Promoting public bike-sharing: A lesson from the unsuccessful Pronto system

In 2014, Seattle implemented its own bike-sharing system, Pronto. However, the system ultimately ceased operation three years later on March 17th, 2017. To learn from this failure, this paper seeks to understand factors that encourage, or discourage, bike-sharing trip generation and attraction at the station level. This paper investigates the effects of land use, roadway design, elevation, bus trips, weather, and temporal factors on three-hour long bike pickups and returns at each docking station. To address temporal autocorrelations and the nonlinear seasonality, the paper implements a generalized additive mixed model (GAMM) that incorporates the joint effects of a time metric and time-varying variables. The paper estimates models on total counts of pickups and returns, as well as pickups categorized by user types and by location. The results clarify that effects of hilly terrain and the rainy weather, two commonly perceived contributors to the failure. Additionally, results suggest that users in the University District, presumably mostly university students, tend to use shared bikes in neighborhoods with a higher household density and a higher percentage of residential land use, and make bike-sharing trips regardless workdays or non-workdays. The paper also contributes to the discussion on the relationship between public transportation service and bike-sharing. In general, users tend to use bike-sharing more at stations that have more scheduled bus trips nearby. However, some bike-sharing users may shift to bus services during peak hours and rainy weather. Several strategies are proposed accordingly to increase bike ridership in the future.     

Safety modeling for two‐way left‐turn lanes and a procedure to identify two‐way left‐turn lane segments with safety concerns

In the past, two‐way left‐turn lane (TWLTL) median treatments have been frequently used in Florida to inexpensively improve traffic and safety performances. In order to identify factors that may have significant impacts on safety operations in TWLTL sections and to identify TWLTL locations that present existing and future safety concerns, a research project was carried out and results are summarized in the paper. In the research, a three‐year crash history database with crashes and section characteristics from a total of 1688 TWLTL sections all over Florida was developed and used. A negative binomial regression model was developed to determine the statistical relationship between the number of crashes per mile per year and several variables such as traffic volume, access density, posted speed, and number of lanes. In regard to the methodology, in order to identify locations with safety concerns, several steps are needed: development of real crash data distribution, determination of statistical distribution models that better represent the actual crash data, determination of percentile values for the average number of crashes, estimation of crash rates for sections with the same characteristics, estimation of critical values for the variables corresponding to the percentile values for average number of crashes, calculation of tables of critical average annual daily traffic values, and generation of a list of TWLTL locations with critical safety concerns. Results presented in the paper have been used in real applications. Copyright © 2012 John Wiley & Sons, Ltd.     

Modeling traffic operation at signalized intersections without explicit left‐turn yielding rules with an enhanced cell transmission model

This paper presents an enhanced cell transmission model (CTM) to capture traffic operation at signalized intersections without explicit permissive left‐turn yielding rules (i.e. aggressive permissive left‐turn maneuvers may not necessarily yield to opposing through traffic), which can be widely observed in many developing countries. Different from previous studies that focus on traffic dynamics on approaching links, this study contributes to modeling traffic operations within the intersection. A novel cell transmission framework with various types of virtual cells is proposed to model the dynamics of traffic movements from approach to exit. The unique phenomenon of competitive occupying of the conflict point between the left turn and opposing through movements is modeled. The cell state indicating its blockage is proposed to capture the dynamic queue formulation and dissipation and to evaluate the operational traffic performance at the intersection. Field validation results show that the proposed model can capture the operation of traffic at signalized intersections without explicit permissive left‐turn yielding rules with significantly higher level of accuracy than traditional traffic flow models. Copyright © 2016 John Wiley & Sons, Ltd.     

CDUBGM-Markov模型在铁路客运量预测中的应用

文章在传统的灰色模型和马尔柯夫模型的基础上,提出了动态无偏灰色马尔柯夫模型,阐述了该模型的建立方法,并采用这三种模型对我国铁路客运量进行了预测,对比结果表明动态无偏灰色马尔柯夫模型的拟合效果较好,预测精度较高,是一种行之有效的预测方法。     

新建地铁车站零距离下穿既有线区间影响分析

暗挖隧道下穿既有地铁施工,势必会引起既有地铁结构附加变形和差异沉降,影响其结构及运营安全,需要进行变形和内力分析,以便采取必要保护措施.文章以某暗挖车站密贴下穿既有地铁区间为例,采用FLAC3D和SAP 2000模拟,分析既有地铁区间变形和内力发展规律,评价结构和运营安全受影响的程度,为安全保护提供决策依据.     

Hybrid model for prediction of bus arrival times at next station

Effective prediction of bus arrival times is important to advanced traveler information systems (ATIS). Here a hybrid model, based on support vector machine (SVM) and Kalman filtering technique, is presented to predict bus arrival times. In the model, the SVM model predicts the baseline travel times on the basic of historical trips occurring data at given time‐of‐day, weather conditions, route segment, the travel times on the current segment, and the latest travel times on the predicted segment; the Kalman filtering‐based dynamic algorithm uses the latest bus arrival information, together with estimated baseline travel times, to predict arrival times at the next point. The predicted bus arrival times are examined by data of bus no. 7 in a satellite town of Dalian in China. Results show that the hybrid model proposed in this paper is feasible and applicable in bus arrival time forecasting area, and generally provides better performance than artificial neural network (ANN)–based methods. Copyright © 2010 John Wiley & Sons, Ltd.     

沉管隧道地震响应分析

文章采用田村重四郎和冈本舜三提出的沉埋隧道地震响应分析的数学模型,对南京长江越江隧道(沉管段)方案进行了地震响应的纵向受力分析。分析中采用了隧址处100年超越概率为2%的人工合成地震加速度,考虑了不同管段的联结方式和不同的计算参数,对初步设计中沉管段结构的安全性进行了论证,所提供的数据曾为设计单位所采纳[1]。     

清管器在输油管道中的运动规律研究

在分析清管物理模型的基础上,建立了清管器前段塞流动的特征参数计算模型、动态数学模型以及相应的数值计算方法,并进行了数值模拟。利用数学模拟方法可以计算清管过程中管线的压力分布,利用压力分布可以跟踪清管球在管线内的运行,这为混输管路的运行管理提供了理论依据。     

快速公交系统站台停靠时间模型研究

快速公交系统停靠站台停车延误是影响快速公交运行车速的关键因素之一,因此构建快速公交系统站台停靠时间模型是提升快速公交服务水平的基础理论研究。本文选取盐城BRT-1号线的起始站、中途站、客流离散站等三类站点为研究对象,综合运用数理统计法与数据挖掘法,构建快速公交系统站台停靠时间模型,并对该模型的合理性进行了检验。研究表明:盐城市BRT-1号线三类站台的快速公交车辆停靠时间与上下车乘客人数呈线性关系,即快速公交车辆停靠时间与上下车乘客人数的检验参数R2均大于0.8。