基于聚类站点客流公共特征的轨道交通车站精细分类

已有城市轨道交通车站分类多基于定性分析，不能满足精细化设计和运营的需要。本文提出一种基于聚类站点公共特征的站点精细分类方法。首先，将来源于AFC(Automatic Fare Collection)的进站客流量数据处理为时间序列数据，并基于K-Means++算法对各个站点的客流量进行聚类；其次，建立客流量聚类结果与土地利用特征多维参数的拟合方程，计算获得居住密集型、工作就业型以及区域中心型等5种大类站点的客流量公共特征。在此基础上，充分考虑属于同一大类站点不同站点的细分特性，使用5类客流量公共特征比重组合精细描述具体站点类型。 实例结果表明，使用本文提出的精细分类方法计算得到的每个站的客流量拟合值与真实客流值间的平均绝对百分比误差控制在14%以内，说明该分类方法具有可行性。     

Integrated models of land use and transportation for the autonomous vehicle revolution

ABSTRACT

The advent of the autonomous vehicle (AV) will affect not only the transportation system, but also future patterns of land development. Integrated land use and transportation models will be critical tools in assessing the path forward with this technology. Key questions with respect to land use impacts of AVs arise from potential changes in sensitivity to travel and reduced demand for parking. It is an open question whether AVs will induce urban sprawl, or whether spatial economies of agglomeration will mitigate any reductions in travel time sensitivity. The deployment of shared fleets of AVs would likely reduce parking demand, producing yet to be explored impacts on property development within existing urban footprints. We perform a critical assessment of currently operational models and their ability to represent the adoption of AVs. We identify the representation of time in such models as a vital component requiring additional development to model this new technology. Existing model applications have focused on the discrete addition of new infrastructure or policy at a fixed point in time, whereas AV adoption will occur incrementally through time. Stated adaptation surveys are recommended as tools to quantify preferences and develop relevant model inputs. It is argued that existing models that assume comparatively static equilibrium have been convenient in the past, but are insufficient to model technology adoption. In contrast, dynamic model frameworks lack sufficient structure to maintain reasonability under large perturbations from base conditions. The ongoing advancement of computing has allowed models to move away from being mechanistic aggregate tools, towards behaviourally rich depictions of individual persons and firms. However, much work remains to move from projections of existing conditions into the future, to the evolution of the spatial economy as it evolves through time in response to new technologies and exogenous stresses. Principles from complex and evolutionary systems theory will be important in the development of models with the capacity to consider such dynamics.     

InSAR技术在采空区铁路选线中的应用

采空区沉降变形会直接影响铁路工程的建设及运营安全,需要对其变形进行勘察分析并评估影响。通过工程实例,应用InSAR(合成孔径雷达干涉测量)技术结合物探及钻探等手段,对某采空区影响进行综合评价。在收集既有地质资料及矿区资料的基础上,首先采用2007~2011年日本ALOSPALSAR及2016~2018年欧空局Sentinel-1A/1B数据进行InSAR分析,框定了采空区范围并断定其发展趋势;然后,针对影响方案的采空区边界段落采用多道地震映像法及“双管单动”钻探手段,查明了浅层小型空洞及采空回填层的分布。实践证明,该方法通过引入空间信息能够有效的细化工作区域,可减少勘察工作量并降低成本。     

Young people and the private car: A love-hate relationship

Urban mobility is one of the main concerns of the public authorities in developed countries. In France, household travel surveys are conducted every ten years in major cities to gather weekday mobility data. They enable decision-makers to better understand travel patterns, their change and their determinants, in order to adapt transport infrastructures to the population′s needs. While the automobile has allowed the level of mobility to increase since 1950, an unexpected finding has emerged from recent surveys in most developed countries, namely that there has been a marked decline in car use. Analyses show that this trend is mainly because young adults (18–34 years old) are less likely to acquire a driver′s license. This paper tries to better understand the decrease in the rate of driver′s license holding among young adults in the Lyon conurbation and to quantify the impact of the main explanatory factors in a temporal perspective. It also aims to analyze the consequences of this trend on private car use as a driver for daily trips. It quantifies the influence of economic, socio-demographic and spatial factors on driver ′ s license holding and car use by considering the responses to the last three household travels surveys conducted in the Lyon conurbation area (1995, 2006 and 2015). The temporal dimension allows us to highlight a change in the relationship between young adults and the private car in the French context.     

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.     

A multiobjective land development optimization model: the case of New Castle County,Delaware

This paper develops a multiobjective optimization model to consider transportation impacts of the future development of land. The output of the model is the best location and type of land use that has minimal negative transportation effects and uses the maximum available public transportation infrastructure. It provides tools for both planners and transportation engineers and enables them to consider different scenarios of possible policies and land development. Since multiple objectives and their nonlinear structures are considered, the model is solved using mixed integer nonlinear programming. The final results are shown in both tabular and graphical format. The effectiveness of the model is applied to the northern part of New Castle County, Delaware. The results show that the model successfully finds the best locations for both residential and commercial land uses in order to meet several criteria discussed in the paper.     

Driver behavior formulation in intersection dilemma zones with phone use distraction via a logit-Bayesian network hybrid approach

Use of cellular phone while driving is one of the top contributing factors that induce traffic crashes, resulting in significant loss of life and property. A dilemma zone is a circumstance near signalized intersections where drivers hesitate when making decisions related to their driving behaviors. Therefore, the dilemma zone has been identified as an area with high crash potential. This article utilizes a logit-based Bayesian network (BN) hybrid approach to investigate drivers' decision patterns in a dilemma zone with phone use, based on experimental data from driving simulations from the National Advanced Driving Simulator (NADS). Using a logit regression model, five variables were found to be significant in predicting drivers' decisions in a dilemma zone with distractive phone tasks: older drivers (50–60 years old), yellow signal length, time to stop line, handheld phone tasks, and driver gender. The identified significant variables were then used to train a BN model to predict drivers' decisions at a dilemma zone and examine probabilistic impacts of these variables on drivers' decisions. The analysis results indicate that the trained BN model was effective in driver decision prediction and variable influence extraction. It was found that older drivers, a short yellow signal, a short time to stop line, nonhandheld phone tasks, and female drivers are factors that tend to result in drivers proceeding through intersections in a dilemma zone with phone use distraction. These research findings provide insight in understanding driver behavior patterns in a dilemma zone with distractive phone tasks.     

The relationship between land use and automobile travel utility: A Multiple Indicators Multiple Causes approach

The relationship between land use and the utility of automobile travel is examined by refining the utility concept, particularly by combining the microeconomic utility theory, which is concerned with the disutility of travel, and the perspective on the positive utility. A conceptual model is accordingly developed and then adjusted considering different purposes of travel. The purpose-specific models are tested through a Multiple Indicators Multiple Causes approach in Seoul, Korea, using datasets from a sample survey and geographic information systems. The major finding is that land use affects the utility mainly by changing synergy and affective utility rather than instrumental utility, which encompasses disutility variables. Among land use variables, the utility is found to be the most sensitive to the number of transit facilities for commuting and shopping travel and land use balance for leisure travel.     

基于信令数据的出行强度影响因素分析模型

为探究城市交通出行强度影响因素及不同因素的影响程度，本文从土地利用与交通基础设施建设两方面出发，分析包括土地利用混合指数，职住混合率熵指数，公共交通站点 500 m覆盖率，路网可达性等17个指标与出行强度的相关关系；基于相关系数和拟合优度分析，提取7个与出行强度强相关指标，基于所识别指标构建北京市中心城区出行强度多元回归模型.模型结果表明，职住混合率熵指数对出行强度的影响最为显著，公共交通站点覆盖率对出行强度的影响比道路网密度和可达性更为明显.此外，给出单一土地利用/交通基础设施指标对出行强度拟合结果的离群特征分析方法，用于评估不同区域基础设施供给与交通出行需求之间的平衡关系.     

Environmental and transport effects of warehouse relocationing: evidence from Norway

Reducing traffic volumes and CO2-emissions from freight transport has proven difficult in many countries. Although the increasing suburbanization of warehouses is seen as a relevant land use trend, comprehensive analyses of their impact remain scarce. This study uses real data in modeling transport, costs, environmental and modal effects from warehouse relocations around Oslo and Trondheim (Norway). Results indicate that for Oslo, traffic performance (ton-km), CO2-emissions, and transport costs increase following warehouse suburbanization. For Trondheim, transport performance and CO2-emissions increase less, while transport costs decrease marginally. We conclude that specific case characteristics (geography and trade patterns) are important in determining the strength and direction of effects, and expect that common concomitant developments (warehouse centralization and consolidation) would lead to more pronounced results. Our findings confirm some, but challenge other, findings from the relatively scarcely literature available. Finally, the study's more general insights and observations can help advance similar analyses beyond Norway.