AS-900HL多平台激光雷达测量系统在河道地形测量中的应用

数字城市的发展对基础数据提出更高的要求,获取地表三维空间信息的需求日渐增加。AS-900HL多平台激光雷达测量系统是一种集成高精度激光扫描仪与惯性导航的系统,可搭载在多种平台进行数据采集,具有工作效率高、扫描速度快、测点密度大、成果形式多样等特点。以鸭绿江白马浪至云峰大坝段河道地形测量为实例,对AS-900HL在河道地形测量的应用进行探讨。该河段地形高低起伏巨大,不具备直接接触测量的条件,由于对岸是朝鲜,无法登陆架设远程测量设备,也不能采用无人机航飞,属于实施难度很大的特殊河段。通过踏勘、路线设计、外业采集、内业处理和成果输出,验证了该系统在河道地形测量中的可行性。该系统适用于河道地形测量,尤其在特殊困难河段,能填补常规测量技术无法施测的空缺,可推动数字航道和数字城市建设。     

国道345线杂多至查吾拉段多年冻土地质踏勘

本文通过对国道345线杂多-查旦乡-查吾拉段公路沿线多年冻土地质情况的现场踏勘,依据踏勘结果分析,初步估计了该路段沿线多年冻土分布情况及主要特征,为沿线多年冻土勘察设计提供基础依据,并为路线设计提供理论参考。     

Development of behavioral context addressable loggers in the shell for travel-activity analysis

This study attempts to develop a small, portable travel-activity measuring instrument that requires no entry from respondents. Conventional surveys have collected identification information such as facility type, transport mode, and activity content through the operation of instruments, questionnaires, etc. However, these complicated surveys burden the respondents and rely on their memory, often leading to recording omissions or incorrect records. We propose a method for estimating behavioral contexts using BCALs (Behavioral Context Addressable Loggers in the Shell), a wearable, behavioral context information-measuring instrument, for re-estimating label information such as facility type and transport mode from ecological and environmental sensors based on learning models. The numerical values observed by these sensors differed greatly among locations or means of transportation, revealing the high possibility of automatic identification of locations and means of transportation using BCALs.     

An exploratory analysis of joint-activity participation characteristics using the American time use survey

This paper presents a detailed exploratory analysis of joint activity participation characteristics using the American Time Use Survey (ATUS). As a very large nationwide survey that explicitly elicited information on both household and non-household companions for each activity episode, the ATUS is ideally suited for this analysis. Several intuitive and interesting results are obtained. Joint episodes are found to be of longer durations, significantly likely to take place at the residence of other people, and often confined to certain time periods of the weekday. In addition, important differences in these characteristics are also observed based on activity purpose, companion type, and the day of the week. These findings are intended to provide the basis for the justification of detailed collection of joint activity–travel participation information in household activity–travel surveys, and also as a stimulant for further empirical analysis and modeling of joint activity participation behavior.

Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

In today’s world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s personal-fleet evolution.Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings.Twenty five-year simulations of Austin’s household vehicle fleet suggest that, under all scenarios modeled, Austin’s vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet’s VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO₂ emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO₂ emissions by 30% (relative to trend).Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year.In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.     

Time use in travel surveys and time use surveys – Two sides of the same coin?

An in-depth understanding of travel behaviour determinants, including the relationship to non-travel activities, is the foundation for modelling and policy making. National Travel Surveys (NTS) and time use surveys (TUS) are two major data sources for travel behaviour and activity participation. The aim of this paper is to systematically compare both survey types regarding travel activities and non-travel activities. The analyses are based on the German National Travel Survey and the German National Time Use Survey from 2002.The number of trips and daily travel time for mobile respondents were computed as the main travel estimates. The number of trips per person is higher in the German TUS when changes in location without a trip are included. Location changes without a trip are consecutive non-trip activities with different locations but without a trip in-between. The daily travel time is consistently higher in the German TUS. The main reason for this difference is the 10-min interval used. Differences in travel estimates between the German TUS and NTS result from several interaction effects. Activity time in NTS is comparable with TUS for subsistence activities.Our analyses confirm that both survey types have advantages and disadvantages. TUS provide reliable travel estimates. The number of trips even seems preferable to NTS if missed trips are properly identified and considered. Daily travel times are somewhat exaggerated due to the 10-min interval. The fixed time interval is the most important limitation of TUS data. The result is that trip times in TUS do not represent actual trip times very well and should be treated with caution.We can use NTS activity data for subsistence activities between the first trip and the last trip. This can potentially benefit activity-based approaches since most activities before the first trip and after the last trip are typical home-based activities which are rarely substituted by out-of-home activities.     

公路交通量及OD调查统计分析软件开发

公路交通量及起讫点(OD)调查的统计分析是开展公路网规划、公路建设项目可行性研究等工作的重要基础。一种统计分析软件的开发弥补了当前流行的交通分析软件在这一方面的不足,实现了公路交通量及OD调查的数据输入、交通流特性的统计分析、断面流量的生成、SQL查询、OD矩阵的操作等功能,提高了统计分析工作的效率和质量,为交通规划、建设与管理的科学决策提供了有力的数据处理工具。     

基于时空聚类算法的轨迹停驻点识别研究

利用手机等智能移动终端获取个体出行GPS轨迹数据,提出了一种新的时空聚类算法AT-DBSCAN识别轨迹中的停驻点.该方法以固定长度的滑窗搜索核心点,以时空邻近条件定义簇间距离,以簇密度大小规定合并次序.提出了出行次数一致性、出行起止时刻误差、停驻点时长误差、停驻点中心偏移距离4个算法验证指标,弥补了传统查全率、查准率等忽略停驻点时空信息准确性的不足.结果表明,识别的停驻点有98%的位置误差在30m以内,100%的时长误差在5min以内,98%的出行起止时刻误差在5min以内.此外,算法对于室内活动、定位飘移、路径重合程度高等复杂轨迹具有较好的泛化能力.     

论船舶安全检查与船舶检验的关系

通过分析船舶安全检查和船舶检验两者之间的联系,从而进一步理顺两者之间的关系,充分发挥两者在船舶安全管理工作的作用,更好地做好船舶安全管理工作.