This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.
WASHINGTON, DC (March 8, 2017)--Interventional radiologists at the University of California at Los Angeles (UCLA) are using technology found in self-driving cars to power a machine learning application that helps guide patients' interventional radiology care, according to research presented today at the Society of Interventional Radiology's 2017 Annual Scientific Meeting. The researchers used cutting-edge artificial intelligence to create a "chatbot" interventional radiologist that can automatically communicate with referring clinicians and quickly provide evidence-based answers to frequently asked questions. This allows the referring physician to provide real-time information to the patient about the next phase of treatment, or basic information about an interventional radiology treatment. "We theorized that artificial intelligence could be used in a low-cost, automated way in interventional radiology as a way to improve patient care," said Edward W. Lee, M.D., Ph.D., assistant professor of radiology at UCLA's David Geffen School of Medicine and one of the authors of the study. "Because artificial intelligence has already begun transforming many industries, it has great potential to also transform health care."
Spatio-temporal problems are ubiquitous and of vital importance in many research fields. Despite the potential already demonstrated by deep learning methods in modeling spatio-temporal data, typical approaches tend to focus solely on conditional expectations of the output variables being modeled. In this paper, we propose a multi-output multi-quantile deep learning approach for jointly modeling several conditional quantiles together with the conditional expectation as a way to provide a more complete "picture" of the predictive density in spatio-temporal problems. Using two large-scale datasets from the transportation domain, we empirically demonstrate that, by approaching the quantile regression problem from a multi-task learning perspective, it is possible to solve the embarrassing quantile crossings problem, while simultaneously significantly outperforming state-of-the-art quantile regression methods. Moreover, we show that jointly modeling the mean and several conditional quantiles not only provides a rich description about the predictive density that can capture heteroscedastic properties at a neglectable computational overhead, but also leads to improved predictions of the conditional expectation due to the extra information and a regularization effect induced by the added quantiles.
With the development of Connected Vehicle (CV) technology, temporal variation of roadway traffic can be captured by sharing Basic Safety Messages (BSMs) from each vehicle using the communication between vehicles as well as with transportation roadside infrastructures (e.g., traffic signal) and traffic management centers. However, the penetration of connected vehicles in the near future will be limited. BSMs from limited CVs could provide an inaccurate estimation of current speed or space headway. This inaccuracy in the estimated current average speed and average space headway data is termed as noise. This noise in the traffic data significantly reduces the prediction accuracy of a machine learning model, such as the accuracy of long short term memory (LSTM) model in predicting traffic condition. To improve the real time prediction accuracy with low penetration of CVs, we developed a traffic data prediction model that combines the LSTM with a noise reduction model (the standard Kalman filter or Kalman filter based Rauch Tung Striebel (RTS)). The average speed and space headway used in this study were generated from the Enhanced Next Generation Simulation (NGSIM) dataset, which contains vehicle trajectory data for every one tenth of a second. Compared to a baseline LSTM model without any noise reduction, for 5 percent penetration of CVs, the analyses revealed that combined LSTM\RTS model reduced the mean absolute percentage error (MAPE) from 19 percent to 5 percent for speed prediction and from 27 percent to 9 percent for space headway prediction. The overall reduction of MAPE value ranged from 1 percent to 14 percent for speed and 2 percent to 18 percent for space headway prediction compared to the baseline model.
Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, some studies have applied machine learning to model travel mode choice and reported higher out-of-sample prediction accuracy than conventional logit models (e.g., multinomial logit). However, there has not been a comprehensive comparison between logit models and machine learning that covers both prediction and behavioral analysis. This paper aims at addressing this gap by examining the key differences in model development, evaluation, and behavioral interpretation between logit and machine-learning models for travel-mode choice modeling. To complement the theoretical discussions, we also empirically evaluated the two approaches on stated-preference survey data for a new type of transit system integrating high-frequency fixed routes and micro-transit. The results show that machine learning can produce significantly higher predictive accuracy than logit models and are better at capturing the nonlinear relationships between trip attributes and mode-choice outcomes. On the other hand, compared to the multinomial logit model, the best-performing machine-learning model, the random forest model, produces less reasonable behavioral outputs (i.e. marginal effects and elasticities) when they were computed from a standard approach. By introducing some behavioral constraints into the computation of behavioral outputs from a random forest model, however, we obtained better results that are somewhat comparable with the multinomial logit model. We believe that there is great potential in merging ideas from machine learning and conventional statistical methods to develop refined models for travel-behavior research and suggest some possible research directions.