An optimal network screening method of hotspot identification for highway crashes with dynamic site length
Introduction
In an effort to alleviate the adverse effect resulting from traffic crashes (Murray et al., 1996) with limited government resources, government agencies developed data-driven procedure (AASHTO, 2010) to detect hotspots (i.e., sites that suffer from high collision concentration and have high potential for safety improvement) and allocate resources to maximize the reductions in crash frequency (Harwood et al., 2010). The performance of such organized effect heavily depends upon types of network screening procedure and safety measure.
There is rich literature on the comparison among various safety measures in the hotspot identification (HSID) process (e.g., Cheng and Washington, 2005; Montella, 2010) in terms of their performance consistency, applicability under data availability, and appropriateness to different purpose of use. Moreover, for a better prediction of the safety level, there have been numerous studies conducted to overcome the limitations of the currently used methods, which are well documented in some review papers such as Mannering and Bhat (2014), Savolainen et al. (2011), and Lord and Mannering (2010). However, finding a better screening method has not been getting great attention compared to developing a better safety measure even though some studies emphasize that improving screening methods can bring additional benefits in terms of robustness and consistency in HSID (e.g., Grembek et al., 2012; Kwon et al., 2013; Medury and Grembek, 2016; Chung and Ragland, 2019).
The Simple Ranking (SR) and Sliding Window (SW) methods are commonly used network screening techniques (AASHTO, 2010; Harwood et al., 2010) associated with a strict definition of candidate sites in terms of their lengths and endpoint locations. One of the critical limitations of the SR method, based on pre-segmented candidate sites, is that they do not consider the crash frequency adjacent to a site such that it can result in selecting only the portion of the potential hotspot. Also, for both methods, the resulting hotspot can markedly vary depending on the predetermined site length (Kwon et al., 2013). If hotspot length is given too short, these methods can suffer from high false positive errors due to random fluctuation in the data. Thus, it is possible to detect a highway section as a hotspot, which is not actually risky. This false positive error can unnecessarily increase the costs of Detailed Engineering Studies (DES), and delay the time for real hotspots to be investigated. Increasing the investigation site length to mitigate the false positive rate can increase false negative rate when the site length of a true hotspot is relatively short compared to the length of the investigation site: it could not be detected since its significance can be averaged-out along the investigation window length.
To address the shortcomings in the SR and SW methods, Chung and Ragland (2007) develop Continuous Risk Profile (CRP) that estimates robust crash frequencies by filtering out the noise in the data and identifies hotspots with dynamic site length. A hotspot is identified where its CRP values are higher than the threshold defined as the upper confidence interval of the safety performance function (SPF), which refers to the observed mathematical relationship between typically crash frequency and explanatory variables of similarly grouped highways, ramps, and intersections (Tegge et al., 2010). Although the CRP is reported to be reproducible over multiple sites (Chung et al., 2009) and can address some of the limitations in other network screening procedures (Grembek et al., 2012), their endpoint locations can still be affected by random fluctuations and the bias in SPF (Lee et al., 2016). The biased hotspot endpoint locations can become an issue when the agency cost for site investigation is limited to a certain covered length or number of hotspots.
To address this issue, Medury and Grembek (2016) propose a screening method based on Dynamic Programming (DP), which maximizes crash frequencies involving pedestrians within identified hotspots associated with flexible site length. This procedure, however, does not correct the regression-to-the-mean (RTM) bias but only counts the naïve number of crashes, and agencies’ resource constraints are not considered.
To this end, this paper developed a novel Dynamic Site Length (DSL) method for detecting hotspots that can maximize the value of any kind of safety performance measures such as the expected average crash frequency with empirical Bayesian (EB) adjustment that accounts for the RTM bias effectively (Hauer, 1997; Hauer et al., 2002b; Huang et al., 2009; Montella, 2010; Wu et al., 2014). The proposed DSL method can also be used to strategically allocate resources and determine the number of hotspots to be invested annually.
The problem formulation with dynamic site length is presented in Section 2 followed by a detailed explanation of the proposed method. Section 3 discusses the logic behind the DSL method. The performance of the DSL method is measured by evaluating tests that are explained in Section 4. The results of the performance test are reported in Section 5. This paper ends with brief concluding remarks in Section 6.
Section snippets
DSL screening method
The proposed DSL screening method identifies hotspots with dynamic site length, which maximizes the value of the safety measures. Hotspots were detected along an extended highway system consisting of corridors, each of which spans in the range of , where is post-kilometer of a location on corridor for all .
The decision variables include the number of hotspots from corridor and within the whole network, denoted by and respectively, and upstream and
Solution methodology
As an optimization solution of the DSL method, we develop a dynamic-programming-based method applicable to any safety measure which guarantees: (i) close-to-optimal results; and (ii) the polynomial solution complexity to the size of network and historical crash data.
The DSL method is comprised of two layers for the system-level problem and the corridor-level problems. The overall process starts from decomposing the system-level problem into corridor-level problems using the Lagrangian
Evaluation test
The performance of the proposed DSL method is compared with two conventional screening methods, SW and CRP, which report hotspots in fixed and flexible lengths respectively (Chung and Ragland, 2019). These methods are elaborated in Appendix A.
As shown in Table 1, SW and CRP have their own strengths. However, SW is forced to have a fixed window length, meaning that it can suffer from false positive when the window length is too short and false negative when it is set to be too long. CRP
CASE STUDY
Traffic collision data (see Table 2) obtained from the Interstate-80 Westbound from post-kilometer 0.74 to 112.90 and the Interstate-880 Northbound near San Francisco California from 2010 to 2014 are evaluated using the DSL, SW, and CRP methods together with the set of SPFs developed by Kwon et al. (2013) following HSM convention. Empirical evaluation of the SPF developed by Kwon et al. indicated improved fit to data over existing SPFs, and these SPFs are used in our case study.
Both corridors
CONCLUSIONS AND FUTURE WORK
This paper aims to design a flexible screening method that locates hotspots in terms of the highest safety measure on a roadway network. The screening method is addressed as a constrained optimization model, where the summation of hotspot length cannot exceed given length or the total number of hotspots is limited. The optimization problem is solved using DP-based screening techniques so that a close-to-optimal result is guaranteed, and its computational complexity is polynomial to the size of
Acknowledgment
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2019R1H1A1080045 and 2018R1A2B6005729).
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