基于DBSCAN的港口泊位自动识别算法设计

为更好地监测船舶动态和船舶在港口的作业情况,通过对K-Means算法和DBSCAN(Density-Based Spa-tial Clustering of Applications with Noise)密度聚类算法进行对比,选择DBSCAN密度聚类算法对港口泊位进行聚类,对港口泊位的位置和大小进行识别.基于船舶自动...     

Transit oriented development and retail: Is variation in success explained by a gap between theory and practice?

Central to the concept of Transit Oriented Development (TOD) is a retail core situated around stations. However, successful retail near light rail transit stations has been elusive. Despite significant implications for land use, transportation, and economic development planning, little research exists to explain the gap between TOD concept and reality. We hypothesize that the density, diversity, and design characteristics central to the theory of TODs drive retail success. We implement a TOD Index proposed in the literature to score 474 light rail station areas in 11 metropolitan areas according to the presence and magnitude of those density, diversity, and design characteristics. A series of robustly-developed multilevel models support our hypothesis: TOD Index scores significantly predict station area retail employment, ceteris paribus. An evaluation of its subcomponents individually (block size, which relates to walkability; land use mix; and activity density) suggests activity density may be the driving force in this relationship. Our research works to move the conversation away from an assumption that transit stations and retail naturally co-exist and toward more intentional station area design choices demonstrated to drive retail employment.     

Joint analysis of the spatial impacts of built environment on car ownership and travel mode choice

Concerns over transportation energy consumption and emissions have prompted more studies into the impacts of built environment on driving-related behavior, especially on car ownership and travel mode choice. This study contributes to examine the impacts of the built environment on commuter’s driving behavior at both spatial zone and individual levels. The aim of this study is threefold. First, a multilevel integrated multinomial logit (MNL) and structural equation model (SEM) approach was employed to jointly explore the impacts of the built environment on car ownership and travel mode choice. Second, the spatial context in which individuals make the travel decisions was accommodated, and spatial heterogeneities of car ownership and travel mode choice across traffic analysis zones (TAZs) were recognized. Third, the indirect effects of the built environment on travel mode choice through the mediating variable car ownership were calculated, in other words, the intermediary nature of car ownership was considered. Using the Washington metropolitan area as the study case, the built environment measures were calculated for each TAZ, and the commuting trips were drawn from the household travel survey in this area. To estimate the model parameters, the robust maximum likelihood (MLR) method was used. Meanwhile, a comparison among different model structures was conducted. The model results suggest that application of the multilevel integrated MNL and SEM approach obtains significant improvements over other models. This study give transportation planners a better understanding on how the built environment influences car ownership and commuting mode choice, and consequently develop effective and targeted countermeasures.     

Specification and estimation of a spatially and temporally autocorrelated seemingly unrelated regression model: application to crash rates in China

In transportation studies, variables of interest are often influenced by similar factors and have correlated latent terms (errors). In such cases, a seemingly unrelated regression (SUR) model is normally used. However, most studies ignore the potential temporal and spatial autocorrelations across observations, which may lead to inaccurate conclusions. In contrast, the SUR model proposed in this study also considers these correlations, making the model more behaviorally convincing and applicable to circumstances where a three-dimensional correlation exists, across time, space, and equations. An example of crash rates in Chinese cities is used. The results show that incorporation of spatial and temporal effects significantly improves the model. Moreover, investment in transportation infrastructure is estimated to have statistically significant effects on reducing severe crash rates, but with an elasticity of only −0.078. It is also observed that, while vehicle ownership is associated with higher per capita crash rates, elasticities for severe and non-severe crashes are just 0.13 and 0.18, respectively; much lower than one. The techniques illustrated in this study should contribute to future studies requiring multiple equations in the presence of temporal and spatial effects.

西延高速铁路引入西安铁路枢纽方案研究

在国家中长期铁路网规划中西安铁路枢纽具有重要的地位。为研究其空间布局优化问题,首先研究枢纽客运布局,缺少衔接南北向高速铁路的主客运站。其次西延高速铁路、西渝高速铁路引入西安铁路枢纽宏观走向方案,综合比较后建议采用城市东侧布线方案,银西高铁与西武高铁也从枢纽东侧连接贯通;再次研究枢纽内主要客运站布局和分工,西安北承担东西向始发和通过动车作业,西安东调整为主客运站,承担南北向的始发和通过动车作业;最后研究南北向高铁引入西安东站后,还应设置引入西安北站、西安站的联络线。结果表明:西安铁路枢纽内各条线路间互连互通、运输效能可以得到有效发挥。     

Bikeshare trip generation in New York City

Cities around the world and in the US are implementing bikesharing systems, which allow users to access shared bicycles for short trips, typically in the urban core. Yet few scholars have examined the determinants of bikeshare station usage using a fine-grained approach. We estimate a series of Bayesian regression models of trip generation at stations, examining the effects bicycle infrastructure, population and employment, land use mix, and transit access separately by season of the year, weekday/weekend, and user type (subscriber versus casual). We find that bikeshare stations located near busy subway stations and bicycle infrastructure see greater utilization, and that greater population and employment generally predict greater usage. Our findings are nuanced, however; for instance, those areas with more residential population are associated with more trips by subscribers and on both weekdays and non-working days; however, the effect is much stronger on non-working days. Additional nuances can be found in how various land use variables affect bikeshare usage. We use our models, based on 2014 data, to forecast the trips generated at new stations opened in 2015. Results suggest there is large variation in predictive power, partly caused by variation in weather, but also by other factors that cannot be predicted. This leads us to the conclusion that the nuances we find in our inferential analysis are more useful for transportation planners.     

Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories

This paper presents a trajectory clustering method to discover spatial and temporal travel patterns in a traffic network. The study focuses on identifying spatially distinct traffic flow groups using trajectory clustering and investigating temporal traffic patterns of each spatial group. The main contribution of this paper is the development of a systematic framework for clustering and classifying vehicle trajectory data, which does not require a pre-processing step known as map-matching and directly applies to trajectory data without requiring the information on the underlying road network. The framework consists of four steps: similarity measurement, trajectory clustering, generation of cluster representative subsequences, and trajectory classification. First, we propose the use of the Longest Common Subsequence (LCS) between two vehicle trajectories as their similarity measure, assuming that the extent to which vehicles’ routes overlap indicates the level of closeness and relatedness as well as potential interactions between these vehicles. We then extend a density-based clustering algorithm, DBSCAN, to incorporate the LCS-based distance in our trajectory clustering problem. The output of the proposed clustering approach is a few spatially distinct traffic stream clusters, which together provide an informative and succinct representation of major network traffic streams. Next, we introduce the notion of Cluster Representative Subsequence (CRS), which reflects dense road segments shared by trajectories belonging to a given traffic stream cluster, and present the procedure of generating a set of CRSs by merging the pairwise LCSs via hierarchical agglomerative clustering. The CRSs are then used in the trajectory classification step to measure the similarity between a new trajectory and a cluster. The proposed framework is demonstrated using actual vehicle trajectory data collected from New York City, USA. A simple experiment was performed to illustrate the use of the proposed spatial traffic stream clustering in application areas such as network-level traffic flow pattern analysis and travel time reliability analysis.     

The value of transportation accessibility in a least developed country city – The case of Rajshahi City,Bangladesh

Little appears to be known about the capitalization of transportation accessibility in South Asian housing markets, which typically differ from those of industrialized countries. This study starts addressing this gap by providing empirical evidence about the nature and the magnitude of the value of accessibility as reflected by residential rents in Rajshahi City, Bangladesh. Results of our SARAR spatial hedonic model estimated on 526 observations from a random sample collected via in-person interviews indicate that the rent of a multi-unit dwelling decreases by 0.0239% for every 1% increase in network access distance to the nearest major road. Moreover, proximity (within 400 m) to a primary school and to a healthcare facility commands rent premiums of respectively 93.55 BDT ($1.40) and 109.45 BDT ($1.64). Surprisingly, whether access roads are paved or not does not statistically impact rents, probably because of the dominance of walking, rickshaws use, and biking, combined with the rarity of personal cars. Likewise, proximity to bus stops and to train stations is not reflected in rents of multi-family dwellings, likely because buses and trains in Rajshahi City only provide regional and national service. Differences in estimates of our spatial models between maximum likelihood (ML) and generalized spatial two-stage-least-squares illustrate the danger of relying on ML in the presence of heteroskedasticity. These results should be useful for planning transportation infrastructure funding measures in least developed country cities like Rajshahi City.     

Using the Bayesian updating approach to improve the spatial and temporal transferability of real-time crash risk prediction models

This study aimed to improve the spatial and temporal transferability of the real-time crash risk prediction models by using the Bayesian updating approach. Data from California’s I-880N freeway in 2002 and 2009 and the I-5N freeway in 2009 were used. The crash risk models for these three datasets are quite different from each other. The model parameters do not remain stable over time or space. The transferability evaluation results show that the crash risk models cannot be directly transferred across time and space. The updating results indicate that the Bayesian updating approach is effective in improving both spatial and temporal transferability even when new data are limited. The predictive performance of the updated model increases with an increase in the sample size of the new data. In addition, when limited new data are available, updating an existing model is better than developing a model using the limited new data.     

空间分析方法在航线设计和航路监视中的应用

探讨在电子海图中，使用GIS空间分析函数自动判断计划航线的偏航极限范围内是否存在危险点、线、面的方法，为航线设计起决策参考，在航行过程中，起实时航路监视作用。