Establishing expert system for prediction based on the project-oriented data warehouse

Many studies have generated cost estimating relationships (CERs) for transportation projects via data analysis. Some studies collected data from databases, while others sourced data from conventional paper-based formats. When cost data were not in a consistent format, many studies failed to discuss the streamlining of pattern recognition, ranging from generating a problem statement, data warehouse and prediction modeling to information management. This study adopts a standard procedure of identifying CERs for transportation projects. For the proposed dimensional data warehouse, a pavement maintenance and rehabilitation project was selected as a case study for extracting data and concealed prediction rules. Linear and log-linear statistical approaches were adopted to create most advantageous models, defined based on their explanatory power and mean absolute prediction error. The resulting favorable estimation models created from the proposed cost data warehouse were integrated into an expert system to facilitate information management and generate preliminary budgets for transportation agencies.     

A computerized causal forecasting system using genetic algorithms in supply chain management

Forecasting activities are widely performed in the various areas of supply chains for predicting important supply chain management (SCM) measurements such as demand volume in order management, product quality in manufacturing processes, capacity usage in production management, traffic costs in transportation management, and so on. This paper presents a computerized system for implementing the forecasting activities required in SCM. For building a generic forecasting model applicable to SCM, a linear causal forecasting model is proposed and its coefficients are efficiently determined using the proposed genetic algorithms (GA), canonical GA and guided GA (GGA). Compared to canonical GA, GGA adopts a fitness function with penalty operators and uses population diversity index (PDI) to overcome premature convergence of the algorithm. The results obtained from two case studies show that the proposed GGA provides the best forecasting accuracy and greatly outperforms the regression analysis and canonical GA methods. A computerized system was developed to implement the forecasting functions and is successfully running in real glass manufacturing lines.     

A Bayesian robust CP decomposition approach for missing traffic data imputation

The inevitable problem of missing data is ubiquitous in the real transportation system, which makes the data-driven intelligent transportation system suffer from incorrect response. We propose a Bayesian robust Candecomp/Parafac (CP) tensor decomposition (BRCP) approach to deal with missing data and outliers by integrating the general form of transportation system domain knowledge. Specifically, when the lower rank tensor captures the global information, the sparse tensor is added to capture the local information, which can robustly predict the distribution of missing items and under the fully Bayesian treatment, the effective variational reasoning can prevent the over fitting problem. Real and reliable traffic data sets are used to evaluate the performance of the model in two data missing scenarios, which the experimental results show that the proposed BRCP model achieves the best imputation accuracy and is better than the most advanced baseline (Bayesian Gaussian CP decomposition (BGCP), high accuracy low-rank tensor completion (HaLRTC) and SVD-combined tensor decomposition (STD)), even in the case of high missed detection rate, the model still has the best performance and robustness.

     

A multi-modal discrete-event simulation model for military deployment

This paper introduces a logistics and transportation simulation that can be used to provide insights into potential outcomes of proposed military deployment plans. More specifically, we model a large-scale real-world military deployment planning problem (DPP) that involves planning the movement of military units from their home bases to their final destinations using different transportation assets on a multi-modal transportation network. We apply, for the first time, the event graph methodology and listener event graph object framework to create a simulation model of the DPP. We use and extend Simkit, an open-source Java Application Programming Interface for creating discrete-event simulation (DES) models. We use a medium-resolution modeling approach, as opposed to either high-resolution or low-resolution modeling paradigms, to reduce lengths of simulation runs without compromising reality. To accurately incorporate real and detailed transportation network data into the simulation, we use GeoKIT, a licensed, state-of-the-art, Java-based geographical information system. While our DES model is not a panacea for all, it allows for testing the feasibility and sensitivity of deployment plans under stochastic conditions prior to committing members of the military into harm’s way. The purpose of the paper is to acquaint the readers with the details of the DPP, the simulation model created, and the results of the analysis of a typical real-world case study.     

基于网络图的产品生产、运输、定价综合决策模型

针对大型垄断厂商在商品生产、运输、定价决策中独立考虑生产定价和运输两个环节的弊端, 提出一种将产量决策、运输决策和定价决策3 个环节作为一个系统进行考虑的综合决策模型. 借助利润网络图, 构建同时考虑生产及运输成本以及市场需求的商品生产、运输和定价综合决策模型, 并探讨模型的解法及其理论基础. 通过给出的应用案例表明, 相比传统决策模型, 利用综合决策模型可以提高15.50% 的利润.

     

A bi-level school bus routing problem with bus stops selection and possibility of demand outsourcing

This paper develops a bi-level mathematical model for the school bus routing problem aiming at designing an efficient transportation system considering the possibility of predicting the students’ response. In the real world, the demand for using private cars depends on how well public transportation systems are operating especially in metropolitan cities. An inefficient public transportation will lead to an increase in the demand for using private cars. This issue will result in problems such as increased traffics and urban pollutions. To address this issue, an efficient public transportation system is designed by developing a new bi-level mathematical model. In the proposed model, the designer of the public transportation system, as the upper-level decision-maker, will locate appropriate bus stops and identify bus navigation routes. Subsequently, the decision regarding the allocation of students to transportation systems or outsourcing them will be made at the lower level which is considered as an operational-level decision-making. To solve this problem, two hybrid metaheuristic approaches named GA-EX-TS and SA-EX-TS have been proposed based on location-allocation-routing (LAR) strategy. The performance of these proposed methods is compared with exact solutions achieved from an explicit enumeration approach followed in the small-scale instances. Finally, the proposed approaches are used to solve 50 random instance problems. Comparing the results of the two tuned hybrid algorithms and conducting the sensitivity analysis of the model provide evidence for the good performance of the proposed approach.     

Identifying important variables for predicting travel time of freeway with non-recurrent congestion with neural networks

The provision of long-distance travel time information has been a major factor facilitating the intelligent transportation system to become more successful. Previous studies have pointed out that non-recurrent congestion is the major cause of freeway delay. The long travel distance complicates the characteristics of traffic flow. Hence, how to improve the prediction capability of long-distance travel time in the case of non-recurrent congestion is an important issue that must be overcome in the field of travel time prediction. This study constructs the travel time prediction model for a segment of 36.1 kms (including eight interchanges) in the National Freeway No. 1, Taiwan, by using the multilayer perceptron. To improve the prediction capability of the model in the case of non-recurrent congestion, this study collects data of average spot speed and heavy vehicle volume gathered by dual-loop vehicle detectors, in addition to rainfall and temporal feature. Furthermore, the historical travel time inferred from the original data of electronic toll collection (ETC) system is also used as the input variable, and the actual travel time inferred from ETC is used as the training target to establish a robust prediction model. As suggested by the results of 168 experimental combinations, the most appropriate prediction model established in this study is a highly accurate forecasting model with MAPE of 6.47 %.     

Performance Evaluation of Public Bus Transportation by Using DEA Models and Shannon’s Entropy: An Example From a Company in a Large City of China

The purpose of this paper is to assess the operational efficiency of a public bus transportation via a case study from a company in a large city of China by using data envelopment analysis (DEA) model and Shannon’s entropy. This company operates 37 main routes on the backbone roads. Thus, it plays a significant role in public transportation in the city. According to bus industry norms, an efficiency evaluation index system is constructed from the perspective of both company operations and passenger demands. For passenger satisfaction, passenger waiting time and passenger-crowding degree are considered, and they are undesirable indicators. To describe such indicators, a super-efficient DEA model is constructed. With this model, by using actual data, efficiency is evaluated for each bus route. Results show that the DEA model with Shannon’s entropy being combined achieves more reasonable results. Also, sensitivity analysis is presented. Therefore, the results are meaningful for the company to improve its operations and management.      

Simulation-based multi-objective model for supply chains with disruptions in transportation

Unpredictable disruptions (e.g., accidents, traffic conditions, among others) in supply chains (SCs) motivate the development of decision tools that allow designing resilient routing strategies. The transportation problem, for which a model is proposed in this paper, consists of minimizing the stochastic transportation time and the deterministic freight rate. This paper extends a stochastic multi-objective minimum cost flow (SMMCF) model by proposing a novel simulation-based multi-objective optimization (SimMOpt) solution procedure. A real case study, consisting of the road transportation of perishable agricultural products from Mexico to the United States, is presented and solved using the proposed SMMCF-Continuous/SimMOpt solution framework. In this case study, time variability is caused by the inspection of products at the U.S.-Mexico border ports of entry. The results demonstrate that this framework is effective and overcomes the limitations of the multi-objective stochastic minimum cost flow problem (which becomes intractable for large-scale instances).     

基于生存分析的GPS轨迹缺失规律挖掘

近年来,智能交通系统(Intelligent Transportation Systems,ITS)已成为提高交通系统性能和增强出行安全性的有效方式。但随着系统数据量的增加,数据缺失问题日益严重,其中由于车载GPS信号丢失导致的轨迹数据缺失是主要的研究问题之一。引起GPS轨迹缺失的原因的多样性造成数据补全工作困难,且至今很少有关于轨迹缺失规律的研究。针对GPS信号丢失原因多样化的问题,基于大量真实数据,首次将生存分析应用于数据缺失领域,提出了基于生存分析的GPS轨迹缺失规律挖掘模型(Survival Analysis-Missing Trajectory Pattern Mining,SA-MTPM)。首先通过生存函数描述信号丢失时长与丢失原因的关系,然后利用Cox回归模型分析信号丢失的关键因素。使用上海市强生出租车公司一个月内13666辆车的数据进行实验,结果表明GPS轨迹缺失存在一定规律,据此可以方便地对信号丢失事件进行识别分类,为进一步对大数据进行研究提供了参考。     

不确定网络结构下的应急物资鲁棒配置模型

灾害发生前的应急物资配置问题具有两个重要的不确定性, 即交通网络中受自然灾害影响而阻断的道路以及受灾点的应急物资需求量. 通过引入两个控制水平参数建立了不确定网络结构下的两阶段应急物资鲁棒配置模型, 并在线性化第2 阶段的回溯问题后提出了求解模型的Benders 分解算法. 数值实验结果表明了所提出的模型的有效性以及所得配置方案的鲁棒性.

     

考虑运输因素的网络化制造资源优化配置方法

针对网络化制造环境下制造资源的优化配置问题,为提高配置结果的实用性,在考虑加工时间和加工成本等因素的基础上,加入企业间运输时间和运输成本因素,建立了综合优化模型。采用带精英保留策略的遗传算法对该问题进行求解。通过实例验证了以上模型和求解算法。研究结果表明,考虑运输因素的网络化制造资源优化配置方法能够有效降低总运输成本和时间,配置方案更贴合实际。     

A graphical interface for production and transportation system modeling: PTS

PTS is a graphical interface for production and transportation system modeling. It provides a means of creating an economic model of production and transportation activities in a system of plants and markets. The model created by PTS is translated into the GAMS language and is solved using that system. The results are then returned to PTS and are displayed graphically. PTS runs under Windows on IBM PCs and compatibles. PTS is used to (1) provide a graphical interface for knowledge-based model development systems, (2) provide a test-bed for studies of parallel model representations and (3) analyze methods of production and transportation model development which are simpler than the knowledge-based methods. The system is currently implemented for linear programming production and transportation problems. Also, the current implementation provides graphical and GAMS representations of the model.     

基于Fabric的出行数据隐私保护方法

针对智能交通出行数据共享中的隐私保护问题,提出一种基于Fabric的出行数据隐私保护模型。利用区块链不可篡改、可追溯的特性,结合AES算法与访问控制列表机制,从出行数据的上传、查询和访问控制等方面对模型中Fabric区块链数据库、智能合约、中间件等模块进行设计和实现。安全性分析和实验结果表明,所提方法具有较好的安全性、保密性以及可行性,可以提高智能交通出行数据共享环境下的个人隐私安全。     

A harmonised model for safety assessment and certification of safety-critical systems in the transportation industries

This paper describes a model for the assessment and certification of safety-critical programmable electronic systems in the transportation industries. The proposed model is founded on the significant commonalities between emerging international safety-related standards in the automotive, railway and aerospace industries. It contains a system development and a safety assessment process which rationalise and unify the common requirements among the standards in these areas. In addition, it defines an evolutionary process for the development of the system’s safety case. The safety case process shows how the evidence produced in the progression of safety assessment can be structured in order to form an overall argument about the safety of the system. We conclude that it is possible to use this model as the basis of a generic approach to the certification of systems across the transportation sector.     

Application of a combined fuzzy multiple criteria decision making and optimization programming model to the container transportation demand split

The container transportation demand split is one of the most important decision issues for government transportation departments and port organizations. In previous studies, many researchers assumed that the shipping carrier would aim to minimize the total operation cost by selecting an appropriate port as the most favorable one to call, and the shipper would aim to minimize the inland freight cost by selecting the nearest port as the most favorable one to import and export international trade containers. Thus, a number of mathematical programming models have been developed. But in practice, the shipping carrier not only aims to minimize the total operation cost but also takes into account other criteria such as the volume of containers and port facility conditions when choosing an appropriate port as the most favorable one to call. The shipper not only aims to minimize the inland freight cost but also takes into account the frequency of ship callings when choosing an appropriate port as the most favorable one to import and export international trade containers.Thus, the purpose of this paper is to formulate a combined fuzzy multiple criteria decision making and optimization programming model for solving the container transportation demand split problem. There are two stages in this combined model: in stage one, we first compute the container transportation demand split rate by using fuzzy multiple criteria decision making (MCDM) method; whereas in stage two, an optimization mathematical programming network model is proposed for determining the inland origin destination (O-D) of import/export containers. The utilization of the proposed model is demonstrated with a case of Taiwanese ports. The results show that the proposed combined fuzzy MCDM and optimization programming model can be used to explain the container transportation demand split practice.     

面向CPS的单车排放配额协同动态分配方法研究

建立个体车辆的排放配额管理机制可以低成本实现道路交通行业污染物和碳排放的减排目标。这需要面向车辆个体的全域全量的动态感知、计算、决策、执行的闭环信息化体系进行支撑,而信息物理系统（cyber-physical system,CPS）技术为此提供了基础条件。但现有道路交通排放配额分配相关研究未研究车辆的污染物与碳排放的协同控制问题,忽略了车辆排放的个体排放差异性与时空动态性;交通信息物理系统相关研究缺少集成精细化的排放量化模型和排放配额管理模型。针对上述研究局限,提出了一种面向信息物理系统的个体车辆污染物与碳排放配额协同动态分配方法,构建了集成个体车辆身份识别及出行行为模型、污染物及二氧化碳排放量化模型、排放配额管理模型的交通信息物理系统框架,并提出了核心流程算法。以宣城市的真实数据进行实验分析,结果表明：提出的交通信息物理系统框架可实现个体车辆污染物和碳排放量及配额量的协同动态计算,配额分配方法与传统方法相比,配额分配颗粒度更加精细,分配对象的确定更加精准,可实现配额分配的动态调整以应对减排目标的时变性,配额分配激励先进、惩罚落后的引导效果更加显著。     

移动对象数据库模型、查询语言及实时交通流分析

提出一种移动对象数据库模型——Dynamic Transportation Network Based Moving Objects Database(简称DTNMOD),并给出了DTNMOD中基于移动对象时空轨迹的网络实时动态交通流分析方法.在DTNMOD中,交通网络被表示成动态的时空网络,可以描述交通状态、拓扑结构以及交通参数随时间的变化过程;网络受限的移动对象则用网络移动点表示.DTNMOD 模型包含了完整的数据类型和查询操作的定义,因此可以在任何可扩充数据库(如PostgreSQL 或SECONDO)中实现,从而得到完整的数据库模型和查询语言.为了对相关模型的性能进行比较与分析,基于PostgreSQL 实现了一个原型系统并进行了一系列的实验.实验结果表明,DTNMOD 提供了良好的区域查询及连接查询性能.     

Intelligent bus routing with heterogeneous human mobility patterns

Optimal planning for public transportation is one of the keys helping to bring a sustainable development and a better quality of life in urban areas. Compared to private transportation, public transportation uses road space more efficiently and produces fewer accidents and emissions. However, in many cities people prefer to take private transportation other than public transportation due to the inconvenience of public transportation services. In this paper, we focus on the identification and optimization of flawed region pairs with problematic bus routing to improve utilization efficiency of public transportation services, according to people’s real demand for public transportation. To this end, we first provide an integrated mobility pattern analysis between the location traces of taxicabs and the mobility records in bus transactions. Based on the mobility patterns, we propose a localized transportation mode choice model, with which we can dynamically predict the bus travel demand for different bus routing by taking into account both bus and taxi travel demands. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. We also leverage the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real-world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips.     

Two-stage stochastic programming supply chain model for biodiesel production via wastewater treatment

This paper presents a two-stage stochastic programming model used to design and manage biodiesel supply chains. This is a mixed-integer linear program and an extension of the classical two-stage stochastic location-transportation model. The proposed model optimizes not only costs but also emissions in the supply chain. The model captures the impact of biomass supply and technology uncertainty on supply chain-related decisions; the tradeoffs that exist between location and transportation decisions; and the tradeoffs between costs and emissions in the supply chain. The objective function and model constraints reflect the impact of different carbon regulatory policies, such as carbon cap, carbon tax, carbon cap-and-trade, and carbon offset mechanisms on supply chain decisions. We solve this problem using algorithms that combine Lagrangian relaxation and L-shaped solution methods, and we develop a case study using data from the state of Mississippi. The results from the computational analysis point to important observations about the impacts of carbon regulatory mechanisms as well as the uncertainties on the performance of biocrude supply chains.