A stochastic analysis of highway capacity: Empirical evidence and implications

This paper presents a stochastic characterization of highway capacity and explores its implications on ramp metering control at the corridor level. The stochastic variation of highway capacity is captured through a Space–Time Autoregressive Integrated Moving Average (STARIMA) model. It is identified following a Seasonal STARIMA model (0, 0, 2₃) × (0, 1, 0)₂, which indicates that the capacities of adjacent locations are spatially–temporally correlated. Hourly capacity patterns further verify the stochastic nature of highway capacity. The goal of this paper is to study (1) how to take advantage of the extra information, such as capacity variation, and (2) what benefits can be gained from stochastic capacity modeling. The implication of stochastic capacity is investigated through a ramp metering case study. A mean–standard deviation formulation of capacity is proposed to achieve the trade-off between traffic operation efficiency and robustness. Following that, a modified stochastic capacity-constraint ZONE ramp metering scheme embedded cell transmission model algorithm is introduced. The numerical experiment suggests that considering capacity variation information would alleviate the spillback effect and improve throughput. Monte Carlo simulation further supports this argument. This study helps verify and characterize the stochastic nature of capacity, validates the benefits of using capacity variation information, and thus enhances the necessity of implementing stochastic capacity in traffic operation.     

Signal coordination model for local arterial with heavy bus flows

Conventional Transit Signal Priority (TSP) controls often reach the limitation for arterials accommodating heavy bus flows since the priority function can significantly increase delay at minor streets. Under such conditions, a proper signal progression plan that accounts for the benefits of buses may offer the potential to improve the reliability of bus operations and increase the bus ridership. This study proposes a bus-based progression model to reduce the delay of buses on local arterials. Given the cycle length and green splits at each intersection, the bus-based progression model, grounded on the same notion as conventional signal progression methods, considers the operational characteristics of transit vehicles, such as the impact of bus dwell time and the capacity constraints at bus stops. Also, to deal with the stochastic nature of dwell time, this study introduces additional constraints to maximize the percentage of buses which can stay within the green band after leaving bus stops. Taking an arterial with five intersections and three two-way bus stops as an example, this study applies VISSIM as an unbiased tool for model evaluation. The simulation results demonstrate that the proposed model can significantly reduce bus passenger delays and the average person delays for the entire arterial, compared with the conventional progression models.     

Development of analytical procedure for selection of control measures to reduce congestions at various freeway bottlenecks

Variable speed limit (VSL) and ramp metering (RM) affect freeway traffic operations in different ways and, accordingly, result in different effects on system travel time. The primary objective of this study is to propose an analytical procedure to help determine which control measure should be selected given different freeway bottlenecks and traffic conditions. The bottlenecks considered included an isolated merge bottleneck, a merge bottleneck with a closely spaced upstream off-ramp, and a diverge bottleneck with a closely spaced upstream on-ramp. Two RM and a VSL control strategies were considered, including the ALINEA, ALINEA/Q and feedback based VSL. The maximum achievable improvements in system efficiency by various control measures were calculated and the results were tested using modified cell transmission models. A coordinated control strategy that combined ALINEA/Q and VSL control was also proposed. The effects of VSL and RM control on system travel time at different freeway bottleneck areas were compared to identify the applicable conditions of different control measures. The analytical procedure was proposed for the selection of control measures at different bottlenecks and the ex-ante estimation of control effects were also discussed.     

复合盾构土舱可视化实时监测系统研究

为了在复合盾构掘进过程中实时监测土舱的情况,获得刀盘的旋转状态、刀具的磨损状况、开挖地层的图像信息和渣土的流动特性,建立一套土舱可视化实时监测系统,该系统通过上位机监控系统实时监测前端设备的工作状态,并将摄像机采集的信号实时传输到视频采集系统。介绍该系统方案,重点探讨其结构设计、硬件设计、软件设计,并通过室内和现场试验进行验证该系统的有效性。结果表明,该系统可实现土舱的实时视频监控、录像、回放以及系统温度、湿度和压力的监测。     

Forecasting container throughput based on wavelet transforms within a decomposition-ensemble methodology: a case study of China

To improve predictive accuracy, new hybrid models are proposed for container throughput forecasting based on wavelet transforms and data characteristic analysis (DCA) within a decomposition-ensemble methodology. Because of the complexity and nonlinearity of the time series of container throughputs at ports, the methodology decomposes the original time series into several components, which are rather simpler sub-sequences. Consequently, difficult forecasting tasks are simplified into a number of relatively easier subtasks. In this way, the proposed hybrid models can improve the accuracy of forecasting significantly. In the methodology, four main steps are involved: data decomposition, component reconstruction based on the DCA, individual prediction for each reconstructed component, and ensemble prediction as the final output. An empirical analysis was conducted for illustration and verification purposes by using time series of container throughputs at three main ports in Bohai Rim, China. The results suggest that the proposed hybrid models are able to forecast better than do other benchmark models. Forecasting may facilitate effective real-time decision making for strategic management and policy drafting. Predictions of container throughput can help port managers make tactical and operational decisions, such as operations planning in ports, the scheduling of port equipment, and route optimization.     

波浪中过度加速度的非线性时域预报

国际海事组织(IMO)船舶设计建造分委会(SDC)第4次会议把过度加速度稳性直接评估的制定提上议程。然而,如何准确预报和评估波浪中的过度加速度依然是一个亟待解决的问题。文章针对此挑战,采用三维时域混合源法进行了波浪中过度加速度的直接预报和评估。首先,采用三维时域混合源法,建立了波浪中船舶大幅横摇运动和船体任意位置处横向加速度的非线性时域预报方法;其次,以4000TEU集装箱船为研究对象,开展规则波和不规则波中的大幅运动模型试验,对计算方法进行验证;然后,对比分析了3DOF(垂荡—纵摇—横摇)和4DOF(横荡—垂荡—纵摇—横摇)耦合数学模型的计算精度;最后,分析了船体横向加速度的影响因素。研究表明,考虑横荡影响的4DOF数学模型计算精度较高;数值计算结果和模型试验结果吻合良好,证明文中建立的非线性时域方法可有效预报波浪中的过度加速度,可用于IMO过度加速度衡准的制定,也可为船舶设计提供评估手段。此外,文中还研究了IMO薄弱性衡准草案中中国和德国联合提出的加速度简化计算方法的适用性,证明该简化计算方法具有一定的保守性,符合衡准的要求,可用于过度加速度薄弱性衡准计算。     

Assessment of factors associated with travel time reliability and prediction: an empirical analysis using probabilistic reasoning approach

Significant efforts have been made in modeling a travel time distribution and establishing measures of travel time reliability (TTR). However, the literature on evaluating the factors affecting TTR is not well established. Accordingly, this paper presents an empirical analysis to determine potential factors that are associated with TTR. This study mainly applies the Bayesian Networks model to assess the probabilistic association between road geometry, traffic data, and TTR. The results from this model reveal that land use characteristics, intersection factors, and posted speed limits are directly associated with TTR. Evaluating the strength of the association between TTR and the directly related variables, the log odds ratio analysis indicates that the land use factor has the highest impact (0.83) followed by the intersection factor (0.57). The findings from this study can provide valuable resources to planners and traffic operators in their decision-making to improve TTR with quantitative evidence.     

Application of Lagrangian relaxation approach to α-reliable path finding in stochastic networks with correlated link travel times

This study investigates the important problem of determining a reliable path in a stochastic network with correlated link travel times. First, the distribution of path travel time is quantified by using trip records from GPS probe vehicles. Second, the spatial correlation of link travel time is explicitly considered by using a correlation coefficient matrix, which is incorporated into the α-reliable path problem by Cholesky decomposition. Third, the Lagrangian relaxation based framework is used to handle the α-reliable path problem, by which the intractable problem with a non-linear and non-additive structure can be decomposed into several easy-to-solve problems. Finally, the path-finding performance of this approach is tested on a real-world network. The results show that 15 iterations of calculation can yield a small relative gap between upper and lower bounds of the optimal solution and the average running time is about 5 s for most OD settings. The applicability of α-reliable path finding is validated by a case study.     

Policy packages for modal shift and CO2 reduction in Lille,France

This paper proposes different policy scenarios to cut CO₂ emissions caused by the urban mobility of passengers. More precisely, we compare the effects of the ‘direct tool’ of carbon tax, to a combination of ‘indirect tools’ – not originally aimed at reducing CO₂ (i.e. congestion charging, parking charges and a reduction in public transport travel time) in terms of CO₂ impacts through a change in the modal split. In our model, modal choices depend on individual characteristics, trip features (including the effects of policy tools), and land use at origin and destination zones. Personal “CO₂ emissions budgets” resulting from the trips observed in the metropolitan area of Lille (France) in 2006 are calculated and compared to the situation related to the different policy scenarios. We find that an increase of 50% in parking charges combined with a cordon toll of €1.20 and a 10% travel time decrease in public transport services (made after recycling toll-revenues) is the winning scenario. The combined effects of all the policy scenarios are superior to their separate effects.     

Link travel time inference using entry/exit information of trips on a network

This paper studies link travel time estimation using entry/exit time stamps of trips on a steady-state transportation network. We propose two inference methods based on the likelihood principle, assuming each link associates with a random travel time. The first method considers independent and Gaussian distributed link travel times, using the additive property that trip time has a closed-form distribution as the summation of link travel times. We particularly analyze the mean estimates when the variances of trip time estimates are known with a high degree of precision and examine the uniqueness of solutions. Two cases are discussed in detail: one with known paths of all trips and the other with unknown paths of some trips. We apply the Gaussian mixture model and the Expectation–Maximization (EM) algorithm to deal with the latter. The second method splits trip time proportionally among links traversed to deal with more general link travel time distributions such as log-normal. This approach builds upon an expected log-likelihood function which naturally leads to an iterative procedure analogous to the EM algorithm for solutions. Simulation tests on a simple nine-link network and on the Sioux Falls network respectively indicate that the two methods both perform well. The second method (i.e., trip splitting approximation) generally runs faster but with larger errors of estimated standard deviations of link travel times.