Discrete Choice Analysis of Temporal Factors on Social Network Growth

Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital world. These networks can be viewed as a collection of nodes and edges, where users and their interactions are represented as nodes and the connections between them as edges. Understanding the factors that contribute to the formation of these edges is important for studying network structure and processes. This knowledge can be applied to various areas such as identifying communities, recommending friends, and targeting online advertisements. Several factors, including node popularity and friends-of-friends relationships, influence edge formation and network growth. This research focuses on the temporal activity of nodes and its impact on edge formation. Specifically, the study examines how the minimum age of friends-of-friends edges and the average age of all edges connected to potential target nodes influence the formation of network edges. Discrete choice analysis is used to analyse the combined effect of these temporal factors and other well-known attributes like node degree (i.e., the number of connections a node has) and network distance between nodes. The findings reveal that temporal properties have a similar impact as network proximity in predicting the creation of links. By incorporating temporal features into the models, the accuracy of link prediction can be further improved.

An empirical study on travel demand management modeling based on discrete choice method

In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based travel demand forecasting model is proposed to demonstrate its applicability to travel demand management. A car-bus discrete choice model is established, including three variables, i. e,, individual socioeconomic characteristics, time, and cost, and the traffic policy-sensitivity is evaluated through two kinds of traffic policies： parking charges and bus priorities. The empirical results show that travel choice is insensitive to the policy of parking charges as 88. 41% of the travelers are insensitive to parking charges; travel choice is, however, sensitive to the policy of bus priorities as 67.70% of the car travelers and 77.02% of the bus travelers are sensitive to bus priorities. The discrete-choice-based travel demand forecasting model is quite policy-sensitive and also has a good adaptability for travel demand management when meeting the basic functions of the demand forecasting model.

Application of discrete choice model in trip mode structure forecast:a case study of Bengbu

In order to find the main factors that influence the urban traffic structure,a relational model between the travelers＇ characteristics and the trip mode choice is built.The data of urban residents＇ characteristics are obtained from statistical data,while the trip mode split data is collected through a trip survey in Bengbu.In addition,the discrete choice model is adopted to build the functional relationship between the mode choice and the travelers＇ personal characteristics,as well as family characteristics and trip characteristics.The model shows that the relationship between the mode split and the personal,as well as family and trip characteristics is stable and changes little as the time changes.Deduced by the discrete model,the mode split result is relatively accurate and can be feasibly used for trip mode structure forecasts.Furthermore,the proposed model can also contribute to find the key influencing factors on trip mode choice,and restructure or optimize the urban trip mode structure.

Comparative study on mode split discrete choice models

Discrete choice model acts as one of the most important tools for studies involving mode split in the context of transport demand forecast. As different types of discrete choice models display their merits and restrictions diversely, how to properly select the specific type among discrete choice models for realistic application still remains to be a tough problem. In this article, five typical discrete choice models for transport mode split are, respectively, discussed, which includes multinomial logit model, nested logit model （NL）, heteroscedastic extreme value model, multinominal probit model and mixed multinomial logit model （MMNL）. The theoretical basis and application attributes of these five models are especially analysed with great attention, and they are also applied to a realistic intercity case of mode split forecast, which results indi- cating that NL model does well in accommodating similarity and heterogeneity across alternatives, while MMNL model serves as the most effective method for mode choice prediction since it shows the highest reliability with the least significant prediction errors and even outperforms the other four models in solving the heterogeneity and similarity problems. This study indicates that conclusions derived from a single discrete choice model are not reliable, and it is better to choose the proper model based on its characteristics.

Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice--A Systematic Review

Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.

A branch-and-bound algorithm for discrete multi-factor portfolio optimization model

In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial optimization. This discrete portfolio model is of integer quadratic programming problems. The separable structure of the model is investigated by using Lagrangian relaxation and dual search. Computational results show that the algorithm is capable of solving real-world portfolio problems with data from US stock market and randomly generated test problems with up to 120 securities.

The Full Potential of Continuous System Simulation Modelling

Continuous System Simulation (CSS) is a powerful way to study the behaviour of differential-algebraic equation models. Differential-algebraic equation modelling goes back to Newton and works well for models of e.g. physical systems where stochasticity plays almost no role, and where only a single attribute of an object is studied over time. However, when results from a deterministic CSS model are compared with results from a Discrete Event Simulation (DES) model, they are often inconsistent. The reasons behind this CSS-DES inconsistency are nowadays well understood. In this paper, we demonstrate that a CSS model can contain both continuous state variables (compartments) that change continuously over time and discrete state variables (also compartments) that model discrete entities and change by integer amounts. In both cases, the time-slicing method is used to advance time. Furthermore, stochasticity can and should play the same role in CSS as it does in DES. This paper first explains how a well-defined conceptual model can be stepwise transformed in a consistent way into a CSS model. These transformation steps provide insights into how to construct a consistent CSS model. In short, this is about preserving uncertainties, attributes and dynamic properties. An additional benefit of this approach is that continuous and discrete sub-models can interact within the same model, without having to combine different types of simulation languages, types of time handling and incongruent concepts. To facilitate the use and understanding of stochastic CSS, this paper is also intended to serve as a guide to performing consistent CSS modelling and simulation. Furthermore, an open source tool for collecting and analyzing the outputs from a stochastic CSS model and analyzing and presenting the results in statistical form is also developed and made available to the reader.

Estimation of charging demand for electric vehicles by discrete choice models and numerical simulations: Application to a case study in Turin

The electrification of vehicles is considered one of the most important strategies for addressing the issues related to energy dependence and climate change.To meet user needs,electric vehicle(EV)management for charging operations is essential.This study uses modelling and simulation of EV user behaviour to forecast possible scenarios for electric charging in cities and to identify potential management problems and opportunities for improvement of EVs and EV charging infrastructures.The conurbation of Turin was selected as a case study to reproduce realistic scenarios by applying discrete choice modelling based on socio-economic and transport system data.One of objectives of the study was to describe user charging behaviour from a geographic perspective to model where users prefer to charge in the area studied according to the variables that may affect decisions.Another objective was to estimate the number of electric vehicles in Turin and the characteristics of their users,both of which are helpful in understanding electric mobility within a city.Analysing these behavioural issues in a modelling framework can provide a set of tools to compare and evaluate a variety of possible modifications,indicating an adequate network of charging infrastructure to facilitate the diffusion of electric vehicles.

Free vibration of a sagged cable with attached discrete elements

An algorithm is presented to analyze the free vibration in a system composed of a cable with discrete elements, e.g., a concentrated mass, a translational spring, and a harmonic oscillator. The vibrations in the cable are modeled and analyzed with the Lagrange multiplier formalism. Some fragments of the investigated structure are modeled with continuously distributed parameters, while the other fragments of the structure are modeled with discrete elements. In this case, the linear model of a cable with a small sag serves as a continuous model, while the elements, e.g., a translational spring, a concentrated mass, and a harmonic oscillator, serve as the discrete elements. The method is based on the analytical solutions in relation to the constituent elements, which, when once derived, can be used to formulate the equations describing various complex systems compatible with an actual structure. The numerical analysis shows that, the method proposed in this paper can be successfully used to select the optimal parameters of a system composed of a cable with discrete elements, e.g., to detune the frequency resonance of some structures.

Clicking through the Clickstream: A Novel Statistical Modeling Approach to Improve Information Usage of Clickstream Data by E-Commerce Entities

Success or failure of an E-commerce platform is often reduced to its ability to maximize the conversion rate of its visitors. This is commonly regarded as the capacity to induce a purchase from a visitor. Visitors possess individual characteristics, histories, and objectives which complicate the choice of what platform features that maximize the conversion rate. Modern web technology has made clickstream data accessible allowing a complete record of a visitor’s actions on a website to be analyzed. What remains poorly constrained is what parts of the clickstream data are meaningful information and what parts are accidental for the problem of platform design. In this research, clickstream data from an online retailer was examined to demonstrate how statistical modeling can improve clickstream information usage. A conceptual model was developed that conjectured relationships between visitor and platform variables, visitors’ platform exit rate, boune rate, and decision to purchase. Several hypotheses on the nature of the clickstream relationships were posited and tested with the models. A discrete choice logit model showed that the content of a website, the history of website use, and the exit rate of pages visited had marginal effects on derived utility for the visitor. Exit rate and bounce rate were modeled as beta distributed random variables. It was found that exit rate and its variability for pages visited were associated with site content, site quality, prior visitor history on the site, and technological preferences of the visitor. Bounce rate was also found to be influenced by the same factors but was in a direction opposite to the registered hypotheses. Most findings supported that clickstream data is amenable to statistical modeling with interpretable and comprehensible models.

基于完形填空的方面级情感四元组预测

方面情感四元组预测(ASQP)任务旨在从给定的评论语句中提取所有方面词以及相应的方面类别、观点表达和情感极性,有助于全面了解用户对产品或服务不同方面的评价情况.现有情感四元组预测方法主要存在以下局限:(1)判别式模型没有利用prompt捕获情感元素之间的语义关系;(2)生成式模型要么简单地将情感元素类型标签组合形成prompt,缺乏理解标签类型涵义的语境;要么将离散模板作为解码器的输入,而编码器则无法捕获到模板中情感元素之间的语义关系.为了缓解这些问题,本文首先基于完形填空思想研制离散和连续2类prompt,提供理解4个情感元素类型涵义的语境,帮助模型更好地捕获情感元素之间的语义关系;然后,基于设计的prompt,提出C-ASQP框架,包含判别式模型DC-ASQP和生成式模型GC-ASQP.在DC-ASQP中,采用2阶段策略,先预测4个情感元素中2个较为容易的情感元素,再将预测结果嵌入到设计的prompt中,帮助模型理解情感元素类型的涵义,从而有效预测另外2个情感元素.在GC-ASQP中,将设计的prompt作为编码器的输入,借助预训练模型的学习模式,充分利用预训练模型蕴含的知识提升四元组的生成效果.实验结果显示,DC-ASQP模型在4个常用数据集上的F1值相比同类判别式最优模型分别提高4.70%、6.48%、6.97%和2.60%,GC-ASQP模型的F1值比最优基准模型分别提高0.86%、1.67%、0.15%和1.02%,验证了将ASQP建模为完形填空任务的有效性,所设计的2类prompt以及C-ASQP框架是有效的.

基于离散-连续耦合的岩溶隧道防突岩体安全厚度预测

为有效预防岩溶隧道施工中的突涌水灾害,首先,调研近年来国内岩溶隧道突涌水典型案例,总结隧道突涌水形成机理及其影响因素;其次,建立离散-连续耦合模型并采用多元回归理论的方法,明确防突岩体破坏的判定准则,划分岩溶隧道防突岩体破坏模式并提出裂缝发育规律;然后,基于对防突岩体最小安全厚度造成影响的各因素,针对溶洞位于隧道上方、下方及侧方3种情况分别建立防突岩体最小安全厚度预测计算式;最后,依托德庆隧道工程进行验证。结果表明:防突岩体破坏模式主要有弯折破坏、整体剪切破坏和复合破坏3种,可基于裂隙发展速度将其破坏过程分为初始阶段、快速发育阶段、平缓发育阶段3个阶段;各因素按影响显著程度由大到小依次为溶腔内水压、围岩黏结参数、溶腔跨度、溶腔高跨比和隧道埋深;计算得到德庆隧道3处施工段的防突岩体最小安全厚度分别为2.964,5.263和0.961 m,突涌水评估结果准确。防突岩体最小安全厚度预测计算式可用于预测并评估岩溶隧道突涌水风险。

考虑心理偏好的枢纽抵站旅客接续出行方式选择模型

为掌握枢纽抵站旅客的出行方式选择特征,解决接续运输运力配置协同优化与效率提升问题,基于高铁枢纽接续出行问卷调查数据,引入上次接续属性、接续方式主观重要度、枢纽服务满意度、接续出行选择倾向特征等潜在变量因素,构建考虑心理倾向的结构方程模型(Structural Equation Model,SEM)和抵站旅客接续出行方式选择多项Logit(Multinomial Logit,MNL)模型相融合的SEM-MNL模型,定量计算枢纽抵站旅客接续出行分方式选择效用函数,预测不同时段旅客接续出行方式选择概率,并对比分析SEM-MNL模型与MNL模型命中率.研究结果表明:引入心理倾向潜在变量后,模型的整体命中率提升了4.6%;对枢纽环境服务满意度越高、接续方式服务重视程度越高、上次出行的时间和距离更短且使用公共交通的出行者,更倾向于选择公共交通方式.通过方式选择模型预测分方式的接续出行需求,开展对外客运枢纽接续运输保障和多模式运力的协调调度,对于实现枢纽接续客流与运力资源的供需平衡,继而提升接续服务质量具有重要意义.

电子围栏背景下共享单车停车行为影响特征分析

单车规范停放问题影响着共享单车系统的可持续发展,而设置电子围栏是促进单车停放规范化的重要手段。用户在电子围栏背景下规范停车行为的影响机理是制定停放管理政策的基础。基于用户停车选择行为问卷调查的结果,在二项Logit模型的基础上,结合调查数据特征建立固定效应选择模型、非对称选择模型等多个停车行为选择模型,探究各类特征对用户停车行为的影响。结果显示,用户的年龄、职业、收入等社会经济特征,用户在平常使用共享单车的时段、时长、频率及停放困难频率等共享单车出行特征,具体在停车场景下合规停放的绕行距离和违规停放的罚款金额等场景特征,均会显著影响用户合规停放的概率。模型结果可为电子围栏背景下的精细化管理提供用户行为定量分析基础。

Model Predictive Control for Discrete and Continuous Timed Petri Nets

The goal of this paper is to propose a unique control method that permits the evolution of both timed continuous Petri net （TCPN） and T-timed discrete Petri net （T-TDPN） from an initial state to a desired one. Model predictive control （MPC） is a robust control scheme against perturbation and a consistent real-time constraints method. Hence, the proposed approach is studied using the MPC. However, the computational complexity may prevent the use of the MPC for large systems and for large prediction horizons. Then, the proposed approach provides some new techniques in order to reduce the high computational complexity; among them one is taking constant control actions during the prediction.

旅游纪念品购买偏好及其异质性来源——基于离散选择实验

购买旅游纪念品是旅游体验的重要组成部分,也是旅游目的地商家的关键业务,了解旅游纪念品自身属性对购买决策的影响程度非常重要。文章设计离散选择实验模拟购物情境,收集旅游者选择数据,利用随机参数Logit模型估计,深入分析旅游者对旅游纪念品属性的选择偏好及偏好异质性来源。研究发现:(1)相较于功能属性,旅游者购买旅游纪念品时更看重价值属性,其中,地方性是旅游者最看重的属性,其次是审美性、文化性、礼品性、实用性、便携性、价格;(2)旅游者在审美性和价格的偏好上存在异质性,随着年龄增大、收入增加,审美性、价格对旅游者购买意愿的影响减小,相比男性旅游者,女性旅游者对旅游纪念品的价格更加敏感。

考虑随机用户均衡的区县级电动汽车快充站规划

电动汽车渗透率的提高和城市化进程的发展使得区县级充电设施的建设具有重要意义。综合考虑用户充电需求时空分布和用户充电决策中的有限理性,提出一种面向区县的电动汽车快充站优化规划模型。首先,针对区县用户的出行特征,构建包含外部连接的交通网络拓扑扩展形式,采用出行链仿真捕捉用户快充需求;其次,结合影响用户充电选择的多种因素,建立用户充电决策模型并给出随机用户均衡条件;然后,建立以投资运行成本、用户绕行距离最小为目标的多场景快充站选址定容优化模型,通过多种线性化手段处理指数等式均衡约束,将优化问题转换为混合整数线性规划以高效求解;最后,以33节点交通系统为例对快充站规划结果进行分析,验证所提模型和线性近似方法的合理性。

中国零碳商用车市场渗透率建模:以重型长途牵引货车为例

商用车碳减排已经成为我国道路交通减碳的关键瓶颈,新能源商用车被视作重型商用车减碳的重要途径,但是新能源商用车的市场渗透率远低于其他车辆部门;但与此同时,现阶段新能源零碳商用车的发展还存在着应用场景复杂、技术路径多样化、同时成本较高的显著的瓶颈。本研究构建了基于新能源汽车总拥有成本(total cost of ownership,TCO)、使用便利性等因素的多元Logit离散选择模型——零碳商用车市场演进模型(discrete choice-based market evolution of green truck model,DC-MEGT),使用自下向上的方法计算TCO,并将车辆使用便利性使用补能时间成本进行货币化量化,构建综合效用函数对纯电动车、燃料电池汽车及零碳燃料等不同动力类型从目前到2060年的市场渗透率演进情况进行预测分析。研究以重型长途牵引场景为例进行分析,结果表明2060年主要的技术路径包括燃料电池汽车、纯电动车、天然气及柴油车,占比分别为48%、28%、12%和10%。政策推广、技术进步、商业模式等因素的不确定性会引发纯电动车和燃料电池汽车2060年市场份额17%~19%的波动。

Combined model of trip mode and destination

This paper analyzes the characteristics of the destination distribution of trips and proposes a stratified sampling strategy for travel mode choice.The stratified sampling strategy can reduce the size of the alternative set;thus,the computation burden of simulation is decreased.Using the stratified sampling strategy,a combined choice model of the trip mode and destination is developed based on the Bayesian theory.Simulations are carried out to verify the proposed model.The results show that the combined choice model of the trip mode and destination can efficiently simulate travelers＇ choice behaviors.Furthermore,the forecasting accuracy of the combined choice model is higher than the one of the gravity model.Therefore,the proposed model is a powerful tool with which to analyze travelers＇ behaviors in selecting the trip mode.

一类时间离散反应扩散对流模型的全局动力学

时间离散空间连续的反应扩散方程模型是描述物种扩散行为的一类重要的研究工具。考虑到物种除了随机扩散外还会存在依赖于局部环境的扩散,文章建立了单个物种关于时间离散空间连续的反应扩散对流模型,然后利用主特征值理论分析了模型平衡点的存在性和稳定性。研究表明,加入依赖于局部环境的扩散对种群的生存是有利的。