Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Exploiting reinforcement learning (RL) for traffic congestion reduction is a frontier topic in intelligent transportation research. The difficulty in this problem stems from the inability of the RL agent simultaneously monitoring multiple signal lights when taking into account complicated traffic dynamics in different regions of a traffic system. Such challenge is even more outstanding when forming control decisions on a large-scale traffic grid, where the RL action space grows exponentially with the number of intersections within the traffic grid. In this paper, we tackle such a problem by proposing a cooperative deep reinforcement learning (Coder) framework. The intuition behind Coder is to decompose the original difficult RL task as a number of subproblems with relatively easy RL goals. Accordingly, we implement Coder with multiple regional agents and a centralized global agent. Each regional agent learns its own RL policy and value functions over a small region with limited actions. Then, the centralized global agent hierarchically aggregates RL achievements from different regional agents and forms the final ${Q}$ -function over the entire large-scale traffic grid. The experimental investigations demonstrate that the proposed Coder could reduce on average 30% congestions in terms of the number of waiting vehicles during high density traffic flows in simulations.

Cooperative Deep Reinforcement Learning for Large-Scale Traffic Grid Signal Control

This paper presents a mathematical model for real-time queue estimation using connected vehicle (CV) technology from wireless sensor networks. The objective is to estimate the queue length for queue-based adaptive signal control. The proposed model can be applied without signal timing, traffic volume, or queue characteristics as basic inputs. The model is also developed so that it can work with both fixed-time signals and actuated signals. Furthermore, a discrete wavelet transform (DWT) is applied to the queue estimation algorithm in this paper for the first time. The purpose of the DWT is to enhance the proposed queue estimation to be more accurate and consistent regardless of the randomness in the penetration ratio. Experimental results are provided to validate the proposed model in both pretimed control and actuated control with a microscopic simulator, i.e., VISSIM. The results indicate that the proposed algorithm is able to estimate the queue length from VISSIM in the test case with pretimed signal control reasonably well. The results in actuated control cases, which have not been studied previously, showed that the proposed algorithm remains as accurate as the pretimed control cases. The accuracy of the proposed queue estimation algorithm is obtained without relying on basic inputs that other models typically require but are often impractical to obtain. Therefore, it is expected that the proposed queue estimation model is applicable for adaptive signal control using CV technology in practice.

Queue Length Estimation Using Connected Vehicle Technology for Adaptive Signal Control

Real-time estimation of the traffic state in urban signalized links is valuable information for modern traffic control and management. In recent years, with the development of in-vehicle and communication technologies, connected vehicle data has been increasingly used in literature and practice. In this work, a novel data fusion approach is proposed for the high-resolution (second-by-second) estimation of queue length, vehicle accumulation, and outflow in urban signalized links. Required data includes input flow from a fixed detector at the upstream end of the link as well as location and speed of the connected vehicles. A probability-based approach is derived to compensate the error associated with low penetration rates while estimating the queue tail location, which renders the proposed methodology more robust to varying penetration rates of connected vehicles. A well-defined nonlinear function based on traffic flow theory is developed to attain the number of vehicles inside the queue based on queue tail location and average speed of connected vehicles. The overall scheme is thoroughly tested and demonstrated in a realistic microscopic simulation environment for three types of links with different penetration rates of connected vehicles. In order to test the efficiency of the proposed methodology in case that data are available at higher sampling times, the estimation procedure is also demonstrated for different time resolutions. The results demonstrate the efficiency and accuracy of the approach for high-resolution estimation, even in the presence of measurement noise.

A data fusion approach for real-time traffic state estimation in urban signalized links

In this study, we develop a real-time estimation approach for lane-based queue lengths. Our aim is to determine the numbers of queued vehicles in each lane, based on detector information at isolated signalized junctions. The challenges involved in this task are to identify whether there is a residual queue at the start time of each cycle and to determine the proportions of lane-to-lane traffic volumes in each lane. Discriminant models are developed based on time occupancy rates and impulse memories, as calculated by the detector and signal information from a set of upstream and downstream detectors. To determine the proportions of total traffic volume in each lane, the downstream arrivals for each cycle are estimated by using the Kalman filter, which is based on upstream arrivals and downstream discharges collected during the previous cycle. Both the computer simulations and the case study of real-world traffic show that the proposed method is robust and accurate for the estimation of lane-based queue lengths in real time under a wide range of traffic conditions. Calibrated discriminant models play a significant role in determining whether there are residual queued vehicles in each lane at the start time of each cycle. In addition, downstream arrivals estimated by the Kalman filter enhance the accuracy of the estimates by minimizing any error terms caused by lane-changing behavior.

/pdf/real-time-estimation-of-lane-based-queue-lengths-at-isolated-3ssntik3zs.pdf

Real-time estimation of lane-based queue lengths at isolated signalized junctions

Queue length estimation is an important component of intersection performance measurement. Different approaches based on different data sources have been presented. With the latest developments in vehicle detection technologies, especially probe vehicle technologies, use of vehicle trajectory data has become possible. In this paper, an improved method for queue length estimation for signalized intersections is proposed. This method is able to provide cycle-by-cycle queue length estimation for signalized intersections with sampled vehicle trajectories as the only input. The keystone of the entire approach is the concept of the critical point (CP), which represents the changing vehicle dynamics. A CP extraction algorithm is introduced to identify CPs from raw trajectories. Using the CPs related to queue formation and dissipation, the authors propose an improved queue length estimation method based on shock waves. The performance of this approach is evaluated with several data sets under different flow and s...

Cycle-by-cycle queue length estimation for signalized intersections using sampled trajectory data

With the ever-increasing travel demand and limited capacity, traffic congestion has become part of daily life. The Texas Transportation Institute (TTI) 2007 mobility report shows traffic congestion is growing worse in American cities of all sizes, creating $78 billion in user costs annually due to lost time. Among all types of roadway facilities, the arterial traffic delay is the most visible because 40% of the nation's vehicle miles traveled are along arterials. However, the progress in providing reliable, real-time traveler information on arterial streets is slow, which is largely due to complicated nature of arterial traffic. This study shows a “proof of concept” that using global positioning system (GPS) based probe vehicle data is useful for identifying performances such as link delay, number of stops, and corridor travel time for arterials. Using the proposed algorithm, a change or need in performance can easily be determined when signal coordination or similar projects are conducted.

Real-Time Arterial Traffic Performance Measures Using GPS-Instrumented Vehicles

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Cycle-by-cycle queue length estimation for signalized intersections using sampled trajectory data

Real-Time Arterial Traffic Performance Measures Using GPS-Instrumented Vehicles