The increasing adoption of electric vehicles underscores the urgent need for efficient and reliable charging infrastructure. Fixed charging stations are crucial for meeting surging demand and ensuring convenient access, yet accurately predicting demand remains highly challenging, because charging patterns change in response to where stations are placed, which creates a cyclical dilemma for planning. However, mobile charging stations (MCSs) offer a novel solution by flexibly relocating across urban areas, they can both deliver energy and act as dynamic probes to collect real-time data on charging demand. Existing studies, however, typically assume prior knowledge of demand distributions, which is rarely available in emerging EV markets or where privacy concerns limit data access. This paper proposes a Deep Reinforcement Learning (DRL) approach, formulated as a Partially Observable Markov Decision Process (POMDP), to optimize the relocation of MCSs in conjunction with fixed charging stations, while simultaneously uncovering latent demand patterns. We employ an Advantage Actor–Critic (A2C) algorithm with Long Short-Term Memory (LSTM) networks to capture temporal dependencies and adapt to stochastic demand. A dynamic Mixed-Integer Programming (MIP) model is developed as a benchmark that represents an idealized case with perfect foresight of demand. We compare the DRL agent against this optimization model in two settings: (i) a synthetic toy environment for controlled testing, and (ii) a realistic simulation of the Frederiksberg municipality in Denmark, calibrated with real charging data. The results show that the DRL framework effectively adapts to stochastic demand, outperforms the optimization baseline under uncertainty, and scales efficiently to larger problem instances. Beyond methodological contributions, the findings highlight how MCSs can serve a dual role as infrastructure supplements and as demand-discovery tools, offering valuable insights for data-driven and adaptive EV charging planning.

The offline pickup and delivery problem with time windows (PDPTW) is a classical combinatorial optimization problem in the transportation community, which has proven to be very challenging computationally. Due to the complexity of the problem, practical problem instances can be solved only via heuristics, which trade-off solution quality for computational tractability. Among the various heuristics, a common strategy is problem decomposition, that is, the reduction of a large-scale problem into a collection of smaller sub-problems, with spatial and temporal decompositions being two natural approaches. While spatial decomposition has been successful in certain settings, effective temporal decomposition has been challenging due to the difficulty of stitching together the sub-problem solutions across the decomposition boundaries. In this work, we introduce a novel temporal decomposition scheme for solving a class of PDPTWs that have narrow time windows, for which it is able to provide both fast and high-quality solutions. We utilize techniques that have been popularized recently in the context of online dial-a-ride problems along with the general idea of rolling horizon optimization. To the best of our knowledge, this is the first attempt to solve offline PDPTWs using such an approach. To show the performance and scalability of our framework, we use the optimization of paratransit services as a motivating example. Due to the lack of benchmark solvers similar to ours (i.e., temporal decomposition with an online solver), we compare our results with an offline heuristic algorithm using Google OR-Tools. In smaller problem instances (with an average of 129 requests per instance), the baseline approach is as competitive as our framework. However, in larger problem instances (approximately 2,500 requests per instance), our framework is more scalable and can provide good solutions to problem instances of varying degrees of difficulty, while the baseline algorithm often fails to find a feasible solution within comparable compute times.

Vehicle routing problems (VRPs) can be divided into two major categories: offline VRPs, which consider a given set of trip requests to be served, and online VRPs, which consider requests as they arrive in real-time. Based on discussions with public transit agencies, we identify a real-world problem that is not addressed by existing formulations: booking trips with flexible pickup windows (e.g., 3 hours) in advance (e.g., the day before) and confirming tight pickup windows (e.g., 30 minutes) at the time of booking. Such a service model is often required in paratransit service settings, where passengers typically book trips for the next day over the phone. To address this gap between offline and online problems, we introduce a novel formulation, the offline vehicle routing problem with online bookings. This problem is very challenging computationally since it faces the complexity of considering large sets of requests—similar to offline VRPs—but must abide by strict constraints on running time—similar to online VRPs. To solve this problem, we propose a novel computational approach, which combines an anytime algorithm with a learning-based policy for real-time decisions. Based on a paratransit dataset obtained from the public transit agency of Chattanooga, TN, we demonstrate that our novel formulation and computational approach lead to significantly better outcomes in this setting than existing algorithms.

We present a probabilistic proactive rebalancing method and speed-up techniques for improving the performance of a state-of-the-art real-time high-capacity fleet management framework. We improve on both computational efficiency and system performance. The speed-up techniques include search-space pruning and I/O cost reduction for parallelization, reducing the computation time by up to 97.67%, in experiments on taxi trips in New York City. The proactive rebalancing routes idle vehicles to future demands based on probabilistic estimates from historical demand, increasing the service rate by 4.8% on average, and decreasing the waiting time and total delay by 5.0% and 10.7% on average, respectively.

The recent emergence of new technologies and systems such as connected and automated vehicles (CAVs), novel incentive and routing platforms, and shared mobility services is making a significant impact on traffic flow in road networks. The rapid development of these innovations, powered by new capabilities in data collection, communication, and vehicle autonomy raises both great opportunities and new challenges for managing and controlling the transportation network efficiently. It is thus imperative to integrate the emerging systems into a dynamic transportation network analysis, and to develop new methodologies, which coherently integrate dynamic traffic models with increasingly available data, and methods for large-scale computation. Consequently, they call for new theories, models, computational methods, and application scenarios to study dynamic transportation networks with the emerging technologies as essential components.

We consider the stochastic on-time arrival (SOTA) problem of finding the optimal routing strategy for reaching a given destination within a pre-specified time budget and provide the first results on using preprocessing techniques for speeding up the query time. We start by identifying some properties of the SOTA problem that limit the types of preprocessing techniques that can be used in this setting, and then define the stochastic variants of two deterministic shortest path preprocessing techniques that can be adapted to the SOTA problem, namely reach and arc-flags. We present the preprocessing and query algorithms for each technique, and also present an extension to the standard reach based preprocessing method that provides additional pruning. Finally, we explain the limitations of this approach due to the inefficiency of the preprocessing phase and present a fast heuristic preprocessing scheme. Numerical results for San Francisco, Luxembourg and a synthetic road network show up to an order of magnitude improvement in the query time for short queries, with even larger gains expected for longer queries.

The Stochastic On-Time Arrival (SOTA) problem has recently been studied as an alternative to traditional shortest-path formulations in situations with hard deadlines. The goal is to find a routing strategy that maximizes the probability of reaching the destination within a pre-specified time budget, with the edge weights of the graph being random variables with arbitrary distributions. While this is a practically useful formulation for vehicle routing, the commercial deployment of such methods is not currently feasible due to the high computational complexity of existing solutions. We present a parallelization strategy for improving the computation times by multiple orders of magnitude compared to the single threaded CPU implementations, using a CUDA GPU implementation. A single order of magnitude is achieved via naive parallelization of the problem, and another order of magnitude via optimal utilization of the GPU resources. We also show that the runtime can be further reduced in certain cases using dynamic thread assignment and an edge clustering method for accelerating queries with a small time budget.

We consider the stochastic on-time arrival (SOTA) routing problem of finding a routing policy that maximizes the probability of reaching a given destination within a pre-specified time budget in a road network with probabilistic link travel-times. The goal of this work is to provide a theoretical understanding of the SOTA problem and present efficient computational techniques to enable the development of practical applications for stochastic routing. We present multiple speedup techniques that include a label-setting algorithm based on the existence of a minimal link travel-time on each road link, advanced convolution methods centered on the Fast Fourier Transform and the idea of zero-delay convolution, and localization techniques for determining an optimal order of policy computation. We describe the algorithms for each speedup technique and analyze their impact on computation time. We also analyze the behavior of the algorithms as a function of the network topology and present numerical results to demonstrate this. Finally, experimental results are provided for the San Francisco Bay Area arterial road network to show how the algorithms would work in an operational setting.

This paper extends the reconfigurable shared scan-in architecture (RSSA) to provide additional ability to change values on the scan configuration signals (scan enable signals) during the scan operation on a per-shift basis. We show that the extra flexibility of reconfiguring the scan chains every shift cycle reduces the number of different configurations required by RSSA while keeping test coverage the same. In addition a simpler analysis can be used to construct the scan chains. This is the first paper of its kind that treats the scan enable signal as a test data signal during the scan operation of a test pattern. Results are presented on some ISCAS as well as industrial circuits.

In this paper, an efficient technique for test data volume reduction based on the shared scan-in (Illinois Scan) architecture and the scan chain reconfiguration (Dynamic Scan) architecture is defined. The composite architecture is created with analysis that relies on the compatibility relation of scan chains. Topological analysis and compatibility analysis are used to maximize gains in test data volume and test application time. The goal of the proposed synthesis procedure is to test all detectable faults in broadcast test mode using minimum scan-chain configurations. As a result, more aggressive sharing of scan inputs can be applied for test data volume and test application time reduction. The experimental results demonstrate the efficiency of the proposed architecture for real-industrial circuits.