The public commute is essential to all urban centers and is an efficient and environment-friendly way to travel. Transit systems must become more accessible and user-friendly. Since public transit is majorly designed statically, with very few improvements coming over time, it can get stagnated, unable to update itself with changing population trends. To better understand transportation demands and make them more usable, efficient, and demographic-focused, we propose a fast, multi-layered transit simulation that primarily focuses on public transit simulation (BTE-Sim). BTE-Sim is designed based on the population demand, existing traffic conditions, and the road networks that exist in a region. The system is versatile, with the ability to run different configurations of the existing transit routes, or inculcate any new changes that may seem necessary, or even in extreme cases, new transit network design as well. In all situations, it can compare multiple transit networks and provide evaluation metrics for them. It provides detailed data on each transit vehicle, the trips it performs, its on-time performance and other necessary factors. Its highlighting feature is the considerably low computation time it requires to perform all these tasks and provide consistently reliable results.

Traffic simulators have many parameters that describe vehicle characteristics and driving behaviors. However, driving behaviors differ across urban, suburban, and rural areas. Even in the same area, driving behavior can be affected by the time of day or weather conditions. Therefore, it is difficult to get an accurate parameter set that is suitable for all scenarios. As a result, default parameters of simulators are usually determined only for a specific test case. To simulate a traffic scenario, researchers need to perform calibration to determine a suitable parameter set, which can provide more reliable simulated traffic than the default parameter set. A popular approach is manual calibration using human experience, but it is usually not effective due to the huge space of possible parameters. Although some studies proposed automated methods using evolutionary algorithms, implementing these methods is a time-consuming job. In this paper, we introduce a toolkit for researchers to easily conduct calibration for their own traffic scenarios. The toolkit supports several state-of-the-art algorithms and is designed to run in parallel to utilize the power of high performance computers. Moreover, by using this toolkit, we conduct in-depth experiments to understand which factors affect the calibration performance.

The volume and velocity of traffic data have in-creased dramatically due to the wide adoption of new technologies such as cameras, Internet-of-Thing devices, and vehicular net-works. That data can help us to optimize Traffic Signal Controls (TSCs) by using adaptive algorithms. Some direct applications of these algorithms are reducing the CO2 emission, fuel consumption, and traveling time. Recently, Deep Reinforcement Learning (DRL) methods are the de-facto solution due to its ability to handle big data with high performance. However, most open source codes and frameworks for TSCs using DRL algorithms have limited flexibility. That causes a difficulty to reuse the codebases for new contexts. Therefore, it will be difficult to have a benchmark for TSCs using different optimization algorithms. For this reason, our paper introduces TSLib – a Python framework for fast prototyping TSCs. Specifically, TSLib is designed as a modular system with high reusability so that researchers can quickly implement and evaluate new ideas of TSCs. Moreover, our work offers a comprehensive implementation of some well known TSCs algorithm including both traditional and DRL-based methods as well as their performance measurements.