A new approach for evaluating regional exposure to particulate matter emissions from motor vehicles

Exposure to fine particulate matter from vehicle exhaust is associated with increased health risk. This study develops a new approach for creating spatially detailed regional maps of fine particulate matter concentration from vehicle exhaust using a dispersion model to better evaluate these risks. The spatial extent, diurnal, and seasonal patterns of concentration fields across Los Angeles County, California are evaluated and population exposure and exposure equity by race and income are investigated. The results demonstrate how this modeling approach can create new knowledge about vehicle emissions exposure. This approach also provides a method for proactively screening out regional plans, or specific projects within these plans, that are likely to cause air quality concerns. A proactive and regional air quality assessment can identify potential problems earlier in the planning process and a wider range of solutions, saving time, money and protecting public health. The detailed concentration maps can also be used to improve the siting of regulatory air quality monitors and provide more accurate exposure data for epidemiology studies.     

Second-order models and traffic data from mobile sensors

Mobile sensing enabled by GPS or smart phones has become an increasingly important source of traffic data. For sufficient coverage of the traffic stream, it is important to maintain a reasonable penetration rate of probe vehicles. From the standpoint of capturing higher-order traffic quantities such as acceleration/deceleration, emission and fuel consumption rates, it is desirable to examine the impact on the estimation accuracy of sampling frequency on vehicle position. Of the two issues raised above, the latter is rarely studied in the literature. This paper addresses the impact of both sampling frequency and penetration rate on mobile sensing of highway traffic. To capture inhomogeneous driving conditions and deviation of traffic from the equilibrium state, we employ the second-order phase transition model (PTM). Several data fusion schemes that incorporate vehicle trajectory data into the PTM are proposed. And, a case study of the NGSIM dataset is presented which shows the estimation results of various Eulerian and Lagrangian traffic quantities. The findings show that while first-order traffic quantities can be accurately estimated even with a low sampling frequency, higher-order traffic quantities, such as acceleration, deviation, and emission rate, tend to be misinterpreted due to insufficiently sampled vehicle locations. We also show that a correction factor approach has the potential to reduce the sensing error arising from low sampling frequency and penetration rate, making the estimation of higher-order quantities more robust against insufficient data coverage of the highway traffic.     

Temporal and spatial variation in allocating annual traffic activity across an urban region and implications for air quality assessments

Patterns of traffic activity, including changes in the volume and speed of vehicles, vary over time and across urban areas and can substantially affect vehicle emissions of air pollutants. Time-resolved activity at the street scale typically is derived using temporal allocation factors (TAFs) that allow the development of emissions inventories needed to predict concentrations of traffic-related air pollutants. This study examines the spatial and temporal variation of TAFs, and characterizes prediction errors resulting from their use. Methods are presented to estimate TAFs and their spatial and temporal variability and used to analyze total, commercial and non-commercial traffic in the Detroit, Michigan, U.S. metropolitan area. The variability of total volume estimates, quantified by the coefficient of variation (COV) representing the percentage departure from expected hourly volume, was 21%, 33%, 24% and 33% for weekdays, Saturdays, Sundays and holidays, respectively. Prediction errors mostly resulted from hour-to-hour variability on weekdays and Saturdays, and from day-to-day variability on Sundays and holidays. Spatial variability was limited across the study roads, most of which were large freeways. Commercial traffic had different temporal patterns and greater variability than non-commercial vehicle traffic, e.g., the weekday variability of hourly commercial volume was 28%. The results indicate that TAFs for a metropolitan region can provide reasonably accurate estimates of hourly vehicle volume on major roads. While vehicle volume is only one of many factors that govern on-road emission rates, air quality analyses would be strengthened by incorporating information regarding the uncertainty and variability of traffic activity.     

Modeling lane-changing behavior in a connected environment: A game theory approach

Vehicle-to-Vehicle communications provide the opportunity to create an internet of cars through the recent advances in communication technologies, processing power, and sensing technologies. A connected vehicle receives real-time information from surrounding vehicles; such information can improve drivers’ awareness about their surrounding traffic condition and lead to safer and more efficient driving maneuvers. Lane-changing behavior, as one of the most challenging driving maneuvers to understand and to predict, and a major source of congestion and collisions, can benefit from this additional information. This paper presents a lane-changing model based on a game-theoretical approach that endogenously accounts for the flow of information in a connected vehicular environment. A calibration approach based on the method of simulated moments is presented and a simplified version of the proposed framework is calibrated against NGSIM data. The prediction capability of the simplified model is validated. It is concluded the presented framework is capable of predicting lane-changing behavior with limitations that still need to be addressed. Finally, a simulation framework based on the fictitious play is proposed. The simulation results revealed that the presented lane-changing model provides a greater level of realism than a basic gap-acceptance model.     

From traces to trajectories: How well can we guess activity locations from mobile phone traces?

Passively generated mobile phone dataset is emerging as a new data source for research in human mobility patterns. Information on individuals’ trajectories is not directly available from such data; they must be inferred. Many questions remain in terms how well we can capture human mobility patterns from these datasets. Only one study has compared the results from a mobile phone dataset to those from the National Household Travel Survey (NHTS), though the comparison is on two different populations and samples. This study is a very first attempt that develops a procedure to generate a simulated mobile phone dataset containing the ground truth information. This procedure can be used by other researchers and practitioners who are interested in using mobile phone data and want to formally evaluate the effectiveness of an algorithm.To identify activity locations from mobile phone traces, we develop an ensemble of methods: a model-based clustering method to identify clusters, a logistic regression model to distinguish between activity and travel clusters, and a set of behavior-based algorithms to detect types of locations visited. We show that the distribution of the activity locations identified from the simulated mobile phone dataset resembles the ground truth better than the existing studies. For home locations, 70% and 97% of identified homes are within 100 and 1000 m from the truth, respectively. For work places, 65% and 86% of the identified work places are within 100 and 1000 m from the true ones, respectively. These results point to the possibility of using these passively generated mobile phone datasets to supplement or even replace household travel surveys in transportation planning in the future.     

移动通信系统中一种新的联合分集方法

提出一种用匹配滤波器作为SIR(信干比,Signal to InterferencePowerRatio)估计方法的SIR加权后检测联合分集方法,估计和利用SIR作为加权因子以补偿严重的信道间干扰。其显著特点是,加权因子SIR是由匹配滤波器的输出和一个简单等式共同估计的,该简单等式表示匹配滤波器的输出与SIR之间的关系。在信道间干扰的环境下,在移动无线瑞利衰落信道中对BPSK信号传输的计算机模拟,证明分集性能的改进。计算机仿真证明,该方法的BER特性大大优于传统的联合分集方法。     

数字与模拟对讲系统的对比分析

无线电对讲系统具有其他通信系统不可替代的实际作用,因而具有重要的研究和应用价值。随着通信技术的进步,数字化对讲系统受到越来越广泛的关注。为此,针对ETSI数字对讲标准DMR和现有模拟对讲系统进行详细分析,阐述数字化对讲系统的优势和所面临的问题。     

水资源开发事业（项目）费用分配的理论研究

在对传统的费用分配方法分析研究的基础上,探讨了以Gam e理论为基础的费用分配方法及其适用条件,并以水资源开发中的多目的大坝建设为例,分析研究了项目开发费用在不同建设主体间的分配,提出了适用多种条件费用分配的计算方法.     

基于移动协同的智能客户端研究

支持移动的智能客户端的研究是移动协同研究领域的又一新课题,本文描述了移动环境下的网络自适应、协作数据传输、移动终端的人机界面、异步协作整合等关键技术,探讨了.NET智能客户端技术的特点及其优越性.在此基础上,提出了基于移动CSCW的智能客户端的系统应用架构,并分析了这种架构的特点以及实现.     

无线局域网高效地址自动分配协议

在无线局域网(MANET-Mobile Ad Hoc Networks)中IP地址如何无冲突的分配一直是一个重要的论题。本文将使用聚集自动分配和有效地址选择给出一个高效的分配方案。无线局域网是由一系列平等的主机所构成,我们需要一个IP地址自动分配协议来对这些移动的主机进行控制,而在该协议制定中很好的考虑到这一特性。