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1. InterTwin: Deep Learning Approaches for Computing Measures of Effectiveness for Traffic Intersections

   https://doi.org/10.3390/app112411637  

   Karnati, Yashaswi; Sengupta, Rahul; Ranka, Sanjay (December 2021, Applied Sciences)

   Microscopic simulation-based approaches are extensively used for determining good signal timing plans on traffic intersections. Measures of Effectiveness (MOEs) such as wait time, throughput, fuel consumption, emission, and delays can be derived for variable signal timing parameters, traffic flow patterns, etc. However, these techniques are computationally intensive, especially when the number of signal timing scenarios to be simulated are large. In this paper, we propose InterTwin, a Deep Neural Network architecture based on Spatial Graph Convolution and Encoder-Decoder Recurrent networks that can predict the MOEs efficiently and accurately for a wide variety of signal timing and traffic patterns. Our methods can generate probability distributions of MOEs and are not limited to mean and standard deviation. Additionally, GPU implementations using InterTwin can derive MOEs, at least four to five orders of magnitude faster than microscopic simulations on a conventional 32 core CPU machine. 

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2. Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks

   https://doi.org/10.1145/3394486.3403127  

   Zhang, Yingxue; Li, Yanhua; Zhou, Xun; Kong, Xiangnan; Luo, Jun (August 2020, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining)

   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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3. Incident management and analysis dashboard for fire departments: ICCPS demo

   https://doi.org/10.1145/3302509.3313329  

   Pettet, Geoffrey; Mukhopadhyay, Ayan; Samal, Chinmaya; Dubey, Abhishek; Vorobeychik, Yevgeniy (January 2019, Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems)

   null (Ed.)

   This work presents a dashboard tool that helps emergency responders analyze and manage spatial-temporal incidents like crime and traffic accidents. It uses state-of-the-art statistical models to learn incident probabilities based on factors such as prior incidents, time and weather. The dashboard can then present historic and predicted incident distributions. It also allows responders to analyze how moving or adding depots (stations for emergency responders) affects average response times, and can make dispatching recommendations based on heuristics. Broadly, it is a one-stop tool that helps responders visualize historical data as well as plan for and respond to incidents. 

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4. Incorporating Queue Dynamics into Schedule-Driven Traffic Control

   Hu, Hsu-Chieh; Hawkes, Allen; Smith, Stephen F. (August 2021, International Joint Conference on Artificial Intelligence)

   null (Ed.)

   Key to the effectiveness of schedule-driven approaches to real-time traffic control is an ability to accurately predict when sensed vehicles will arrive at and pass through the intersection. Prior work in schedule-driven traffic control has assumed a static vehicle arrival model. However, this static predictive model ignores the fact that the queue count and the incurred delay should vary as different partial signal timing schedules (i.e., different possible futures) are explored during the online planning process. In this paper, we propose an alternative arrival time model that incorporates queueing dynamics into this forward search process for a signal timing schedule, to more accurately capture how the intersection’s queues vary over time. As each search state is generated, an incremental queueing delay is dynamically projected for each vehicle. The resulting total queueing delay is then considered in addition to the cumulative delay caused by signal operations. We demonstrate the potential of this approach through microscopic traffic simulation of a real-world road network, showing a 10 − 15% reduction in average wait times over the schedule-driven traffic signal control system in heavy traffic scenarios. 

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5. cST-ML: Continuous Spatial-Temporal Meta-Learning for Traffic Dynamics Prediction

   https://doi.org/10.1109/icdm50108.2020.00187  

   Zhang, Yingxue; Li, Yanhua; Zhou, Xun; Luo, Jun (November 2020, 2020 IEEE International Conference on Data Mining (ICDM))

   null (Ed.)

   Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. However, it is very challenging to solve this problem due to spatial-temporal dependencies and traffic uncertainties. In this paper, we solve the traffic dynamics prediction problem from Bayesian meta-learning perspective and propose a novel continuous spatial-temporal meta-learner (cST-ML), which is trained on a distribution of traffic prediction tasks segmented by historical traffic data with the goal of learning a strategy that can be quickly adapted to related but unseen traffic prediction tasks. cST-ML tackles the traffic dynamics prediction challenges by advancing the Bayesian black-box meta-learning framework through the following new points: 1) cST-ML captures the dynamics of traffic prediction tasks using variational inference; 2) cST-ML has novel designs in architecture, where CNN and LSTM are embedded to capture the spatial-temporal dependencies between traffic status and traffic related features; 3) novel training and testing algorithms for cST-ML are designed. We also conduct experiments on two real-world traffic datasets (taxi inflow and traffic speed) to evaluate our proposed cST-ML. The experimental results verify that cST-ML can significantly improve the urban traffic prediction performance and outperform all baseline models. 

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