钱江四桥抗震性能分析

根据钱江四桥地震危险性分析得到的地震动参数,基于两水准设防、两阶段设计的抗震设计思想,采用反应谱和时程分析两种方法对该桥的抗震性能进行了比较分析,分析时考虑了桩-土的相互作用及材料、伸缩缝、支座等非线形效应影响。研究结果表明:钱江四桥的抗震性能满足预期的设计性能目标,不会因地震而破坏。     

FSQ6125客车设计

简要介绍FSQ6125客车的设计目的、整车造型、主要结构特点和技术参数。     

新疆公路三级区划中环境参数的提出

自然环境条件对公路工程的影响，主要体现在路线选取、路基强度和稳定性、路面稳定性和耐久性、公路主要自然病害(包括冻融、翻浆、雪害、风沙害、崩塌、滑坡和地震灾害等)、施工条件和养护运营环境等5个方面，分析自然条件和公路工程的关系，提出公路区划中地质地貌环境参数和水热状况环境参数。阐述了环境参数的提出过程，为新疆公路自然区划三级区的划分提供依据。     

Aggregating mobile object trajectories: cumulative time geographic density estimation for GPS data

We present new approaches that expand upon the time geographic density estimation (TGDE) framework previously employed to estimate potential path trees. In the past, TGDE metrics have identified possible locations an individual moving object may have passed between, given known origin and destination points. This paper utilizes a new form of TGDE to investigate taxicab GPS traces over a specified time horizon with position ‘gaps’. To this end, we propose a new extension to the TGDE framework, TGDE-C, which is used to determine the cumulative TGDE values for a group of GPS traces, at a given location. These metrics are applied to multiple taxis and allow for time of day analysis. Additionally, we combine these new extensions with existing TGDE metrics that allow us to determine how accessible individual or groups of vehicles are to urban opportunities.     

Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.     

Estimation of origin-destination matrices from link counts and sporadic routing data

Estimation of origin-destination (OD) matrices from link count data is a challenging problem because of the highly indeterminate relationship between the observations and the latent route flows. Conversely, estimation is straightforward if we observe the path taken by each vehicle. We consider an intermediate problem of increasing practical importance, in which link count data is supplemented by routing information for a fraction of vehicles on the network. We develop a statistical model for these combined data sources and derive some tractable normal approximations thereof. We examine likelihood-based inference for these normal models under the assumption that the probability of vehicle tracking is known. We show that the likelihood theory can be non-standard because of boundary effects, and provide conditions under which such irregular behaviour will be observed in practice. For regular cases we outline connections with existing generalised least squares methods. We then consider estimation of OD matrices under estimated and/or misspecified models for the probability of vehicle tracking. Theoretical developments are complemented by simulation experiments and an illustrative example using a section of road network from the English city of Leicester.     

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.     

Estimating traffic volumes for signalized intersections using connected vehicle data

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.     

动态载荷识别的自适应延迟逆模型方法

为减小载荷识别问题对原系统先验知识的依赖,采用系统的自适应延迟逆模型识别时域载荷。采用自适应算法辨识延迟逆模型,代替了一般识别方法中的系统特性矩阵求逆过程,避免了病态问题。随后将工作状态下的响应作为逆模型的输入,则其输出就是时域载荷的延迟估计。通过对两端简支梁结构进行载荷识别的仿真研究,以及对双层隔振试验台架的试验研究,识别了稳态激励和瞬态激励,验证了该方法的有效性。该方法不需要了解系统的数学模型及参数,因此能够应用于工程实践中。     

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.