列车运行图可调整度评价系统研究

在详细分析了列车运行图缓冲时间的大小及其分布规律对列车晚点传播影响的基础上，通过建立列车运行图调整系统，对给定晚点程度的列车进行模拟调整，从而计算出列车晚点恢复率，晚点传播区大小，连带晚点时间和关键列车运行线等。指标来达到对列车运行图可高速程度优劣评判的目的。     

高速公路常发性交通拥挤路段实时判定与跟踪研究

提出的自动判定和实时跟踪高速公路常发性拥挤路段的方法包含3个算法：①路段平均车速算法；②交通流区段类型判定算法；③排队类型判定算法。考虑采用两个前后相邻车辆检测站之间的路段平均车速来实时判定和跟踪常发性交通拥挤路段的情况，使得模型所提供的信息更能反映路段的真实交通状况。采用路段平均车速的方法克服了目前采用点速度来跟踪车队方法的局限性，并且采用“客观”标定临界车速作为基于现场数据判定车队状态的一种方法。     

大跨度钢管混凝土拱桥地震反应分析

利用有限元法分析大跨度钢管混凝土拱桥的地震反应，考虑了剪切变形的影响，用子空间迭代法分析了其振动规律和动力特性，应用反应谱法和时程分析法分析了该拱桥的地震反应，得出了一些有价值的结论。     

公共交通系统营运可靠性研究

优先发展公共交通是大城市解决交通拥堵,实现城市交通可持续发展的一项重要措施,然而,公交营运水平的低下制约着公交的发展。本文借鉴可靠性理论对公共交通营运可靠性进行定义,并对公交营运时间和乘客服务可靠性分别进行了描述,据此建立起公交系统营运可靠性模型,然后采用随机模拟技术(即Monte Carlo模拟)进行求解,通过算例说明模型的可行性,最后通过分析可靠性模型得出大型活动期间改善公交营运的途径。     

超大跨度斜拉桥施工过程力学行为

斜拉桥结构效应在施工过程中具有典型的时空变化特性，但目前对效应的时程特性及时空包络关注较少。建立精细的几何非线性分析模型，分析主跨1088m的苏通大桥施工过程力学行为，包括施工阶段效应增量、效应时程、效应包络及恒载效应。研究结果有助于结构设计和施工控制。     

大直径钢管桩承载力恢复过程试验研究

针对目前预制桩承载力恢复特性研究与工程应用中的不足,依托西非某海工工程,提出高应变法。采用对同一钢管桩进行初打与不同休止时间复打相结合的试验方法,研究了大直径钢管桩沉桩后的承载力恢复过程。结合地质情况、沉桩与试验结果,得到了钢管桩承载力、侧阻力及端阻力随时间变化的一般规律及影响因素,并通过静载试验对研究结果进行了验证。该研究在提高项目施工效率、降低成本的同时,还得到了有意义的规律与结论,可为后续类似项目提供参考。     

Strava Metro data for bicycle monitoring: a literature review

ABSTRACT

Monitoring bicycle trips is no longer limited to traditional sources, such as travel surveys and counts. Strava, a popular fitness tracker, continuously collects human movement trajectories, and its commercial data service, Strava Metro, has enriched bicycle research opportunities over the last five years. Accrued knowledge from colleagues who have already utilised Strava Metro data can be valuable for those seeking expanded monitoring options. To convey such knowledge, this paper synthesises a data overview, extensive literature review on how the data have been applied to deal with drivers’ bicycle-related issues, and implications for future work. The review results indicate that Strava Metro data have the potential—although finite—to be used to identify various travel patterns, estimate travel demand, analyse route choice, control for exposure in crash models, and assess air pollution exposure. However, several challenges, such as the under-representativeness of the general population, bias towards and away from certain groups, and lack of demographic and trip details at the individual level, prevent researchers from depending entirely on the new data source. Cross-use with other sources and validation of reliability with official data could enhance the potentiality.     

Spatiotemporal influence of land use and household properties on automobile travel demand

Understanding the patterns of automobile travel demand can help formulate policies to alleviate congestion and pollution. This study focuses on the influence of land use and household properties on automobile travel demand. Car license plate recognition (CLPR) data, point-of-interest (POI) data, and housing information data were utilized to obtain automobile travel demand along with the land use and household properties. A geographically and temporally weighted regression (GTWR) model was adopted to deal with both the spatial and temporal heterogeneity of travel demand. The spatial-temporal patterns of GTWR coefficients were analyzed. Also, comparative analyses were carried out between automobile and total person travel demand, and among travel demand of taxis, heavily-used private cars, and total automobiles. The results show that: (I) The GTWR model has significantly higher accuracy compared with the Ordinary Least Square (OLS) model and the Geographically Weighted Regression (GWR) model, which means the GTWR model can measure both the spatial and temporal heterogeneity with high precision; (II) The influence of built environment and household properties on automobile travel demand varies with space and time. In particular, the temporal distribution of regression coefficients shows significant peak phenomenon; and (III) Comparative analyses indicate that residents’ preference for automobiles over other travel modes varies with their travel purpose and destination. The above findings indicate that the proposed method can not only model spatial-temporal heterogeneous travel demand, but also provide a way to analyze the patterns of automobile travel demand.     

Modeling duration choice in space–time multi-state supernetworks for individual activity-travel scheduling

Multi-state supernetworks have been advanced recently for modeling individual activity-travel scheduling decisions. The main advantage is that multi-dimensional choice facets are modeled simultaneously within an integral framework, supporting systematic assessments of a large spectrum of policies and emerging modalities. However, duration choice of activities and home-stay has not been incorporated in this formalism yet. This study models duration choice in the state-of-the-art multi-state supernetworks. An activity link with flexible duration is transformed into a time-expanded bipartite network; a home location is transformed into multiple time-expanded locations. Along with these extensions, multi-state supernetworks can also be coherently expanded in space–time. The derived properties are that any path through a space–time supernetwork still represents a consistent activity-travel pattern, duration choice are explicitly associated with activity timing, duration and chain, and home-based tours are generated endogenously. A forward recursive formulation is proposed to find the optimal patterns with the optimal worst-case run-time complexity. Consequently, the trade-off between travel and time allocation to activities and home-stay can be systematically captured.     

Autonomous cars: The tension between occupant experience and intersection capacity

Systems that enable high levels of vehicle-automation are now beginning to enter the commercial marketplace. Road vehicles capable of operating independently of real-time human control under an increasing set of circumstances will likely become more widely available in the near future. Such vehicles are expected to bring a variety of benefits. Two such anticipated advantages (relative to human-driver vehicle control) are said to be increased road network capacity and the freeing up of the driver-occupant’s time to engage in their choice of leisurely or economically-productive (non-driving) tasks.In this study we investigate the implications for intersection capacity and level-of-service of providing occupants of automated (without real-time human control), autonomously-operating (without vehicle-to-X communication) cars with ride quality that is equivalent (in terms of maximum rates of longitudinal and lateral acceleration) to two types of rail systems: [urban] light rail transit and [inter-urban] high-speed rail. The literature suggests that car passengers start experiencing discomfort at lower rates of acceleration than car drivers; it is therefore plausible that occupants of an autonomously-operating vehicle may wish to instruct their vehicle to maneuver in a way that provides them greater ride comfort than if the vehicle-control algorithm simply mimicked human-driving-operation.On the basis of traffic microsimulation analysis, we found that restricting the dynamics of autonomous cars to the acceleration/deceleration characteristics of both rail systems leads to reductions in a signalized intersection’s vehicle-processing capacity and increases in delay. The impacts were found to be larger when constraining the autonomous cars’ dynamics to the more-restrictive acceleration/deceleration profile of high-speed rail. The scenarios we analyzed must be viewed as boundary conditions, because autonomous cars’ dynamics were by definition never allowed to exceed the acceleration/deceleration constraints of the rail systems. Appropriate evidence regarding motorists’ preferences does not exist at present; establishing these preferences is an important item for the future research agenda.This paper concludes with a brief discussion of research needs to advance this line of inquiry.