With the transformation to smarter cities and the development of technologies, a large amount of data is collected from sensors in real time. Services provided by ride-sharing systems such as taxis, mobility-on-demand autonomous vehicles, and bike sharing systems are popular. This paradigm provides opportunities for improving transportation systems’ performance by allocating ride-sharing vehicles toward predicted demand proactively. However, how to deal with uncertainties in the predicted demand probability distribution for improving the average system performance is still a challenging and unsolved task. Considering this problem, in this work, we develop a data-driven distributionally robust vehicle balancing method to minimize the worst-case expected cost. We design efficient algorithms for constructing uncertainty sets of demand probability distributions for different prediction methods and leverage a quad-tree dynamic region partition method for better capturing the dynamic spatial-temporal properties of the uncertain demand. We then derive an equivalent computationally tractable form for numerically solving the distributionally robust problem. We evaluate the performance of the data-driven vehicle balancing algorithm under different demand prediction and region partition methods based on four years of taxi trip data for New York City (NYC). We show that the average total idle driving distance is reduced by 30% with the distributionally robust vehicle balancing method using quad-tree dynamic region partitions, compared with vehicle balancing methods based on static region partitions without considering demand uncertainties. This is about a 60-million-mile or a 8-million-dollar cost reduction annually in NYC.

As electric vehicle (EV) technologies become mature, EV has been rapidly adopted in modern transportation systems, and is expected to provide future autonomous mobility-on-demand (AMoD) service with economic and societal benefits. However, EVs require frequent recharges due to their limited and unpredictable cruising ranges, and they have to be managed efficiently given the dynamic charging process. It is urgent and challenging to investigate a computationally efficient algorithm that provide EV AMoD system performance guarantees under model uncertainties, instead of using heuristic demand or charging models. To accomplish this goal, this work designs a data-driven distributionally robust optimization approach for vehicle supply-demand ratio and charging station utilization balancing, while minimizing the worstcase expected cost considering both passenger mobility demand uncertainties and EV supply uncertainties. We then derive an equivalent computationally tractable form for solving the distributionally robust problem in a computationally efficient way under ellipsoid uncertainty sets constructed from data. Based on E-taxi system data of Shenzhen city, we show that the average total balancing cost is reduced by 14.49%, the average unfairness of supply-demand ratio and utilization is reduced by 15.78% and 34.51% respectively with the distributionally robust vehicle balancing method, compared with solutions which do not consider model uncertainties.

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Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline, i.e., training, pruning and retraining (fine-tuning) significantly increases the overall training trails. In this paper, we develop a systematic weight-pruning optimization approach based on Surrogate Lagrangian relaxation (SLR), which is tailored to overcome difficulties caused by the discrete nature of the weight-pruning problem while ensuring fast convergence. We further accelerate the convergence of the SLR by using quadratic penalties. Model parameters obtained by SLR during the training phase are much closer to their optimal values as compared to those obtained by other state-of-the-art methods. We evaluate the proposed method on image classification tasks using CIFAR-10 and ImageNet, as well as object detection tasks using COCO 2014 and Ultra-Fast-Lane-Detection using TuSimple lane detection dataset. Experimental results demonstrate that our SLR-based weight-pruning optimization approach achieves higher compression rate than state-of-the-arts under the same accuracy requirement. It also achieves a high model accuracy even at the hard-pruning stage without retraining (reduces the traditional three-stage pruning to two-stage). Given a limited budget of retraining epochs, our approach quickly recovers the model accuracy.

With the development of communication technologies, connected autonomous vehicles (CAVs) can share information with each other. Besides basic safety messages, they can also share their future plan. We propose a behavior planning method for CAVs to decide whether to change lane or keep lane based on the information received from neighbors and a policy learned by deep reinforcement learning (DRL). Our state design based on shared information is scalable to the number of vehicles. The proposed feedback deep Q-learning algorithms integrate the policy learning process with a continuous state space controller, which in turn gives feedback about actions and rewards to the learning process. We design both centralized and distributed DRL algorithms. In experiments, our behavior planning method can help increase traffic flow and driving comfort compared with a traditional rule-based control method. It also shows the distributed learning result is comparable to the centralized learning result, which reveals the possibility of improving the policy of behavior planning online. We also validate our algorithm in a more complicated scenario where there are two road closures on a freeway.