The use of gait parameters to evaluate pedestrian behavior at scramble phase signalized intersections

Pedestrian scramble phasing is usually implemented to reduce pedestrian‐vehicle conflicts and therefore increase the safety of the intersection. However, to adequately determine the benefits of scramble phasing, it is necessary to understand how pedestrians react to such an unconventional design. This study investigates changes in pedestrian crossing behavior following the implementation of a scramble phase by examining the spatiotemporal gait parameters (step length and step frequency). This detailed microscopic‐level analysis provides insight into changes in pedestrian walking mechanisms as well as the effect of various pedestrian and intersection characteristics. The study uses video data collected at a scramble phase signalized intersection in Oakland, California. Gait parameters were found to be influenced by pedestrian gender, age, group size, crosswalk length, and pedestrian signal indications. Both average step length and walking speed were significantly higher for diagonally crossing pedestrians compared with pedestrians crossing on the conventional crosswalks. Pedestrians were found to have the tendency to increase their step length more than their step frequency to increase walking speed. It was also found that, compared with men, women generally increase their walking speed by increasing their step frequency more than step length. However, when in non‐compliance with signal indications, women increase their walking speed by increasing their step length more than step frequency. It was also found that older pedestrians do not significantly change their walking behavior when in non‐compliance with signal indications. Copyright © 2014 John Wiley & Sons, Ltd.     

Within day rescheduling microsimulation combined with macrosimulated traffic

The concept of rescheduling is essential to activity-based modeling in order to calculate effects of both unexpected incidents and adaptation of individuals to traffic demand management measures. When collaboration between individuals is involved or timetable based public transportation modes are chosen, rescheduling becomes complex. This paper describes a new framework to investigate algorithms for rescheduling at a large scale. The framework allows to explicitly model the information flow between traffic information services and travelers. It combines macroscopic traffic assignment with microscopic simulation of agents adapting their schedules. Perception filtering is introduced to allow for traveler specific interpretation of perceived macroscopic data and for information going unnoticed; perception filters feed person specific short term predictions about the environment required for schedule adaptation. Individuals are assumed to maximize schedule utility. Initial agendas are created by the FEATHERS activity-based schedule generator for mutually independent individuals using an undisturbed loaded transportation network. The new framework allows both actor behavior and external phenomena to influence the transportation network state; individuals interpret the state changes via perception filtering and start adapting their schedules, again affecting the network via updated traffic demand. The first rescheduling mechanism that has been investigated uses marginal utility that monotonically decreases with activity duration and a monotonically converging relaxation algorithm to efficiently determine the new activity timing. The current framework implementation is aimed to support re-timing, re-location and activity re-sequencing; re-routing at the level of the individual however, requires microscopic travel simulation.     

Modeling the behavioral determinants of travel behavior: An application of latent transition analysis

This paper applies the relatively new method of latent class transition analysis to explore the notion that qualitative differences in travel behavior patterns are substantively meaningful and therefore relevant from explanatory point of view. For example, because the bicycle may function as an important access and egress mode, a car user who also (occasionally) uses the bicycle may be more likely to switch to a public transit profile than someone who only uses the car. Data from the Dutch mobility panel are used to inductively reveal travel behavior patterns and model transitions in these patterns over time. Additionally, the effects of seven exogenous variables, including two important life events (i.e. moving house and changing jobs), on cluster membership and the transition probabilities are assessed. The results show that multiple-mode users compared to single-mode users are more likely to switch from one behavioral profile to another. In addition, age, the residential environment, moving house and changing jobs have strong influences on the transition probabilities between the revealed behavioral patterns over time.     

Using automated walking gait analysis for the identification of pedestrian attributes

Collecting microscopic pedestrian behavior and characteristics data is important for optimizing the design of pedestrian facilities for safety, efficiency, and comfortability. This paper provides a framework for the automated classification of pedestrian attributes such as age and gender based on information extracted from their walking gait behavior. The framework extends earlier work on the automated analysis of gait parameters to include analysis of the gait acceleration data which can enable the quantification of the variability, rhythmic pattern and stability of pedestrian’s gait. In this framework, computer vision techniques are used for the automatic detection and tracking of pedestrians in an open environment resulting in pedestrian trajectories and the speed and acceleration dynamic profiles. A collection of gait features are then derived from those dynamic profiles and used for the classification of pedestrian attributes. The gait features include conventional gait parameters such as gait length and frequency and dynamic parameters related to gait variations and stability measures. Two different techniques are used for the classification: a supervised k-Nearest Neighbors (k-NN) algorithm and a newly developed semi-supervised spectral clustering. The classification framework is demonstrated with two case studies from Vancouver, British Columbia and Oakland, California. The results show the superiority of features sets including gait variations and stability measures over features relying only on conventional gait parameters. For gender, correct classification rates (CCR) of 80% and 94% were achieved for the Vancouver and Oakland case studies, respectively. The classification accuracy for gender was higher in the Oakland case which only considered pedestrians walking alone. Pedestrian age classification resulted in a CCR of 90% for the Oakland case study.     

Incorporating human-factors in car-following models: A review of recent developments and research needs

Over the past decades there has been a considerable development in the modeling of car-following (CF) behavior as a result of research undertaken by both traffic engineers and traffic psychologists. While traffic engineers seek to understand the behavior of a traffic stream, traffic psychologists seek to describe the human abilities and errors involved in the driving process. This paper provides a comprehensive review of these two research streams.It is necessary to consider human-factors in CF modeling for a more realistic representation of CF behavior in complex driving situations (for example, in traffic breakdowns, crash-prone situations, and adverse weather conditions) to improve traffic safety and to better understand widely-reported puzzling traffic flow phenomena, such as capacity drop, stop-and-go oscillations, and traffic hysteresis. While there are some excellent reviews of CF models available in the literature, none of these specifically focuses on the human factors in these models.This paper addresses this gap by reviewing the available literature with a specific focus on the latest advances in car-following models from both the engineering and human behavior points of view. In so doing, it analyses the benefits and limitations of various models and highlights future research needs in the area.     

新型GFRP组合梁翼缘有效分布宽度分析

利用能量变分法推导出均布线荷载作用下新型GFRP组合梁翼缘有效分布宽度的理论公式,通过有限元数值模拟验证理论公式可靠性。并借鉴传统钢-混凝土组合梁分析方法,对新型GFRP组合梁翼缘有效分布宽度各种影响因素进行了参数分析。     

32 m超低高度混凝土T梁动力性能试验与分析

为了研究开行120km/h货车后既有铁路32m超低高度混凝土T梁的运营状况、安全性和加固对策,选取1孔32m超低高度混凝土T梁进行自振频率、挠度、振幅等主要动力性能测试。结果发现跨中横向振幅超过限值,不能满足120km/h货车运行,需要提高该梁的竖向和横向刚度。     

某飞鸟式拱桥拱座开裂加固后静动载试验与分析

针对某飞鸟式拱桥拱座加固后结构性能进行静动载试验。分析了静载试验中控制位置变形、应变,吊杆轴力等控制指标,观测了拱座裂缝变化情况。并采用模态分析法比较动载试验所确定桥面前两阶振型及频率,对实测加速度及动挠度变化规律与理论结果进行比较,计算确定冲击系数。试验结果分析表明,该桥检测指标均能满足要求,行车性能较好。表明该桥拱座加固效果良好,使用性能比较理想,可适应设计荷载等级的要求。     

桩-土-结构相互作用对大跨度连续刚架钢桁拱桥地震响应影响研究

以广州新光大桥为例,研究软弱分层土、较软分层土、较硬分层土和岩石地基与群桩相互作用下大跨度连续刚架钢桁拱桥动力特性以及地震响应。研究表明在各种地震时程激励下,不同地基土对大跨度连续刚架钢桁拱桥拱肋以及V形刚架的内力和位移影响很大。随着地基土刚度的增加,在横桥向、顺桥向和竖向地震激励下,主拱肋和边拱肋上弦杆、V形刚架靠边跨斜腿轴力基本呈递减趋势,而V形刚架面内和面外弯矩随地基土刚度变化的规律性不强。     

An analysis of the determinants of children’s weekend physical activity participation

This paper examines the out-of-home, weekend, time-use patterns of children aged 5–17 years, with a specific emphasis on their physical activity participation. The impact of several types of factors, including individual and household demographics, neighborhood demographics, built environment characteristics, and activity day variables, on physical activity participation is analyzed using a joint nested multiple discrete–continuous extreme value-binary choice model. The sample for analysis is drawn from the 2000 San Francisco Bay Area Travel Survey. The model developed in the paper can be used to assess the impacts of changing demographics and built environment characteristics on children’s physical activity levels.