新冠肺炎交通防控政策对长沙市人口流动的影响

为探究新冠肺炎疫情下交通防控政策对长沙市人口流动的影响,本文根据长沙市在新冠 肺炎疫情期间颁布的交通防控政策和疫情实时防控情况划分防控阶段,基于百度迁徙大数据,利 用双重差分模型,识别长沙市不同阶段的交通防控政策以及量化防控效果,分析交通防控政策对 长沙市人口流动的影响。结果显示,长沙市在交通管制阶段,平均人口迁出强度、平均人口迁入 强度及城市内部出行强度分别下降了83.68%、69.24%及59.74%,有效地控制了人口流动,降低了 疫情扩散危险。在交通恢复阶段,长沙市人口流动强度逐渐反弹,城市内部出行强度基本恢复到 2019年同期水平。本文研究结果显示了交通管制对疫情扩散限制的有效性,为常态化疫情防控 下精准防控政策和复工复产政策制定提供参考。     

Cellpath: Fusion of cellular and traffic sensor data for route flow estimation via convex optimization

A new convex optimization framework is developed for the route flow estimation problem from the fusion of vehicle count and cellular network data. The issue of highly underdetermined link flow based methods in transportation networks is investigated, then solved using the proposed concept of cellpaths for cellular network data. With this data-driven approach, our proposed approach is versatile: it is compatible with other data sources, and it is model agnostic and thus compatible with user equilibrium, system-optimum, Stackelberg concepts, and other models. Using a dimensionality reduction scheme, we design a projected gradient algorithm suitable for the proposed route flow estimation problem. The algorithm solves a block isotonic regression problem in the projection step in linear time. The accuracy, computational efficiency, and versatility of the proposed approach are validated on the I-210 corridor near Los Angeles, where we achieve 90% route flow accuracy with 1033 traffic sensors and 1000 cellular towers covering a large network of highways and arterials with more than 20,000 links. In contrast to long-term land use planning applications, we demonstrate the first system to our knowledge that can produce route-level flow estimates suitable for short time horizon prediction and control applications in traffic management. Our system is open source and available for validation and extension.     

Categorizing bicycling environments using GPS-based public bicycle speed data

A promising alternative transportation mode to address growing transportation and environmental issues is bicycle transportation, which is human-powered and emission-free. To increase the use of bicycles, it is fundamental to provide bicycle-friendly environments. The scientific assessment of a bicyclist’s perception of roadway environment, safety and comfort is of great interest. This study developed a methodology for categorizing bicycling environments defined by the bicyclist’s perceived level of safety and comfort. Second-by-second bicycle speed data were collected using global positioning systems (GPS) on public bicycles. A set of features representing the level of bicycling environments was extracted from the GPS-based bicycle speed and acceleration data. These data were used as inputs for the proposed categorization algorithm. A support vector machine (SVM), which is a well-known heuristic classifier, was adopted in this study. A promising rate of 81.6% for correct classification demonstrated the technical feasibility of the proposed algorithm. In addition, a framework for bicycle traffic monitoring based on data and outcomes derived from this study was discussed, which is a novel feature for traffic surveillance and monitoring.     

Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SSRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.     

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.     

Travel analytics: Understanding how destination choice and business clusters are connected based on social media data

Understanding how destination choice and business clusters are connected is of great importance for designing sustainable cities, fostering flourishing business clusters, and building livable communities. As sharing locations and activities on social media platforms becomes increasingly popular, such data can reveal destination choice and activity space which can shed light on human-environment relationships. To this end, this research models the relationship between characteristics of business clusters and check-in activities from Los Angeles County, California. Business clusters are analyzed via two lenses: the supply side (employment data by industry) and the demand side (on-line check-in data). Spatial and statistical analyses are performed to understand how land use and transportation network features affect the popularity of the identified clusters and their relationships. Our results suggest that a cluster with more employment opportunities and more types of employment is associated with more check-ins. A business cluster that has access to parks or recreational services is also more popular. A business cluster with a longer road network and better connectivity of roads is associated with more check-ins. The visualization of the common visitors between clusters reveals that there are a few clusters with outstanding strong ties, while most have modest ties with each other. Our findings have implications on the influence of urban design on the popularity of business clusters.     

Toward using social media to support ridesharing services: challenges and opportunities

Ridesharing has been attracting increasing attention from both academia and industry. One of the challenges posed by the study of ridesharing is to identify the most valuable information for improving the ridesharing decisions taken by participants. Another challenge is to use harvesting techniques to extract specific types of travel-related information. Many methods have been developed by the community in order to solve these issues. However, due to a lack of information sharing between different transit authorities and the difficulty of identifying subjective perceptions of the experience of ridesharing, understanding and evaluating how social media data might support or obstruct goals for mobility, safety and environmental sustainability in ridesharing is a difficult task. In this survey, we first analyze the literature on ridesharing with a focus on the properties and model of service, and introduce a framework to describe the major components required for a ridesharing service. Then, we illustrate the potential value of information extracted from social media and present the rationale for harvesting travel-related data. Finally, we detail some possible directions and different approaches for using social media data, and highlight their assets and drawbacks.     

Link travel time inference using entry/exit information of trips on a network

This paper studies link travel time estimation using entry/exit time stamps of trips on a steady-state transportation network. We propose two inference methods based on the likelihood principle, assuming each link associates with a random travel time. The first method considers independent and Gaussian distributed link travel times, using the additive property that trip time has a closed-form distribution as the summation of link travel times. We particularly analyze the mean estimates when the variances of trip time estimates are known with a high degree of precision and examine the uniqueness of solutions. Two cases are discussed in detail: one with known paths of all trips and the other with unknown paths of some trips. We apply the Gaussian mixture model and the Expectation–Maximization (EM) algorithm to deal with the latter. The second method splits trip time proportionally among links traversed to deal with more general link travel time distributions such as log-normal. This approach builds upon an expected log-likelihood function which naturally leads to an iterative procedure analogous to the EM algorithm for solutions. Simulation tests on a simple nine-link network and on the Sioux Falls network respectively indicate that the two methods both perform well. The second method (i.e., trip splitting approximation) generally runs faster but with larger errors of estimated standard deviations of link travel times.     

城市轨道交通设施安全智能监测预警系统设计

城市轨道交通线网规模和客运量的日益增加对城市轨道交通安全运营的要求越来越高,城市轨道交通设施安全智能监测是轨道交通系统安全运营的重要保障.设计了一种城市轨道交通设施安全智能监测预警系统,实现轨道交通设施监测数据采集、数据传输、数据保存、智能预警功能,对系统功能及布局提出具体的设计要求,同时根据安全性的需求划分预警级别.     

UML Galaxy Diagram： An Approach of Conceptual Data Integration for OLAP

IntroductionWith the development of contemporary elec-tronic commerce,the multidimensional data analy-sis for On-Line Analytical Processing(OLAP)onthe web makes it necessary to integrate various da-ta sources such as Extensible Markup Language(XML)data and relational data on the conceptuallevel.XML is quickly becoming the new standardfor data representation and exchange on the WorldWide Web[1].At present,the most widespreadconceptual model is the Unified Modeling Lan-guage(UML)which …