An investigation of multiplier effects generated by implementing queue jump lanes at multiple intersections

This paper investigates the combination effects of queue jump lanes (QJLs) on signalised arterials to establish if a multiplier effect exists, that is, the benefit from providing QJLs at multiple intersections is higher than the sum of benefits from providing them individually at each of those intersections. To explore the combination effects on bus delay and total person delay, a delay estimation model is developed using kinematic wave theory, kinematic equations and Monte Carlo simulation. In addition, to investigate the combination effects in offset settings optimised for bus delay or total person delay, offset optimisation models are proposed. Validation results using traffic micro‐simulation indicate the effectiveness and computational efficiency of the proposed models. Results of a modelling test bed suggest that providing QJLs at multiple intersections can create a multiplier effect on one‐directional bus delay savings with signal offsets that provide bus progression. Furthermore, optimising offsets to minimise bus delay tends to create a multiplier effect on one‐directional bus delay savings, particularly when variations in dwell times are not high. The reason for the multiplier effect may be that providing QJLs reduces variations in bus travel times, which makes signal coordination for buses perform more effectively. From a policy perspective, the existence of a multiplier effect suggests that a corridor‐wide scale implementation of QJLs has considerable merit. Copyright © 2016 John Wiley & Sons, Ltd.     

Microscopic decision model for pedestrian route choice at signalized crosswalks

In this paper, two‐tier mathematical models were developed to simulate the microscopic pedestrian decision‐making process of route choice at signalized crosswalks. In the first tier, a discrete choice model was proposed to predict the choices of walking direction. In the second tier, an exponential model was calibrated to determine the step size in the chosen direction. First, a utility function was defined in the first‐tier model to describe the change of utility in response to deviation from a pedestrian's target direction and the conflicting effects of neighboring pedestrians. A mixed logit model was adopted to estimate the effects of the explanatory variables on the pedestrians' decisions. Compared with the standard multinomial logit model, it was shown that the mixed logit model could accommodate the heterogeneity. The repeated observations for each pedestrian were grouped as panel data to ensure that the parameters remained constant for individual pedestrians but varied among the pedestrians. The mixed logit model with panel data was found to effectively address inter‐pedestrian heterogeneity and resulted in a better fit than the standard multinomial logit model. Second, an exponential model in the second tier was proposed to further determine the step size of individual pedestrians in the chosen direction; it indicates the change in walking speed in response to the presence of other pedestrians. Finally, validation was conducted on an independent set of observation data in Hong Kong. The pedestrians' routes and destinations were predicted with the two‐tier models. Compared with the tracked trajectories, the average error between the predicted destinations and the observed destinations was within an acceptable margin. Copyright © 2016 John Wiley & Sons, Ltd.     

Efficient solution algorithm for finding spatially dependent reliable shortest path in road networks

Travel times are generally stochastic and spatially correlated in congested road networks. However, very few existing route guidance systems (RGS) can provide reliable guidance services to aid travellers planning their trips with taking account explicitly travel time reliability constraint. This study aims to develop such a RGS with particular consideration of travellers' concern on travel time reliability in congested road networks with uncertainty. In this study, the spatially dependent reliable shortest path problem (SD‐RSPP) is formulated as a multi‐criteria shortest path‐finding problem in road networks with correlated link travel times. Three effective dominance conditions are established for links with different levels of travel time correlations. An efficient algorithm is proposed to solve SD‐RSPP by adaptively using three established dominance conditions. The complexities of road networks in reality are also explicitly considered. To demonstrate the applicability of proposed algorithm, a comprehensive case study is carried out in Hong Kong. The results of case study show that the proposed solution algorithm is robust to take account of travellers' multiple routing criteria. Computational results demonstrate that the proposed solution algorithm can determine the reliable shortest path on real‐time basis for large‐scale road networks. Copyright © 2016 John Wiley & Sons, Ltd.     

Impact of family in-home quality time on person travel demand

This paper introduces the concept of Primary Family Priority Time (PFPT), which represents a high priority household decision to spend time together for in-home activities. PFPT is incorporated into a fully specified and operational activity based discrete choice model system for Copenhagen, called COMPAS, using the DaySim software platform. Structural tests and estimation results identify two important findings. First, PFPT has a place high in the model hierarchy, and second, strong interactions exist between PFPT and the other day level activity components of the model system. Forecasts are generated for a road pricing and congestion scenario by COMPAS and a comparison version of the model system that excludes PFPT. COMPAS with PFPT exhibits less mode changing and time-of-day shifting in response to pricing and congestion than the comparison version.     

Precise estimation of connections of metro passengers from Smart Card data

The aim of this study is to estimate both the physical and schedule-based connections of metro passengers from their entry and exit times at the gates and the stations, a data set available from Smart Card transactions in a majority of train networks. By examining the Smart Card data, we will observe a set of transit behaviors of metro passengers, which is manifested by the time intervals that identifies the boarding, transferring, or alighting train at a station. The authenticity of the time intervals is ensured by separating a set of passengers whose trip has a unique connection that is predominantly better by all respects than any alternative connection. Since the connections of such passengers, known as reference passengers, can be readily determined and hence their gate times and stations can be used to derive reliable time intervals. To detect an unknown path of a passenger, the proposed method checks, for each alternative connection, if it admits a sequence of boarding, middle train(s), and alighting trains, whose time intervals are all consistent with the gate times and stations of the passenger, a necessary condition of a true connection. Tested on weekly 32 million trips, the proposed method detected unique connections satisfying the necessary condition, which are, therefore, most likely true physical and schedule-based connections in 92.6 and 83.4 %, respectively, of the cases.     

Travel mode choice and travel satisfaction: bridging the gap between decision utility and experienced utility

Over the past decades research on travel mode choice has evolved from work that is informed by utility theory, examining the effects of objective determinants, to studies incorporating more subjective variables such as habits and attitudes. Recently, the way people perceive their travel has been analyzed with transportation-oriented scales of subjective well-being, and particularly the satisfaction with travel scale. However, studies analyzing the link between travel mode choice (i.e., decision utility) and travel satisfaction (i.e., experienced utility) are limited. In this paper we will focus on the relation between mode choice and travel satisfaction for leisure trips (with travel-related attitudes and the built environment as explanatory variables) of study participants in urban and suburban neighborhoods in the city of Ghent, Belgium. It is shown that the built environment and travel-related attitudes—both important explanatory variables of travel mode choice—and mode choice itself affect travel satisfaction. Public transit users perceive their travel most negatively, while active travel results in the highest levels of travel satisfaction. Surprisingly, suburban dwellers perceive their travel more positively than urban dwellers, for all travel modes.     

Do new sources of traffic data make the application of Chaos Theory to traffic management a realistic possibility?

Current signal systems for managing road traffic in many urban areas around the world lack a coordinated approach to detecting the spatial and temporal evolution of congestion across control regions within city networks. This severely inhibits these systems’ ability to detect reliably, on a strategic level, the onset of congestion and implement effective preventative action. As traffic is a time-dependent and non-linear system, Chaos Theory is a prime candidate for application to Urban Traffic Control (UTC) to improve congestion and pollution management. Previous applications have been restricted to relatively uncomplicated motorway and inter-urban networks, arguably where the associated problems of congestion and vehicle emissions are less severe, due to a general unavailability of high-resolution temporal and spatial data that preserve the variability in short-term traffic patterns required for Chaos Theory to work to its full potential. This paper argues that this restriction can now be overcome due to the emergence of new sources of high-resolution data and large data storage capabilities. Consequently, this opens up the real possibility for a new generation of UTC systems that are better able to detect the dynamic states of traffic and therefore more effectively prevent the onset of traffic congestion in urban areas worldwide.     

Performance decay of stern bearing based on lubrication numerical model and state parameters北大核心CSCD

[目的]为了实现对船舶艉轴承润滑状态的监测和评估,提出一种结合润滑性能衰变模型和支持向量机(SVM)算法的艉轴承润滑性能评估方法。[方法]针对船舶艉轴承润滑状态难以监测和识别的问题,建立轴承润滑衰变数值模型,并运用实验数据对该模型进行验证,研究载荷、粗糙度和半径间隙对润滑状态衰变机理的影响。基于SVM算法,构建润滑状态分类器,通过网格搜索算法优化超参数,利用不同润滑状态的数据集进行训练,最后实现对艉轴承润滑状态的评估。[结果]结果显示,随着外部载荷、粗糙度和半径间隙的增大,轴承润滑状态恶化的临界速度增大,动压润滑工作范围减小,混合润滑工作范围增大;由仿真数据集对润滑状态识别模型的验证表明,所提的润滑状态识别方法准确率达96.88%。[结论]所提方法能监测轴承的润滑性能特征,有效识别轴承的润滑状态。     

上软下硬地层大直径土压平衡盾构下穿民房建筑群沉降控制北大核心CSCD

依托广州地铁18号线盾构施工,针对其地层分布复杂、软硬差异大及穿越密集民房建筑群等特点,通过注浆加固、现场动态监测、优化掘进参数等一系列主动措施,解决实际工程中掘进参数合理取值与地表建筑沉降变形控制两大难题。研究结果表明:(1)在该类软硬地层中掘进时,刀盘在通过不同岩层断面分界线时扭矩、推力等参数波动较大,总推力与扭矩有良好的相关性,各掘进参数宜控制为:推力30000~35000 kN、扭矩4500~6000 kN·m、推进速度35~45 mm/min;(2)提出的多段式封孔洞内超前注浆工艺可使刀盘前18 m范围内的地表上抬3~5 mm,最大上抬值能达9.02 mm,同时能缓解土体在盾构下穿时及后续固结稳定的沉降趋势;(3)因刀具磨损、掘进参数波动大、螺旋机卡死等引起的临时停机会造成地表建筑日沉降速率超过3 mm/d,在预设停机点位置前采取洞内超前注浆和克泥效工法能有效缓解沉降趋势,在带压开舱期间日沉降速率控制在2 mm/d以下;(4)左右隧道轴线附近的地表建筑沉降基本可分为“微小隆起—沉降较大—逐渐稳定”三个阶段,测点最大沉降值最终稳定在-21.38 mm、-22.49 mm,均小于控制值。     

隧道-洞口滑坡平行体系受力变形模式与计算方法研究北大核心CSCD

我国目前对隧道-滑坡工程的设计尚无可供参照的行业标准,尤其是滑坡洞口段隧道缺少相应的计算理论。文章首先以平行体系中隧道-洞口滑坡为研究对象,通过归纳总结滑坡地段隧道衬砌的病害特征,构建了相应的工程地质模型;然后将剩余滑坡推力视为导致隧道变形破坏的直接原因,通过荷载传递规律得到作用于隧道结构上的附加荷载,将其与围岩压力叠加推导出了隧道外荷载的计算公式;接着采用弹性地基梁理论,推导出滑坡推力作用下的隧道内力计算方法,从而得到隧道-洞口滑坡的受力变形模式及计算理论;最后通过模型试验对其合理性进行了验证分析,结果表明该方法与实际工程相符,能够为滑坡地段洞口隧道的设计提供参考。