3.2. Regression Results and Analysis
The variance inflation factor (VIF) of all independent variables was detected before the regression models. The VIF values for polycentricity, CT, PC, PB, PUA, and PD were 1.073, 1.228, 2.065, 1.987, and 1.632, respectively. Values that are less than 4 indicate that the multiple collinearity between variables does not affect the analysis of the results [4
]. The statistics of variables in the regression equation are shown in Table 1
The regression results of polycentric and urban commuting efficiency (represented by both CDI and MTS) are shown in the Table 2
and Table 3
. Each table shows six sets of regression results from the linear relationship between polycentricity and CDI or MTS. In turn, the models adopted a method of adding a control variable.
The results of Models 1, 2, 3, 4, 5, and 6 show that polycentricity always has a significant negative relationship with CDI and positive relationship with MTS (significant at 0.05 level) regardless of the control variables. Thus, high polycentricity means low congestion index and fast average commuting speed. In Model 1, only the independent polycentricity index was analyzed using CDI and MTS. The results show that for every 1% increase in polycentricity, urban congestion coefficient decreases by 0.687%, whereas traffic speed increases by 14.218%. Polycentric spatial structure improves urban commuting efficiency.
In Model 2, the CT control variable slightly alters the effects of polycentricity on CDI and MTS. The promotion effect on MTS is enhanced by approximately 2.31%; the reduction effect on CDI, by 1.46%. Therefore, a compact spatial form can enhance the effects of polycentricity on commuter efficiency. Reasonable commuting distance improves the effectiveness of public transport in compact cities with controlled scales of urban development. Moreover, appropriate travel time, comfortable environment and cheap travel cost make public transportation an attractive and rational choice for urban dwellers. Several rules and policies can be promulgated to restrain the increase private cars and, thus, alleviate traffic pressure.
In Model 3, which has PC variables, the effect of polycentricity on CDI decreases with the influence of CT and PC. For every 1% increase in polycentricity, the reduction of CDI decreases from 0.697% of Model 2 to 0.597% of Model 3. On the contrary, the effect of polycentricity on MTS is enhanced. Ceteris paribus, the traffic speed increases from 14.547% to 14.606%. The improvement of the living standard of the residents results in an annual increase in car ownership and private car traffic. Families with cars are no longer constrained by traffic distance when choosing house locations, which accelerates the trend of suburbanization of living space. Loose and smooth traffic has been a main mechanism to improve the commuting speed of polycentricity. However, the increased number of vehicles entering the city would increase the possibility of traffic jams under the same number of commuting destinations (polycenters). Cars represent private travel, whereas PB (Model 4) represents urban public transport. Although the impact of PB on CDI and MTS does not have statistical significance, it enhances the effect of polycentricity on commuting efficiency. Compared with Model 3, the enhancement effect on MTS was increased by 2.73% and reduction effect on CDI was increased by 4.19%. Public transport has many advantages, such as wide coverage, large capacity, convenience and speed. The demand for passenger flow and the completion level of road facilities infrastructure in the suburbs is not similar to that of the main urban area. Public transportation mainly affects the commuting situation of the main urban area, where urban population centers (employment centers) are mainly distributed. Therefore, the promotion of public transport can improve the utilization rate of traffic resources, the congestion caused by too many cars and the speed of road traffic.
Urban commuter capacity is also related to the city’s road facilities and population density. PUA and PD variables are added in the sequence, and Models 5 and 6 are constructed. The PUA variable increases the effect of polycentricity on the MTS, making it the largest among the six groups (15.446 in Model 5 > 15.505 in Model 4 > 14.606 in Model 3 > 14.547 in Model 2 > 14.066 in Model 6). However, after PD is added, the effect reaches minimum despite the effect of polycentricity on CDI and MTS. Complete road infrastructure is an important measure to promote urban commuting efficiency in a polycentric city. However, high population density makes frequent commuters to aggravate the pressure of urban commuting, resulting in unobstructed traffic and urban congestion.
To identify which factor can increase the effect of polycentricity, the change of polycentricity effect was observed by adding only one variable at a time (Table 4
). Under similar conditions, the order of effect magnitude of polycentricity on MTS was PD > PC > CT > PUA > PB (absolute value indicates the effect magnitude, the same below). However, the order of effect magnitude of polycentricity on CDI was PD > PC > PB > CT > PUA. PC has the greatest positive effect on polycentricity, promoting MTS, with a 2.73% increase. On the contrary, PD has the most significant negative effect. It reduces the effect of polycentricity on MTS by 13.67%. In terms of the effect of polycentricity on CDI, PD most significantly increased the congestion index, whereas CT is the most significantly reduced congestion.
3.3. Compared with Previous Studies
Although the calculations indicate that polycentricity is statistically significantly greater than monocentricity in this study, we could not take the results for granted because it need to be scale and data specific. A careful comparison was made among the previous studies related to this topic, and conflicting results were found. The explanations for inconsistent conclusions could be summarized as follows:
First, the study areas are inconsistent. Some of them are conducted within a city or a metropolitan area [15
], While others take numerous cities rather a single city as study area [43
]. Therefore, the results may or may not be the same with alternative research scales, and each result may only be applicable for the specific scale.
Second, part of the explanation could be the inconsistent data source even for the same indicator. For example, Gordon et al. [28
] and Cervero et al. [18
] found different relationships between urban spatial form and commuting efficiency even though using the same indicator. This may due partially to the inconsistent data obtained from different national surveys. Further studies are needed to predict whether the results remain the same when using the same dataset.
Admittedly, city-wide data collecting is usually time-consuming and costly, and it is difficult to be applied extensively. The emergence of new urban data opens new opportunities of conducting multi-city analysis with the same dataset source at fine-grained scale. This study found positive and significant impact of polycentric urban form on urban commuting for 100 Chinese cities with the use of fined-grained geographic data, contributing to the literature in the main debate focusing on whether to support monocentric or polycentricity urban structures.
Fortunately, we found that all the existing studies aiming at Chinese cities have achieved the same conclusion that polycentric spatial structure helps to improve commuting efficiency even based on traditional socioeconomic data or new big data, and numerous cities [43
] or a single city [61
]. All the findings help to verify the necessity of the implementation of ‘polycentric urban patterns’ in China.
3.4. Reason for Polycentricity and Suggestions for Urban Planning
In urban planning and construction, people often overlook the importance of urban form. Only when the change of urban form seriously affects production, life and environment, can people find defects [56
]. China’s urbanization rate in 2016 was only 57.35% (National Bureau of Statistics, 2016). However, the ‘2013 China Human Development Report’ released by the United Nations Development Program pointed out that China’s urbanization will reach over 70% by 2030. Therefore, Chinese cities are expected to expand for a certain period [56
]. Urban space will continue to undergo profound changes, which is both a challenge and an opportunity. The challenge is if Chinese cities can eliminate urban sprawl, which is likened to a ‘spread the pie’ around a single center. Meanwhile, the opportunity is to leave enough room to reshape urban form.
Currently, many cities in China are facing major urban diseases, such as overcrowding, traffic jams and high housing prices. In particular, the urban problems in the developed regions of Eastern China are quite serious. Unlike Western cities, China’s cities generally have large populations. According to statistics from the National Development and Reform Commission in 2014, 142 Chinese cities have a population of over 1 million, of which six cities have a population of over 10 million. With such large populations, the mode of one or few centers leads to excessive concentration of resources. Moreover, commuters share a similar target area, which brings enormous commuting pressure in the downtown area and causes very large economic losses. Building new towns with mixed-use and encouraging people to work close to home in suburban areas can reduce the population and employment pressure from downtown, thereby solving commuting problem. International and domestic disputes on monocentric and polycentric cities remain unresolved. Even Cervero et al. [62
], the strongest supporter of monocentric cities, acknowledged that the residents of the former central area who have moved to the employment area have a much shorter commuting time than those who still live in the central area. The main reason for such a disagreement is the mechanism under which a polycentric urban spatial structure will be formed. If subcenters realize the complementary function during formation that the residents can get close to the employment, then the polycentric urban spatial structure will undoubtedly reduce the volume of traffic gathered in the single center. Such a structure will shorten the commuting distance and time of residents. On the contrary, if residence and employment places occupy different locations of the city, then the interregional travel of the residents will exacerbate urban traffic. Therefore, a polycentric spatial structure must balance employment and residence. Future urban planning should consider the six following aspects:
(1) To guide population transfer: It could be reasonably inferred from the model results that excessive population density can significantly reduce the function of polycentric spatial structure in increasing commuting efficiency. Population agglomeration mainly results from unequal spatial distribution of social resources. Therefore, population distribution can be improved by promoting equal public utilities in different regions. Preferential policies, such as household registration and taxation, must also be implemented.
(2) To guide the city’s compact development: Compact city can significantly reduce congestion. It is considered a smart growth model, which can greatly reduce the dependence on road traffic, especially private cars. Compact city alleviates road traffic pressure, consumption of oil and other resources, and air pollution [63
]. The creation of a compact city can be encouraged by measures, such as development of landfills [56
] and delineation of urban growth boundaries.
(3) To improve the urban public transportation system: A 1% increase in buses owned by 10,000 people increases MTS by roughly 2.73% and reduces CDI by approximately 4.19%. Public transport has substantial coverage and transport capacity. Therefore, establishing public transport system may alleviate urban transport problems in China. Public transportation must be developed around subcenters, between main centers and subcenters as well as between subcenters. A public transport oriented development model (TOD) is also suggested. A polycentric urban structure usually include the main center and the subcenters which take stations over radial public transport lines as core areas, forming orderly polycentric development.
(4) To strengthen road infrastructure: Model 6 reveals the significant role of PUA in exerting polycentricity on MTS. As the most basic municipal engineering, roads link production and living spaces. In addition to increasing capital investment, the government should consider improving the design of road network features. These features must be designed in a way that increases the degree of urban road area per capita network center in the area where people gather and shortens the road network distance between different network nodes.
(5) To promote rational consumption of cars: The mainstream mode of transport has important constraints and guidance on the structure and form of the city, and the transportation system shapes land use patterns. A large number of cars have caused serious traffic congestion and environmental pollution, which has led to a low-density spreading of the city as the wheels roll forward. The government can implement regulations to control private purchase and use of cars. Such regulations include license plate queuing measures implemented in Beijing and Shanghai, which have curbed the excessive increase of cars to a certain extent.
(6) Jobs-housing balance: Polycentric cities are favorable to short commutes only when they have a balanced number of jobs and resident workers in each center. Therefore, ‘polycentrism’ plans and policies is required to take into consideration jobs-housing balance, not only the number, but also the type of jobs and urban housing.