Spatial Analysis of the Contribution of Parking Service Facilities to Traffic Crashes along Limited-access Roadways

Abstract

Background and Objective Parking service facilities (PSFs) are integral components of road networks and are typically designated to offer road users, stakeholders, and external entities various services and opportunities. However, the current literature manifests contradictions among studies regarding the contribution of PSFs to crashes in the mainline traffic stream. This study aims to assess the contribution of PSFs to traffic crashes on limited-access expressways using hotspot analysis of traffic crashes in GIS. Methods Planar Getis-Ord Gi* and network KDE were used to detect crash hotspots along a study road based on five criteria: crash frequency of all vehicle types, frequency of all vehicles involved in crashes, frequency of heavy vehicles involved in crashes, injury severity index and iRAP star rating. Results The study identified and ranked the safety hazard of PSFs along the study road via the interference and risk level of crash hotspots with influential segments of PSFs. Getis-Ord Gi* analysis showed that almost 65% of PSFs along the study road were potentially high-risk facilities, with their influential segments interfering with crash hotspots based on the five hotspot analysis criteria. On the other hand, network KDE identified that almost 100% of PSFs along the study road were potentially high-risk facilities of either serious, significant or moderate risk levels, based on the same hotspot analysis criteria. Conclusion The two spatial analyses indicated that crashes in the proximity to PSFs may increase when the number of lanes of their influential road segments increases and traffic flow is interrupted by other road facilities. They also indicated that crash frequency and severity increase in the proximity to PSFs with high speed and high traffic volume. Furthermore, the two analyses indicated that heavy vehicle crashes are more likely to increase in the proximity to PSFs in rural areas.

Evaluating Expressway Safety Based on Fuzzy Comprehensive Evaluation with AHP–Entropy Method: A Case Study of Jinliwen Expressway in Zhejiang Province, China

“…Let X denote the entropy weight matrix, where x mj (m ∈ M, j ∈ J) denotes the value of indicator j for road section m. In order to eliminate the influence of different indicator units on the evaluation results, each indicator is standardized. The step transformation method is a standardization method used commonly, as shown in Equations ( 8) and (9).…”

“…The third type is accident factor analysis [6,7], which aims to identify the causes and related factors, such as speeding, drunk driving, and road conditions, which increase the probability of accidents. The fourth type is the use of GIS hotspot analysis of traffic accidents for traffic safety assessment [8,9]. Specifically, these studies reveal the geographical distribution patterns and hotspots of road traffic accidents, which aid analysts in achieving a better understanding of the spatial characteristics and influencing factors of accident occurrences.…”

Enhancing Pavement Sustainability: Prediction of the Pavement Condition Index in Arid Urban Climates Using the International Roughness Index

“…Developing IRI-based models for performance prediction is common as pavements deteriorate [3,19,20]. Increasing fuel consumption, greenhouse gas emissions, decreasing vehicle efficiency, and causing traffic accidents are well-correlated with increasing pavement roughness, which collectively costs USD one million every year [18,[21][22][23]. Owed to its versatility, the IRI became a key performance index in pavement management systems (PMSs) in many countries, especially for estimating vehicle operating costs [19].…”