In recent years, the Chinese vehicle fleet has experienced rapid growth. The production and sales of vehicles in China has been ranked first in the world [1
]. However, according to statistics from National Bureau of Statistics, in 2006 the death rate per million registered vehicles in China was up to 6.2, whereas the number in USA was estimated to be approximately 1.77, the number in Japan was only 0.77 [2
]. Fatality in the transportation industry is 77.6% of the total fatality number in safety production, 15 times the number in the area coal mining industry [3
]. Traffic crash (TC) has been the number one “killer” that threatens people’s lives and property in China [4
]. How to keep public transit safety and sustainable has become a major concern for the Department of Road Administration. Wuhan, with thoroughfares to nine provinces in China, is one of the largest cities in the country, with a continuing expansion of urban area and highway construction. In the period of 2000–2010, the number of motor vehicle grew at 25 percent and continues to grow with confidence, however, the road mileage increased by 3 percent [5
]. As the motor vehicle trip demand increases, the tense situation between traffic supply and demand emerged. Frequent TCs and traffic congestions have kept adversely affecting people’s everyday life and social development. Stringent efforts should be made to uplift the traffic safety standards and control traffic congestion for a sustainable development of transportation and urbanization. Besides, it is confirmed that TCs contribute around 10 to 15 percent of random traffic congestion, and cause the greatest amount of lost time due to congestion delays [6
]. A systematic analysis of TC scenario, proper traffic control devices, suitable roadway design practices and effective traffic police activities can often help to reduce TC. Moreover, it has been proved that spatial analysis could provide an effective solution to detect the pattern and suggest reasons for the pattern characteristics [7
]. The detection of TC pattern and identification of hot spots is the first step of TC strategies, including identification, ranking, profiling and treatment [10
]. Nevertheless, city-level empirical research on spatial pattern of TC and risk road identification in China is lacking.
Two decades ago, it had been noted that “there has been little published on the geography of traffic crashes” [11
]. This clearly has changed over the last two decades [12
]. In a broad sense, TC is the result of interactions between human activities and geographical environment. In geographical space, TC was abstracted as discrete event in area or line. When considering factors associated with TC, there are driver factors, motor vehicle conditions, roadway conditions, traffic characteristics and environmental factors [13
]. Driver factors, defined as subjective judgments, are always involved in reacting to objective conditions, such as roadway conditions. Therefore, the improvement of objective conditions can result in the decrease of TCs. In one sense, knowing the influence of road condition has on crashes would help target the maintenance effort for the road system. Nevertheless, considering the costs and resources for the improvement of objective conditions, the question becomes how to determine the road with priority to be mended, particularly with respect to which roadway segments are riskier than others. RRSs are defined as segments with more TCs in the same time interval and the equal roadway length. Considering that TC is a kind of point event, which often occurs along roadways, RRSs can be identified by detecting the cluster pattern of TC along roadways at a the city scale. Thus, the detection of spatial clusters of TC is an essential approach to identifying RRSs for the appropriate allocation of resources for road safety improvements [14
Research on the spatial pattern of TC has substantially progressed during the last years. Previous work has shown that the distribution of TC have apparent spatial cluster characteristics [14
, there are TC hot spots, hot road segments or hot areas that are a combined geographic unit of high-risk points, road segments or areas [10
]. According to the scale of the research object and its research area, there are two main methods for analyzing the spatial pattern of geographic events: the area statistics method and the discrete event method.
As for area statistics, due to the spatial heterogeneity and spatial dependence of areas, the global spatial autocorrelation and local indicators of spatial association (LISA) are used to measure the cluster degree of attributes of the area, such as TC from different areas [7
]. The global spatial autocorrelation method, including global Moran’s I, global Geary’s C, and Getis-Ord Gi*, can be used to describe the distribution of TC across the study area, however the location of TC clusters and the differences among each TC clusters were ignored [23
]. Afterwards, the LISA method, such as local Getis-Ord Gi* improved from global Getis-Ord Gi*, had been proved to be available for detecting hot areas and identifying the center of a cluster at a significant level [24
]. Due to the fact that TC is spatial event in planar space but constrained in road network, the network-constrained LISA named local indicators of network-constrained clusters (LINCS) was proposed [25
]. The GLINCS, based on G statistics, and ILINCS, based on I statistics, are mostly used LINCS in network space.
In regard to the spatial cluster of discrete events, there are approaches, including descriptive analysis, quadrat analysis and distance analysis. Furthermore, the most typical methods based on discrete events may be the nearest neighbor distance method, Ripley’s K function methods [26
], Kernel Density Estimation (KDE) methods [27
] and others. Traditionally, the KDE methods have been widely used in point-pattern analyses for discrete events, especially in TC analyses [14
]. Although no single technique has emerged as the “best” for detecting and predicting TC clusters, recent research suggests that KDE outperforms other approaches due to its simplicity and easy implementation [31
]. Also, the KDE method may outperform the empirical Bayesian method in the identification of hazardous road segments when only the location of the crash can be used for the analysis [32
]. However, although the KDE has shown acceptable properties using density values, its homogeneous 2D assumption for events distributed in 1.5D space, such as TC on a road network, seems to be irrelevant [33
]. To overcome this limitation, Okabe proposed the idea of the spatial analysis based on a network, Network-Constrained Kernel Density Estimation (NKDE), which can overcome the shortcomings of the KDE method and reduce the deviation of its results [39
]. Furthermore, research has demonstrated the validity of NKDE to analyze network-based phenomena, such as TC [35
Although KDE and NKDE are useful methods for the spatial cluster analysis in TC research, they had some limitations. One inevitable problem is the local maximums and boundary effects due to the derivation of the kernel function. Therefore, deciding which clusters are statistically significant is necessary. Nevertheless, there is not enough attention paid to the statistical significance of KDE in the current literature [48
]. Meanwhile, the same question has been proposed by some researchers, such as Xie and Anderson [14
]. Xie noted that NKDE has one of the same fundamental drawbacks as planar KDE. Moreover, Plug said that KDE is better for visualization purposes than for identification of black spots [49
Hence, in this paper, firstly KDE and NKDE are compared to portray the spatial cluster characteristic of TC in area scale and network scale, respectively. Still, as to each road network polyline, the NKDE generates a smoothing density surface with reduction of data noise and statistical bias. Secondly, considering the statistical significance of NKDE, GLINCS method is used to identify high-risk road segments by using the density value as input attributes. Next, the result of NKDE-GLINCS is compared with the GLINCS.
This paper aims to evaluate and represent the TC pattern to contribute to the traffic safety in Wuhan City. The detection can help to identify vulnerable locations and road segments that require remedial measures. A spatial method for visualization of TC spatial cluster and identification of risky road segments is expounded The remainder of this manuscript is organized in the following manner: first, descriptions of data used in the current study; second, explanation of methods; third, results of our analyses; and finally, discussion of implications and limitations of the methods, and suggestions for future research.
4. Summary and Conclusions
The intense demand of roads to cater to the rapid economic development has made road traffic crashes causing traffic congestion one of the most pervasive forms of “bottle neck” in Wuhan, China. Secure and efficient transportation and mobility are key components and central to sustainable development of all-round urbanization. For an effective solution of the TC problem with a limited budget, risky road segments with higher probability of TCs should be given the priority to be maintained. The detection of spatial clusters of TC is the first vital step for the appropriate allocation of resources for safety improvement in a sustainable way. To identify riskier road segments in Wuhan, a two-step approach using NKDE, extended from KDE in planar space, and GLINCS, based on Getis-Ord Gi* was illustrated. As presented, TC on the road network in Wuhan, with a total of 3113 crashes between motor vehicles, were selected for testing and verifying. It is confirmed that NKDE-GLINCS perform better than traditional GLINCS in identifying the cluster due to the preprocessing of NKDE smoothing. The case study also provides evidence of effectiveness and robustness of the NKDE-GLINCS method. In addition, the top 20 roads with high-high TC density at the significance level of 0.1 are listed and presented in 3-D visualization. The results of this case study should be useful in assisting transportation agencies and motorists to identify risky roads quickly and play an important role in the further analysis and prediction of TC.
Compared with conventional TC analysis methods, NKDE can be used not only for analyzing the properties of point events and measuring the variation in the mean values of spatial processes but also for a preprocess for a smoothing density value from the origin data. The main advantage of the NKDE method is that the uncertainty about the process can be understood and implement easily. However, NKDE methods may always be used as visualization tools, due to the absence of significance testing. Herein, NKDE result was input as attribute for GLINCS to use the density indicator formally for evaluating the significant locations with high-density values.
Although the NKDE-GLINCS method for detecting the cluster pattern of TC has availability and advantage, there are still some fields to be improved. As discussed previously, in this study, only the spatial characteristics of TC was analyzed, whereas previous research has shown that the factors associated with TC may be diverse and complicated [15
]. Thus, further study is needed to add other parameters to the kernel function and weight matrix, such as road density, road accessibility and land-use of the study area. Despite these improvements existing, TC distribution analysis using NKDE-GLINCS in other areas or cities in different scales are still expected. Besides, some other applications for geographical events constrained by or associated with networks are encouraged in the future.