Abstract
Locating the bottlenecks in cities where traffic congestion usually occurs is essential prior to solving congestion problems. Therefore, this paper proposes a low-frequency probe vehicle data (PVD)-based method to identify turn-level intersection traffic congestion in an urban road network. This method initially divides an urban area into meter-scale square cells and maps PVD into those cells and then identifies the cells that correspond to road intersections by taking advantage of the fixed-location stop-and-go characteristics of traffic passing through intersections. With those rasterized road intersections, the proposed method recognizes probe vehicles’ turning directions and provides preliminary analysis of traffic conditions at all turning directions. The proposed method is map-independent (i.e., no digital map is needed) and computationally efficient and is able to rapidly screen most of the intersections for turn-level congestion in a road network. Thereby, this method is expected to greatly decrease traffic engineers’ workloads by providing information regarding where and when to investigate and solve traffic congestion problems.
| Original language | English |
|---|---|
| Pages (from-to) | 320-339 |
| Number of pages | 20 |
| Journal | Transportation Research Part C: Emerging Technologies |
| Volume | 108 |
| DOIs | |
| Publication status | Published - Nov 2019 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 11 Sustainable Cities and Communities
Free Keywords
- Big data
- Floating car data
- Road intersection
- Traffic congestion
- Urban road network
ASJC Scopus subject areas
- Civil and Structural Engineering
- Automotive Engineering
- Transportation
- Management Science and Operations Research
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