Boundedly rational user equilibria (BRUE): Mathematical formulation and solution sets

Boundedly rational user equilibria (BRUE) represent traffic flow distribution patterns where travellers can take any route whose travel cost is within an ‘indifference band’ of the shortest path cost. Those traffic flow patterns satisfying the above condition constitute a set, named the BRUE solution set. It is important to obtain all the BRUE flow patterns, because it can help predict the variation of the link flow pattern in a traffic network under the boundedly rational behavior assumption. However, the methodology of constructing the BRUE set has been lacking in the established literature. This paper fills the gap by constructing the BRUE solution set on traffic networks with fixed demands. After defining ε-BRUE, where ε is the indifference band for the perceived travel cost, we formulate the ε-BRUE problem as a nonlinear complementarity problem (NCP), so that a BRUE solution can be obtained by solving a BRUE–NCP formulation. To obtain the BRUE solution set encompassing all BRUE flow patterns, we propose a methodology of generating acceptable path set which may be utilized under the boundedly rational behavior assumption. We show that with the increase of the indifference band, the acceptable path set that contains boundedly rational equilibrium flows will be augmented, and the critical values of indifference band to augment these path sets can be identified by solving a family of mathematical programs with equilibrium constraints (MPEC) sequentially. The BRUE solution set can then be obtained by assigning all traffic demands to the acceptable path set. Various numerical examples are given to illustrate our findings.     

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting.     

Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

In today’s world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s personal-fleet evolution.Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings.Twenty five-year simulations of Austin’s household vehicle fleet suggest that, under all scenarios modeled, Austin’s vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet’s VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO₂ emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO₂ emissions by 30% (relative to trend).Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year.In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.     

Development and evaluation of a statistically reliable traffic counting program

This paper reviews the existing procedures to compute the seasonal factors, growth factors, and the number of automatic traffic recorder stations (ATRs) for developing a statistically reliable traffic counting program. The review is based on, (i) theoretical evaluation of the procedures, (ii) analysis of the results obtained from applying these procedures to extensive real life data, and (iii) experience of past researchers with these methods. The paper primarily deals with existing statistical procedures for determining seasonal factors, since it is believed that these factors play the most important role in estimating Annual Average Daily Traffic (AADTs) from short counts. The procedure of cluster analysis (which is most commonly used to determine seasonal factors) is critically reviewed. The shortcomings of this method motivated the development of a different statistical procedure, based on regression analysis, to determine seasonal factors. It is shown that this newly developed method gives better results than the cluster analysis techniques. Furthermore, separate sections are dedicated to a thorough analysis of determination of number of ATR stations and computation of growth factors.     

GTM法在公路沥青路面施工中的应用研究

结合GTM法技术在河北省张石高速公路中的实践应用,介绍GTM法的设计原理、施工工艺和质量控制体系,尤其是对施工过程质量控制点和沥青路面最大密实度标准的研究分析,有效地降低了工程施工费用,提高了路面的服务寿命。     

公路安全保障工程对下坡急弯路段车辆运行特征影响分析

为指导公路安全保障工程的深入开展，进行了大量追踪工作。选定国道108某路段，对公路安全保障工程前后下坡急弯路段车辆运行特征数据进行了观测，主要包括公路安全保障工程实施前后事故情况、车速情况、刹车点位置情况等。基于对上述观测数据的分析，发现在该下坡上公路安全保障工程的实施使得车辆运行特征发成了变化，标志、薄层铺装等设施都起到了预期的安全效果。最后，对薄层铺装等设施的合理设计提出建议。     

复合式路面沥青混凝土加铺层设计方法探讨

目前,国内在对CC-AC复合式路面加铺沥青混凝土层时,仍然参照沥青混凝土路面或水泥混凝土路面加铺沥青混凝土层的设计方法,至今没有一个效果非常令人满意。在广泛收集并分析国内外原有沥青混凝土路面加铺沥青混凝土层有关资料的基础上,综合弹性层状体系与有效厚度法,设计时考虑材料的非线性以及旧路面破损或者其他缺陷对加铺层使用寿命的可能影响,探讨了基于FW D检测和有效厚度的弹性层状体系CC-AC复合式路面加铺层设计方法。该方法将原复合式路面的面层等效为水泥混凝土板,按照水泥混凝土路面加铺沥青混凝土层进行设计,并验算沥青混凝土加铺层的抗剪稳定性,验算旧水泥混凝土板与沥青混凝土层界面之间的抗剪稳定性。最后,还研究了复合式路面加铺层典型结构。     

孔压静力触探(CPTU)探求高速公路软基固结系数研究

基于孔压静力触探(CPTU)的原理,简要介绍了利用CPTU试验求解地基土固结系数的理论方法,并以Tor-stensson理论模型为例,详细叙述了利用CPTU试验资料探求地基土固结系数的方法和步骤。结合连盐高速公路灌云三标的CPTU试验进行了实例计算,结果表明,现场所得的固结系数较室内试验的固结系数大1~2个数量级,与其他研究者的结果一致;在淤泥土层内,现场固结系数较室内试验所得的固结系数变化小,与现场资料反演值很接近;若采用此方法为实际工程提供设计固结系数时,进行修正后效果会更好。     

基于非参数回归的快速路行程速度短期预测算法

基于北京市快速路上的检测器所采集的历史数据，经过数据筛选，剔除判别，小波滤噪平稳处理，聚类分析等过程，建立了交通状态演变系列的历史样本数据库。基于所构建的历史数据库，通过数值试验，确定了状态向量、距离匹配原则，K近邻值等参量，构建了一种基于K近邻的非参数回归短时交通预测模型，实现了对路段行程速度的短时预测。最后，利用随机选取的历史数据系列对预测模型的精度进行了检验。结果表明，预测算法的精度可以达到90％以上，可以很好地满足ITS应用系统对于交通预测数据的精度要求。     

汽车电动助力转向系统

对目前汽车配置的助力转向系统做了简要比较,指出了机械液压和电子液压助力的缺点,介绍了电动助力转向系统的构成、工作原理、以及主要设计参数和控制特性。此系统的优点是节约能源,提高操作稳定性,是将来动力转向的发展趋势。