基于自注意力机制与图自编码器的路网交通流数据修复模型

针对城市交通流数据修复问题，提出一种基于图卷积网络和多头自注意力机制的自注意 力图自编码器模型。该模型包括基于拓扑图结构和图信号捕获交通流时空关联性的 STGCN (Spatial-temporal Graph Convolutional Networks)网络。在该网络中使用 LSTM(Long Short-Term Memory)网络学习数据中时序规律，通过注意力网络计算道路自注意力及一阶临近道路注意力系 数，用图卷积网络对图信号重组，达到对缺失数据的精确修复。同时，采用多头自注意力网络计 算数据的注意力权值并对数据重组，捕获交通流数据中的二阶及高阶临近道路空间关联性，提取 已知数据与缺失数据的时间关系，以残差链的形式加入到模型中，作为对STGCN功能的补充。 基于真实数据的实验表明，在多种缺失模式和缺失率下，该模型能够学习路网拓扑关系，捕获数 据中的时间规律性和时空关联性，有效地修复缺失数据。     

基于网络交通分配方法族谱的交通分配 一体化技术与工程应用

为满足交通规划、建设与管理等应用场景对交通分配多样化的需求，结合目前交通分配方 法族谱中的众多模型与方法，本文构建能够满足族谱中所有交通分配特征的一体化交通分配技 术框架，提出交通网络交通分配一体化技术体系，并将该体系嵌入交通分析平台软件“交运之星- TranStar”中。该技术体系包括：“模型关键参数”“交通阻抗函数”“交通网络交通分配基础模型与 快速算法”3部分模块组合的分析模型一体化；面向步行、自行车、机动车及公共交通等多模式交 通网络的分析对象一体化；针对城市土地开发，交通网络建设，交通管理控制，公共交通系统，以 及交通政策制定等应用场景一体化。选取南京市道路网络和公交网络进行实证分析。结果表 明，本文提出的交通分配一体化技术具有处理超万节点多模式交通网络的能力，对各类交通模 式、典型业务场景的分析结果可为城市交通系统规划、建设与管理提供决策支持。     

Optimal development of alternative fuel station networks considering node capacity restrictions

A potential solution to reduce greenhouse gas (GHG) emissions in the transport sector is the use of alternative fuel vehicles (AFV). As global GHG emission standards have been in place for passenger cars for several years, infrastructure modelling for new AFV is an established topic. However, as the regulatory focus shifts towards heavy-duty vehicles (HDV), the market diffusion of AFV-HDV will increase as will planning the relevant AFV infrastructure for HDV. Existing modelling approaches need to be adapted, because the energy demand per individual refill increases significantly for HDV and there are regulatory as well as technical limitations for alternative fuel station (AFS) capacities at the same time. While the current research takes capacity restrictions for single stations into account, capacity limits for locations (i.e. nodes) – the places where refuelling stations are built such as highway entries, exits or intersections – are not yet considered. We extend existing models in this respect and introduce an optimal development for AFS considering (station) location capacity restrictions. The proposed method is applied to a case study of a potential fuel cell heavy-duty vehicle AFS network. We find that the location capacity limit has a major impact on the number of stations required, station utilization and station portfolio variety.     

基于BP-DS证据理论的航标状态识别模型研究

内河航标标示了可航行水域的边界,是保障内河船舶安全航行的重要前提。面向内河航标不同故障模式,构建一种基于BP-DS证据理论的航标状态识别模型。采用两个BP神经网络分别训练航标基础属性数据与环境因素数据,计算独立的证据值;通过DS证据理论实现航标运行状态识别结果的决策融合。结果表明,基于BP-DS证据理论的航标状态识别模型精度达到80%以上,高于传统经验阈值判别法。     

Prioritizing road extension projects with interdependent benefits under time constraint

Since transportation projects are costly and resources are limited, prioritizing or sequencing the projects is imperative. This study was inspired by a client who asked: “I have tens of approved road extension projects, but my financial resources are limited. I cannot construct all the projects simultaneously, so can you help me prioritize my projects?” To address this question, the benefits and costs of all the possible scenarios must be known. However, the impacts (or benefit) of road extension projects are highly interdependent, and in sizable cases cannot be specified thoroughly. We demonstrate that the problem is analogous to the Traveling Salesman Problem (TSP). Dynamic change in travel demand during construction is another aspect of the complexity of the problem. The literature is yet to provide efficient methods for large cases. To this end, we developed a heuristic methodology in which the variation of travel demand during the construction period is considered. We introduce a geometrical objective function for which a solution-finding policy based on “gradient maximization” is developed. To address the projects’ interdependency, a special neural network (NN) model was devised. We developed a search engine hybridized of Ant Colony and Genetic Algorithm to seek a solution to the TSP-like problem on the NN based on gradient maximization. The algorithm was calibrated and applied to real data from the city of Winnipeg, Canada, as well as two cases based on Sioux-Falls. The results were reliable and identification of the optimum solution was achievable within acceptable computational time.     

Competitiveness of container terminal operating companies in South Korea and the industry–university–government network

Burgeoning container port facilities have fostered intensified competition among container terminal operating companies (CTOCs). However, despite research into their survival strategies which identified antecedents of competitiveness including hard factors such as facilities, available cargo and cargo processing ability, softer factors spanning human resource management, networks and strategic alliances with universities and government agencies in industry–university–government (I–U–G) networks have been overlooked. This study aims to examine both hard and softer antecedents of competitiveness as perceived by 152 professionals in South Korean CTOCs; empirical relationships among these antecedents, I–U–G networks, and competitiveness itself; and the significance of the I–U–G network in establishing and improving competitiveness. Posited antecedents of competitiveness included human resources, facilities, service quality, customer orientation, reputation, and government support policy as independent variables; the I–U–G network as a moderating variable; and competitiveness as a dependent variable. Empirical structural relationships revealed that excepting government support policy, each variable significantly affected CTOC competitiveness. Further, the I–U–G network moderated the relationships between the antecedents of competitiveness and competitiveness. Because an effective I–U–G network was pivotal in controlling CTOC competitiveness, improved competitiveness requires not only differentiation of human resources, facilities, service quality, customer orientation, and reputation factors but also I–U–G network developments.     

A modified reinforcement learning algorithm for solving coordinated signalized networks

This study proposes Reinforcement Learning (RL) based algorithm for finding optimum signal timings in Coordinated Signalized Networks (CSN) for fixed set of link flows. For this purpose, MOdified REinforcement Learning algorithm with TRANSYT-7F (MORELTRANS) model is proposed by way of combining RL algorithm and TRANSYT-7F. The modified RL differs from other RL algorithms since it takes advantage of the best solution obtained from the previous learning episode by generating a sub-environment at each learning episode as the same size of original environment. On the other hand, TRANSYT-7F traffic model is used in order to determine network performance index, namely disutility index. Numerical application is conducted on medium sized coordinated signalized road network. Results indicated that the MORELTRANS produced slightly better results than the GA in signal timing optimization in terms of objective function value while it outperformed than the HC. In order to show the capability of the proposed model for heavy demand condition, two cases in which link flows are increased by 20% and 50% with respect to the base case are considered. It is found that the MORELTRANS is able to reach good solutions for signal timing optimization even if demand became increased.     

Driver behavior formulation in intersection dilemma zones with phone use distraction via a logit-Bayesian network hybrid approach

Use of cellular phone while driving is one of the top contributing factors that induce traffic crashes, resulting in significant loss of life and property. A dilemma zone is a circumstance near signalized intersections where drivers hesitate when making decisions related to their driving behaviors. Therefore, the dilemma zone has been identified as an area with high crash potential. This article utilizes a logit-based Bayesian network (BN) hybrid approach to investigate drivers' decision patterns in a dilemma zone with phone use, based on experimental data from driving simulations from the National Advanced Driving Simulator (NADS). Using a logit regression model, five variables were found to be significant in predicting drivers' decisions in a dilemma zone with distractive phone tasks: older drivers (50–60 years old), yellow signal length, time to stop line, handheld phone tasks, and driver gender. The identified significant variables were then used to train a BN model to predict drivers' decisions at a dilemma zone and examine probabilistic impacts of these variables on drivers' decisions. The analysis results indicate that the trained BN model was effective in driver decision prediction and variable influence extraction. It was found that older drivers, a short yellow signal, a short time to stop line, nonhandheld phone tasks, and female drivers are factors that tend to result in drivers proceeding through intersections in a dilemma zone with phone use distraction. These research findings provide insight in understanding driver behavior patterns in a dilemma zone with distractive phone tasks.     

基于多层次模糊评价法的船闸引航道基坑开挖风险评估

船闸项目建设过程中的风险因素复杂多样,特别是株洲二线船闸引航道基坑施工面临着与原有一线船闸近、临水度汛造成工期紧等特点。针对这一情况,将层次分析法与模糊综合评价法相结合,采用BIM技术实时复核项目土石方开挖工程量,使风险评价过程更加客观。模糊评价风险评估结果表明,株洲二线船闸引航道基坑开挖的风险较大,存在的主要风险原因要素是:工程环境风险现场管理风险施工作业风险技术因素风险组织与管理等其他风险。评价结果可作为工程实践参考,对船闸工程项目施工的风险控制有一定的参考价值。     

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.