###### Abstract  
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. deep learning; deep simulators; traffic signal timing optimization; intelligent transportation systems +
Footnote †: 2021: _Applied_  
11637. [[https://doi.org/10.3390/app11241637](https://doi.org/10.3390/app11241637)]([https://doi.org/10.3390/app11241637](https://doi.org/10.3390/app11241637))
an MOE can be easily calculated. We demonstrate the feasibility of achieving this for a single intersection with high accuracy. The main advantage of our approach is that when implemented on modern GPU-based processors, it is four to five orders of magnitude faster than running the microscopic simulators on a typical 32-core CPU machine. Clearly, if the actual vehicle trajectories are required on a given intersection, our method is not useful or appropriate. Our deep learning approach (called InterTwin as a short form for Intersection Twin) uses a large amount of data from different intersection topologies and signal timing plans, so as to capture the underlying traffic behavior at an intersection. While several real-world datasets exist [5], they are limited to only currently-used signal timing plans. We use microscopic simulators in conjunction with real world data to derive MOE distributions for a variety of scenarios that encompass a broad range of signal timing plans. Using this data, we built our models. The following are the key contributions of our work:  
1. We develop a novel two-module deep learning approach that captures the intrinsic properties of traffic behavior at an intersection. The first module corresponds to a spatial graph convolution that is used to extract spatial features from the detector waveforms leveraging the relationship between intersection lanes and signal timing phases. This makes our modeling relatively independent of the intersection topology. The second module is an encoder-decoder with temporal attention architecture, to capture the temporal dynamic behavior of the traffic flow for each phase based on the signal timing plan. These two modules are stacked together for obtaining the final prediction. 2. We show that the InterTwin-trained models are able to accurately predict MOE distributions generated by traffic simulators. After training, when these models are used in inference mode, these models are four to five orders of magnitude faster compared to microscopic simulations. Additionally, it can model multiple intersection topologies without painstakingly redrawing a new base map for each intersection (that is typically required by a microscopic simulator). 3. For training our models, we use data generated using a significant extension of SUMO [3], an open source microscopic traffic simulator to make the data generation more realistic. We use real-world recorded data from high resolution loop detectors for input traffic patterns.