Are young Americans carless across the United States? A spatial analysis

This study uses the National Household Travel Survey (NHTS) data to investigate the most recent correlates of vehicle ownership among young Americans. This study performs a spatial analysis to examine the potentially non-stationary relationships between sociodemographic factors and vehicle ownership. Consistent with previous studies, modeling results from this study showed that young Americans are more likely to be carless than older adults. The spatial analysis answers the research question – in which regions(s) young Americans are even less likely to have a car. The results highlighted the Northeast states for the young American’s extra-lower vehicle ownership if the influences of all other factors are held constant. The cost of living and availability of transportation alternatives are possible reasons. Further, this study built separate models for young adults (25–34 years old) and three older age groups. The vehicle ownership correlates within the young adults are found to be generally consistent with the correlates among all adults. Among young adults, vehicle ownership is still significantly related to their gender, educational attainment, employment status, household characteristics, and travel demand. However, young adults’ vehicle ownership seems to be less sensitive to household income than mid-age adults’ (35–44 years old), perhaps because young people may not perceive financial stress such as child support and mortgage. This study contributes by using a spatial analysis approach to reveal the non-stationary correlates of vehicle ownership. This approach is useful for future travel behavior research and transportation policy considering the spatial heterogeneity.     

Assessment of spatial transferability of an activity-based model,TASHA

Spatial transferability has been recognized as a useful validation test for travel demand models. To date, however, transferability of activity-based models has not been frequently assessed. This paper assesses the spatial transferability of an activity-based model, TASHA (Travel Activity Scheduler for Household Agents), which has been developed for the Greater Toronto Area (GTA), Canada. TASHA has been transferred to the context of the Island of Montreal, Canada using the 2003 Origin–Destination (O–D) travel survey and the 2001 Canadian Census. It generates daily schedules of activities (individual and joint) for each individual in this region. The modelled activity attributes (frequency, start time, duration and distance) from TASHA and observed attributes from the 2003 O–D travel survey are compared for five different activities (i.e. work, school, shopping, other, and return to home). At the aggregate level, TASHA provides quite reasonable outcomes (in some cases – better results than for the Toronto Area) for all four attributes for work, school and return to home activities with few exceptions (for instance, school start time). The model outcomes are also promising for shopping frequency and start times; however, TASHA provides larger differences for average shopping durations and distances. Only the forecasts for all four attributes for the ‘other’ activity type differ greatly with the observed attributes for the Montreal Island. These large differences most likely indicate the differences in behaviour between the Montreal Island and the Toronto Area. In general, we conclude that re-estimation of model parameters and the use of local activity attribute distributions (frequency, start time and duration) is a desirable step in the transfer of the TASHA model from one context to another.     

Factors affecting public transportation usage rate: Geographically weighted regression

As the number of private vehicles grows worldwide, so does air pollution and traffic congestion, which typically constrain economic development. To achieve transportation sustainability and continued economic development, the dependency on private vehicles must be decreased by increasing public transportation usage. However, without knowing the key factors that affect public transportation usage, developing strategies that effectively improve public transportation usage is impossible. Therefore, this study respectively applies global and local regression models to identify the key factors of usage rates for 348 regions (township or districts) in Taiwan. The global regression model, the Tobit regression model (TRM), is used to estimate one set of parameters that are associated with explanatory variables and explain regional differences in usage rates, while the local regression model, geographically weighted regression (GWR), estimates parameters differently depending on spatial correlations among neighbouring regions. By referencing related studies, 32 potential explanatory variables in four categories, social-economic, land use, public transportation, and private transportation, are chosen. Model performance is compared in terms of mean absolute percentage error (MAPE) and spatial autocorrelation coefficient (Moran’ I). Estimation results show that the GWR model has better prediction accuracy and better accommodation of spatial autocorrelation. Seven variables are significantly tested, and most have parameters that differ across regions in Taiwan. Based on these findings, strategies are proposed that improve public transportation usage.     

智能公交乘客信息服务数据库设计

本文从车载、站台以及控制中心三方面分析了智能公交乘客信息服务系统,重点介绍了站台触摸屏公交查询系统中数据库的设计.查询系统数据库以ACCESS进行描述,创建了公交信息的存储结构,以ADO.NET与数据库进行连接访问,实现了对公交站点线路的查询.以VB.NET编程实现了起始站点名称选择和点击地图两种方式的公交站点查询、线路查询以及具有最少换乘次数的出行路径查询.     

Monitoring Median Queries over Moving Objects

The k-median problem has attracted a number of researchers. However, few of them have considered both the dynamic environment and the issue of accuracy. In this paper, a new type of query is studied, called continuous median monitoring (CMM) query. It considers the k-median problem under dynamic environment with an accuracy guarantee. A continuous group nearest neighbor based (CGB) algorithm and an average distance medoid (ADM) algorithm are proposed to solve the CMM problem. ADM is a hill climbing schemed algorithm and achieves a rapid converging speed by checking only qualified candidates. Experiments show that ADM is more efficient than CGB and outperforms the classical PAM (partitioning around medoids) and CLARANS (clustering large applications based on randomized search) algorithms with various parameter settings.     

An individual information system for visualizing the bus and subway network

The system described in this paper allows the user to visualize the Montreal transportation system. The user is provided with several different maps.

The complete subway network and all regular bus lines have been included. The subway network and the bus lines can be selectively displayed. The most attractive feature of this system is the possibility of obtaining all possible routes between two different places. The input is based on the menu technique.

The system can be easily adapted to other large urban transportation systems.     

围岩空间变异性对隧道结构可靠度的影响分析

采用随机场模型能够有效地模拟围岩参数的空间变异性。文章将局部平均随机场理论、有限差分法和Monte-Carlo模拟结合在一起,得到了Monte-Carlo随机有限差分法,基于该方法编制了可考虑围岩空间变异性的隧道结构可靠度计算程序;并将围岩密度、弹性模量和内摩擦角等参数视为三维正态随机场,研究了围岩的竖向与水平向相关距离的变化对隧道结构可靠度的影响。计算结果表明,围岩空间变异性会显著影响隧道结构的可靠度,为了获得更为经济合理的支护方案,在支护设计时考虑围岩参数的空间变异性很有必要;围岩竖向或水平向相关距离的增大均会引起隧道结构失效概率的增加,但影响程度不同;隧道结构失效概率对围岩竖向空间变异性更加敏感,故在工程勘察费用较少时,可考虑将勘察费用更多地投入到竖向相关距离的勘测,水平向相关距离可参考已有经验数据取值。     

Carbon dioxide emissions from port container distribution: Spatial characteristics and driving factors

Port carbon dioxide (CO₂) emissions in China have become an ever-increasing public concern due to their significant impacts on human health and the environment. However, existing studies focus mainly on CO₂ emissions from vessels calling at the ports and cargo handling within the ports, paying little attention to the inland distribution networks. To fill this gap, this paper proposes an easily implemented method for calculating CO₂ emissions from port container distribution (PCD) and investigates their spatial characteristics and driving factors. By analyzing 30 container ports in China, the main findings are as follows. First, road transportation is the major contributor of CO₂ emissions from PCD due to the lack of rail and inland water transportation. Second, PCD carbon emissions exhibit significant local spatial clustering. That is, ports with similar geographical locations tend to present a similar pattern of PCD carbon emissions. Third, as suggested by the spatial Durbin model, PCD carbon emissions are negatively determined by local gross domestic product, number of port berths, but are positively determined by local tertiary industry value and highway freight volume, and waterway freight volume in both local and neighboring ports. These results provide empirical insights into cross-port collaboration in reducing PCD carbon emissions.     

New approaches to transportation forecasting models

In 1992, the Federal Highway Administration awarded small research contracts to four teams of transportation researchers to design alternative approaches for improving the urban travel demand forecasting process. The purpose of these contracts was to enable each research team to explain how transportation planning models could and should be improved to meet the new forecasting requirements brought on by recent legislation, to address the impacts of new transportation technology, and to exploit the travel behavior theories and methodologies that have developed over the past two decades.This paper presents a summary and synthesis of the ideas which emerged from the four research reports. Its purpose is to identify common themes suggested by several of the research teams, to point out what appear to be critical elements missing from some approaches, and to combine the best aspects of the four approaches into a research plan for improving the current generation of travel demand models.Abbreviations CAAA Clean Air Act Amendments - FHWA Federal Highway Administration - GIS Geographic Information System - IIA Independence of Irrelevant Alternatives - IT Information Technology - IVHS Intelligent Vehicle Highway System - SUE Stochastic User Equilibrium - TCM Transportation Control Measures - UTPS Urban Transportation Planning System - VMT Vehicle Miles of Travel The paper was prepared as a report for the Federal Highway Administration.     

On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level

This paper proposes a new spatial multivariate count model to jointly analyze the traffic crash-related counts of pedestrians and bicyclists by injury severity. The modeling framework is applied to predict injury counts at a Census tract level, based on crash data from Manhattan, New York. The results highlight the need to use a multivariate modeling system for the analysis of injury counts by road-user type and injury severity level, while also accommodating spatial dependence effects in injury counts.