Optimal Parking Planning for Shared Autonomous Vehicles

Parking is a crucial element of the driving experience in urban transportation systems. Especially in the coming era of Shared Autonomous Vehicles (SAVs), parking operations in urban transportation networks will inevitably change. Parking stations will serve as storage places for unused vehicles and depots that control the level-of-service of SAVs. This study presents an Analytical Parking Planning Model (APPM) for the SAV environment to provide broader insights into parking planning decisions. Two specific planning scenarios are considered for the APPM: (i) Single-zone APPM (S-APPM), which considers the target area as a single homogeneous zone, and (ii) Two-zone APPM (T-APPM), which considers the target area as two different zones, such as city center and suburban area. S-APPM offers a closed-form solution to find the optimal density of parking stations and parking spaces and the optimal number of SAV fleets, which is beneficial for understanding the explicit relationship between planning decisions and the given environments, including demand density and cost factors. In addition, to incorporate different macroscopic characteristics across two zones, T-APPM accounts for inter- and intra-zonal passenger trips and the relocation of vehicles. We conduct a case study to demonstrate the proposed method with the actual data collected in Seoul Metropolitan Area, South Korea. Sensitivity analyses with respect to cost factors are performed to provide decision-makers with further insights. Also, we find that the optimal densities of parking stations and spaces in the target area are much lower than the current situations.Comment: 27 pages, 9 figures, 9 table

Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach

In many spatial trajectory-based applications, it is necessary to map raw trajectory data points onto road networks in digital maps, which is commonly referred to as a map-matching process. While most previous map-matching methods have focused on using rule-based algorithms to deal with the map-matching problems, in this paper, we consider the map-matching task from the data-driven perspective, proposing a deep learning-based map-matching model. We build a Transformer-based map-matching model with a transfer learning approach. We generate trajectory data to pre-train the Transformer model and then fine-tune the model with a limited number of ground-truth data to minimize the model development cost and reduce the real-to-virtual gap. Three metrics (Average Hamming Distance, F-score, and BLEU) at two levels (point and segment level) are used to evaluate the model performance. The results indicate that the proposed model outperforms existing models. Furthermore, we use the attention weights of the Transformer to plot the map-matching process and find how the model matches the road segments correctly.Comment: 25 pages, 9 figures, 4 table

Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting

Deep learning-based multivariate and multistep-ahead traffic forecasting models are typically trained with the mean squared error (MSE) or mean absolute error (MAE) as the loss function in a sequence-to-sequence setting, simply assuming that the errors follow an independent and isotropic Gaussian or Laplacian distributions. However, such assumptions are often unrealistic for real-world traffic forecasting tasks, where the probabilistic distribution of spatiotemporal forecasting is very complex with strong concurrent correlations across both sensors and forecasting horizons in a time-varying manner. In this paper, we model the time-varying distribution for the matrix-variate error process as a dynamic mixture of zero-mean Gaussian distributions. To achieve efficiency, flexibility, and scalability, we parameterize each mixture component using a matrix normal distribution and allow the mixture weight to change and be predictable over time. The proposed method can be seamlessly integrated into existing deep-learning frameworks with only a few additional parameters to be learned. We evaluate the performance of the proposed method on a traffic speed forecasting task and find that our method not only improves model performance but also provides interpretable spatiotemporal correlation structures.Comment: 11 pages, 4 figures, 2 tabl

Applications of Bioinspired Reversible Dry and Wet Adhesives: A Review

<jats:p>Bioinspired adhesives that emulate the unique dry and wet adhesion mechanisms of living systems have been actively explored over the past two decades. Synthetic bioinspired adhesives that have recently been developed exhibit versatile smart adhesion capabilities, including controllable adhesion strength, active adhesion control, no residue remaining on the surface, and robust and reversible adhesion to diverse dry and wet surfaces. Owing to these advantages, bioinspired adhesives have been applied to various engineering domains. This review summarizes recent efforts that have been undertaken in the application of synthetic dry and wet adhesives, mainly focusing on grippers, robots, and wearable sensors. Moreover, future directions and challenges toward the next generation of bioinspired adhesives for advanced industrial applications are described.</jats:p&gt

Enhanced Thermal Transport across Self-Interfacing van der Waals Contacts in Flexible Thermal Devices

Minimizing the thermal contact resistance (TCR) at the boundary between two bodies in contact is critical in diverse thermal transport devices. Conventional thermal contact methods have several limitations, such as high TCR, low interfacial adhesion, a requirement for high external pressure, and low optical transparency. Here, a self-interfacing flexible thermal device (STD) that can form robust van der Waals mechanical contact and low-resistant thermal contact to planar and non-planar substrates without the need for external pressure or surface modification is presented. The device is based on a distinctive integration of a bioinspired adhesive architecture and a thermal transport layer formed from percolating silver nanowire (AgNW) networks. The proposed device exhibits a strong attachment (maximum 538.9 kPa) to target substrates while facilitating thermal transport across the contact interface with low TCR (0.012 m(2) K kW(-1)) without the use of external pressure, thermal interfacial materials, or surface chemistries