Related Experiment Video
Updated: Sep 30, 2025
14:55
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
Published on: January 20, 2023
3.5K
Hotspot identification on urban arterials at the meso level
1Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China.
Accident; Analysis and Prevention
|March 13, 2022
Summary
Identifying traffic crash hotspots on urban arterials requires considering both individual road segments and intersections (micro-level) and combined units (meso-level). The empirical Bayesian and potential for safety improvement methods showed better consistency for hotspot identification.
Area of Science:
- Traffic Engineering and Safety
- Urban Planning
- Transportation Systems Analysis
Background:
- Urban arterials are critical infrastructure with high traffic volumes and frequent crashes.
- Traditional micro-level analysis of road segments and intersections as isolated units has limitations for urban arterials due to short signal spacing and interaction effects.
- Practical traffic safety management often targets larger, multi-segment hotspots rather than single points.
Purpose of the Study:
- To evaluate the suitability of meso-level units (combined intersections and adjacent road segments) for hotspot identification on urban arterials.
- To compare the consistency of different hotspot identification (HSID) methods at both micro- and meso-levels.
- To determine the most reliable HSID methods for urban arterial safety management.
Main Methods:
- Data from 21 urban arterials in Shanghai, China, were analyzed.
- Hotspots were identified at micro- (intersections, road segments) and meso-levels (combined units) using crash frequency, empirical Bayesian (EB), potential for safety improvement (PSI), and full Bayesian (FB) methods.
- Hotspot consistency was evaluated over a two-year period.
Main Results:
- The empirical Bayesian (EB) and potential for safety improvement (PSI) methods demonstrated superior performance and consistency across both micro- and meso-level analyses.
- Significant inconsistencies were observed between hotspots identified at the micro-level compared to the meso-level.
- No single method or unit level consistently identified all critical areas.
Conclusions:
- Meso-level analysis is valuable for understanding traffic safety on urban arterials, complementing traditional micro-level approaches.
- The empirical Bayesian (EB) and potential for safety improvement (PSI) methods are recommended for their reliability in HSID on urban arterials.
- A combined approach utilizing both micro- and meso-level hotspot identification is recommended for comprehensive safety management of urban arterials.
Related Concept Videos
01:29Levels of Use of a GIS
112
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
112
01:25Selected Data About Geographic Locations
80
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
80
01:21Manipulation and Analysis
81
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
81
01:30Thematic Layering in GIS
91
In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
91
01:17Design Example: Alignment of a Road Line Using GIS
121
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
121
01:30Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device
186
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
186