Spatiotemporal Analysis of Three-Wheeled Motorized Rickshaw Crashes in Rawalpindi Spatiotemporal Analysis of Three-Wheeled Motorized Rickshaw Crashes in Rawalpindi

• Published in: Applied spatial analysis and policy Vol. 18; no. 2
• Main Authors: Moeed, Abdul, Khan, Muhammad Asif, Ud-Din, Sameer, Habib, Muhammad Faisal, Ahmed, Kamran
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2025; Springer Nature B.V
• Subjects: Airports; Developing countries; Hospitals; Human Geography; Landscape/Regional and Urban Planning; LDCs; Public health; Regional/Spatial Science; Residential areas; Roads & highways; Safety; Social Sciences; Spatial analysis; Traffic accidents & safety; Transportation; Rawalpindi Pakistan; Pakistan; Spatiotemporal Analysis; Road Safety Analysis; Moran's; Threshold Distance; Emerging Hotspots; Getis-Ord Gi; 3-Wheeler Safety
• ISSN: 1874-463X; 1874-4621
• DOI: 10.1007/s12061-025-09676-0

The three-wheeled motorized rickshaw (3-MR) is the dominant mode of transportation in developing countries, primarily for short trips with a passenger capacity of four to six and is also used for small-scale goods transport. 3-MRs are often linked to road traffic crashes, which create serious socioeconomic and public health challenges. While early research focused primarily on the safety of two- and four-wheeled vehicles, there has been limited investigation into the safety dynamics of 3-MR crashes. This study aims to identify crash hotspots using 3-MR crash data (January 2022 to April 2023) from Rawalpindi, Pakistan. The study uses advanced spatial analytic methods, including emerging hotspot analysis with a space–time cube spatial autocorrelation, which has recently gained popularity for incorporating the temporal dimension in spatial analysis. Incremental spatial autocorrelation was first used to determine the threshold distance, followed by Moran's I and Getis-Ord Gi* to reveal recurring hotspot patterns in 3-MR crashes. Further utilizing the emerging hotspot analysis technique, substantial spatiotemporal clustering is identified. Hotspots were found to be associated with major bus stops, commercial areas, intersections, hospitals, airports, and high-density residential areas. The findings of this study provide valuable insights for stakeholders to identify hotspot locations, supporting targeted policies to reduce 3-MR crashes and improve road safety, particularly in developing countries with similar transportation modes, such as Bangladesh, Pakistan, India, and Sri Lanka.