Research and application in intervening opportunity class models for predicting human mobility
- Autores: Erjian Liu
- Directores de la Tesis: Xiaoyong Xiaoyong (dir. tes.), José Javier Ramasco Sukia (dir. tes.), Maria Rosa López Gonzalo (tut. tes.)
- Lectura: En la Universitat de les Illes Balears ( España ) en 2023
- Idioma: inglés
- Número de páginas: 167
- Tribunal Calificador de la Tesis: Bin Jia (presid.), Pere Colet Rafecas (secret.), Lucas Lacasa Saiz de Arce (voc.)
- Programa de doctorado: Programa de Doctorado en Física por la Universidad de las Illes Balears
- Materias:
- Enlaces
- Tesis en acceso abierto en: RepositoriUIB
- Resumen
- English
The research on human mobility has been an important topic in many fields such as transportation science, sociology and economic geography. With the fast development of urbanization and globalization, human travel has become increasingly complex. Understanding and predicting human mobility is of great theoretical significance to explain various complex phenomena related to human activities and has practical applications in many fields. For example, in terms of urban planning, the research on human mobility can help us better understand the travel demand and travel characteristics of urban residents, so as to optimize the urban road network and improve the efficiency of the públic transport system; in terms of migration, the research on human mobility can help us better understand migration patterns, so as to provide a scientific basis for the government to develop population management policies; in terms of the spread of diseases, the research on human mobility can help us better understand the travel patterns and travel characteristics of different groups of people, so as to more accurately predict and control the spread of disease within humans. Through the mining of large-scale human travel data, this thesis takes the trip distribution prediction as the main research object, uses the System science thinking and combines data statistical analysis methods to establish a model that can predict human mobility at different spatiotemporal scales, and further explores its application on spatial interaction and enriches the equipotential line display method of model prediction results. The main results of this thesis are as follows: (1) Mobility datasets collection and spatiotemporal feature analysis. We collect a variety of mobility datasets at different spatiotemporal scales, including commuting datasets, migration datasets, freight datasets, intracity trip datasets and intercity travel datasets, providing a reliable data basis for the research of human travel behavior. Based on these datasets, we count travel distance distribution, travel fluxes distribution and normalized entropy, and show the spatial distribution characteristics by drawing the map of trip generation, trip attraction and desire line. (2) Research on intervening opportunity class model for predicting human mobility. We propose a new interventional opportunity class model named universal opportunity model to predict commuting, migration, freight, intracity trip and intercity travel, which has broad applicability. The universal opportunity model assumes that individuals will comprehensively consider the benefits of each location when choosing a destination, and it reflects two human behavioral tendencies: one is the exploratory tendency and the other is the cautious tendency. The exploratory tendency describes the individual tend to choose the destination whose benefit is higher than the benefits of the origin and the intervenint opportunities, and the cautious tendency describes the individual tend to choose the destination whose benefit is higher than the benefit of the origin, and the benefit of the origin is higher than the benefits of the intervening opportunities. Validation results on múltiple datasets show that the universal opportunity model can better predict human mobility than previous intervening opportunity class models. The universal opportunity model establishes a new framework in intervening opportunity class models and covers the classical radiation model and opportunity priority selection model, which can help us better understand the underlying mechanism of the individual’s destination selection behavior. (3) Research and application of the model considering interactions between cities. By extending the universal opportunity model, we propose an interactive city choice model to measure the interaction intensity between cities and city interaction intensity. The model assumes that the probability of an individual choosing to interact with a city is proportional to the number of opportunities in the destination city and inversely proportional to the number of intervening opportunities between the origin city and the destination city, calculated using the travel time in the transportation network. By using this model to measure the interaction intensity and combining the outgoing interaction intensity and the incoming interaction intensity to quantify city interaction intensity, we analyze the impact of changes in the Chinese land transportation network from 2005 to 2018 on the intercity and city interaction intensity. The results show that the travel time between cities has decreased and the interaction intensity between large cities has increased due to the development of land transportation. In particular, the interaction intensity of cities along high-speed railways has greatly increased. Compared with the gravity model and the radiation model, the interactive city choice model can better measure the interaction intensity between cities and city interaction intensity. (4) Research, application and extension of travel distribution feature display method. We propose a new method to display the prediction results of the universal opportunity model by establishing a vector field and drawing the equipotential lines. Compared with the previous commuting vector field method, our method is more general and can be applied not only to commuting but also to passenger travel and freight travel. Further, we expand it and propose a generalized vector framework, which includes two indicators: the vector field and track orientation. Among them, the vector field can be used to describe the characteristics of the field formed by individuals’trajectories, and the track orientation divides the trajectory into positive, negative and balanced according to whether the individual tend to move toward the starting point or away from it during the trip process. Through the analysis of freight datasets in 21 Chinese cities and Foursquare check-in datasets in New York, we find that there are more trips point to the starting point than away from the starting point, and the ratio of trips away from the starting point and trips back to it is similar, which indicates that there is a universal rule in different cities. Finally, we introduce the distance rand model, the distance traveling salesman model and the distance rand-traveling salesman mix model to generate the trajectory, and apply the generalized vector framework to study the characteristics of the trajectory generated by these models. The results show that the distance rand-traveling salesman mix model can reproduce the universal rule that the ratio of trips away from the starting point and trips back to it is similar in different cities. The above research shows that the generalized vector field framework can not only display the spatial characteristic of population mobility, but also quantify the spatial characteristic of individual mobility.
- 中文
人类出行行为研究是交通科学、社会学、经济地理学、区域经济学等诸多领 域长期以来的一个重要研究主题。随着城市化和全球化的不断发展，人类出行变 得日趋复杂。理解和预测人的出行行为对于解释与人类活动相关的各种复杂现象 具有重要理论意义，在许多领域也具有广泛的应用价值。例如，在城市规划方面，人类出行行为研究可以帮助我们更好地理解城市的出行需求和出行特征，从而优化城市道路和公共交通系统，提高城市的出行效率和舒适性；在人口迁移方面，该研究可以帮助我们更好地理解人口的迁移模式和趋势，为政府制定人口管理政策提供科学依据；在疾病传播防控方面，该研究可以帮助我们更好地理解不同人群的出行模式和出行特征，从而更加精准地预测和控制疾病传播。本文通过对大规模人类出行数据的挖掘，以出行分布预测为主要研究对象，运用系统科学思想，结合数据统计分析方法，建立了能够预测不同时空尺度出行的模型，进一步开展了其在空间交互测量方面的应用，并丰富了模型结果的等势线展示方法。主要研究工作如下： （1）出行数据收集与时空特征分析。本文收集了多种不同时空尺度下的出行数据，包括通勤数据、迁移数据、货运数据、城市内部出行数据和城市间出行数 据，为研究人类出行行为提供了可靠的数据基础。在这些出行数据的基础上，本 文统计了出行距离分布、出行量分布、归一化熵等不同时空尺度出行的数据特征，并通过绘制地点出行发生量分布图、地点出行吸引量分布图、期望线图来展示出行分布的空间特征。 （2）出行分布预测介入机会类模型研究。本文提出了一个名为统一机会模型的介入机会类模型，用于预测通勤、人口迁移、货物运输、城市内出行、城市间出行等不同时空尺度下的出行分布矩阵，具有广泛适用性。该模型假设个体在选择目的地时会综合考虑各地点的收益，并反映了个体选择收益比起点收益以及介入机会收益高的目的地的倾向和个体选择收益比起点收益高且介入机会的收益比起点收益低的目的地的倾向。统一机会模型在多种数据集上的验证结果表明，相较于其他介入机会类出行分布预测模型，它具有更高的预测精度。该模型囊括了经典的辐射模型和机会优先选择模型，建立了一个新的框架，有助于我们更深入地理解不同类型出行的底层机制。 （3）考虑城市间交互的选择模型研究与应用。本文对统一机会模型做了进一步扩展，提出了一个考虑城市间交互的选择模型来测量城市间的交互强度，并将其应用于中国城市间交互活力量化。该模型假设个体选择某个城市的概率与该城市的GDP 成正比，与起点城市和目的地城市之间用陆路交通网络最短出行时间计算的介入机会数成反比。通过使用该模型来测量城市间的交互强度，并结合城市的交互发生强度和吸引强度来量化城市交互活力，分析了中国陆路交通网络 2005年到2018 年的变化对城市间交互的影响。结果显示，随着中国陆路交通网络的发展，城市间的出行时间明显缩短，中远距离城市间的交互强度显著提高。此外，中国高铁的快速发展使高铁沿线城市的交互活力普遍提高，而距离高铁线路较远的城市的交互活力则普遍下降。与引力模型和辐射模型相比，考虑城市间交互的选择模型能够更好地量化城市交互活力和城市间交互强度。 （4）出行分布特征展示方法研究应用与扩展。本文提出了一种展示统一机会模型结果的新方法，通过建立出行向量场并绘制出行等势线来展示出行分布空间特征。相比之前通勤出行向量场的方法，本文提出的方法更具一般性，不仅能够应用于通勤还可以应用于城市内部的客货出行。进一步地，本文又对其进行了扩展，提出了一个一般化的矢量场框架。该框架包括平均向量场和能反映个体出行空间特征的轨迹取向两个指标。其中，平均向量场可以用来描述个体在连续出行过程中所形成场的特性，而个体轨迹取向指标则根据个体在出行过程中是指向起点还是远离起点，将出行轨迹分为正、负和平衡三种情况。通过对北京、上海、成都等多个城市的货运数据和纽约Foursquare 签到数据的分析，发现指向起点的出行要比远离起点的出行多，远离起点出行与返回起点出行比值相近，这说明在不同的城市中存在一个普适性规律。最后，本文建立了距离随机模型、距离旅行商模型和距离随机-旅行商混合模型来生成轨迹，并将上述矢量场框架应用于这三种模型来研究它们生成轨迹的特点。结果表明，距离随机-旅行商混合模型可以很好地再现不同城市的远离起点出行与返回起点出行比值相近这一普适性规律。上述研究说明，一般化的矢量场框架不仅能够展示群体出行分布空间特征，还可以量化个体出行空间特征。
- English
