Car dependence, traffic congestion, long commuting distance, and associated air pollution and Greenhouse Gas (GHG) emissions in metropolises have become a serious global area of concern [1
]. At the same time, rapid urbanization and population growth, rising incomes, increased car ownership, land use changes, and weak traffic management have resulted in an increase in commuting time in cities [3
]. In order to deal with these issues, different counter measures have been proposed. The jobs–housing balance has been considered by planners, researchers, and policy makers to be the most effective counter measure [7
]. This balance reflects the distribution of both residential and employment opportunities within an urban area, being the “spatial relation between the number of jobs and housing units within a given area” [15
]. The jobs–housing balance can also be used as a ratio to measure jobs and housing opportunities via spatial units, such as Traffic Analysis Zones (TAZ) [16
]. If a spatial unit achieves a certain ratio of jobs and housing opportunities, it is in a “quantitative balance”; a “quantitative imbalance” is achieved when this is not the case [1
]. More generally, commuting time can be a proxy for the jobs–housing balance. An imbalance is suggested whether workers live far (in either space or time) from opportunities or not. The resulting jobs–housing imbalance (JHI) has been analyzed theoretically and empirically by urban economists, geographers, and planners, and three issues have been identified: (i) longer commuting distance—JHI can induce a longer total commuting, e.g., “wasteful” commuting; (ii) single-occupant commuting—JHI increases the rate of solo-driving trips; and (iii) social exclusion—JHI influences commuting of both workers and job-seekers who do not have their own cars. As these issues can be related to traffic congestion and deteriorating air quality, the jobs–housing imbalance has been examined as solutions are sought to address these social and environmental related problems [18
Empirical studies have quantified the spatial relationship between jobs and housing using different geographical methods. The ratio of the number of jobs to the number of working people within a given region is probably one of the most convenient, simple, and prevalent measurements used. This measurement has previously been defined by Boussauw et al. [19
]. Currently, different ratios reflecting a suitable balance of jobs to housing have been proposed. Margolis [16
] used a ratio of 0.75–1.25 for the community level; Cervero [21
] proposed a reasonable ceiling of 1.5 at the nationwide level; and Peng [15
], based on traffic analysis zones, suggested a range of 1.2–2.8. Other measurements include theoretical minimum/maximum commuting distance, excess commuting, commuting potential, and observed or reported individual-level or aggregated commuting distance [22
]. Cervero [9
] emphasized that the balance between an ideal job and housing is an abstract concept that is difficult to measure, and it has long been thought that people tend to gather in places where there are more jobs; residents therefore believe that jobs are more likely to be found in neighborhoods where housing is concentrated [27
], constituting an analytic parameter for these measurements. Boussauw et al. [19
] found that that the spatial proximity represents, for example, the jobs–housing balance or the number of potentially accessible jobs, and it can be used as non-linear predictors for reported commuting distances. This finding was confirmed by Horner who thought that the balance of jobs–housing, excess commuting distance, and job accessibility were interrelated in urban areas [28
]. At the same time, a number of investigations were undertaken on spatial models of the interactions of jobs and housing site selection. For example, Hincks and Wong [30
] empirically examined the spatial process of housing and the interaction with the labor market using a case study in north west England, and Sener et al. [31
] analyzed housing choices using a generalized spatially correlated logit model based on survey data from San Francisco, USA. These studies adopted traditional ‘top-down’ methods, which are limited in reflecting and expounding individual behavior leading to spatial population dynamics [32
]. Due to spatial population dynamics based on individuals looking for work and choosing where to live, it is therefore hard to simulate complicated individual behavior using these models [38
The urban ecosystem is complex, involving multiple factors such as population, economy, transportation, and the environment. There are complex inter-relationships between internal factors that influence and/or restrict each other. Among many simulation methods of complex systems, agent-based models (ABMs) are an important tool to simulate complex systems which have increased in popularity [39
]. ABMs utilize a ‘bottom-up’ approach to simulate the complex behaviors of interacting individual agents [43
]. These models are advantages by dynamically connecting social and economic factors, and simulating the process of individual decision making and interactions [47
]. ABMs can describe the behavior of individual agents, which can be governments, the environment, individuals or enterprises, depending on the specific conditions. ABMs also often focus on decision-making processes, such as which objectives to examine and decision rules. Therefore, ABM approaches can potentially be used in modeling spatial dynamics evolving from individual behavior [49
]. Liu and Ye [49
] explored the evolvement of firms’ environmental behavior and influencing factors using an adaptive agent-based modeling approach, and the results revealed that firms’ environmental behavior followed this evolvement path: defensive behavior, preventive behavior, and enthusiastic behavior using empirical data from 167 firms in China. Adrestani et al. [50
] proposed an agent-based model of residential segregation, which contributes to the same realistic modelling direction for analyzing the effect of residential location decision of individual residents on the spatial ethnic mosaic pattern of the central Auckland region (New Zealand metropolis). Yue et al. [51
] built a simulation model of the energy-saving behavior of urban residents using agent-based modeling, and analyzed the subsequent effect of behavioral outcomes due to the short- and long-term influence of energy-saving behavior and intentions under different policy situations. Therefore, agent-based models (ABMs) are ideally suited for simulating individual behavior differences in a complex system.
With the large-scale agglomeration of populations in some large cities, and the rapid expansion of urban space, serious large-scale urban diseases such as unbalanced occupations, traffic congestion, and environmental pollution have emerged, which have severely restricted the sustainable development of the region and the construction of ecological civilization. As a typical single-center layout city in China, Beijing has a large-scale agglomeration of urban population and a rapid expansion of urban space. Serious urban issues, such as jobs–housing separation (JHS), traffic congestion, and environmental pollution, have significantly restricted the sustainable development of Beijing. Therefore, in order for Beijing to successfully develop in the post-Olympic era, solutions to solve urban traffic congestion, to relieve the current situation of JHS, and to shorten commuting time and distance are urgently needed. In this study, we propose an ABM to simulate spatial location selection behavior of agents by considering the impact of environment and economy factors on employment behavior and the residential decisions of individuals. This approach will also simulate residential decisions made by individuals. The difference of resident agents’ income level has a significant impact on residential location decision-making, and housing price is the primary factor affecting the decision of residents to choose their residential location. Based on the simulation results of location selection of agents, the density simulation results of resident population and employment population on a street level in Beijing, as spatial units, will be obtained. Using this approach, the spatial distribution of jobs–housing in Beijing under the maximization of the micro-agent location utility will also be identified. The spatial relationship distribution of jobs–housing in Beijing and the imbalance of the jobs–housing relationship in the central city has improved. Compared with the initial distribution, the number of jobs–housing balance areas in Beijing has increased. Our aim is to simulate the adaptive behavior of each agent on the jobs–housing environment by constructing the location selection method framework of agents. Moreover, we used the modular and hierarchical modelling characteristics of the Anylogic platform to analyze the urban jobs–housing location selection and spatial relationship. Our study is an exploration of a complex multi-agent system model on the jobs–housing relationship, and results provide suggestions for improving spatial relationships of jobs and housing to achieve a balance. At the same time, our study has a certain theoretical and practical significance to scientifically formulate policy measures for improving the jobs–housing relationship and green low-carbon transportation development strategies.
In this study we introduced a multi-agent approach to examine the jobs–housing relationship under the maximum location utility of residents and enterprises. The jobs/housing ratio was initially used to measure the balance of the number of jobs–housing relationships. JHS of Beijing in 2010 and 2014 was then compared and analyzed using district, county, and street scales. Results from this analysis identified that rapid population growth in the 6th Ring Road, a mismatch between housing and jobs, and the surrounding urban areas not being able to provide sufficient housing has resulted in an imbalance in the jobs–housing relationship in Beijing. The jobs–housing relationship of the central urban areas (Dongcheng and Xicheng) is still in an obvious imbalance, and it greatly exceeds the limit, indicating that it is impossible to provide corresponding accommodation for people in the area. From street and township scales, parks, historical, and traditional cultural areas of Nanyuan, Beiyuan, Tiantan, Tsinghua Park, and Yushu Street in the 6th Ring Road are all in a relatively balanced state. For some large residential areas in Beijing (such as Tiantongyuan, Huilongguan, and Huoying), an obvious imbalance in the jobs–housing relationship exists due to a lack of jobs; some residents living in these areas have to work in other external areas. Due to a lack of housing, forcing people to live in other areas, the streets of Haidian, Dongzhimen, and Jianwai are also in an unbalanced state.
An agent-based model was proposed to simulate spatial location selection behavior of agents by considering the influence of environment and economy on the residential decisions of individuals. Simulation results for six resident agents and enterprise agents experiments examining the spatial location selection process of residents in Beijing were analyzed. Resident agents with a middle and high income were mainly concentrated in the urban area, and areas with better environmental characteristics. Low-income residents, however, are mainly concentrated in the new city area and the urban development new area. This result indicated that the difference of resident agents’ income level has a significant impact on residential location decision-making, and housing price is the primary factor affecting the decision of residents to choose their residential location. At the same time, financial and technological innovation service enterprises are mainly concentrated in urban areas and the key science park areas; although social service enterprises are more geographically dispersed, they are mainly concentrated in the 6th Ring Road. The location of industrial manufacturing enterprises are mainly distributed outside the 6th Ring Road, concentrated in the urban development new zone. The spatial distribution of jobs–housing in Beijing under the maximization of micro-agent location utility was also obtained. The spatial relationship distribution of jobs–housing in Beijing and the imbalance of the jobs–housing relationship in the central city has improved. Compared with the initial distribution, the number of jobs–housing balance areas in Beijing has increased.
The current situation of serious urban issues, such as jobs–housing separation (JHS), traffic congestion, and environmental pollution, have significantly restricted the sustainable development. The reasonable urban jobs–housing adjustment policy not only improves traffic congestion, but also improves urban residents’ commuting efficiency and reduces commuting time. Local governments can accelerate the implementation of policy combinations to encourage the closest residence or employment, thereby achieving a real sense of a jobs–housing relationship balance. These include the establishment of urban sub-centers, the orderly promotion of the transfer of all or part of municipal administrative institutions to sub-centers, minimization of traffic congestion caused by commuting, and promotion of job–housing balance. Meanwhile, the construction of new surrounding areas should be accelerated, focusing on creating a non-capital function decentralized centralized load-bearing area, effectively alleviating diseases in large cities, and focusing on alleviating the pressure on urban populations. At the same time, it is necessary to speed up the implementation of the means and programs that focus on adjusting the jobs–housing relationship, reduce the travel demand of residents from the source, shorten the commute time and distance of commuters, alleviate the pressure of tidal traffic, and achieve the purpose of controlling the demand for urban travel, and achieve jobs–housing balance from urban transport.
There are some potential limitations of this study. We used ABM models to simulate the local jobs–housing relationship based on different scenarios. In order to facilitate the construction of the model we simplified the behavior rules and decision-making of agents. In real life, behavior rules and decisions of micro-agents are affected by urban spatial planning, land use control, financial regulation, etc. Future research based on our findings can continue to improve this model, thereby improving the accuracy and the reference of simulation results. In addition, due to government control and supervision of people, the behavior of enterprises can be affected by many factors. Future studies need to combine the situation of the local jobs–housing relationship adjustment policy and government control to analyze its impact on the behavior of enterprise agents.