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.     

A new logit‐artificial neural network ensemble for mode choice modeling: a case study for border transport

Logit model is one of the statistical techniques commonly used for mode choice modeling, while artificial neural network (ANN) is a very popular type of artificial intelligence technique used for mode choice modeling. Ensemble learning has evolved to be very effective approach to enhance the performance for many applications through integration of different models. In spite of this advantage, the use of ANN‐based ensembles in mode choice modeling is under explored. The focus of this study is to investigate the use of aforementioned techniques for different number of transportation modes and predictor variables. This study proposes a logit‐ANN ensemble for mode choice modeling and investigates its efficiency in different situations. Travel between Khobar‐Dammam metropolitan area of Saudi Arabia and Kingdom of Bahrain is selected for mode choice modeling. The travel on this route can be performed mainly by air travel or private vehicle through King Fahd causeway. The results show that the proposed ensemble gives consistently better accuracies than single models for multinomial choice problems irrespective of number of input variables. Copyright © 2015 John Wiley & Sons, Ltd.     

Cellpath: Fusion of cellular and traffic sensor data for route flow estimation via convex optimization

A new convex optimization framework is developed for the route flow estimation problem from the fusion of vehicle count and cellular network data. The issue of highly underdetermined link flow based methods in transportation networks is investigated, then solved using the proposed concept of cellpaths for cellular network data. With this data-driven approach, our proposed approach is versatile: it is compatible with other data sources, and it is model agnostic and thus compatible with user equilibrium, system-optimum, Stackelberg concepts, and other models. Using a dimensionality reduction scheme, we design a projected gradient algorithm suitable for the proposed route flow estimation problem. The algorithm solves a block isotonic regression problem in the projection step in linear time. The accuracy, computational efficiency, and versatility of the proposed approach are validated on the I-210 corridor near Los Angeles, where we achieve 90% route flow accuracy with 1033 traffic sensors and 1000 cellular towers covering a large network of highways and arterials with more than 20,000 links. In contrast to long-term land use planning applications, we demonstrate the first system to our knowledge that can produce route-level flow estimates suitable for short time horizon prediction and control applications in traffic management. Our system is open source and available for validation and extension.     

考虑空车调配协调的货物列车开行方案优化研究

为实现空车调配与货物列车开行方案协调优化，结合基本运行图架构与车流径路，构建货运时空服务拓展网络。考虑配空与装卸取送、集编发等环节的时间接续要求，节点与区段不对流空车要求，以重车流全程运送与空车配送等广义总费用最少为目标，建立整数规划弧路模型。针对既有算法设计局限性，结合重车或空车配空的时间接续要求，提出将不同的 k 短路重车流方案与空车配空方案相关联的改进可行解构造方法，设计混合差分进化求解算法。实例研究表明，考虑空车调配进行重车、空车流组织协调优化，能够减少空车走行费用，及时满足装车需求，有效保证作业车流配合中转车流集结编组及时挂线，提高方案可实施性。     

高精度陀螺全站仪在长大铁路隧道CPⅡ平面控制网分段建网测量中的应用

为了确保隧道贯通前CPⅡ分段建网的精度,保障隧道的顺利施工,以格库铁路阿尔金山隧道为工程背景,采用高精度陀螺全站仪对洞内CPⅡ平面控制网加测多条陀螺边,并提出了陀螺定向精度观测精度内检核、多条陀螺边定向复核的方法,对陀螺定向边位置的选择、陀螺方位角观测中误差、陀螺方位角观测值的应用区间进行探讨。应用高精度陀螺定向成果对比分析洞内CPⅡ分段控制网成果,从理论上探讨了加测陀螺边对CPⅡ控制网贯通预计精度的影响,解决了隧道贯通前CPⅡ平面控制网精度无法验证的问题,确保了CPⅡ分段建网的精度,总结出一套高精度陀螺全站仪在长大铁路隧道CPⅡ平面控制网分段建网测量中的应用方法,其中包括陀螺定向边间距约2 km、陀螺定向边采用对向观测、每测站数不小于4测回且观测方向平均测角中误差应小于仪器精度(3.6″)、依据陀螺观测计算方位角与导线推算方位角较差值并将成果应用划为三个应用区间、依据陀螺定向观测精度变换权重降低贯通预计值、优化约束平差计算方案等。     

货车轨迹数据在公路货运系统中应用研究综述

随着轨迹数据可获取性及精度的持续提高，货车轨迹数据被广泛应用于公路货运系统的 规划与管理中，同时，人工智能和大数据分析技术的快速发展也为公路货运系统研究带来新的机 遇与挑战。本文全面梳理并总结了公路货运轨迹数据应用领域的相关研究，从基于轨迹数据的 货运出行信息辨识、货运系统关键特征预测、货运轨迹数据进一步应用3个方面回顾现有文献的 研究目标、主要内容和研究方法。通过文献分析发现：货运出行信息辨识研究聚焦于货运停留 点、车辆和货物、活动出行模式等热点主题，但现有辨识方法多移植于旅客出行研究，需要更多地 考虑货运出行的独特特征。在货运系统关键特征预测方面，研究者主要针对货运行程时间、空间 位置、出行需求等主题展开研究，并证明了基于轨迹数据预测货运特征的可行性，但预测时空范 围较为局限，需要根据具体的货运任务、货车司机特征和货运政策进行深入研究。此外，轨迹数 据也被应用于货运出行路径选择行为、货运停车休息行为、行驶安全、货运排放和能耗分析、货运 政策评估等研究。最后，在总结现有研究不足的基础上，本文认为未来研究应重点将货运轨迹数 据与其他多源数据相结合，从3个关键技术进行突破：一是针对货运实践个体，重点探索高效货车 驾驶员的出行特征和出行模式，并在货运系统中进行推广应用；二是针对交通运输新技术和新形 势，重点开发和优化自动驾驶技术和重大应急事件影响下的货运组织模式与策略；三是针对货运 供需关系及匹配机制，重点研究货运全流程供需状态辨识与预测，并结合深度学习等方法训练和 开发智能供需匹配模型，从而优化货运系统调度，助力社会散乱运力资源整合，提高货运系统的 综合效率。     

Capturing correlation with a mixed recursive logit model for activity-travel scheduling

Representing activity-travel scheduling decisions as path choices in a time–space network is an emerging approach in the literature. In this paper, we model choices of activity, location, timing and transport mode using such an approach and seek to estimate utility parameters of recursive logit models. Relaxing the independence from irrelevant alternatives (IIA) property of the logit model in this setting raises a number of challenges. First, overlap in the network may not fully characterize perceptual correlation between paths, due to their interpretation as activity schedules. Second, the large number of states that are needed to represent all possible locations, times and activity combinations imposes major computational challenges to estimate the model. We combine recent methodological developments to build on previous work by Blom Västberg et al. (2016) and allow to model complex and realistic correlation patterns in this type of network. We use sampled choices sets in order to estimate a mixed recursive logit model in reasonable time for large-scale, dense time-space networks. Importantly, the model retains the advantage of fast predictions without sampling choice sets. In addition to estimation results, we present an extensive empirical analysis which highlights the different substitution patterns when the IIA property is relaxed, and a cross-validation study which confirms improved out-of-sample fit.     

How the environmental impact affects the design of logistics networks based on cost minimization

The degree of centralization of the logistics network is one of the important strategic decisions to take. This issue has often been studied but mainly from a cost minimization focus. This research studies the influence of incorporating the environmental impact in logistics network design models, comparing the results with those obtained considering cost minimization only. A fuzzy biobjective optimization model is used to carry out a number of experiments. By weighting the relative importance of the two objectives, the differences with respect to the base cost minimization solution can be ascertained. Our results show that in general more material is moved through warehouses when the environmental impact is considered and more decentralized facilities are opened.     

基于神经网络对电控柴油机运行特性影响因素仿真研究

基于AVL-BOOST软件仿真平台建立某船用四缸柴油机仿真模型,标定后的模型进行柴油机全工况仿真计算.仿真出来的3 200组数据作为人工神经网络输入数据,采用贝叶斯统计方法对网络进行训练建立2层的反馈神经网络仿真模型.并分别通过实验、AVL-BOOST和神经网络数据曲线的对比分析,验证人工神经网络预测的准确性.利用验证好的人工神经网络模型预测进排气压力对柴油机转矩的影响,以及预测压缩比和供油定时对柴油机排放性能和动力性能的影响,最后利用扰动法分析不同工况下柴油机各个参数对柴油机性能的影响程度.     

The value of options for time charterparty extension: an artificial neural networks (ANN) approach

The most frequently associated options in the physical shipping market are options to extend the charter period on time charters and additional shipment options on contracts of affreightment. The value of freight options, in practice, is estimated mostly by referring to forward curves. An option on freight has different properties from its financial counterparts, and the straightforward adoption of theoretical models does not produce promising results. In this paper, extension options, which have the property of options on futures, were transformed into regular European options before the application of the Black-Scholes model (BSM). The efficient market hypothesis, which justifies the parity of the performance of a long-term charter to that of repetitive short-term charters, worked as the basis for the transformation. The option values determined by the BSM were compared with actual realized values. Additionally, the artificial neural networks (ANN) was employed to derive the option values. This study is meaningful as the first-time application of both the closed-form solution and the ANN to the valuation of physical freight options. The research results can contribute to the quality of chartering decisions. The results could also be used in quantifying credit risk, as extension options tend to be granted to charterers with more creditability.