基于数据挖掘的城市机动车停车调查分析研究

进行城市机动车停车场规划前需要开展停车场调查,以得到停车场的基本特征参数、机动车进出记录和停车者的停车行为等信息。通过数据挖掘中的一些方法可以在数据库中挖掘出有用的信息。根据机动车进出停车场的记录计算一系列的停车指标,并借助多元统计分析中典型相关分析的方法,分析停车指标与停车场基本特征参数之间的关系。然后利用关联规则挖掘停车问询调查数据中的潜在信息,分析停车者的行为特征,以掌握更多的停车信息。     

XML技术在交通综合数据库中的应用研究

交通综合数据库是交通综合信息平台的核心和基础。本文首先分析了交通综合数据库的功能以及其所存储的信息类型,分析了XML技术在异构数据源之间进行数据共享和交换方面的优势以及目前许多国家和地区对XML在交通数据交换中的研究状况,在此基础上提出了基于XML技术的交通综合数据库的系统结构,重点研究了利用XML实现数据交换的理论与技术途径,通过在实际研发工作中的应用,结果表明XML技术在交通综合数据库中有良好的应用前景。     

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.     

Modeling real-time human mobility based on mobile phone and transportation data fusion

Even though a variety of human mobility models have been recently developed, models that can capture real-time human mobility of urban populations in a sustainable and economical manner are still lacking. Here, we propose a novel human mobility model that combines the advantages of mobile phone signaling data (i.e., comprehensive penetration in a population) and urban transportation data (i.e., continuous collection and high accuracy). Using the proposed human mobility model, travel demands during each 1-h time window were estimated for the city of Shenzhen, China. Significantly, the estimated travel demands not only preserved the distribution of travel demands, but also captured real-time bursts of mobility fluxes during large crowding events. Finally, based on the proposed human mobility model, a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams.     

Highway voting system: Embracing a possible paradigm shift in traffic data acquisition

The integration of internet and mobile phones has opened the door to a new wave of utilizing private vehicles as probes not only for performance evaluation but for traffic control as well, gradually replacing the role of traffic surveillance systems as the dominant source of traffic data. To prepare for such a paradigm shift, one needs to overcome some key institutional barriers, in particular, the privacy issue. A Highway Voting System (HVS) is proposed to address this issue in which drivers provide link- and/or path-based vehicle data to the traffic management system in the form of “votes” in order to receive favorable service from traffic control. The proposed HVS offers a platform that links data from individual vehicles directly with traffic control. In the system, traffic control responds to voting vehicles in a way similar to the current system responding to prioritized vehicles and providing the requested services accordingly. We show in the paper that the proposed “voting” system can effectively resolve the privacy issue which often hampers traffic engineers from getting detailed data from drivers. Strategies to entice drivers into “voting” so as to increase the market penetration level under all traffic conditions are discussed. Though the focus of the paper is on addressing the institutional issues associated with data acquisition from individual vehicles, other research topics associated with the proposed system are identified. Two examples are given to demonstrate the impact of the proposed system on algorithm development and traffic control.     

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.     

On the variance of recurrent traffic flow for statistical traffic assignment

This paper generalizes and extends classical traffic assignment models to characterize the statistical features of Origin-Destination (O-D) demands, link/path flow and link/path costs, all of which vary from day to day. The generalized statistical traffic assignment (GESTA) model has a clear multi-level variance structure. Flow variance is analytically decomposed into three sources, O-D demands, route choices and measurement errors. Consequently, optimal decisions on roadway design, maintenance, operations and planning can be made using estimated probability distributions of link/path flow and system performance. The statistical equilibrium in GESTA is mathematically defined. Its multi-level statistical structure well fits large-scale data mining techniques. The embedded route choice model is consistent with the settings of O-D demands considering link costs that vary from day to day. We propose a Method of Successive Averages (MSA) based solution algorithm to solve for GESTA. Its convergence and computational complexity are analyzed. Three example networks including a large-scale network are solved to provide insights for decision making and to demonstrate computational efficiency.     

“s”型覆岩空间结构的冲击地压危险区预测

基于微地震监测和覆岩空间结构理论，以山东华丰矿1410工作面为例，分析了“s”型覆岩空间结构的形成，利用理论方法和微地震监测预测并圈定了华丰矿1410工作面发生冲击地压的危险区域，研究了“s”型覆岩空间结构的冲击地压危险区分布规律，为现场预测和控制冲击地压灾害提供了理论基础。     

Commuter ride-sharing using topology-based vehicle trajectory clustering: Methodology,application and impact evaluation

This paper illustrates a ride matching method for commuting trips based on clustering trajectories, and a modeling and simulation framework with ride-sharing behaviors to illustrate its potential impact. It proposes data mining solutions to reduce traffic demand and encourage more environment-friendly behaviors. The main contribution is a new data-driven ride-matching method, which tracks personal preferences of road choices and travel patterns to identify potential ride-sharing routes for carpool commuters. Compared with prevalent carpooling algorithms, which allow users to enter departure and destination information for on-demand trips, the proposed method focuses more on regular commuting trips. The potential effectiveness of the approach is evaluated using a traffic simulation-assignment framework with ride-sharing participation using the routes suggested by our algorithm. Two types of ride-sharing participation scenarios, with and without carpooling information, are considered. A case study with the Chicago tested is conducted to demonstrate the proposed framework’s ability to support better decision-making for carpool commuters. The results indicate that with ride-matching recommendations using shared vehicle trajectory data, carpool programs for commuters contribute to a less congested traffic state and environment-friendly travel patterns.     

Dynamic demand estimation and prediction for traffic urban networks adopting new data sources

Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far.The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements.Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF).Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.