寒区近海混凝土桥梁性能衰退机理与损伤行为评估方法

受到周期性潮汐变化的影响,中国寒区近海混凝土桥梁长期遭受海水冻融及多种耦合腐蚀作用,这严重影响了桥梁的服役性能与使用寿命。为了准确预测桥梁的性能衰退规律,针对桥梁所处环境,分析了桥梁结构的性能退化机理,并借助近场动力学方法在多尺度方面的优势,利用MATLAB-ABAQUS联合仿真方法建立了桥梁劣化损伤预测与评估方法。在此基础上,从混凝土海水冻融损伤出发,考虑温度分布特性、临界饱和度模型、孔隙结晶规律与孔隙压力理论,结合不同孔隙的结晶温度与孔隙累积,通过孔隙变形加载方式建立混凝土微观尺度的冻融循环数值模拟计算方法。随后,根据混凝土微观尺度冻融损伤的计算结果,建立考虑冻融损伤分布的RC桥墩等效数值模拟计算方法。最终,设计制作了RC桥墩节段,进行了海水环境下RC桥墩的冻融循环试验,并利用超声层析成像技术,得到RC桥墩的海水冻融损伤分布。试验与计算结果表明：通过与超声层析成像试验结果的对比,混凝土微观尺度计算模型可以很好地模拟冻融循环过程中孔隙累积与孔隙转化引起的混凝土冻融损伤行为;受到温度分布与相对饱和度的影响,当冻融大于50次后,试件出现明显的冻融损伤界限（冻融深度）,且随着冻融次数的增加,冻融深度的增速呈现先增大后减小的趋势。除此之外,等效计算模型的滞回曲线计算结果与试验结果吻合较好,随着冻融次数的增加,RC桥墩的峰值荷载逐渐减低,海水冻融对RC桥墩力学性能的影响极为严重,且建立的计算方法很好地预测了海水冻融作用下RC桥墩的力学性能退化。     

大跨连续刚构桥箱梁抗剪性能数值分析

以苏通大桥的大跨度连续刚构辅航道桥为工程背景,利用非线性有限元分析方法,建立中墩附近节段和跨中弯矩变号附近节段的三维实体单元模型,在考虑原有结构受力状况的条件下,对2个关键区段的抗剪性能进行数值分析,检验原有设计相应截面的抗剪承载力,得到相应结构安全系数、应力分布规律和截面破坏形态。     

The impacts of built environment on home-based work and non-work trips: An empirical study from Iran

This paper aims to explore the impact of built environment attributes in the scale of one quarter-mile buffers on individuals’ travel behaviors in the metropolitan of Shiraz, Iran. In order to develop this topic, the present research is developed through the analysis of a dataset collected from residents of 22 neighborhoods with variety of land use features. Using household survey on daily activities, this study investigates home-based work and non-work (HBW and HBN) trips. Structural equation models are utilized to examine the relationships between land use attributes and travel behavior while taking into account socio-economic characteristics as the residential self-selection. Results from models indicate that individuals residing in areas with high residential and job density, and shorter distance to sub-centers are more interested in using transit and non-motorized modes. Moreover, residents of neighborhoods with mixed land uses tend to travel less by car and more by transit and non-motorized modes to non-work destinations. Nevertheless, the influences of design measurements such as street density and internal connectivity are mixed in our models. Although higher internal connectivity leads to more transit and non-motorized trips in HBW model, the impacts of design measurements on individuals travel behavior in HBN model are significantly in contrast with research hypothesis. Our study also shows the importance of individuals’ self-selection impacts on travel behaviors; individuals with special socio-demographic attributes live in the neighborhoods with regard to their transportation patterns. The findings of this paper reveal that the effects of built environment attributes on travel behavior in origins of trips do not exactly correspond with the expected predictions, when it comes in practice in a various study context. This study displays the necessity of regarding local conditions of urban areas and the inherent differences between travel destinations in integrating land use and transportation planning.     

Freight Demand Management and the Potential of Receiver-Led Consolidation programs

The paper defines the field of Freight Demand Management (FDM), and positions it as an important component of transportation policy and management. To establish the rationale for FDM, the paper studies the effects of the agent interactions at the core of supply chains, and identifies the important role played by the receivers of supplies in determining when and how deliveries are made. The paper classifies the various modalities of FDM, and summarizes the real-life experiences of their implementation. To illustrate the potential of FDM, the paper analyzes Receiver-Led Consolidation (RLC) programs. The paper provides background on consolidation programs, and estimates a behavioral model to shed light on the factors explaining receivers’ interest in cargo consolidation. The resulting model is used to estimate expected participation in a RLC program in New York City. These results are complemented with freight-trip generation analyses, and a behavioral micro-simulation to estimate potential reductions in freight traffic and vehicle-miles-traveled. The results show that RLC programs could bring significant benefits to large metropolitan areas, reducing freight vehicle-miles-traveled and congestion levels.     

Validation of an agent-based building evacuation model with a school drill

An effective evacuation of buildings is critical to minimize casualties due to natural or anthropogenic hazards. Building evacuation models help in preparing for future events and shed light on possible shortcomings of current evacuation designs. However, such models are seldom compared or validated with real evacuations, which is a critical step in assessing their predictive capacities. This research focuses on the evacuation of a K-12 (kindergarten to 12th grade) school located within the tsunami inundation zone of Iquique, Chile. An agent-based evacuation model was developed to simulate the evacuation of approximately 1500 children and staff from the school during a global evacuation drill carried out for the entire city. The model simulates the motions of heterogeneous human agents, and the simulations were validated using video analysis of the real event. Resulting error estimations between predicted versus measured flow rates and evacuation times are 13.5% and 5.9%, respectively. The good agreement between the simulated and measured values can be attributed to the known distribution of students and staff at the start of the drill, and their known exposure to emergency preparedness protocols. However, the results presented herein show that this mathematical evacuation model can be used for logistical changes in the emergency planning.     

Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment

Under the Connected Vehicle environment where vehicles and road-side infrastructure can communicate wirelessly, the Advanced Driver Assistance Systems (ADAS) can be adopted as an actuator for achieving traffic safety and mobility optimization at highway facilities. In this regard, the traffic management centers need to identify the optimal ADAS algorithm parameter set that leads to the optimization of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. Once the ADAS-equipped drivers implement the optimal parameter set, they become active agents that work cooperatively to prevent traffic conflicts, and suppress the development of traffic oscillations into heavy traffic jams. Measuring systematic effectiveness of this traffic management requires am analytic capability to capture the quantified impact of the ADAS on individual drivers’ behaviors and the aggregated traffic safety and mobility improvement due to such an impact. To this end, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through a multi-objective optimization approach that uses the Genetic Algorithm. The developed methodology is tested at a freeway facility under low, medium and high ADAS market penetration rate scenarios. The case study reveals that fine-tuning the ADAS algorithm parameter can significantly improve the throughput and reduce the traffic delay and conflicts at the study site in the medium and high penetration scenarios. In these scenarios, the ADAS algorithm parameter optimization is necessary. Otherwise the ADAS will intensify the behavior heterogeneity among drivers, resulting in little traffic safety improvement and negative mobility impact. In the high penetration rate scenario, the identified optimal ADAS algorithm parameter set can be used to support different control objectives (e.g., safety improvement has priority vs. mobility improvement has priority).     

A gradient boosting logit model to investigate driver’s stop-or-run behavior at signalized intersections using high-resolution traffic data

Driver’s stop-or-run behavior at signalized intersection has become a major concern for the intersection safety. While many studies were undertaken to model and predict drivers’ stop-or-run (SoR) behaviors including Yellow-Light-Running (YLR) and Red-Light-Running (RLR) using traditional statistical regression models, a critical problem for these models is that the relative influences of predictor variables on driver’s SoR behavior could not be evaluated. To address this challenge, this research proposes a new approach which applies a recently developed data mining approach called gradient boosting logit model to handle different types of predictor variables, fit complex nonlinear relationships among variables, and automatically disentangle interaction effects between influential factors using high-resolution traffic and signal event data collected from loop detectors. Particularly, this research will first identify a series of related influential factors including signal timing information, surrounding traffic information, and surrounding drivers’ behaviors using thousands drivers’ decision events including YLR, RLR, and first-to-stop (FSTP) extracted from high-resolution loop detector data from three intersections. Then the research applies the proposed data mining approach to search for the optimal prediction model for each intersection. Furthermore, a comparison was conducted to compare the proposed new method with the traditional statistical regression model. The results show that the gradient boosting logit model has superior performance in terms of prediction accuracy. In contrast to other machine learning methods which usually apply ‘black-box’ procedures, the gradient boosting logit model can identify and rank the relative importance of influential factors on driver’s stop-or-run behavior prediction. This study brings great potential for future practical applications since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly.     

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.     

Safety impacts of signalized lane merge control at highway work zones

Lane closures due to highway work zones present many challenges to the goal of ensuring smooth traffic operations and a safe environment for both drivers and workers. Late merge behavior at a work zone closure is a dangerous behavior that impacts the traffic conflicts upstream of work zone closures. This paper analyzes the safety impacts of using a signalized lane control strategy at the work zone merge points. To achieve the objective of this research, a field study has been conducted at a highway work zone to collect traffic and driver behavior data, and a two-stage, simulation-based approach is used to analyze the safety impacts of implementing a signalized lane merge control strategy at the studied work zone. In the first stage, micro-simulation models are developed and calibrated based on field data to generate vehicle trajectories. In the second stage, the U.S. Federal Highway Administration’s Surrogate Safety Assessment Model is employed to identify potential conflicts under different traffic conditions. The paper concludes that a proposed signal control device could significantly reduce lane-change conflicts at work zone merge points. In addition, recommendations on the signal cycle length and timing splits are provided.     

Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks

Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts.