# The Impact of Road Functions on Road Congestions Based on POI Clustering: An Empirical Analysis in Xi’an, China

Authors: Hui Zhu; Kaiqi Zhang; Changjun Wang; Lujin Jia; Siyu Song
Journal: Journal of Advanced Transportation
 (2023)

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## Abstract

In new-tier cities with rapid urbanization, the reorganization of urban spatial functions and the development of road networks have brought novel challenges to traffic congestion control. Urban land use patterns have a significant correlation with urban traffic congestion. However, whether and how land use patterns of cities close to the roads affect road congestion is less to be discussed. This article investigated the relationship between land use patterns close to the urban trunk road network and traffic congestion in new tier cities Xi’an, China. We adopted the DBSCAN algorithm to cluster POIs and use the mixed POI clusters to label the socio-economic functions of roads. We found the spatial heterogeneity of POIs on the trunk road network and identified the impacts of the scales and types of POI on road congestion based on the empirical analysis. Compared to the POIs as origin and destination of the trips, the POIs as stopover points of the trips cause significantly more road congestion. The POIs with bidirectional flows at entrances/exits are more likely to cause road congestion than the POIs with unidirectional flows. Moreover, the POIs with flexible traffic flows increase road congestion, while the POIs with predicted traffic flows have no statistically significant correlation with road congestion. The results help urban planners to plan the scale, type, and location of POIs close to roads and to optimize the socio-economic functions of roads and alleviate road congestion.


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## Body

## 1. Introduction

Urban sprawl and population growth are changing the spatial distribution of urban functions and affecting the urban traffic system in rapid urbanization [1]. A proper urban land use pattern is essential to improve the efficiency of the urban traffic system [2]. As a global urban governance issue, traffic congestion has caused heavy environmental pollution [3], energy consumption [4], traffic safety issues [5], and other negative externalities [6]. With the rapid urbanization in China, land use pattern is also challenging for the urban traffic system. Notably, the rapid reorganization of urban land use patterns in some emerging new-tier cities in China, such as Chengdu, Hangzhou, Chongqing, and Xi’an, caused exceptionally prominent traffic congestion. Moreover, some empirical evidence in daily traffic management is inconsistent with some common sense or traffic assignment theories on traffic congestion. Therefore, traffic congestion has become an essential issue in the sustainable development of cities and has received continuous attention from local governments and traffic management authorities. Moreover, the empirical findings also motivate us to explore the correlation between the urban land use and traffic congestion.Land use-transport interaction (LUTI) policy is a sustainable solution to urban traffic congestion, and numerous studies have discussed the impact of urban land use patterns on urban traffic congestion [7, 8]. However, due to the complex relationship between the traffic demand generation and the land use, it is still a challenge to understand the urban traffic congestion formation mechanism based on specific land use patterns. There is a general perspective that compact and mixed urban land use planning is more conducive to alleviating urban traffic congestion [7, 9], while that overcompact urban land use planning may generate greater traffic demands and cause traffic congestion [10]. Urban roads, an essential type of urban land use, provide the traffic functions for daily travel. Moreover, the urban primary socio-economic activities are also concentrated along the roads. Therefore, it may cause a stronger correlation between the land use pattern along the road and traffic congestion at the road level (hereafter referred to as road congestion). However, to our knowledge, limited research has considered the relationship between land use patterns and road congestion at the road level, while most research is analyzed at the urban area level, such as the relationship between the type, density, and mixing of land use within the urban area and traffic congestion. Apparently, the land use pattern along the road may differ from that of the area due to their geometry structure, and spatial distribution. Therefore, whether and how the land use pattern along the road affects road congestion is an issue that needs to be discussed.In response to the previous questions, it is necessary to depict the land use pattern along the road and establish the relationship between land use pattern and road congestion. Therefore, we proposed a DBSCAN-based algorithm to cluster POIs (points of interest) and associate the clusters of POI with roads. We defined the socio-economic function of POIs as the road functions to depict the land use pattern at the road level, and set several road function scenarios to revealed the correlations between road functions and road congestions based on empirical analysis. In the rest of this paper, Section2 reviews existing studies on the influence of road features and urban land use on congestions, Section 3 introduces the data acquisition and research methodology, and Section 4 provides the result analysis and discussions, followed by the conclusion in Section 5.


## 2. Literature Review

### 2.1. Impact of Urban Road Features on Road Congestion

Based on the view of traffic engineering, traffic congestion reflects the conflict between traffic travel demand and urban road limitations [11, 12]. The number of road lanes directly describes the road capacity. However, the relationship between the number of road lanes and traffic congestion in realistic environments is more complex, considering various factors such as the saturation [11], the spatial location of roads [13], and the restriction of traffic management measures for roads [14]. In addition, external factors, such as time of day and weather, may also affect the relationship between the number of lanes and traffic congestion [15–18]. On the other hand, the rank of the road is another criterion for evaluating the road capacity, and there are significant differences in the capacity of the different ranks of roads [19]. Studies have shown that urban traffic flows preferentially concentrate on the urban trunk roads. Moreover, the spatial location of the road, road network structure, and adjacent road properties such as the rank of the road, the number of road lanes, and the length of the road will significantly affect the traffic flow on the road. As a result, the road congestion levels of different ranks of roads usually have significant distinctions [12, 16, 17, 19]. In addition, microscopic features of roads, such as the duration of road signals [20], the distribution of turn lanes [21], the length of the road [17, 22], and the number of public transport facilities on the road [23], are correlated with the traffic congestion [24, 25].Based on the view of network science, the position of urban roads in the road network also significantly affects the road congestion level [13]. Existing studies discuss the impact of network structural features on network transport efficiency and road congestion level in abstract network structures [26, 27]. Meanwhile, recent studies have found that urban road networks generally are self-similarity, connectivity, and self-evolving [28–31]. Moreover, road network density significantly affects the performance of urban transportation systems. The local road congestion level is lower in a denser road network [32, 33]. Furthermore, with the same traffic volume, the single link which stands for the road with high betweenness in scale-free networks are more likely to have traffic congestion [22]. However, the road network with the Small-World feature cannot significantly affect the traffic flow in the network due to the spatial functions of the city [12].A comprehensive view shows a specific correlation between road features and traffic congestion. However, the current research mainly discuss the relationship between engineering features, topological features, and traffic congestion. In addition, whether the socio-economic functions or facilities in the vicinity of the road will impact the road congestion level is still an issue worth discussing.


### 2.2. Impact of Land Use on Road Congestion

Land use patterns are closely related to urban traffic congestion [8, 34, 35]. Many traffic demand forecasting methods are mainly based on OD matrices. The OD matrix estimation relies on the urban land use data. Therefore, traffic flows generated by land use pattern attraction help understand the causes of urban traffic congestion [36]. Urban traffic is closely related to land use types [8], land use density [37, 38], and the mixing of land use [39]. The travel behavior of urban residents, especially commuting behavior, is usually influenced by urban land use patterns. Furthermore, commuting time and average delay time has a significant negative correlation with the mixing of land use. However, the overcompact urban structure may also induce urban traffic congestion due to the density of travel demand [2, 9]; On the other hand, the urban built environment significantly impacts the state of urban traffic [17, 28].Similar to land use patterns, the urban built environment correlates significantly with traffic congestion. However, the urban built environment is highly spatially heterogeneous in its impact on traffic states. Specifically, the number of urban public facilities, commercial and residential facilities, and the density of the public transportation network all impact the traffic states in a region [15]. In addition, the distance from the city center impacts the coordination relationship between the built environment and traffic congestion [40]. Moreover, the urban built environment can impact urban residents’ travel behavior [28] and the choice of residents’ travel patterns may impact traffic congestion [41]. Furthermore, at the road level, existing studies have discussed that chronically congested roads are usually associated with the number of bus stops and shopping mall density [17]. However, these researches are limited and mainly focus on the built environment on the road.Points of interest (POI) data, a common data to represent urban land use patterns, can better quantify urban land attributes and simplify the complexity of urban land use patterns [42]. Currently, the utilization of POIs data mainly focuses on two aspects. The first is to depict land use patterns, such as by calculating urban development density, agglomeration, and mixing of land use to analyze land use patterns [37, 38]. The second is to reflect the types of the urban spatial structure according to the density of POIs in a particular area or block, such as identifying the aggregation characteristics of urban space through clustering POIs [43–45]. As a result, POIs data can effectively describe the land use types, density, degree of land use structure, and existing built-up areas in cities [38]. Moreover, combining POIs data and road network data can effectively establish the quantitative relationship between urban traffic and land use patterns, and reveal the impact of land use patterns on traffic congestion at the area level [17, 37].However, most of these researches have evaluated the impact of urban land patterns and built environments on urban traffic congestion based on the macro dimension. For example, evaluate the traffic congestion based on the built environment in a specific area [46], and use the SAR model to establish the relationship between the urban built environment and the spatial and temporal distribution of the OD flow [47]. There are significant geometric differences between urban areas and urban roads, and land use patterns within the polygon (urban area) will obviously differ from those along the line (urban road). Therefore, the existing researches at the area level may not effectively depict the land use patterns along the road and reveal the impact of the socio-economic function along the road on the road congestion, especially for the rapidly changing socio-economic functions at the road level. In this article, we will propose a method to depict land use patterns at the road level based on POIs data and reveal the impact of road function on road congestion.


## 2.1. Impact of Urban Road Features on Road Congestion

Based on the view of traffic engineering, traffic congestion reflects the conflict between traffic travel demand and urban road limitations [11, 12]. The number of road lanes directly describes the road capacity. However, the relationship between the number of road lanes and traffic congestion in realistic environments is more complex, considering various factors such as the saturation [11], the spatial location of roads [13], and the restriction of traffic management measures for roads [14]. In addition, external factors, such as time of day and weather, may also affect the relationship between the number of lanes and traffic congestion [15–18]. On the other hand, the rank of the road is another criterion for evaluating the road capacity, and there are significant differences in the capacity of the different ranks of roads [19]. Studies have shown that urban traffic flows preferentially concentrate on the urban trunk roads. Moreover, the spatial location of the road, road network structure, and adjacent road properties such as the rank of the road, the number of road lanes, and the length of the road will significantly affect the traffic flow on the road. As a result, the road congestion levels of different ranks of roads usually have significant distinctions [12, 16, 17, 19]. In addition, microscopic features of roads, such as the duration of road signals [20], the distribution of turn lanes [21], the length of the road [17, 22], and the number of public transport facilities on the road [23], are correlated with the traffic congestion [24, 25].Based on the view of network science, the position of urban roads in the road network also significantly affects the road congestion level [13]. Existing studies discuss the impact of network structural features on network transport efficiency and road congestion level in abstract network structures [26, 27]. Meanwhile, recent studies have found that urban road networks generally are self-similarity, connectivity, and self-evolving [28–31]. Moreover, road network density significantly affects the performance of urban transportation systems. The local road congestion level is lower in a denser road network [32, 33]. Furthermore, with the same traffic volume, the single link which stands for the road with high betweenness in scale-free networks are more likely to have traffic congestion [22]. However, the road network with the Small-World feature cannot significantly affect the traffic flow in the network due to the spatial functions of the city [12].A comprehensive view shows a specific correlation between road features and traffic congestion. However, the current research mainly discuss the relationship between engineering features, topological features, and traffic congestion. In addition, whether the socio-economic functions or facilities in the vicinity of the road will impact the road congestion level is still an issue worth discussing.


## 2.2. Impact of Land Use on Road Congestion

Land use patterns are closely related to urban traffic congestion [8, 34, 35]. Many traffic demand forecasting methods are mainly based on OD matrices. The OD matrix estimation relies on the urban land use data. Therefore, traffic flows generated by land use pattern attraction help understand the causes of urban traffic congestion [36]. Urban traffic is closely related to land use types [8], land use density [37, 38], and the mixing of land use [39]. The travel behavior of urban residents, especially commuting behavior, is usually influenced by urban land use patterns. Furthermore, commuting time and average delay time has a significant negative correlation with the mixing of land use. However, the overcompact urban structure may also induce urban traffic congestion due to the density of travel demand [2, 9]; On the other hand, the urban built environment significantly impacts the state of urban traffic [17, 28].Similar to land use patterns, the urban built environment correlates significantly with traffic congestion. However, the urban built environment is highly spatially heterogeneous in its impact on traffic states. Specifically, the number of urban public facilities, commercial and residential facilities, and the density of the public transportation network all impact the traffic states in a region [15]. In addition, the distance from the city center impacts the coordination relationship between the built environment and traffic congestion [40]. Moreover, the urban built environment can impact urban residents’ travel behavior [28] and the choice of residents’ travel patterns may impact traffic congestion [41]. Furthermore, at the road level, existing studies have discussed that chronically congested roads are usually associated with the number of bus stops and shopping mall density [17]. However, these researches are limited and mainly focus on the built environment on the road.Points of interest (POI) data, a common data to represent urban land use patterns, can better quantify urban land attributes and simplify the complexity of urban land use patterns [42]. Currently, the utilization of POIs data mainly focuses on two aspects. The first is to depict land use patterns, such as by calculating urban development density, agglomeration, and mixing of land use to analyze land use patterns [37, 38]. The second is to reflect the types of the urban spatial structure according to the density of POIs in a particular area or block, such as identifying the aggregation characteristics of urban space through clustering POIs [43–45]. As a result, POIs data can effectively describe the land use types, density, degree of land use structure, and existing built-up areas in cities [38]. Moreover, combining POIs data and road network data can effectively establish the quantitative relationship between urban traffic and land use patterns, and reveal the impact of land use patterns on traffic congestion at the area level [17, 37].However, most of these researches have evaluated the impact of urban land patterns and built environments on urban traffic congestion based on the macro dimension. For example, evaluate the traffic congestion based on the built environment in a specific area [46], and use the SAR model to establish the relationship between the urban built environment and the spatial and temporal distribution of the OD flow [47]. There are significant geometric differences between urban areas and urban roads, and land use patterns within the polygon (urban area) will obviously differ from those along the line (urban road). Therefore, the existing researches at the area level may not effectively depict the land use patterns along the road and reveal the impact of the socio-economic function along the road on the road congestion, especially for the rapidly changing socio-economic functions at the road level. In this article, we will propose a method to depict land use patterns at the road level based on POIs data and reveal the impact of road function on road congestion.


## 3. Data and Methodology

### 3.1. Study Area

We choose the famous historical-cultural city in China, Xi’an, for this research. Xi’an has seen a rapid expansion of its urban road network and continuous changes in urban land use in the process of urbanization. Thus, the development contradiction between the historical-cultural district and the emerging economic activity district in Xi’an city is highly representative.Xi’an has a radial arrangement of its roads. The Second Ring Roads and its extended road network (hereafter referred to as the Second Ring Road Network), along with its vicinity areas, are one of the most congested areas in Xi’an. The Second Ring Road Network connects the most prospering areas and historical districts in the city and gathers heavy loads of traffic from various types of functional urban districts, such as administrative, historical, industrial, cultural, educational, commercial, and economic districts (Figure1). Therefore, it provides a good research sample for this study.Figure 1
The study areas and second ring road network. The red lines, brown lines, and black lines correspond to the second ring road, extension roads directly connected to the second ring road, and linking the extension road of the extension road, respectively.More importantly, we have observed numerous counterintuitive congestions in the Second Ring Road Network in our daily traffic management, which existing theories cannot explain. For example, congestion levels near residential areas or office buildings are lower during the morning peak, although these areas would theoretically generate a large volume of traffic. In contrast, road congestion levels tend to be higher near some facilities that generate temporary travel demand, such as schools and hospitals. In addition, congestion levels near parking lots fluctuate significantly over time, and congestion levels near parking lots are significantly higher in the evening than in the morning. So, all these attractive observations motivated us to choose this area for this study.


### 3.2. Data Sources and Processing

Three types of data were collected to explore the correlation between urban road functions and traffic congestion: road property, vehicle speed data, and POI data.


#### 3.2.1. The Road Property Data

Based on the previously described, the roads included in this analysis include three types: the trunk roads of Second Ring Roads, the extension roads directly linked to the trunk roads, and the subextension roads linked to the extension roads. According to this sampling criterion, we combined the official vehicle velocity data from the Xi’an traffic governance department with the open data from the OpenStreetMap to obtain the spatial coordinates, road vector shape, road width, and road rank. We finally obtained the property data of 163 roads, including road design speed, capacity, length, and the number of road lanes. Then, we used the K-means clustering method to classify the sample roads into six ranks (Table1) by referring to OpenStreetMap’s classification of road ranks and corrected the clustering results by manual proofreading.Table 1
The ranks and the ratio of the sample roads.
CountRatio (%)Trunk148.59Primary2112.88Secondary3018.41Tertiary6741.10Local1911.66Residential127.36Total163100.00In our classification, the trunk is the Second Ring Roads. The primary, the secondary, and the tertiary are extension roads directly linked to the Second Ring Road, and their cumulative proportion is more than is more than 50%. And the local and the residential are the subextension roads linked to the extension roads, which are the peripheral or internal roads of residential or industrial areas. Finally, it is essential to note that we have clustered the roads using K-mean, thus avoiding the influence of different roads or directions on the road congestion. Therefore, our sampling ignores the lane direction of the road and the distinction between the primary and secondary lanes of one road, as we focus more on the function given to the road by the adjacent area.


#### 3.2.2. The Average Road Speed Data

We obtained the average road speed data of the sample roads in September 2021 via the Xi’an Traffic Police Detachment. We excluded the data during the weekends and public holidays in September and kept it for weekdays, considering the daily functions of the Second Ring Network and its surrounding areas, and the sampling frequency of data was hourly. Finally, we obtained 164,736 rows of data for analysis. As shown in Figure2, the sample roads’ average speed generally follows the urban’s circadian rhythm. Meanwhile, the road average speed varies significantly. The initial analysis suggests that the sampled speed data can describe the daily travel behavior.Figure 2
Hourly average speed data. The distribution data are from 164,736 rows of data for analysis.


#### 3.2.3. Points-of-Interest (POIs) Data

We obtained the POI data within the Third Ring Road of Xi’an in 2020 through Xi’an Urban Planning Bureau, including the name, the address, the coordinate, and the type of POIs. There are 19 primary and 133 secondary types of POIs data. To align the type of POIs with the empirical evidence in daily traffic management, we reorganized the types of POI for our research design. Firstly, we removed the POIs data without generating traffic demand, such as the roads, the bridges, the road entrances and exits, the intersections, and the entrances and exits of areas. Secondly, we reorganized the primary type according to the travel demands they generate. For example, we distinguished the industrial parks and office buildings as business and company, and the residential buildings as a residence, although they are all categorized as Real Estate. Similarly, we combined living services, leisure and entertainment, and a beauty salon as living services that satisfy the daily demands of residents. Thirdly, we combined the bus stations, subway stations, railway stations, airports, and bus garages as transportation facilities, and set the parking services, and set car care services, repair, and car rental and sale services as vehicle services. In addition, we remove the POIs that may belong to more than one type. Finally, we obtained 16 types, and their counts and percentages are shown in Table2.Table 2
Types and percent of POIs.
CountPercent (%)Retail service2400622.46Living service1808316.92Catering service1273111.91Business and company1365012.77Parking Service43054.03Financial service36603.42Educational service41033.84Government agency45494.26Medical service37103.47Hotel service28272.64Transportation service60445.65Vehicle service25752.41Residence34703.25Sports service11721.10Tourist service15871.48Culture service4090.38


### 3.3. Methods for Quantifying Road Congestion Level

The road congestion features of the roads are the explained variables in this article. We used the traffic performance index (hereafter referred to as TPI) to estimate the level of congestion on the roads. For roadxi, its TPI can be defined by(1)TPI=RVshVd,where vs is the average speed of road xi within time t, vd is the free-flow speed of the road, and R is a normalization function which can help us transform the ratio into a distribution between 0 and 10 (Appendix A). This function is a method used by the Xi’an traffic governance department. After normalization, the TPI range is (0–10). Moreover, it can be divided into five levels, which are free (0–2), partial-free (2–4), mild congestion (4–6), moderate congestion (6–8), and severe congestion (8–10). Moreover, to control the fluctuations of the external factors, such as traffic accidents, emergencies, extreme weather, and other uncertainties on the road speed, we excluded the top 5% and bottom 5% of the original data when calculating the average travel speed. Table 3 shows the distribution of TPI for different ranks of roads after calculation.Table 3
TPI of different ranks of road.
CountMeanStdMinMaxTrunk175422.0261.7360.0019.470Primary282231.7621.4330.0009.126Secondary383761.7081.2820.0009.240Tertiary900652.3601.1930.0009.012Local266312.6991.1420.0028.979Residential172722.9221.0000.0048.046We estimated the temporal distribution of TPI for different ranks of roads (Figure3(a)). The road congestion level of the morning and evening peaks (8:00 and 18:00) are the most prominent (Figure 3(a)). Moreover, there are more significant fluctuations on the trunk roads during the morning and evening peaks (Figure 3(b)), which indicates that the congestion distribution on the trunk roads is heterogeneous. Furthermore, the tertiary and lower ranks of roads maintain a higher TPI throughout the day (7:00–21:59), but fluctuations of these roads are relatively small, which indicates that the congestion on these roads may be a general phenomenon due to the road features.Figure 3
Hourly TPI distribution of different ranks of roads. (a) Hourly average TPI of the different ranks of roads. The color is closer to red, and the road is more congested, on the contrary, the road is smooth. (b) Hourly standard deviation of the different ranks of roads. The color is closer to red, and the TPI changes greater over time, on the contrary, the change is not obvious.
(a)(b)In addition, to reduce the research’s complexity, we further divide the road congestion level into four periods based on the temporal distribution of the TPI in Figure3, which are morning peak (7:00–9:59, 3 hours), evening peak (17:00–19:59, 3 hours), off-peak (10:00–16:59, 7 hours), and night hours (20:00–22:59, 3 hours). Due to the low congestion between 0:00 and 6:00, we ignored this period in this article. and the subsequent empirical analysis in this article will focus on the four periods previously.


### 3.4. Quantification of Road Functions Based on POIs Data

The function of the road is the primary explanatory variable in this paper to explain the congestion on the road. For this purpose, we use the types and scales of POIs along the roads to define the road function. Therefore, we propose a labeling method based on the DBSCAN clustering algorithm for associating POIs with roads.Specifically, our method includes two steps. In the first step, we used the DBSCAN algorithm (Appendix B) to cluster each type of POI based on the Euclidean distance between each POI. In practice, we limited the upper bound of the neighborhood distance threshold Dmin to be less than the average length of the sampled roads and used a greedy algorithm to obtain the optimal parameters of the DBSCAN algorithm based on the silhouette coefficientSc (Figure 4). This setting ensures that the range of each cluster cannot exceed the length of the sampled roads so that we can use the cluster to represent the road function in the second step.Figure 4
The distribution of silhouette coefficient of business and company POI clustering results under different parameter combinations. The colors represent the value of the silhouette coefficient of the clustering results. The horizontal axis is the minimum number of clustersNmin, and the vertical axis is the neighborhood distance threshold Dmin. Considering the realistic need for spatial clustering, the units of Dmin are meters.In the second step, we calculated the shortest distance from the core points of the clusters to the sampled roads and match the core points of the clusters with the closest sampled road (Appendix C). We assume that each road has a buffer area (Figure5), a container of POIs, where travelers can realize their travel demands. In practice, We preanalyzed based on the sampled roads in second ring road network. On the one hand, the stores and other facilities along the road are evenly distributed within an area 100 meters wide from the road; if the buffer area is too small, we cannot obtain more POIs to label the road functions. On the other hand, we calculated the average distance between parallel roads. Setting the width of the buffer area to 100 m can avoid overlapping areas for parallel roads. Thus, we set the boundary of the buffer area to 100 m. We selected the clusters and their POIs whose cores are in the buffer area and remove the clusters and their POIs whose cores are more than 100 m away from the road, though some POIs are in the buffer area. Finally, we will use the types of the selected clusters and the scale of POIs in clusters to represent the road functions. According to the previous method, Table 4 presents the optimal parameters of DBSCAN for different types of POI and the statistical characteristics of the resampled POIs and includes the use of the DBSCAN parameter, such as Nmin, Dmin which has the same meaning as in the algorithm.Figure 5
A buffer area of POIs was selected. Different types of POI are distributed on the roads. By using the DBSCAN clustering algorithm (Appendix B) based on the Euclidean distance between the POIs to cluster the POIs acquire the core of the cluster. In addition, by setting a boundary (100 m) for filtering the core points of the clusters (the buffer area’s scale is not the actual situation). The blue and red circles correspond to the selected and unselected clusters, respectively. The star and error symbols indicate POIs selected and POIs unselected, respectively. Finally, the function of the road will use the types of the retained clusters and the scale of POIs in those clusters to describe.Table 4
Clustering results of different types of POI.
CountPercent (%)NminDminNumber of clustersMaxMinMeanStdRetail service538025.99287434500212.3735.63Living service265012.80212627617529.5717.29Catering service252612.2029528917228.7113.78Business and company245711.8727934137627.1822.03Parking service15247.36211527225325.5815.96Financial service10605.122792699923.936.18Educational service9074.382221957329.4513.91Government agency8504.11215215614425.4111.67Medical service7073.4122011019826.9311.73Hotel service6903.332201907127.5812.01Transportation service6593.1822661136825.788.53Vehicle service5022.422292814226.127.13Residence3611.7434641574422.5620.94Sports service2741.322423393726.857.33Tourist service1010.492464153526.318.41Culture service550.272464122024.234.76


### 3.5. Analytical Model and Study Design

Due to the complex spatial correlation between different POI types, most studies use entropy to describe the mixing pattern of POIs and label the function of specific areas [37, 38]. However, the entropy cannot effectively verify the correlation between specific POI types and the explained variables. So, we cannot reveal which POI type or combination of POIs may cause congestion and their impact on the road congestion level. For this purpose, we design an analytical framework to reveal the correlation between POI types and road congestion levels. In addition, to better understand the relationship between road function and road congestion, we model the road function and road congestion based on the following three observations that are contrary to the common sense from daily traffic management evidence.(1)
Road congestion is more severe near public facilities such as schools and hospitals along commuting routes, although vehicles do not stay there for long(2)
There is no significant road congestion near companies and businesses with many vehicles(3)
There are different characteristics of traffic flow at the entrance and exit of the road during the morning peakTherefore, we used a regular regression model to represent the relationship between road function and road congestion levels, which is a common model to reveal correlations between variables. In this model, we set the road congestion level as the dependent variables, the scales of different types of POIs as the independent variables, and the ranks of the road as a control variable. The regression equation might appear as follows:(2)TPIi,t=β0+∑c=1nβcPOIi,c+βn+1Ranki+εi,where TPI is the average traffic performance index of the road i during the time t, representing the each road congestion level, POIc is the scales of different types of POIs within the buffer area of road i, representing the road function, β is the parameter to be estimated, and ε is the random error term. In practice, we used the ordinary least squares to estimate the parameters of the model.Based on the actual observation of traffic management in real life, we separated the 16 POI types into four scenarios and discussed the impact of different POI combinations on road congestion levels separately.(1)
The influence of POIs in the 50th percentile of type scale on road congestions, namely, retail service, living service, and catering service(2)
The influence of POIs in the commuting-related types of road congestion, namely, residence, business and company, and educational service(3)
The influence of POIs in the public service types on road congestion, namely, government agency, transportation service, educational service, medical service, and parking service(4)
The influence of POIs in other types on road congestion.Notably, since the number of Tourist Service and Culture Service POIs is less than 1%, they will not be discussed in the above model design and subsequent model discussions.


## 3.1. Study Area

We choose the famous historical-cultural city in China, Xi’an, for this research. Xi’an has seen a rapid expansion of its urban road network and continuous changes in urban land use in the process of urbanization. Thus, the development contradiction between the historical-cultural district and the emerging economic activity district in Xi’an city is highly representative.Xi’an has a radial arrangement of its roads. The Second Ring Roads and its extended road network (hereafter referred to as the Second Ring Road Network), along with its vicinity areas, are one of the most congested areas in Xi’an. The Second Ring Road Network connects the most prospering areas and historical districts in the city and gathers heavy loads of traffic from various types of functional urban districts, such as administrative, historical, industrial, cultural, educational, commercial, and economic districts (Figure1). Therefore, it provides a good research sample for this study.Figure 1
The study areas and second ring road network. The red lines, brown lines, and black lines correspond to the second ring road, extension roads directly connected to the second ring road, and linking the extension road of the extension road, respectively.More importantly, we have observed numerous counterintuitive congestions in the Second Ring Road Network in our daily traffic management, which existing theories cannot explain. For example, congestion levels near residential areas or office buildings are lower during the morning peak, although these areas would theoretically generate a large volume of traffic. In contrast, road congestion levels tend to be higher near some facilities that generate temporary travel demand, such as schools and hospitals. In addition, congestion levels near parking lots fluctuate significantly over time, and congestion levels near parking lots are significantly higher in the evening than in the morning. So, all these attractive observations motivated us to choose this area for this study.


## 3.2. Data Sources and Processing

Three types of data were collected to explore the correlation between urban road functions and traffic congestion: road property, vehicle speed data, and POI data.


### 3.2.1. The Road Property Data

Based on the previously described, the roads included in this analysis include three types: the trunk roads of Second Ring Roads, the extension roads directly linked to the trunk roads, and the subextension roads linked to the extension roads. According to this sampling criterion, we combined the official vehicle velocity data from the Xi’an traffic governance department with the open data from the OpenStreetMap to obtain the spatial coordinates, road vector shape, road width, and road rank. We finally obtained the property data of 163 roads, including road design speed, capacity, length, and the number of road lanes. Then, we used the K-means clustering method to classify the sample roads into six ranks (Table1) by referring to OpenStreetMap’s classification of road ranks and corrected the clustering results by manual proofreading.Table 1
The ranks and the ratio of the sample roads.
CountRatio (%)Trunk148.59Primary2112.88Secondary3018.41Tertiary6741.10Local1911.66Residential127.36Total163100.00In our classification, the trunk is the Second Ring Roads. The primary, the secondary, and the tertiary are extension roads directly linked to the Second Ring Road, and their cumulative proportion is more than is more than 50%. And the local and the residential are the subextension roads linked to the extension roads, which are the peripheral or internal roads of residential or industrial areas. Finally, it is essential to note that we have clustered the roads using K-mean, thus avoiding the influence of different roads or directions on the road congestion. Therefore, our sampling ignores the lane direction of the road and the distinction between the primary and secondary lanes of one road, as we focus more on the function given to the road by the adjacent area.


### 3.2.2. The Average Road Speed Data

We obtained the average road speed data of the sample roads in September 2021 via the Xi’an Traffic Police Detachment. We excluded the data during the weekends and public holidays in September and kept it for weekdays, considering the daily functions of the Second Ring Network and its surrounding areas, and the sampling frequency of data was hourly. Finally, we obtained 164,736 rows of data for analysis. As shown in Figure2, the sample roads’ average speed generally follows the urban’s circadian rhythm. Meanwhile, the road average speed varies significantly. The initial analysis suggests that the sampled speed data can describe the daily travel behavior.Figure 2
Hourly average speed data. The distribution data are from 164,736 rows of data for analysis.


### 3.2.3. Points-of-Interest (POIs) Data

We obtained the POI data within the Third Ring Road of Xi’an in 2020 through Xi’an Urban Planning Bureau, including the name, the address, the coordinate, and the type of POIs. There are 19 primary and 133 secondary types of POIs data. To align the type of POIs with the empirical evidence in daily traffic management, we reorganized the types of POI for our research design. Firstly, we removed the POIs data without generating traffic demand, such as the roads, the bridges, the road entrances and exits, the intersections, and the entrances and exits of areas. Secondly, we reorganized the primary type according to the travel demands they generate. For example, we distinguished the industrial parks and office buildings as business and company, and the residential buildings as a residence, although they are all categorized as Real Estate. Similarly, we combined living services, leisure and entertainment, and a beauty salon as living services that satisfy the daily demands of residents. Thirdly, we combined the bus stations, subway stations, railway stations, airports, and bus garages as transportation facilities, and set the parking services, and set car care services, repair, and car rental and sale services as vehicle services. In addition, we remove the POIs that may belong to more than one type. Finally, we obtained 16 types, and their counts and percentages are shown in Table2.Table 2
Types and percent of POIs.
CountPercent (%)Retail service2400622.46Living service1808316.92Catering service1273111.91Business and company1365012.77Parking Service43054.03Financial service36603.42Educational service41033.84Government agency45494.26Medical service37103.47Hotel service28272.64Transportation service60445.65Vehicle service25752.41Residence34703.25Sports service11721.10Tourist service15871.48Culture service4090.38


## 3.2.1. The Road Property Data

Based on the previously described, the roads included in this analysis include three types: the trunk roads of Second Ring Roads, the extension roads directly linked to the trunk roads, and the subextension roads linked to the extension roads. According to this sampling criterion, we combined the official vehicle velocity data from the Xi’an traffic governance department with the open data from the OpenStreetMap to obtain the spatial coordinates, road vector shape, road width, and road rank. We finally obtained the property data of 163 roads, including road design speed, capacity, length, and the number of road lanes. Then, we used the K-means clustering method to classify the sample roads into six ranks (Table1) by referring to OpenStreetMap’s classification of road ranks and corrected the clustering results by manual proofreading.Table 1
The ranks and the ratio of the sample roads.
CountRatio (%)Trunk148.59Primary2112.88Secondary3018.41Tertiary6741.10Local1911.66Residential127.36Total163100.00In our classification, the trunk is the Second Ring Roads. The primary, the secondary, and the tertiary are extension roads directly linked to the Second Ring Road, and their cumulative proportion is more than is more than 50%. And the local and the residential are the subextension roads linked to the extension roads, which are the peripheral or internal roads of residential or industrial areas. Finally, it is essential to note that we have clustered the roads using K-mean, thus avoiding the influence of different roads or directions on the road congestion. Therefore, our sampling ignores the lane direction of the road and the distinction between the primary and secondary lanes of one road, as we focus more on the function given to the road by the adjacent area.


## 3.2.2. The Average Road Speed Data

We obtained the average road speed data of the sample roads in September 2021 via the Xi’an Traffic Police Detachment. We excluded the data during the weekends and public holidays in September and kept it for weekdays, considering the daily functions of the Second Ring Network and its surrounding areas, and the sampling frequency of data was hourly. Finally, we obtained 164,736 rows of data for analysis. As shown in Figure2, the sample roads’ average speed generally follows the urban’s circadian rhythm. Meanwhile, the road average speed varies significantly. The initial analysis suggests that the sampled speed data can describe the daily travel behavior.Figure 2
Hourly average speed data. The distribution data are from 164,736 rows of data for analysis.


## 3.2.3. Points-of-Interest (POIs) Data

We obtained the POI data within the Third Ring Road of Xi’an in 2020 through Xi’an Urban Planning Bureau, including the name, the address, the coordinate, and the type of POIs. There are 19 primary and 133 secondary types of POIs data. To align the type of POIs with the empirical evidence in daily traffic management, we reorganized the types of POI for our research design. Firstly, we removed the POIs data without generating traffic demand, such as the roads, the bridges, the road entrances and exits, the intersections, and the entrances and exits of areas. Secondly, we reorganized the primary type according to the travel demands they generate. For example, we distinguished the industrial parks and office buildings as business and company, and the residential buildings as a residence, although they are all categorized as Real Estate. Similarly, we combined living services, leisure and entertainment, and a beauty salon as living services that satisfy the daily demands of residents. Thirdly, we combined the bus stations, subway stations, railway stations, airports, and bus garages as transportation facilities, and set the parking services, and set car care services, repair, and car rental and sale services as vehicle services. In addition, we remove the POIs that may belong to more than one type. Finally, we obtained 16 types, and their counts and percentages are shown in Table2.Table 2
Types and percent of POIs.
CountPercent (%)Retail service2400622.46Living service1808316.92Catering service1273111.91Business and company1365012.77Parking Service43054.03Financial service36603.42Educational service41033.84Government agency45494.26Medical service37103.47Hotel service28272.64Transportation service60445.65Vehicle service25752.41Residence34703.25Sports service11721.10Tourist service15871.48Culture service4090.38


## 3.3. Methods for Quantifying Road Congestion Level

The road congestion features of the roads are the explained variables in this article. We used the traffic performance index (hereafter referred to as TPI) to estimate the level of congestion on the roads. For roadxi, its TPI can be defined by(1)TPI=RVshVd,where vs is the average speed of road xi within time t, vd is the free-flow speed of the road, and R is a normalization function which can help us transform the ratio into a distribution between 0 and 10 (Appendix A). This function is a method used by the Xi’an traffic governance department. After normalization, the TPI range is (0–10). Moreover, it can be divided into five levels, which are free (0–2), partial-free (2–4), mild congestion (4–6), moderate congestion (6–8), and severe congestion (8–10). Moreover, to control the fluctuations of the external factors, such as traffic accidents, emergencies, extreme weather, and other uncertainties on the road speed, we excluded the top 5% and bottom 5% of the original data when calculating the average travel speed. Table 3 shows the distribution of TPI for different ranks of roads after calculation.Table 3
TPI of different ranks of road.
CountMeanStdMinMaxTrunk175422.0261.7360.0019.470Primary282231.7621.4330.0009.126Secondary383761.7081.2820.0009.240Tertiary900652.3601.1930.0009.012Local266312.6991.1420.0028.979Residential172722.9221.0000.0048.046We estimated the temporal distribution of TPI for different ranks of roads (Figure3(a)). The road congestion level of the morning and evening peaks (8:00 and 18:00) are the most prominent (Figure 3(a)). Moreover, there are more significant fluctuations on the trunk roads during the morning and evening peaks (Figure 3(b)), which indicates that the congestion distribution on the trunk roads is heterogeneous. Furthermore, the tertiary and lower ranks of roads maintain a higher TPI throughout the day (7:00–21:59), but fluctuations of these roads are relatively small, which indicates that the congestion on these roads may be a general phenomenon due to the road features.Figure 3
Hourly TPI distribution of different ranks of roads. (a) Hourly average TPI of the different ranks of roads. The color is closer to red, and the road is more congested, on the contrary, the road is smooth. (b) Hourly standard deviation of the different ranks of roads. The color is closer to red, and the TPI changes greater over time, on the contrary, the change is not obvious.
(a)(b)In addition, to reduce the research’s complexity, we further divide the road congestion level into four periods based on the temporal distribution of the TPI in Figure3, which are morning peak (7:00–9:59, 3 hours), evening peak (17:00–19:59, 3 hours), off-peak (10:00–16:59, 7 hours), and night hours (20:00–22:59, 3 hours). Due to the low congestion between 0:00 and 6:00, we ignored this period in this article. and the subsequent empirical analysis in this article will focus on the four periods previously.


## 3.4. Quantification of Road Functions Based on POIs Data

The function of the road is the primary explanatory variable in this paper to explain the congestion on the road. For this purpose, we use the types and scales of POIs along the roads to define the road function. Therefore, we propose a labeling method based on the DBSCAN clustering algorithm for associating POIs with roads.Specifically, our method includes two steps. In the first step, we used the DBSCAN algorithm (Appendix B) to cluster each type of POI based on the Euclidean distance between each POI. In practice, we limited the upper bound of the neighborhood distance threshold Dmin to be less than the average length of the sampled roads and used a greedy algorithm to obtain the optimal parameters of the DBSCAN algorithm based on the silhouette coefficientSc (Figure 4). This setting ensures that the range of each cluster cannot exceed the length of the sampled roads so that we can use the cluster to represent the road function in the second step.Figure 4
The distribution of silhouette coefficient of business and company POI clustering results under different parameter combinations. The colors represent the value of the silhouette coefficient of the clustering results. The horizontal axis is the minimum number of clustersNmin, and the vertical axis is the neighborhood distance threshold Dmin. Considering the realistic need for spatial clustering, the units of Dmin are meters.In the second step, we calculated the shortest distance from the core points of the clusters to the sampled roads and match the core points of the clusters with the closest sampled road (Appendix C). We assume that each road has a buffer area (Figure5), a container of POIs, where travelers can realize their travel demands. In practice, We preanalyzed based on the sampled roads in second ring road network. On the one hand, the stores and other facilities along the road are evenly distributed within an area 100 meters wide from the road; if the buffer area is too small, we cannot obtain more POIs to label the road functions. On the other hand, we calculated the average distance between parallel roads. Setting the width of the buffer area to 100 m can avoid overlapping areas for parallel roads. Thus, we set the boundary of the buffer area to 100 m. We selected the clusters and their POIs whose cores are in the buffer area and remove the clusters and their POIs whose cores are more than 100 m away from the road, though some POIs are in the buffer area. Finally, we will use the types of the selected clusters and the scale of POIs in clusters to represent the road functions. According to the previous method, Table 4 presents the optimal parameters of DBSCAN for different types of POI and the statistical characteristics of the resampled POIs and includes the use of the DBSCAN parameter, such as Nmin, Dmin which has the same meaning as in the algorithm.Figure 5
A buffer area of POIs was selected. Different types of POI are distributed on the roads. By using the DBSCAN clustering algorithm (Appendix B) based on the Euclidean distance between the POIs to cluster the POIs acquire the core of the cluster. In addition, by setting a boundary (100 m) for filtering the core points of the clusters (the buffer area’s scale is not the actual situation). The blue and red circles correspond to the selected and unselected clusters, respectively. The star and error symbols indicate POIs selected and POIs unselected, respectively. Finally, the function of the road will use the types of the retained clusters and the scale of POIs in those clusters to describe.Table 4
Clustering results of different types of POI.
CountPercent (%)NminDminNumber of clustersMaxMinMeanStdRetail service538025.99287434500212.3735.63Living service265012.80212627617529.5717.29Catering service252612.2029528917228.7113.78Business and company245711.8727934137627.1822.03Parking service15247.36211527225325.5815.96Financial service10605.122792699923.936.18Educational service9074.382221957329.4513.91Government agency8504.11215215614425.4111.67Medical service7073.4122011019826.9311.73Hotel service6903.332201907127.5812.01Transportation service6593.1822661136825.788.53Vehicle service5022.422292814226.127.13Residence3611.7434641574422.5620.94Sports service2741.322423393726.857.33Tourist service1010.492464153526.318.41Culture service550.272464122024.234.76


## 3.5. Analytical Model and Study Design

Due to the complex spatial correlation between different POI types, most studies use entropy to describe the mixing pattern of POIs and label the function of specific areas [37, 38]. However, the entropy cannot effectively verify the correlation between specific POI types and the explained variables. So, we cannot reveal which POI type or combination of POIs may cause congestion and their impact on the road congestion level. For this purpose, we design an analytical framework to reveal the correlation between POI types and road congestion levels. In addition, to better understand the relationship between road function and road congestion, we model the road function and road congestion based on the following three observations that are contrary to the common sense from daily traffic management evidence.(1)
Road congestion is more severe near public facilities such as schools and hospitals along commuting routes, although vehicles do not stay there for long(2)
There is no significant road congestion near companies and businesses with many vehicles(3)
There are different characteristics of traffic flow at the entrance and exit of the road during the morning peakTherefore, we used a regular regression model to represent the relationship between road function and road congestion levels, which is a common model to reveal correlations between variables. In this model, we set the road congestion level as the dependent variables, the scales of different types of POIs as the independent variables, and the ranks of the road as a control variable. The regression equation might appear as follows:(2)TPIi,t=β0+∑c=1nβcPOIi,c+βn+1Ranki+εi,where TPI is the average traffic performance index of the road i during the time t, representing the each road congestion level, POIc is the scales of different types of POIs within the buffer area of road i, representing the road function, β is the parameter to be estimated, and ε is the random error term. In practice, we used the ordinary least squares to estimate the parameters of the model.Based on the actual observation of traffic management in real life, we separated the 16 POI types into four scenarios and discussed the impact of different POI combinations on road congestion levels separately.(1)
The influence of POIs in the 50th percentile of type scale on road congestions, namely, retail service, living service, and catering service(2)
The influence of POIs in the commuting-related types of road congestion, namely, residence, business and company, and educational service(3)
The influence of POIs in the public service types on road congestion, namely, government agency, transportation service, educational service, medical service, and parking service(4)
The influence of POIs in other types on road congestion.Notably, since the number of Tourist Service and Culture Service POIs is less than 1%, they will not be discussed in the above model design and subsequent model discussions.


## 4. Results and Discussion

### 4.1. The Distribution of POIs on the Roads

We firstly analyzed the scale of POIs along the roads, estimated the distribution of the scale of POIs using the maximum likelihood estimate, and tested whether the distribution followed a specific distribution using the Kolmogorov–Smirnov test. The distribution of the scale of POIs along the roads is well approximated by an exponential distribution (Figure6(a)):(3)PCt∼expCtk,with exponent k = 127.03, suggesting that the scale of POIs is heterogeneous and exponential decays with a certain step width. Furthermore, we counted the scale of each types POIs in sampled roads and estimated their distribution. The scales for the retail, living, catering, financial, and parking services follow the exponential distribution (Figure 6(b)), while the scales for other types are approximated by the exponential cut-off power-law distribution (Figure 6(c)). These indicate that the distribution of the road capacity for all POIs is a mixture of different POI types. The scale of POIs along the roads following the exponential distribution decay stepwise, and the scale for POIs following the truncated power-law distribution decay sharply after a specific position, which means that the road only sustains up to a certain scale of POIs. The heterogeneity of the road capacity indicates the heterogeneity of road function, while the exponential or power-law distribution implies that a few roads may take on more road functions, which may affect the road congestion levels.Figure 6
The distribution of the scale of POIs along the sampled road: (a) the distribution of the total scale of POIs along the roads, (b) the distribution of retail service, living service, catering service, financial service, and parking service types and the exponential distribution fit line, and (c) the distribution of other types of POI and the exponential cut-off power-law distribution fit line.
(a)(b)(c)Furthermore, we counted the average scale of POIs for different types in different ranks of roads. As shown in Table5, in terms of the total scale, POIs primarily concentrated on the trunk roads (230.28 per road), while less on residential roads (42.90 per road). Regarding different types of POI, retail services, catering services, parking services, financial services, educational services, government agencies, transportation services, and sports services, are primarily on the trunk roads. living services, medical services, and hotel services are primarily on the primary roads and the residences is primarily on the secondary roads. We set the significance at p<0.05, then the average scale of POIs is significantly variants across the different ranks of roads for most POI types. These indicate that there are more types and larger scales of POIs along the trunk roads, i.e., the second ring roads, and their road functions are more complex than those of other roads. Meanwhile, the significant variants in the scale of POIs between different ranks of roads indicated that different ranks of roads will attract different types of POIs. Furthermore, as shown in Figure 6, a large scale of POIs is concentrated on a limited number of roads. Therefore, the traffic generated by different types of POIs will be concentrated on a limited number of roads, and the combination of different types of POIs may also result in different congestion levels on the different ranks of road.Table 5
Average number of different POI in different ranks of road.
MeanFp valueTrunkPrimarySecondaryTertiaryLocalResidentialRetail service59.0054.6228.9026.7236.6110.642.080.06Living service23.7129.6719.5313.4710.224.732.430.03Catering service24.8619.0519.6313.5011.789.271.750.11Business & company31.9327.5716.1710.6310.064.184.85<0.001Parking service21.7113.6212.276.835.112.187.45<0.001Financial service11.2111.1910.274.772.720.096.65<0.001Educational service10.578.718.703.952.062.183.55<0.001Government agency9.367.958.233.473.391.735.32<0.001Medical service5.869.004.273.203.892.641.480.19Hotel service6.718.335.932.882.331.552.100.06Transportation service11.006.435.772.660.940.8212.23<0.001Vehicle service5.215.103.572.421.891.732.320.04Residence3.002.293.831.582.440.912.780.01Sports service4.142.002.471.310.780.186.64<0.001Total230.28207.47150.9097.8594.9442.905.50<0.001


### 4.2. Correlation Between Scale of POIs and Road Congestion Levels

In this section, we further discussed the correlation between the total scale of POIs along the road and road congestion level for four periods using Pearson correlation analysis. The correlation coefficients between the total scale of POIs and road congestion levels are all significantly correlated, but the direction of the correlation is not the same at four periods (Figure7).Figure 7
Correlation between the scale of POIs and TPI of the roads in different time periods. The scale of POI on road affects the TPI of the roads significantly, and the direction of the influence on the TPI is different in different time periods. The downward direction indicates a negative effect, on the contrary, it indicates a positive effect. The higher the TPI coefficient, the more obvious the effect.Firstly, there is a positive correlation between the total scale of POIs and the road congestion level during evening peak hours. This suggests that an increase in the total scale of POIs along a road is associated with higher levels of congestion. Interestingly, this pattern does not hold true for morning peak hours, where a decrease in congestion level is observed with an increase in the total scale of POIs along the road. During off-peak and night periods, there is a negative relationship between the total scale of POIs and the road congestion level. This relationship is particularly strong during the night hours, with a correlation coefficient close to −1. This means that the higher the scale of POIs along the road, the lower the congestion level.In order to control the impact of the ranks of roads on the previous patterns, we further examined the correlation patterns between road capacity and the road congestion level in the different ranks of roads using the same method mentioned previously. As shown in Figure8, there are positive correlations between the road capacity and the road congestion level during the evening peak, regardless of the ranks of roads, while the correlation is more diverse in other periods.Figure 8
Correlation between the scale of POIs and road congestion levels of different ranks of road. The scale of POI in the evening peak is positively correlated with the TPI of the road on all roads, and the correlation is most prominent on primary roads and secondary roads. The scale of POI shows a positive relationship with the TPI on trunk roads and secondary roads during the morning peak, but other ranks of roads have a negative relationship.Regarding the ranks of roads, it is clear that the road capacity of trunk, primary and secondary roads are positively correlated with road congestion levels throughout the day. For other ranks of roads, the correlation pattern for tertiary roads is consistent with the pattern (Figure8). In addition, the capacity of the local roads is positively correlated with the road congestion level except during the morning peak. However, conversely, the correlation between the capacity and the residential road congestion level is only slight positive during the evening peak but negative in other periods. The previous results indicate that for the main roads, such as the trunk, primary and secondary roads, and roads covering more POIs mean more congestion. Moreover, for the other ranks of roads, the correlation between the road capacity and congestion levels has a more apparent temporal pattern, which is more likely to be related to the specific function of the road or the travel patterns of nearby travelers. For example, the functions of the residential roads, such as grocery stores and delivery stations, are most likely to attract neighborhood travelers who cannot drive to achieve those functions. So, the correlation between the capacity and the residential road congestion level is always negative.


### 4.3. Impact of Different Types of POI on the Roads on Road Congestion

Based on the observations and inference from daily traffic management, we estimated the impact of road functions represented by the types and scale of the POI on road congestion levels and discussed the impact of specific POI types on road congestions.Firstly, we estimated the relationship between the scale of POIs in three types and road congestion level, since their cumulative share is more than 50%. As shown in Table6, we find a significant positive relationship between the number of retail services and the road congestion levels during all periods except the morning peak; however, we also find the number of living services and catering services during all periods do not have a significant effort on road congestion level except the evening peak and night hours, respectively. Among all the significant relationships, the POIs of living service strongly affected road congestion during the evening peak (1.689), indicating that necessary travels of citizens’ daily life may worsen the road congestion during evening hours. Nevertheless, the results also show that the POIs of the carting service may reduce road congestion during the night hours (−0.707), which may be because the night hours are not the major service time and most restaurants are closed during the night hours. Therefore, the relationship between the scale of POIs along the road and road congestion cannot be established by ignoring the types of POI. More POIs in a road’s buffer area does not mean the road will be more congested. The type of POI in the road and its related properties are more critical for the impact of congestion. In addition, more flexible travel at night (including evening peak and night hours) may cause the impact of POI on road congestion levels to be more significant.Table 6
Impact of POIs on road congestion in retail service, living service, and catering service.
AM peakOff peakPM peakNight hoursRetail service0.3971.547∗1.191∗∗0.960∗∗Living service0.3530.4401.689∗∗0.883Catering service−0.475−0.451−0.598−0.707∗∗Road Ranks (Ref: Trunk)Primary−0.919∗∗∗−0.343∗∗∗−0.634∗∗−0.304Secondary−0.831∗∗∗−0.333−0.544∗∗−0.168Tertiary−0.471∗∗0.301−0.1550.433∗∗Local−0.1400.803∗∗0.0240.573∗∗Residential0.0110.867∗∗0.0661.005∗∗∗Notes:∗∗p<0.05 and ∗p<0.1.Secondly, we estimated the relationship between the scale of POIs related to commuting and road congestion. The types of POIs included in the analysis were business and companies, educational services, and residences. As shown in Table7, the scale of POIs for all types was significantly related to road congestion levels. However, surprisingly, the relationship between the scale of POIs for business and companies and the road congestion levels was negative. In contrast, the relationship between the scale of POIs for educational service and the road congestion levels do not have the same impact. This suggests that stopovers during a commute, such as at educational services, are more likely to cause congestion on adjacent roads than on roads adjacent to commuting destinations such as businesses and companies. In fact, this finding is consistent with and validates our daily traffic management experience and observations. However, it may seem counterintuitive, as businesses and companies as commuting destinations typically attract more traffic and thus cause congestion. Based on our daily traffic management experience, one plausible explanation for this finding is that businesses and companies often plan more parking spaces to accommodate commuter traffic flow, while educational services, for example, may not provide enough parking spaces for pick-up and drop-off vehicles. This could lead to more congestion on roads adjacent to educational services. We found the same pattern during the evening peak and night hours.Table 7
Impact of daily commuter POIs on road congestion.
AM peakOff peakPM peakNight hoursResidence0.5968∗0.1550.268−0.003Business and company−1.1914∗∗∗−0.348−0.573∗−0.896∗∗Educational service1.0032∗∗0.7841.549∗∗∗0.807∗Road Ranks (Ref: Trunk)Primary−0.866∗∗∗−0.302−0.525∗∗−0.256Secondary−0.982∗∗∗−0.464∗−0.669∗∗∗−0.302Tertiary−0.482∗∗0.248−0.1510.337∗Local−0.1420.805∗∗0.0770.525∗∗Residential−0.0090.767∗∗0.0150.859∗∗∗Notes:∗∗∗p<0.01; ∗∗p<0.05; ∗p<0.1.In addition, we find that the POI of residence has a minimal impact on road congestion, indicating that the traffic volume and the circadian rhythm of the residential communities have been taken into account in the traffic planning, and a regular traffic volume cannot cause a sharp variation on road congestion. However, notably, the TPI of the residential roads is high throughout the day, which may also be one of the reasons why the scale of the POI of the residence has minimal impact on road congestion.Thirdly, we estimated the impact of the scale of POIs related to urban public services on congestion, including government agencies, medical services, sports services, transportation services, and parking services. However, we ignored the educational service already discussed in the commuting scenario, even though it is also an urban public service facility in China. Results in Table8 shows that, except for Government Agency and Sports Service, the scale of POIs of the public service significantly affects road congestion levels during the morning peak hours, off-peak hours, and evening peak hours, while only the scale of POIs related to transportation service has an impact on road congestion during night hours. Moreover, there is always a significant positive relationship between the scale of POIs related to medical service and road congestion, especially during the morning peak hours. Similar to the case of education facilities, we find that hospitals facilities have a significantly higher impact on road congestion than other facilities, and these facilities are stopover points in the commuting route rather than the final commuting destination. Especially in the context of rapid urbanization, the development of urban medical services usually fails to adapt to the rapid growth of the population, and accordingly, the traffic planning around the medical facilities fails to satisfy the travel demands of medical services. Thus, it is reasonable to assume the medical facilities at stopover points worsen the road congestion.Table 8
Impact of public service POIs on road congestion.
AM peakOff peakPM peakNight hoursMedical service0.607∗∗∗0.251∗∗0.172∗∗−0.273Transportation service0.639∗∗∗−0.427∗∗∗0.838∗∗∗−0.126∗Parking service−0.519∗∗∗0.379∗∗∗0.740∗∗∗0.404∗∗Government agency−0.0330.101−0.162∗−0.651Sports service0.086−0.090−0.0920.171Road ranks (ref: trunk)Primary−0.891∗∗∗−0.374−0.398∗−0.239Secondary−0.759∗∗−0.452∗−0.372∗−0.187Tertiary−0.361∗∗0.1690.0980.378∗Local−0.0010.701∗∗0.3450.564∗∗Residential0.115∗0.687∗0.3120.915∗∗∗Notes:∗∗∗p<0.01, ∗∗p<0.05, and ∗p<0.1.We also find that the scale of public transport service has a positive effect on road congestion during morning and evening peak hours while a negative effect during off-peak and night hours. This relationship is evident because public transport and subway stations may be the hub for daily travel and activity, and public transport facilities such as buses or stops may occupy road and cause more congestions.In addition, we find the entrance/exit flow of most parking lots or spaces along the road is usually unidirectional during morning peaks, while bidirectional during other periods. Based on our daily observations of traffic management, we can assume that during the morning peak, residents will either leave the parking lots near their homes or arrive at the parking lots at their workplaces. This unidirectional flow does not significantly impact the level of congestion on the adjacent roads. However, at other times, the complexity of travel behavior can cause the flow at the entrance/exit of the parking lot to be more likely to be bidirectional, particularly during the evening peak. Therefore, the results in Table8 support our observation and inference that the flow direction of parking lot entrances and exits has a significant impact on road congestion.Finally, we estimated the impact of the scale of POIs not included in the above scenarios on road congestion in Table9. The types included financial, hotel, and vehicle services. We find that the scale of POIs for financial service and hotel service does not significantly affect road congestion, while the scale of POIs for vehicle service only has a negative impact on road congestion during the off-peak and night hours. Like parking service during the morning peak hours, the entrance/exit flow of vehicle services is also unidirectional mostly.Table 9
Impact of financial, hotel, and vehicle service POIs on road congestion.
AM peakOff peakPM peakNight hoursFinancial service0.292−0.0420.457−0.108Hotel service−0.2750.4830.4410.260Vehicle service−0.206−0.487∗−0.331−0.449∗Road ranks (ref: trunk)Primary−0.880∗∗∗−0.343−0.586∗∗−0.264Secondary−0.856∗∗∗−0.470∗−0.644∗∗∗−0.257Tertiary−0.458∗∗0.164−0.1890.330∗Local−0.1050.696∗∗0.0290.479∗∗Residential0.0380.694∗∗0.0050.839∗∗∗Notes:∗∗∗p<0.01, ∗∗p<0.05, and ∗p<0.1.Summarizing the previous analysis results, we can summarize the impact of the scale of POIs in the road buffer area on road congestion in the following three aspects:(i)
The road congestion level is related to the function of the POI along the road during the trip. We consider that the final destination of a trip can reduce the maximum possible number of vehicles on the road. Conversely, the stopover point of a trip will only hinder the speed of vehicles but not reduce the number of vehicles on the road. As in the case of medical and educational facilities, vehicles waiting to access medical care and those picking up and dropping off students may affect other vehicles and increase the level of congestion on the roads. Therefore, reducing the number of stopover POIs on the roads without sufficient temporary parking spaces, relocating the entrances and exits of the stopover POIs, or dredging idling vehicles generated by those can help improve road congestion.(ii)
The road congestion level is related to the direction of the entrance/exit flow of vehicles to/from the POI along the road. The roads adjacent to POI entrances and exits that generate bidirectional traffic flow tend to be more congested than those adjacent to entrances and exits that generate unidirectional traffic flow. We considered this pattern applies not only to parking services but also to other types of POI that can provide parking spaces. Actually, the parking problem has become another vital issue in urban traffic research. From the results of this study, we concluded that the relationship between the flow generated by POIs and other vehicles on the road is essential for road congestion, and we should do more analysis on the flow direction of the parking services. So, we need to improve the relationship between POI and road by increasing the parking space of POI, improving the efficiency of entrances and exits of POIs, or repositioning entrances and exits of POIs.(iii)
The road congestion level is related to the planning traffic volume of the adjacent POI. It is well known that stable traffic is essential for optimizing traffic planning and reducing traffic congestion. Such as residences, steady and rhythmic traffic does not significantly increase road congestion, while flexible trips such as shopping and entertainment during the night increase the potential for road congestion. Therefore, by accounting for the expected traffic generated by a particular type of POI, we can better predict and manage congestion on the surrounding roads. Advantageously, numerous studies have discussed the relationship between POIs and urban mobility. Therefore, we need to propose more accurate prediction models based on empirical data to reveal the relationship between POIs and urban mobility, estimate the urban mobility by the POI, and use the estimated results to alleviate congestion.In addition to the discussion of POIs, the effect of the control variable- road rank- share a similar pattern in different scenarios. In the morning and evening peak hours, primary and secondary roads always have significantly lower congestions than trunk roads. However, during the off-peak and night hours, local and residential roads are significantly more congested than trunk roads. This pattern helps us to understand the spatial and temporal patterns of congestion. In particular, by combining the distribution of POIs in different ranks of roads, we can assume that POIs in the buffer area of primary and secondary roads are less likely to interfere with other road vehicles than on trunk roads. However, due to the limitations of road width, road conditions, and other features, even during off-peak and night hours, local and residential roads can also be more congested.


## 4.1. The Distribution of POIs on the Roads

We firstly analyzed the scale of POIs along the roads, estimated the distribution of the scale of POIs using the maximum likelihood estimate, and tested whether the distribution followed a specific distribution using the Kolmogorov–Smirnov test. The distribution of the scale of POIs along the roads is well approximated by an exponential distribution (Figure6(a)):(3)PCt∼expCtk,with exponent k = 127.03, suggesting that the scale of POIs is heterogeneous and exponential decays with a certain step width. Furthermore, we counted the scale of each types POIs in sampled roads and estimated their distribution. The scales for the retail, living, catering, financial, and parking services follow the exponential distribution (Figure 6(b)), while the scales for other types are approximated by the exponential cut-off power-law distribution (Figure 6(c)). These indicate that the distribution of the road capacity for all POIs is a mixture of different POI types. The scale of POIs along the roads following the exponential distribution decay stepwise, and the scale for POIs following the truncated power-law distribution decay sharply after a specific position, which means that the road only sustains up to a certain scale of POIs. The heterogeneity of the road capacity indicates the heterogeneity of road function, while the exponential or power-law distribution implies that a few roads may take on more road functions, which may affect the road congestion levels.Figure 6
The distribution of the scale of POIs along the sampled road: (a) the distribution of the total scale of POIs along the roads, (b) the distribution of retail service, living service, catering service, financial service, and parking service types and the exponential distribution fit line, and (c) the distribution of other types of POI and the exponential cut-off power-law distribution fit line.
(a)(b)(c)Furthermore, we counted the average scale of POIs for different types in different ranks of roads. As shown in Table5, in terms of the total scale, POIs primarily concentrated on the trunk roads (230.28 per road), while less on residential roads (42.90 per road). Regarding different types of POI, retail services, catering services, parking services, financial services, educational services, government agencies, transportation services, and sports services, are primarily on the trunk roads. living services, medical services, and hotel services are primarily on the primary roads and the residences is primarily on the secondary roads. We set the significance at p<0.05, then the average scale of POIs is significantly variants across the different ranks of roads for most POI types. These indicate that there are more types and larger scales of POIs along the trunk roads, i.e., the second ring roads, and their road functions are more complex than those of other roads. Meanwhile, the significant variants in the scale of POIs between different ranks of roads indicated that different ranks of roads will attract different types of POIs. Furthermore, as shown in Figure 6, a large scale of POIs is concentrated on a limited number of roads. Therefore, the traffic generated by different types of POIs will be concentrated on a limited number of roads, and the combination of different types of POIs may also result in different congestion levels on the different ranks of road.Table 5
Average number of different POI in different ranks of road.
MeanFp valueTrunkPrimarySecondaryTertiaryLocalResidentialRetail service59.0054.6228.9026.7236.6110.642.080.06Living service23.7129.6719.5313.4710.224.732.430.03Catering service24.8619.0519.6313.5011.789.271.750.11Business & company31.9327.5716.1710.6310.064.184.85<0.001Parking service21.7113.6212.276.835.112.187.45<0.001Financial service11.2111.1910.274.772.720.096.65<0.001Educational service10.578.718.703.952.062.183.55<0.001Government agency9.367.958.233.473.391.735.32<0.001Medical service5.869.004.273.203.892.641.480.19Hotel service6.718.335.932.882.331.552.100.06Transportation service11.006.435.772.660.940.8212.23<0.001Vehicle service5.215.103.572.421.891.732.320.04Residence3.002.293.831.582.440.912.780.01Sports service4.142.002.471.310.780.186.64<0.001Total230.28207.47150.9097.8594.9442.905.50<0.001


## 4.2. Correlation Between Scale of POIs and Road Congestion Levels

In this section, we further discussed the correlation between the total scale of POIs along the road and road congestion level for four periods using Pearson correlation analysis. The correlation coefficients between the total scale of POIs and road congestion levels are all significantly correlated, but the direction of the correlation is not the same at four periods (Figure7).Figure 7
Correlation between the scale of POIs and TPI of the roads in different time periods. The scale of POI on road affects the TPI of the roads significantly, and the direction of the influence on the TPI is different in different time periods. The downward direction indicates a negative effect, on the contrary, it indicates a positive effect. The higher the TPI coefficient, the more obvious the effect.Firstly, there is a positive correlation between the total scale of POIs and the road congestion level during evening peak hours. This suggests that an increase in the total scale of POIs along a road is associated with higher levels of congestion. Interestingly, this pattern does not hold true for morning peak hours, where a decrease in congestion level is observed with an increase in the total scale of POIs along the road. During off-peak and night periods, there is a negative relationship between the total scale of POIs and the road congestion level. This relationship is particularly strong during the night hours, with a correlation coefficient close to −1. This means that the higher the scale of POIs along the road, the lower the congestion level.In order to control the impact of the ranks of roads on the previous patterns, we further examined the correlation patterns between road capacity and the road congestion level in the different ranks of roads using the same method mentioned previously. As shown in Figure8, there are positive correlations between the road capacity and the road congestion level during the evening peak, regardless of the ranks of roads, while the correlation is more diverse in other periods.Figure 8
Correlation between the scale of POIs and road congestion levels of different ranks of road. The scale of POI in the evening peak is positively correlated with the TPI of the road on all roads, and the correlation is most prominent on primary roads and secondary roads. The scale of POI shows a positive relationship with the TPI on trunk roads and secondary roads during the morning peak, but other ranks of roads have a negative relationship.Regarding the ranks of roads, it is clear that the road capacity of trunk, primary and secondary roads are positively correlated with road congestion levels throughout the day. For other ranks of roads, the correlation pattern for tertiary roads is consistent with the pattern (Figure8). In addition, the capacity of the local roads is positively correlated with the road congestion level except during the morning peak. However, conversely, the correlation between the capacity and the residential road congestion level is only slight positive during the evening peak but negative in other periods. The previous results indicate that for the main roads, such as the trunk, primary and secondary roads, and roads covering more POIs mean more congestion. Moreover, for the other ranks of roads, the correlation between the road capacity and congestion levels has a more apparent temporal pattern, which is more likely to be related to the specific function of the road or the travel patterns of nearby travelers. For example, the functions of the residential roads, such as grocery stores and delivery stations, are most likely to attract neighborhood travelers who cannot drive to achieve those functions. So, the correlation between the capacity and the residential road congestion level is always negative.


## 4.3. Impact of Different Types of POI on the Roads on Road Congestion

Based on the observations and inference from daily traffic management, we estimated the impact of road functions represented by the types and scale of the POI on road congestion levels and discussed the impact of specific POI types on road congestions.Firstly, we estimated the relationship between the scale of POIs in three types and road congestion level, since their cumulative share is more than 50%. As shown in Table6, we find a significant positive relationship between the number of retail services and the road congestion levels during all periods except the morning peak; however, we also find the number of living services and catering services during all periods do not have a significant effort on road congestion level except the evening peak and night hours, respectively. Among all the significant relationships, the POIs of living service strongly affected road congestion during the evening peak (1.689), indicating that necessary travels of citizens’ daily life may worsen the road congestion during evening hours. Nevertheless, the results also show that the POIs of the carting service may reduce road congestion during the night hours (−0.707), which may be because the night hours are not the major service time and most restaurants are closed during the night hours. Therefore, the relationship between the scale of POIs along the road and road congestion cannot be established by ignoring the types of POI. More POIs in a road’s buffer area does not mean the road will be more congested. The type of POI in the road and its related properties are more critical for the impact of congestion. In addition, more flexible travel at night (including evening peak and night hours) may cause the impact of POI on road congestion levels to be more significant.Table 6
Impact of POIs on road congestion in retail service, living service, and catering service.
AM peakOff peakPM peakNight hoursRetail service0.3971.547∗1.191∗∗0.960∗∗Living service0.3530.4401.689∗∗0.883Catering service−0.475−0.451−0.598−0.707∗∗Road Ranks (Ref: Trunk)Primary−0.919∗∗∗−0.343∗∗∗−0.634∗∗−0.304Secondary−0.831∗∗∗−0.333−0.544∗∗−0.168Tertiary−0.471∗∗0.301−0.1550.433∗∗Local−0.1400.803∗∗0.0240.573∗∗Residential0.0110.867∗∗0.0661.005∗∗∗Notes:∗∗p<0.05 and ∗p<0.1.Secondly, we estimated the relationship between the scale of POIs related to commuting and road congestion. The types of POIs included in the analysis were business and companies, educational services, and residences. As shown in Table7, the scale of POIs for all types was significantly related to road congestion levels. However, surprisingly, the relationship between the scale of POIs for business and companies and the road congestion levels was negative. In contrast, the relationship between the scale of POIs for educational service and the road congestion levels do not have the same impact. This suggests that stopovers during a commute, such as at educational services, are more likely to cause congestion on adjacent roads than on roads adjacent to commuting destinations such as businesses and companies. In fact, this finding is consistent with and validates our daily traffic management experience and observations. However, it may seem counterintuitive, as businesses and companies as commuting destinations typically attract more traffic and thus cause congestion. Based on our daily traffic management experience, one plausible explanation for this finding is that businesses and companies often plan more parking spaces to accommodate commuter traffic flow, while educational services, for example, may not provide enough parking spaces for pick-up and drop-off vehicles. This could lead to more congestion on roads adjacent to educational services. We found the same pattern during the evening peak and night hours.Table 7
Impact of daily commuter POIs on road congestion.
AM peakOff peakPM peakNight hoursResidence0.5968∗0.1550.268−0.003Business and company−1.1914∗∗∗−0.348−0.573∗−0.896∗∗Educational service1.0032∗∗0.7841.549∗∗∗0.807∗Road Ranks (Ref: Trunk)Primary−0.866∗∗∗−0.302−0.525∗∗−0.256Secondary−0.982∗∗∗−0.464∗−0.669∗∗∗−0.302Tertiary−0.482∗∗0.248−0.1510.337∗Local−0.1420.805∗∗0.0770.525∗∗Residential−0.0090.767∗∗0.0150.859∗∗∗Notes:∗∗∗p<0.01; ∗∗p<0.05; ∗p<0.1.In addition, we find that the POI of residence has a minimal impact on road congestion, indicating that the traffic volume and the circadian rhythm of the residential communities have been taken into account in the traffic planning, and a regular traffic volume cannot cause a sharp variation on road congestion. However, notably, the TPI of the residential roads is high throughout the day, which may also be one of the reasons why the scale of the POI of the residence has minimal impact on road congestion.Thirdly, we estimated the impact of the scale of POIs related to urban public services on congestion, including government agencies, medical services, sports services, transportation services, and parking services. However, we ignored the educational service already discussed in the commuting scenario, even though it is also an urban public service facility in China. Results in Table8 shows that, except for Government Agency and Sports Service, the scale of POIs of the public service significantly affects road congestion levels during the morning peak hours, off-peak hours, and evening peak hours, while only the scale of POIs related to transportation service has an impact on road congestion during night hours. Moreover, there is always a significant positive relationship between the scale of POIs related to medical service and road congestion, especially during the morning peak hours. Similar to the case of education facilities, we find that hospitals facilities have a significantly higher impact on road congestion than other facilities, and these facilities are stopover points in the commuting route rather than the final commuting destination. Especially in the context of rapid urbanization, the development of urban medical services usually fails to adapt to the rapid growth of the population, and accordingly, the traffic planning around the medical facilities fails to satisfy the travel demands of medical services. Thus, it is reasonable to assume the medical facilities at stopover points worsen the road congestion.Table 8
Impact of public service POIs on road congestion.
AM peakOff peakPM peakNight hoursMedical service0.607∗∗∗0.251∗∗0.172∗∗−0.273Transportation service0.639∗∗∗−0.427∗∗∗0.838∗∗∗−0.126∗Parking service−0.519∗∗∗0.379∗∗∗0.740∗∗∗0.404∗∗Government agency−0.0330.101−0.162∗−0.651Sports service0.086−0.090−0.0920.171Road ranks (ref: trunk)Primary−0.891∗∗∗−0.374−0.398∗−0.239Secondary−0.759∗∗−0.452∗−0.372∗−0.187Tertiary−0.361∗∗0.1690.0980.378∗Local−0.0010.701∗∗0.3450.564∗∗Residential0.115∗0.687∗0.3120.915∗∗∗Notes:∗∗∗p<0.01, ∗∗p<0.05, and ∗p<0.1.We also find that the scale of public transport service has a positive effect on road congestion during morning and evening peak hours while a negative effect during off-peak and night hours. This relationship is evident because public transport and subway stations may be the hub for daily travel and activity, and public transport facilities such as buses or stops may occupy road and cause more congestions.In addition, we find the entrance/exit flow of most parking lots or spaces along the road is usually unidirectional during morning peaks, while bidirectional during other periods. Based on our daily observations of traffic management, we can assume that during the morning peak, residents will either leave the parking lots near their homes or arrive at the parking lots at their workplaces. This unidirectional flow does not significantly impact the level of congestion on the adjacent roads. However, at other times, the complexity of travel behavior can cause the flow at the entrance/exit of the parking lot to be more likely to be bidirectional, particularly during the evening peak. Therefore, the results in Table8 support our observation and inference that the flow direction of parking lot entrances and exits has a significant impact on road congestion.Finally, we estimated the impact of the scale of POIs not included in the above scenarios on road congestion in Table9. The types included financial, hotel, and vehicle services. We find that the scale of POIs for financial service and hotel service does not significantly affect road congestion, while the scale of POIs for vehicle service only has a negative impact on road congestion during the off-peak and night hours. Like parking service during the morning peak hours, the entrance/exit flow of vehicle services is also unidirectional mostly.Table 9
Impact of financial, hotel, and vehicle service POIs on road congestion.
AM peakOff peakPM peakNight hoursFinancial service0.292−0.0420.457−0.108Hotel service−0.2750.4830.4410.260Vehicle service−0.206−0.487∗−0.331−0.449∗Road ranks (ref: trunk)Primary−0.880∗∗∗−0.343−0.586∗∗−0.264Secondary−0.856∗∗∗−0.470∗−0.644∗∗∗−0.257Tertiary−0.458∗∗0.164−0.1890.330∗Local−0.1050.696∗∗0.0290.479∗∗Residential0.0380.694∗∗0.0050.839∗∗∗Notes:∗∗∗p<0.01, ∗∗p<0.05, and ∗p<0.1.Summarizing the previous analysis results, we can summarize the impact of the scale of POIs in the road buffer area on road congestion in the following three aspects:(i)
The road congestion level is related to the function of the POI along the road during the trip. We consider that the final destination of a trip can reduce the maximum possible number of vehicles on the road. Conversely, the stopover point of a trip will only hinder the speed of vehicles but not reduce the number of vehicles on the road. As in the case of medical and educational facilities, vehicles waiting to access medical care and those picking up and dropping off students may affect other vehicles and increase the level of congestion on the roads. Therefore, reducing the number of stopover POIs on the roads without sufficient temporary parking spaces, relocating the entrances and exits of the stopover POIs, or dredging idling vehicles generated by those can help improve road congestion.(ii)
The road congestion level is related to the direction of the entrance/exit flow of vehicles to/from the POI along the road. The roads adjacent to POI entrances and exits that generate bidirectional traffic flow tend to be more congested than those adjacent to entrances and exits that generate unidirectional traffic flow. We considered this pattern applies not only to parking services but also to other types of POI that can provide parking spaces. Actually, the parking problem has become another vital issue in urban traffic research. From the results of this study, we concluded that the relationship between the flow generated by POIs and other vehicles on the road is essential for road congestion, and we should do more analysis on the flow direction of the parking services. So, we need to improve the relationship between POI and road by increasing the parking space of POI, improving the efficiency of entrances and exits of POIs, or repositioning entrances and exits of POIs.(iii)
The road congestion level is related to the planning traffic volume of the adjacent POI. It is well known that stable traffic is essential for optimizing traffic planning and reducing traffic congestion. Such as residences, steady and rhythmic traffic does not significantly increase road congestion, while flexible trips such as shopping and entertainment during the night increase the potential for road congestion. Therefore, by accounting for the expected traffic generated by a particular type of POI, we can better predict and manage congestion on the surrounding roads. Advantageously, numerous studies have discussed the relationship between POIs and urban mobility. Therefore, we need to propose more accurate prediction models based on empirical data to reveal the relationship between POIs and urban mobility, estimate the urban mobility by the POI, and use the estimated results to alleviate congestion.In addition to the discussion of POIs, the effect of the control variable- road rank- share a similar pattern in different scenarios. In the morning and evening peak hours, primary and secondary roads always have significantly lower congestions than trunk roads. However, during the off-peak and night hours, local and residential roads are significantly more congested than trunk roads. This pattern helps us to understand the spatial and temporal patterns of congestion. In particular, by combining the distribution of POIs in different ranks of roads, we can assume that POIs in the buffer area of primary and secondary roads are less likely to interfere with other road vehicles than on trunk roads. However, due to the limitations of road width, road conditions, and other features, even during off-peak and night hours, local and residential roads can also be more congested.


## 5. Conclusion

Understanding the relationship between road function and road congestion is essential to alleviate urban traffic congestion. In this article, we use POIs to label the functions of roads and use the DBSCAN algorithm to cluster POIs and associate POIs with roads. We found a heterogeneous distribution of POIs on roads, indicating the heterogeneity of road functions. The POIs in a small portion of roads are numerous and complex, while most roads are small and simple. Meanwhile, we found that the scale of POIs in the road buffer area significantly impacts road congestion, and the temporal pattern of the impact differs significantly in different ranks of roads. Most importantly, we revealed the correlation between the types of POI and road congestion. The role of POI in a trip, the flow direction of POI entrance and exit, and the stability of traffic generated by the POIs are all critical factors affecting road congestion. Therefore, we concluded that the function of the road has a significant effect on road congestion. We can estimate the impact of road functions on road congestion by counting the scale of POIs, evaluating the types of POIs, and identifying combinations of POIs.Based on the findings of this article, we can plan the existing road POIs effectively to improve road congestion by limiting the scale of POIs, optimizing the flow and orientation of POI entrances and exits, or concerning and diverting the vehicles near the highlighted POI. Indeed, our findings also help to identify whether a type of POI is a stopover point during the trip, optimize the parking space in its vicinity, and assign traffic police to divert the surrounding vehicles. It is essential for urban planners, traffic administrators, and policymakers of cities with rapid urbanization. In future work, discussing more comprehensive samples and considering exceptional cases such as extreme weather and traffic accidents will be an issue worth studying. Moreover, revealing the mechanism of the spatial heterogeneity of road functions on congestion is a valuable study in the future.



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*Source: 6144048-2023-02-18.xml*