## 3 Simulator for Dataset Generation  
MOE prediction algorithms must be trained using a large amount of realistic datasets for a wide variety of parameters for them to be predictive in realistic scenarios. These parameters include intersection topology, signal timing parameters, and input traffic distributions for all directions. The modern road network infrastructure (signal controllers and detectors) continuously generates data that opens up a new space of possibilities for using them for deriving realistic signal timing parameters, input traffic patterns, and a combination thereof. In particular, high resolution signalized intersection controller logs comprise of a listing of timestamped events at 10 Hz using induction loop detectors [18]at stop-bar and in some cases at upstream (40 m-100 m) locations. The data collected by such systems [19] at each intersection can be broadly divided into signal timing data and loop detector data. The former consists of traffic movement timing for different phases (including pedestrian calls), while the latter consists of arrival, departure, and occupancy information of loop detectors. Table 1 shows a sample of high resolution raw data collected by the loop detectors. It also comes with metadata that describe different event codes and event parameters; for example, eventcode 81 indicates a vehicle departure, and event code 2 indicates start of green phase. An event parameter identifies the particular detector channel or phase in which the event was captured. This data can be used to drive simulations and capture performance measures such as travel times, wait times, queue lengths, etc. One of the novelty of our approach is that we use this real-world high-resolution data to drive our simulations, the focus being to be able to run simulations for different signal timing parameter combinations. Vehicle equations at these detectors can be considered as pulse waveform, can represent the arrival/departure pattern at a given detector. For a given traffic stream on an approach (based on traffic from a neighboring interaction), the waveform at the advanced and stop bar detector, in general, is dependent on the signal phase timing for that intersection. This makes it challenging to develop combinations where we would like to predict MOEs for combinations that are not available in the datasets. To address this problem, we leverage our earlier work [6] that uses neural network architectures for inflow waveform reconstruction using stopbar, advance detector, and signal timing data. The inflow waveform can be thought of as the incoming waveform that has exited the upstream intersection and is still sufficiently far away from the intersection of interest and thus is not yet affected by the queues and signal timing plan of the intersection of interest. The simulations can be run using this inflow waveform with different candidate signal timing plans. We use Simulator of Urban Mobility (SUMO) [3], an open source microscopic traffic simulator as our choice of physical simulator. The overall workflow for dataset generation is as follows:  
* Reconstruct the unperturbed inflow waveform using stop bar, advance detector actuations, and signal timing information;
* Extract signal timing details from controller logs;
* Create the intersection base map in SUMO based on Google Maps satellite imagery using SUMO NETEDIT;
* The signal timing plan is perturbed within feasible limits to generate viable counterfactual simulations;
* Run multiple simulations in parallel for different signal timing plans;
* The vehicle traces along with detector output files are parsed to store route wise distribution of travel times, wait times along with waveforms at stopbar, advance detectors, and signal timing information. The signal timing control type is either pre-timed or actuated in SUMO, but for effective operation, ring and barrier control is often employed in practice (Figure 1). To