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All Journal Journal of Technology Informatics and Engineering International Journal of Graphic Design
Ruiyan Ma
Software Engineering, UC Irvine, CA, USA
Arts Computer Science & IT

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Digital-Twin Dispatching for Urban Mobility via Spatio-Temporal Transformers and Offline Reinforcement Learning Long Zhang; Ruiyan Ma; Peter Greg
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.501

Abstract

This study addresses the challenge of optimizing ride-hailing dispatch and repositioning under data limitations by proposing an end-to-end digital-twin dispatching framework that integrates spatio-temporal demand forecasting with offline reinforcement learning. Using publicly available NYC FOIL ride-hailing data aggregated at the dispatching-base level, the research aims to evaluate whether coarse-grained data can still support reliable, reproducible decision-making pipelines. The methodology consists of two main components: (i) multivariate time-series forecasting using baseline models, a temporal convolutional network (TCN), and a spatio-temporal transformer to predict next-day demand; and (ii) a digital-twin simulation combined with an action-constrained offline reinforcement learning approach, including behavior cloning (BC) and Conservative Q-Learning (CQL), to optimize fleet repositioning decisions. Experimental results show that the TCN achieves the best forecasting accuracy on the test period, although dominant demand regions largely drive performance gains. In the control phase, conservative policies such as CQL demonstrate stable performance with reduced repositioning costs, but do not significantly outperform behavior cloning due to limited training data. The findings indicate that, in coarse aggregate settings, operational improvements are more influenced by controlling policy sensitivity than by marginal forecasting gains. This study contributes a reproducible benchmark pipeline and highlights the importance of conservative control strategies, transparent assumptions, and sensitivity analysis when deploying AI-driven mobility systems based on limited or aggregated data.
Cross-Dataset Parcel Workload Priors for Last-Mile Capacity Forecasting: Integrating Package Segmentation with Delivery Operation Signals Ruiyan Ma; Long Zhang; Annie Bai
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.564

Abstract

This paper evaluates a cross-dataset framework for parcel segmentation, next-day last-mile operational-intensity forecasting, and capacity-oriented error analysis. The operational study uses 4,514,661 delivery tasks and 6,136,147 pickup tasks from LaDe-D and LaDe-P across five cities (May–October 2022), while the visual study uses 2,197 Package Segmentation images with 7,643 annotated package instances. A segmentation model estimates package count, foreground area, and instance-area dispersion to construct an additive workload descriptor. Because the images are not paired with operational records, city-day visual variables are represented as cross-dataset distributional proxies derived from empirical-rank mapping. Segmentation baselines are evaluated independently of forecasting. The random-forest pixel classifier achieves the best segmentation performance (IoU 0.5323, Dice 0.6450), outperforming YOLO11n-seg (IoU 0.3439, Dice 0.4591). Operational intensity is represented by the first principal component of delivery orders, active couriers, areas of interest, regions, and area types, explaining 89.19% of training variance. On a 230 city-day chronological holdout, ElasticNet Operational achieves the best forecasting accuracy (MAE 2.0521). Within XGBoost models, Vision-Prior records MAE 2.4988, slightly outperforming Delivery+Pickup (MAE 2.5154) and a shuffled-prior control (MAE 2.5973). However, the 0.0166 MAE improvement is not statistically significant under a paired moving-block bootstrap (95% CI: −0.0558 to 0.0319). A separate analysis of 6,112 Amazon routes and 1,457,175 packages similarly shows only a 0.24% MAE reduction from parcel-mix features. Overall, the proposed proxy provides only limited gains, while the operational ElasticNet remains the strongest baseline. Time- and location-paired visual observations are needed to establish practical operational benefits from computer vision.
Computer-Vision-Informed Visual Explanation Cards for Autonomous-Driving Traffic-Sign Alerts: Localization, Classification, and Retrieved Evidence on GTSDB Ruiyan Ma; Long Zhang; Tiffany Song
International Journal of Graphic Design Vol. 3 No. 2 (2025): October| IJGD: International Journal of Graphic Design
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/ijgd.v3i2.3991

Abstract

This paper develops a computer-vision-informed visual explanation card for traffic-sign alerts in autonomous-driving and driver-assistance interfaces. The card organizes a detected sign crop, predicted class, confidence, default semantic display-priority tier, retrieved visual precedents, scene-location cue, and concise action prompt. The empirical study uses the complete German Traffic Sign Detection Benchmark (GTSDB), comprising 900 road scenes in the standard 600-scene development and 300-scene evaluation portions. The same scenes support localization, crop classification, retrieval, calibration, and end-to-end analysis. The first 600 scenes were divided at the scene level into training and validation subsets; the 300 evaluation scenes were held out until model choices, retrieval depth, fusion weight, and detector threshold had been fixed. A learned class-agnostic localizer filters color-connected-component proposals with a histogram-of-oriented-gradients and color classifier. Five crop classifiers and four nearest-neighbor settings were evaluated, with retrieval treated primarily as example-based explanation support. On the held-out scenes, the selected localizer achieved an AP at IoU 0.50 of 0.211, an AP averaged over IoU 0.50–0.95 of 0.098, and a recall of 0.260 at the validation-selected operating point. The selected crop classifier achieved 0.784 accuracy and 0.574 macro-F1 on ground-truth crops. With predicted crops, correct-class end-to-end coverage was 0.177, and correct-tier end-to-end coverage was 0.244. These results define the information that the proposed card can receive from the evaluated vision pipeline. They do not measure driver comprehension, glance behavior, response time, trust, usability, or deployment safety, which require separate human-centred evaluation.
Co-Authors Annie Bai Long Zhang Peter Greg Tiffany Song