CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic environment and coordinating thousands of agents including vehicles and pedestrians. A key factor in the success of modern reinforcement learning relies on a good simulator to generate a large number of data samples for learning. The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios. This motivates us to create a new traffic simulator CityFlow with fundamentally optimized data structures and efficient algorithms. CityFlow can support flexible definitions for road network and traffic flow based on synthetic and real-world data. It also provides user-friendly interface for reinforcement learning. Most importantly, CityFlow is more than twenty times faster than SUMO and is capable of supporting city-wide traffic simulation with an interactive render for monitoring. Besides traffic signal control, CityFlow could serve as the base for other transportation studies and can create new possibilities to test machine learning methods in the intelligent transportation domain.Comment: WWW 2019 Demo Pape

Revised Condition Rating Survey Models to Reflect All Distresses: Volume 1

Pavement condition assessment plays a key role in infrastructure programming and planning processes. Similar to other state agencies, the Illinois Department of Transportation (IDOT) has been using a system to evaluate the condition of pavements since 1974. Since 1994–1995, IDOT has been using a system to project future pavement performance as well. The condition rating survey (CRS) value is the index between 1 (failed) and 9 (new), representing the overall condition of pavement. The purpose of this study was to update and revise the existing CRS calculation and prediction models using new data. To accomplish the goals of the study, the CRS data was received for the years 2000–2014. The data was initially processed and cleaned in preparation for modeling. CRS prediction models were prepared for Interstate and Non-Interstate pavement types. The two-slope model was used for all asphalt-surfaced pavements, whereas a new model was proposed for concrete-surfaced pavements. The proposed model for concrete-surfaced pavements is a nonlinear survival type designed to capture the distinct deterioration patterns of concrete pavements with little to no reduction in CRS—followed by a rapid and linear deterioration and a flatter region at the end, once the pavement is saturated with damage. The CRS calculation models were updated to incorporate new distresses. Based on the literature review and the analysis of distress composition, it was found that IDOT’s distress ratings are generally in agreement with the ASTM standard—with the exception of alligator cracking. A database containing recorded distresses, used by experts, was referenced to add missing distresses, such as alligator cracking, for each Interstate model.IDOT-R27-150Ope

Application of Lagrange Relaxation to Decentralized Optimization of Dispatching a Charging Station for Electric Vehicles

To improve the computation efficiency of optimally dispatching large-scale cluster electric vehicles (EVs) and to enhance the profit of a charging station (CS) for EVs, this study investigates the optimal dispatch of the CS based on a decentralized optimization method and a time-of-use (TOU) price strategy. With the application of the Lagrange relaxation method (LRM), a decentralized optimization model with its solution is proposed that converts the traditional centralized optimization model into certain sub-problems. The optimization model aims to maximize the profit of CS, but it comprehensively considers the charging preference of EV users, the operation constraints of the distribution network, and the TOU strategy adopted by the CS. To validate the proposed decentralized optimal dispatching method, a series of numerical simulations were conducted to demonstrate its effect on the computation efficiency and stability, the profit of the CS, and the peak-load shifting. The result indicates that the TOU strategy markedly increases the profit of the CS in comparison with the fixed electricity price mechanism, and the computation efficiency and stability are much better than those of the centralized optimization method. Although it does not compensate the load fluctuation completely, the proposed method with the TOU strategy is helpful for filling the valley of power use. Document type: Articl

Networked Time Series Prediction with Incomplete Data

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation, environment monitoring to smart grid management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, etc. In this paper, we study the problem of NETS prediction with incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose Graph Temporal Attention Networks, which incorporate the attention mechanism to capture both inter-time series and temporal correlations. We conduct extensive experiments on four real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods, reducing the MAE by up to 25%