Quantifying Percent Traffic Congestion (pTC) and Mobility Bottleneck Dynamics at Atlanta’s Spaghetti Junction
Bibliographic Data
| ID | 22032875 |
|---|---|
| Authors | Jeong Chang Seong (0000-0001-9969-8063, University of West Georgia, corresponding author), Jiwon Yang (0000-0002-6370-5775, Kyung Hee University), Jina Jang (0000-0002-4318-9355, Kyung Hee University), Soo Hoo Choi (0000-0003-4695-5462, University of West Georgia), Seung Hee Choi (0000-0001-6949-422X, University of West Georgia), Brian Vann (Georgia Department of Transportation), Chul Sue Hwang (0000-0001-9933-2432, Kyung Hee University) |
| Year | 2025 |
| Volume | 14 |
| Issue | 12 |
| Pages | 482 |
| Publication date | 2025-12-06 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14120482 |
| OpenAlex | W4417114582 |
| Language | EN |
| References cited | 23 |
Highway interchanges are vulnerable components of transport networks, often prone to congestion and crashes. Traditional monitoring methods like loop detectors or travel time queries often fail to capture the granular spatiotemporal distribution of bottlenecks in detail. To address this gap, this study introduces a new approach to quantify congestion and analyze bottleneck dynamics at Atlanta’s Tom Moreland Interchange, one of the nation’s most congested sites. A percent Traffic Congestion (pTC) metric was developed from the Google Maps Traffic Layer for twelve directional routes and validated against observed travel times obtained independently through the Google Maps Routes API. Traffic imagery collected every ten minutes for four months and 746 crash records were analyzed. Findings reveal distinct spatial patterns and temporal dynamics of congestion, with northbound I-85 and eastbound I-285 most affected during afternoon peaks. A quadratic model provided the best fit between pTC and travel times (R2 = 0.85), confirming pTC as a reliable congestion indicator. An LSTM model using pTC time series also accurately predicted mobility trends at the I-285 west to I-85 north bottleneck. Additionally, Seasonal-Trend decomposition using LOESS (STL) identified congestion anomalies, and their association was analyzed with crashes. The proposed methodology offers transportation agencies a cost-effective framework for monitoring, measuring, and understanding congestion in complex interchanges
Bottleneck · Congestion management · Network congestion · Traffic congestion · Traffic congestion reconstruction with Kerner's three-phase theory · Travel time · Automated Road and Building Extraction · Traffic control and management · Traffic Prediction and Management Techniques
| Highly cited | No |
|---|