Examining the relationship between built environment and metro ridership at station-to-station level

Very few studies have examined the impact of built environment on urban rail transit ridership at the station-to-station (origin-destination) level. Moreover, most direct ridership models (DRMs) tend to involve simple a prior assumed linear or log-linear relationship in which the estimated parameters are assumed to hold across the entire data space of the explanatory variables. These models cannot detect any changes in the linear (or non-linear) effects across different values of the features of built environment on urban rail transit ridership, which possibly induces biased results and hides some non-negligible and detailed information. Based on these research gaps, this study develops a time-of-day origin-destination DRM that uses smart card data pertaining to the Nanjing metro system, China. It applies a gradient boosting regression trees model to provide a more refined data mining approach to investigate the non-linear associations between features of the built environment and station-to-station ridership. Data related to the built environment, station type, demographics, and travel impedance including a less used variable – detour, were collected and used in the analysis. The empirical results show that most independent variables are associated with station-to-station ridership in a discontinuous non-linear way, regardless of the time period. The built environment on the origin side has a larger effect on station-to-station ridership than the built environment on the destination side for the morning peak hours, while the opposite holds for the afternoon peak hours and night. The results also indicate that transfer times is more important variables than detour and route distance.     

Climate change modeling and the weather-related road accidents in Canada

The objective of this research is to study the impact of climate change on the hazardous weather-related road accidents in the New Brunswick, Canada. We develop an Exposure to Weather-Accident Severity (EWAS) index multiplying accident and weather severity. The Negative Binomial Regression and Poisson regression models are applied to estimate the spatial–temporal relationship between the EWAS index and weather-related explanatory variables of road accidents. The regression results show that the surface-weather condition, weather, driver’s gender, weather-driver’s age, weather-driver’s experience, and weather-vehicle’s age have strong positive correlation with the EWAS index, while the surface-road alignment and surface-road characteristics have negative relationship with the EWAS index. The climate change model also indicates that the number of accidents declines during snowy and freezing days—most people stay at home and those who travel extra cautious—accidents do occur. The study suggests that the Road Safety Strategy 2015 of the Transport Canada should take a holistic approach to help minimize the incidences of severe road accident during the normal as well as hazardous weather conditions.     

Minimum acceptable time for turning off idling engines: Evidence from Taiwan

This paper examines the influence of different factors on motorist willingness to comply with idling stop regulations, as determined using stated preference analysis. Motorists were surveyed at urban roadsides in Taiwan, and the results obtained were analyzed using a partially adaptive model. The analysis showed that the standing time and turnoff idling engine while parking are both significant variables, and arise from risk aversion behavior. Environmental perceptions and convenience of use are the most influential factors, according to elasticity analysis. The study also verifies that a partially adaptive model is an appropriate model to consider censored data in a Triple-Bounded Dichotomous Choice analysis. These results will be useful as a reference for improving implementation of idling reduction regulations.     

Have tropical marine protected areas worked? An initial analysis of their success

A multinomial choice framework was used to analyze data from hypothetical storm forecast scenarios administered via mail survey to a random sample of U.S. Gulf Coast residents. Results indicate that the issuance of a mandatory evacuation notice and the presence of higher wind speeds had the largest influence on increasing the likelihood of evacuation. Age, race, disability, distance, and education were significant in explaining one's decision to wait relative to choosing to evacuate. Blacks and disabled individuals were strictly less likely to wait and more likely to make an immediate evacuation decision. Hurricane Katrina evacuees and those with an evacuation destination identified were also more likely to decide to evacuate, but were also more likely to wait before deciding. Results indicate that residents of mobile homes were more likely to either evacuate or wait before making a decision, but strictly less likely not to evacuate. Respondents very confident in being rescued were strictly more likely not to evacuate. Results indicate that not having an evacuation destination identified was the most influential factor regarding the likelihood of not knowing what choice to make.     

Origin软件在隧道监控量测数据处理中的应用

介绍Origin软件的基本功能及其应用。利用Origin软件实现了对量测数据的绘图、非线性拟合等处理,获得可靠的参数及直观的图形。以石龙山隧道施工监控中量测数据处理的实例,并据此指导了隧道的开挖作业和支护结构的设计与施工。     

公轨两用拱桥吊杆的疲劳分析

提出了一种公轨两用拱桥吊杆的疲劳分析方法。依据交通流量调查和相关规范中汽车及轻轨车荷载谱的规定,基于Monte-carlo原理,模拟产生了通过桥梁的双向多车道汽车车流及双向轻轨车车流。针对典型吊杆的影响线开展模拟车流加载,得到杆件应力历程,通过泄水法得到杆件应力谱,按照Palmgren-Miner线性累积损伤理论,计算得到各吊杆的等效应力幅并进行设计基准期内的损伤度评估。以某公轨两用拱桥进行实例分析,同时考虑6线行车道公路荷载和双线轻轨荷载,对吊杆开展了疲劳分析。计算结果表明:采用模拟车流法可方便开展公轨两用特大桥构件的疲劳分析和疲劳剩余寿命评估。     

桥梁超重荷载与限载标准的确定

为了分析公路运营中超重荷载对桥梁的影响，以陕西省府谷-店塔公路上运煤超载车辆为调查对象，根据车辆装载特点和轴型分布特征，将超载车辆分为9种类型，分析了9种车型的超载、超限情况；利用线性回归，得出超载与超限的函数关系。利用等代荷载方法，与公路-Ⅰ级荷载相比较，确定出9种车型的限载系数和限载标准，评价了限载后车辆荷载特性，认为车辆限载后宜使用多轮、多轴运营车型。     

Theoretical analysis of natural frequency of externally prestressed concrete beam based on rigidity correction

A dynamic test on externally prestressed simply supported concrete beams separately with three typical types of tendon distributions was conducted. The results show that the natural frequencies of the beams increase with the increase in the prestressing force at,the tensioning stage, and the natural frequencies decrease after the cracks occur in the beams. Following the calculation formula of natural frequency of externally prestressed beam, which was reported in a literature, the natural frequencies of the experimental beams are calculated, and big errors are found between the test results and the calculated ones of natural frequency values. As a result, this paper has tried to adopt two methods to correct the rigidity parameter of the concrete beam in the formula for natural frequency calculation, and to use the corrected formula to calculate the frequencies of the experimental beams. The calculation results indicate a good consistency with the experimental ones, which verifies the feasibility of the corrected formula.     

Lane-harmonised passenger car equivalents for heterogeneous expressway traffic

In order to account for variations in traffic composition during traffic analysis, passenger car equivalent (PCE) factors are used to convert flow rates of various vehicle classes into flow rates in terms of passenger car units (PCUs). Earlier studies have developed various methods to estimate PCE values but only a few of them are based on uninterrupted traffic flow, particularly for flow regimes with heterogeneous traffic where differential (lower) speed limits are imposed on commercial vehicles. This paper proposes a lane-harmonisation approach, which leverages on the high variation in traffic composition across the lanes, to estimate PCE factors for urban expressways. Multiple linear regression is used and the PCE factors obtained for motorcycles, light goods vehicles, and heavy goods vehicles are 0.65, 1.53, and 2.75, respectively. The estimated capacity flow rate after the application of the obtained PCE factors is around 2200 PCUs per hour per lane.     

散货船运输市场周期性分析与预测

国际散货船航运市场在国际航运市场中占有举足轻重的地位,其走势具有周期性,可以预测船舶航运市场的整体趋势,进一步对全球经济形势分析预测。通过对克拉克森报告进行分析和调研,根据调研结果并结合新冠疫情影响确定了散货船航运市场的主要影响因素,采用相关股票指数和数据对各影响因素量化,对历史数据建立时滞模型并进行多元回归分析,进而建立时滞回归模型,构造了代价函数并采用梯度下降法建立多变量线性回归模型,得出表征散货船市场情况的波罗的海指数(BDI)与影响其走势的几种主要因素之间的代数关系,从而用近几年各影响因素的值来预测未来几个月内波罗的海指数的发展趋势,为船市的进一步布局规划提供参考。