Given an urban development plan and the historical traffic observations over the road network, the Conditional Urban Traffic Estimation problem aims to estimate the resulting traffic status prior to the deployment of the plan. This problem is of great importance to urban development and transportation management, yet is very challenging because the plan would change the local travel demands drastically and the new travel demand pattern might be unprecedented in the historical data. To tackle these challenges, we propose a novel Conditional Urban Traffic Generative Adversarial Network (Curb-GAN), which provides traffic estimations in consecutive time slots based on different (unprecedented) travel demands, thus enables urban planners to accurately evaluate urban plans before deploying them. The proposed Curb-GAN adopts and advances the conditional GAN structure through a few novel ideas: (1) dealing with various travel demands as the "conditions" and generating corresponding traffic estimations, (2) integrating dynamic convolutional layers to capture the local spatial auto-correlations along the underlying road networks, (3) employing self-attention mechanism to capture the temporal dependencies of the traffic across different time slots. Extensive experiments on two real-world spatio-temporal datasets demonstrate that our Curb-GAN outperforms major baseline methods in estimation accuracy under various conditions and can produce more meaningful estimations.
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Identification of Spatiotemporal Relationships in Travel Speeds along Individual Roadways using Probe Vehicle Data
The existence of spatiotemporal correlations in traffic behavior on links in a transportation network is potentially very useful. However, traffic metrics are often strongly correlated simply because of natural variations in travel demand patterns and these temporal trends might obstruct more meaningful relationships caused by the physics of traffic. To overcome this challenge, the present paper proposes a non-parametric, moving average detrending method that can be used to remove these background trends, even during non-stationary periods in which traffic states are changing with time. Cross-correlations performed on the detrended data are then used to identify more meaningful trends. The proposed method can also incorporate temporal lags in correlations between individual links, which accounts for the time it takes for information to travel between them. Links that exhibit strong correlations after detrending can then be grouped into communities which behave together using graph theory methods, and this community structure can be leveraged to improve prediction of link performance when information is missing. The proposed methodology is applied to a case study network using real-time link travel speeds obtained from probe vehicles. The results reveal that the 40 links in the network can be grouped into between eight and 12 communities, depending on the day of the week. This suggests that only a handful of links may need to be monitored to estimate travel speeds across the entire network. Furthermore, the significant overlap in the community structure across these days reveals that the network structure plays a large role in spatiotemporal correlations in link travel speeds in a network.
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- Award ID(s):
- 1749200
- PAR ID:
- 10134407
- Date Published:
- Journal Name:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume:
- 2673
- Issue:
- 11
- ISSN:
- 0361-1981
- Page Range / eLocation ID:
- 546 to 560
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Recent advancements in network science showed that the topological credentials of the elements (i.e., links) in a network carry important implications. Likewise, roadway segments (i.e., links) in a road network should be assessed based on their network position along with traffic conditions at a given geographic scale. The goal of this study is to present a framework that can identify and select critical links in a road network based on their topological importance such as centrality, and the effects of systematic interventions conducted on such links in improving overall system performance (vehicle delay, travel time) to provide an adequate level of service (LOS). A real-world road network (Boise downtown) is investigated by applying lane interventions on roadways experiencing high congestion. Microscopic traffic simulation and analyses are conducted to estimate the traffic flow parameters hence the performance of the road segments. The findings of this study show that interventions applied to critical and congested road segments improve the serviceability from LOS F to LOS E as well as from LOS D to LOS C. Besides, reduced travel time and vehicular delay (after applying intervention on critical components) are also observed for high demand OD pairs of the road network. As such the proposed framework has the potential to incorporate the topological credentials with traffic flow parameters and improve the performance of the road network. This systematic approach will help traffic managers and practitioners to develop strategies that enhance road network performance.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/0361198119842823
