Exploring public transport sustainability with neutrosophic logic

Sustainable transportation is a significant component of overall sustainable development. Increasingly, evaluation and decision-making with respect to major complex projects (for example, transportation and land use projects), a multiple attribute perspective is taken. This paper illustrates a multiple attribute decision-making approach for selecting sustainable public transportation systems under uncertainty, that is, with partial or incomplete information represented by single-valued neutrosophic sets (SVNSs). A SVNS is a generalization of a classical set, a fuzzy set, and an intuitionistic fuzzy set. Here, SVNSs and SVNS connectives are illustrated in the context of a ‘Public Transit Sustainable Mobility Analysis Tool’ (PTSMAT) which involves a composite (multiple attribute) sustainability index. A case study of PTSMAT is provided for the UBC Corridor study in Vancouver, Canada. As expected, similar results are obtained to the original study, though the neutrosophic formalism opens a wide range of possibilities for recognition of uncertainty in sustainability assessment.     

A methodology to control urban traffic noise under the constraint of environmental capacity: A case study of a double-decision optimization model

This paper presents a novel methodology to control urban traffic noise under the constraint of environmental capacity. Considering the upper limits of noise control zones as the major bottleneck to control the maximum traffic flow is a new idea. The urban road network traffic is the mutual or joint behavior of public self-selection and management decisions, so is a typical double decision optimization problem.The proposed methodology incorporates theoretically model specifications. Traffic noise calculation model and traffic assignment model for O–D matrix are integrated based on bi-level programming method which follows an iterated process to obtain the optimal solution. The upper level resolves the question of how to sustain the maximum traffic flow with noise capacity threshold in a feasible road network. The user equilibrium method is adopted in the lower layer to resolve the O–D traffic assignment.The methodology has been applied to study area of QingDao, China. In this illustrative case, the noise pollution level values of optimal solution could satisfy the urban environmental noise capacity constraints. Moreover, the optimal solution was intelligently adjusted rather than simply reducing the value below a certain threshold. The results indicate that the proposed methodology is feasible and effective, and it can provide a reference for a sustainable development and noise control management of the urban traffic.     

指挥决策问题的可拓模型研究

针对指挥决策中含有的大量矛盾、不相容问题,寻求可能的解决途径。引入可拓学理论和方法,采取物元变换思想,对决策系统进行相容度分析,将其中的矛盾、不相容问题转换为相容问题进行处理,最后根据评价准则对方案进行损耗和效果评价。如果决策结果满足决策的目标与要求,就作为决策方案。编队防空仿真实验进一步验证了模型的可行性和合理性,为解决指挥决策中的对立问题提供了依据。     

A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul

This paper provides a hierarchical customer satisfaction framework to measure rail transit lines’ performances in Istanbul. The problems related to rail transit line systems are addressed via customer satisfaction surveys. Then, a framework is proposed combining statistical analysis, fuzzy analytic hierarchy process, trapezoidal fuzzy sets and Choquet integral to evaluate customer satisfaction levels. Next, the criteria need to be improved are determined and specific recommendations to enhance the operation for specific lines are suggested. The proposed framework provides directions for the future investments and it also can be used at a more macroscopic level to determine the operational deficiencies. Furthermore, it can be generalized and applied to complex decision making problems that include uncertain and subjective data or vague information.     

Airline-driven ground delay programs: A benefits assessment

Three decades of research studies in ground delay program (GDP) decision-making, and air traffic flow management in general, have produced several analytical models and decision support tools to design GDPs with minimum delay costs. Most of these models are centralized, i.e., the central authority almost completely decides the GDP design by optimizing certain centralized objectives. In this paper, we assess the benefits of an airline-driven decentralized approach for designing GDPs. The motivation for an airline-driven approach is the ability to incorporate the inherent differences between airlines when prioritizing, and responding to, different GDP designs. Such differences arise from the airlines’ diverse business objectives and operational characteristics. We develop an integrated platform for simulating flight operations during GDPs, an airline recovery module for mimicking the recovery actions of each individual airline under a GDP, and an algorithm for fast solution of the recovery problems to optimality. While some of the individual analytical components of our framework, model and algorithm share certain similarities with those used by previous researchers, to the best of our knowledge, this paper presents the first comprehensive platform for simulating and optimizing airline operations under a GDP and is the most important technological contribution of this paper. Using this framework, we conduct detailed computational experiments based on actual schedule data at three of the busiest airports in the United States. We choose the recently developed Majority Judgment voting and grading method as our airline-driven decentralized approach for GDP design because of the superior theoretical and practical benefits afforded by this approach as shown by multiple recent studies. The results of our evaluation suggest that adopting this airline-driven approach in designing the GDPs consistently and significantly reduces airport-wide delay costs compared to the state-of-the-research centralized approaches. Moreover, the cost reduction benefits of the resultant airline-driven GDP designs are equitably distributed across different airlines.     

Energy impact evaluation for eco-routing and charging of autonomous electric vehicle fleet: Ambient temperature consideration

This paper studies the heterogeneous energy cost and charging demand impact of autonomous electric vehicle (EV) fleet under different ambient temperature. A data-driven method is introduced to formulate a two-dimensional grid stochastic energy consumption model for electric vehicles. The energy consumption model aids in analyzing EV energy cost and describing uncertainties under variable average vehicle trip speed and ambient temperature conditions. An integrated eco-routing and optimal charging decision making framework is designed to improve the capability of autonomous EV’s trip level energy management in a shared fleet. The decision making process helps to find minimum energy cost routes with consideration of charging strategies and travel time requirements. By taking advantage of derived models and technologies, comprehensive case studies are performed on a data-driven simulated transportation network in New York City. Detailed results show us the heterogeneous energy impact and charging demand under different ambient temperature. By giving the same travel demand and charging station information, under the low and high ambient temperature within each month, there exist more than 20% difference of overall energy cost and 60% difference of charging demand. All studies will help to construct sustainable infrastructure for autonomous EV fleet trip level energy management in real world applications.     

大数据背景下的交通模型发展思考

将传统交通调查和模型与新的信息技术相结合,根据大数据的特征改进交通模型的方法体系,是交通模型发展面临的极大挑战.分析交通大数据对交通模型研发的促进作用,特别是在人口及就业岗位分布、综合交通网络数据和基于交通调查的模型参数等核心数据方面的支撑作用.从相关定义、市场细分以及信息完整性三个方面阐述大数据与交通模型的一致性问题.深入讨论应用大数据改进交通调查技术、4D模型研发、出行分布目的地选择模型、轨道交通出行接驳模型、出行成本校核等交通模型关键技术.通过对比大数据和交通模型的决策分析过程,指出大数据与交通模型互动发展的途径.     

Individual trip rate transferability analysis based on a decision tree approach

Transferring trip rates to areas without local survey data is a common practice which is typically performed in an ad hoc fashion using household-based cross-classification tables. This paper applies a rule-based decision tree method to develop individual-level trip generation models for eight different trip purposes as defined in the US National Household Travel Survey in addition to daily vehicle miles traveled. For each trip purpose, the models are obtained by finding the best fitted statistical distribution to each of the final decision tree clusters while considering the correlation between the trip rates for other trip purposes. The rule-based models are sensitive to changes in demographics. The performance of the models is then tested and validated in a transferability application to the Phoenix Metropolitan Region. These models can be employed in a disaggregate microsimulation framework to generate trips with different purposes at the individual or household level.     

基于优化赋权法的船型技术经济模糊综合评判

多目标、多准则决策问题中,决策结果的准确性与属性权重的合理性和评价系统的可行度密切相关。分析了主观赋权法、客观赋权法的优缺点, 提出了基于优化的组合赋权方法。该方法应用序列二次优化方法求解组合权向量,使最终的组合权向量与专家统计评分法、层次分析法、信息熵法、变异系数法这四种单一的主客观赋权方法相似度最高。将这种组合赋权方式与模糊综合评价法结合应用于秦皇岛至温州电厂航线散装运煤船方案决策分析,并与灰色关联评价法结果比较,决策结果证明该综合方法能合理、客观的给出排序方案,同时也能推广应用于船舶与海洋工程决策的相关领域。     

城市轨道交通线网方案的灰局势决策方法

在分析目前国内城市轨道交通线网规划指标体系的基础上,建立了城市轨道交通线网规划评价指标体系,并结合专家咨询意见和层次分析法计算结果,确定了指标权重。在评价指标体系的基础上,通过引入灰局势决策理论,对西安市城市快速轨道交通线网方案中的4个预选方案进行了优化决策,结果表明预选方案四为满意方案。