查不能及时准确地获取居民出行信息。针对该问题,提出了一种基于手机信令数据的城市居民动态OD矩阵提取方法。首先,针对信令数据中的两种复杂噪声:乒乓切换和漂移数据,提出了基于窗口阈值的检测与等效位置替换方法,以及复杂漂移点的检测和标记处理方法;然后,提出一种改进的ST-DBSCAN聚类方法,引入一种等时化方法将时间信息与空间信息相结合,识别出行过程中的驻留点;最后,基于地理信息系统构建含有道路关键节点的路网,将居民出行OD与路网节点相匹配,有效推导出城市居民动态OD矩阵。实验结果表明:与ST-DBSCAN算法相比,所提改进的ST-DBSCAN算法在聚类效果和识别速度上分别提升了6.10%和5.26%;与统计方法和二阶统计量方法相比,基于改进的ST-DBSCAN算法的动态OD矩阵提取方法在均方误差(MSE)上分别降低了16.98%和21.55%。以北京市为例,运用提出的动态OD矩阵提取方法,能够及时有效地分析城市居民日常与高峰时段的出行特征。

有限数据条件下智能体仿真模型构建方法

与传统的集计评估模型不同,智能体仿真模型能够捕捉个体之间的行为交互,在交通政策精细化评估中具有优势。但智能体仿真模型对数据要求较高,在实践中面临活动计划生成、参数标定和结果验证等难点。基于MATSim建立了在有限数据条件下面向应用研究的可操作、可复现的智能体仿真模型,并且以上海市拥堵收费评估场景为例详细介绍了模型的建模流程和关键技术。重点介绍基于手机轨迹数据的活动计划生成方法以及模型的参数标定和结果验证细节。结果表明,智能体仿真模型能够满足交通政策评估需求,基准场景的上海市域出行方式划分平均相对误差为8%、快速路交通量平均相对误差为17%。进而从集计和个体两个层面对仿真结果进行分析,展示了智能体仿真模型的应用潜力。

基于多源数据的交通出行数据扩样及位置匹配研究

随着城市交通出行规划的日益发展,需要精细的居民交通出行数据作为数据支撑以实现更准确的居民交通出行需求预测。文章基于多源数据,包括地块数据、居民交通出行样本数据以及总人口家庭特征数据,采用迭代比例更新算法(Iterative Proportional Updating,IPU),结合蒙特卡洛模拟,实现了获取小尺度的精细居民交通出行数据的目标,为精细交通出行数据的获取提供了有益的参考和借鉴。

灾害应对措施下的北京道路拥堵时空变化

暴雨灾害给城市正常运转带来了巨大挑战,道路交通由于直接暴露在户外、承载多种出行方式和海量刚需活动,需要重点关注与积极应对。该研究以北京五环内为研究区,使用精细的道路拥堵数据,从时空维度评价了2021年北京最大暴雨事件及多种应对措施下的交通状况。结果表明:①道路拥堵在常态下具有鲜明的日际、周际规律,而降雨和应对措施会导致极其显著的变化;②拥堵变化的时间序列呈现对勾形态,即暴雨当日拥堵缓解,而前后日拥堵加重,体现了应对措施的阶段性效果;③城市内部的拥堵变化具有空间异质性,暴雨当日北部和东部地区的拥堵大幅缓解,而次日拥堵整体加重且高度聚集;④分时段分区域统计显示,早高峰和环线的拥堵变化最为强烈;⑤相关性分析识别了应对措施的有效作用对象,证实了众多行业部门对于暴雨预警的积极响应以及弹性上下班建议的执行成效。研究发现,暴雨应对措施在局部上有效减轻了交通压力,但伴随的拥堵转移现象需要在实施应对措施前被充分考虑。

高速铁路旅客智慧出行服务数据体系研究

新一代信息技术的发展给出行方式带来了巨大变化,旅客对出行服务提出了更高的要求。智慧出行服务有望利用数据信息分析与处理技术提升服务质量,但目前还存在数据标准不一、数据治理平台尚无、数据共享困难等难题。以高速铁路旅客服务为中心,贯穿出行前与售后、枢纽乘降与接驳和列车在途3个阶段,梳理旅客出行全过程的相关数据;以出行数据全生命周期管理为主线,从数据汇集、存储、治理、共享、应用与安全6个方面,设计高速铁路旅客智慧出行服务数据体系框架,并提出高速铁路旅客出行服务数据标准体系的建设思路,以期发挥高速铁路旅客智慧出行数据体系的数据价值,为智慧出行服务和运营管理发展提供理论依据。

不依赖GNSS的输电线路双端行波故障测距

双端行波故障测距技术是实现输电线路快速故障恢复的有效手段。传统双端行波测距方法依赖全球卫星导航系统(global navigation satellite system,GNSS)同步线路两侧采样数据,存在信号误差、丢失等风险。基于行波的传播时延和时移不变性,该文提出一种不依赖GNSS的双端行波故障测距方案。理论层面,定量分析时间同步误差及其对故障前工频方向行波相角关系的影响,推导证明通过本地计算方向行波相量进行双端故障测距的可行性。算法层面,首先,通过小波变换检测故障初始行波到达时间;其次,通过对故障前工频方向行波的傅里叶变换求解附加相角差,进而完成双端测距。仿真结果表明,在考虑电压互感器传变特性的条件下,所提方案在不同故障情况、采样频率和噪声干扰等影响下均保持了良好的测距性能。由于无须增设GNSS、硬件时间戳等辅助硬件设备,所提方案便于现存设备的技术更新与工程应用。

手机信令数据在职住空间、出行行为和交通碳排放研究中的应用进展与前景

手机信令数据具有覆盖范围广、获取成本低、时空精度较高、稳定实时追踪等优势,能够有效识别大规模人群的空间活动和出行特征,已成为应用最广泛的交通大数据类型之一。文章在手机信令数据的分类和特征基础上,总结了其在职住空间关系和交通出行行为研究中的技术应用,随后结合上述应用成果和已有文献对其在交通碳排放研究中的应用潜力和场景进行了探讨,最后总结了手机信令数据在职住空间、出行行为和交通碳排放研究中的应用框架、应用机遇与挑战以及未来研究内容与技术创新方向。目前,手机信令数据在职住空间领域中的应用包括职住地识别、职住关系和通勤网络特征及其影响因素解析,在出行行为领域中的应用包括驻留-出行识别、出行方式和路径识别,以及人群移动普适规律解析。以上技术应用能够有效服务交通碳排放领域研究,为交通碳排放测算以及城市空间结构、居民出行行为对交通碳排放的影响研究奠定了基础。未来,相关研究应进一步关注长时序动态追踪、大范围对比分析以及人口和交通新现象研究,并注重多源数据的融合、传统方法与机器学习的结合以及数字孪生模型的构建。

基于居民属性数据的出行碳排放预测模型

准确分析居民出行方式的碳排放及方式选择影响因素的重要性和敏感性,是精准制定交通减排措施的基础。根据居民出行调查的家庭属性、个人属性、出行属性和环境属性等影响因素综合分析,基于LightGBM(Light Gradient Boosting Machine)构建了居民出行方式预测模型并进行验证,结合出行活动水平、各种能源类型的碳排放系数、标准煤系数等参数,构建了基于居民属性数据的出行碳排放预测模型;最后,以广州市为例进行实证分析,对居民出行方式和碳排放总量进行预测,并分析了出行方式选择影响因素的重要程度和重要因素敏感性。结果表明:基于居民属性数据构建的碳排放预测模型,能较为精确地预测各种出行方式的碳排放,较好地分析碳排放的影响因素重要性和敏感性,以及全面揭示出行行为、出行方式和出行碳排放之间的关系。其中,起终点距最近公交站的距离或距最近地铁站的距离、自驾车费用、出行距离等是影响居民出行方式选择的重要因素。当起终点距最近地铁站距离下降55%时,地铁出行竞争力随着距离缩短而明显提升;在公交站点密度较大的区域,起终点距最近公交站距离对居民出行方式选择不敏感;当碳排放费用增加400%时为居民出行方式和碳排放的转折点,超过转折点后小汽车出行方式难以转移;当出行距离下降幅度在90%以内时,碳排放下降速度最快,最大降幅为90.4%。

手机信令定位频率对交通方式识别的影响

作为影响手机信令数据定位质量的关键因素,定位频率对交通方式的识别精度具有重要影响.为量化定位频率与交通方式识别精度之间的变化规律,首先,提出一种基于随机森林的交通方式识别模型;其次,在通信运营商的协助下,通过开展实地数据采集实验,完成手机信令数据及对应真实出行信息的同步采集,并利用该数据集对本文提出的交通方式识别模型进行验证;最后,通过数据抽样形成一系列拥有不同定位频率的手机信令数据集,利用该系列数据集对不同定位频率下的交通方式识别精度进行评估研究.研究结果表明:本文模型对步行、非机动车、汽车和公共交通4种交通方式的总体识别准确率为79.2%;每种交通方式对定位频率的敏感性不同,其中非机动车与公交的敏感性更高,步行和汽车的敏感性相对较低;随着平均定位频率从48 s/条下降至241 s/条,非机动车和公交的整体识别精度下降幅度分别约为19.2%和21.5%,而步行与汽车的整体识别精度则分别下降12.8%与11.5%;综合考虑识别准确率与计算效率两方面的需求,建议将60 s/条作为用户筛选与数据抽样的最佳阈值.