A hybrid simulation-assignment modeling framework for crowd dynamics in large-scale pedestrian facilities

This paper presents a hybrid simulation-assignment modeling framework for studying crowd dynamics in large-scale pedestrian facilities. The proposed modeling framework judiciously manages the trade-off between ability to accurately capture congestion phenomena resulting from the pedestrians’ collective behavior and scalability to model large facilities. We present a novel modeling framework that integrates a dynamic simulation-assignment logic with a hybrid (two-layer or bi-resolution) representation of the facility. The top layer consists of a network representation of the facility, which enables modeling the pedestrians’ route planning decisions while performing their activities. The bottom layer consists of a high resolution Cellular Automata (CA) system for all open spaces, which enables modeling the pedestrians’ local maneuvers and movement decisions at a high level of detail. The model is applied to simulate the crowd dynamics in the ground floor of Al-Haram Al-Sharif Mosque in the City of Mecca, Saudi Arabia during the pilgrimage season. The analysis illustrates the model’s capability in accurately representing the observed congestion phenomena in the facility.     

Turning meter transactions data into occupancy and payment behavioral information for on-street parking

Over 95% of on-street paid parking stalls are managed by parking meters or kiosks. By analyzing meter transactions data, this paper provides a methodology to estimate on-street time-varying parking occupancy and understand payment behavior in an effective and inexpensive way. We propose a probabilistic payment model to simulate individual payment and parking behavior for each parker. Aggregating the payment/parking of all transactions leads to time-varying occupancy estimation. Two data sets are used to evaluate the methodology, parking spaces near Carnegie Mellon University (CMU) campus, and near the Civic Center in San Francisco. The proposed model generally provides reliable estimations of occupancies at a low error rate and substantially outperforms other naive models in the literature. From the results of the experiments we find that people generally tend to slightly underpay in CMU area, whereas for Civic Center area, payment behavior varies by time of day and day of week. For Fridays, people generally tend to overpay and stay longer in the mornings, compared to underpaying and parking for shorter durations in the late afternoons. Parkers’ payment behavior, in general, is more variable and noisier around Civic Center than around CMU. Moreover, we explore the effective granularity, defined as the highest spatial resolution for this model to perform reliably. For CMU areas, the effective granularity is around 10–20 spaces for each block of streets, while it is 150–200 spaces for the Civic Center area due to more random parking behavior.     

Data-driven spatio-temporal discretization for pedestrian flow characterization

We propose a novel approach to pedestrian flow characterization. The definitions of density, flow and velocity existing in the literature are extended through a data-driven spatio-temporal discretization framework. The framework is based on three-dimensional Voronoi diagrams. Synthetic data is used to empirically investigate the performance of the approach and to illustrate its advantages. Our approach outperforms the considered approaches from the literature in terms of the robustness with respect to the simulation noise and with respect to the sampling frequency. Additionally, the proposed approach is by design (i) independent from an arbitrarily chosen discretization; (ii) appropriate for the multidirectional composition of pedestrian traffic; (iii) able to reflect the heterogeneity of the pedestrian population; and (iv) applicable to pedestrian trajectories described either analytically or as a sample of points.     

区段占用状态对联锁系统的 影响研究

基于通信的列车控制系统 CBTC 得到了广泛应用,并成为城市轨道交通信号系统的新标准,相对既往信 号系统具有列车追踪间隔时间短的显著优势,这与信号系统对轨道区段的划分方式密切相关。CBTC 系统通过将 物理区段细分为多个较短的逻辑区段,使得同一个大物理区段可以容纳多列车追踪运行,但同时也引入了新的问 题,需处理两种区段状态;而区段占用状态有两个信息来源,在特定情况下会出现两者信息不一致的情况,造成 信号故障关闭或进路不能正常解锁,影响运营效率。为了更可靠地获得区段占用状态,从计轴故障占用、列车定 位误差、系统延时方面对两种区段状态信息不一致的原因进行研究,分析具体场景中对区段状态的处理方法及存 在的问题,并提出在“区段融合”基础上增加“列车跨压信号机信息”或“即时占用,延时出清”判断策略,不 仅确保了信号系统的安全性和可靠性,还提高了列车运行效率。     

高速铁路路基微冻胀填料冻胀发育机制研究

为了揭示微冻胀填料的冻胀发育机制,解决寒区高速铁路路基冻胀问题,从理论上分析了微冻胀填料的组成,各组分的冻胀特点,在此基础上分析了填充料的受力及水分迁移情况。结果表明:微冻胀填料主要由骨架颗粒与填充料组成,其中填充料包括细骨料与细颗粒,当无外界约束作用或填充料膨胀力大于外界约束作用时,微冻胀填料将表现为宏观冻胀。微冻胀填料内部离散性较大,水分迁移通道不连续,由此造成冻结为"原位冻胀",细颗粒冻胀是微冻胀填料冻胀的主要原因,细颗粒冻胀时水分迁移造成空间体积占位,加大了冻胀程度。结论:微冻胀填料冻胀主要由细颗粒冻胀及其水分迁移引起。     

交通灯控制下基于元胞自动机的行人过街模型研究

本文提出了一种交通灯控制下的基于元胞自动机的行人过街穿越模型,该模型由行人元胞子模型、机动车元胞子模型及相互作用子模型三部分组成,用于分析在带有人行横道的一维道路上,由行人与机动车构成的混和文通流的特性。该模型着重考虑了行人的行为特性,在考虑诸多实际影响因素的情况下,研究了行人的产生概率,行人不遵守交通规则的概率、行人群效应等对于车流量、行人流量和等待时间的影响,并据此提出混和交通流情况下城市交通管理方案,以保证行人安全,提高通行效率,减少城市拥堵。