arXiv subjects

Jiaqing Lu

Publications and source records attributed to Jiaqing Lu.

5 recordsLinked to original sources

arXiv2025

Measuring Nonlinear Relationships and Spatial Heterogeneity of Influencing Factors on Traffic Crash Density Using GeoXAI

This study applies a Geospatial Explainable AI (GeoXAI) framework to analyze the spatially heterogeneous and nonlinear determinants of traffic crash density in Florida. By combining a high-performing machine learning model with GeoShapley, the framework provides interpretable, tract-level insights into how roadway characteristics and socioeconomic factors contribute to crash risk. Specifically, results show that variables such as road density, intersection density, neighborhood compactness, and educational attainment exhibit complex nonlinear relationships with crashes. Extremely dense urban areas, such as Miami, show sharply elevated crash risk due to intensified pedestrian activities and roadway complexity. The GeoShapley approach also captures strong spatial heterogeneity in the influence of these factors. Major metropolitan areas including Miami, Orlando, Tampa, and Jacksonville display significantly higher intrinsic crash contributions, while rural tracts generally have lower baseline risk. Each factor exhibits pronounced spatial variation across the state. Based on these findings, the study proposes targeted, geography-sensitive policy recommendations, including traffic calming in compact neighborhoods, adaptive intersection design, speed management on high-volume corridors such as I-95 in Miami, and equity-focused safety interventions in disadvantaged rural areas of central and northern Florida. Moreover, this paper compares the results obtained from GeoShapley framework against other established methods (e.g., SHAP and MGWR), demonstrating its powerful ability to explain nonlinearity and spatial heterogeneity simultaneously.

stat.ME↗

arXiv2025

Equity-aware Design and Timing of Fare-free Transit Zoning under Demand Uncertainty

We propose the first analytical stochastic model for optimizing the configuration and implementation policies of fare-free transit. The model focuses on a transportation corridor with two transportation modes: automobiles and buses. The corridor is divided into two sections, an inner one with fare-free transit service and an outer one with fare-based transit service. Under the static version of the model, the optimized length and frequency of the fare-free transit zone can be determined by maximizing total social welfare. The findings indicate that implementing fare-free transit can increase transit ridership and reduce automobile use within the fare-free zone while social equity among the demand groups can be enhanced by lengthening the fare-free zone. Notably, the optimal zone length increases when both social welfare and equity are considered jointly, compared to only prioritizing social welfare. The dynamic model, framed within a market entry and exit real options approach, solves the fare policy switching problem, establishing optimal timing policies for activating or terminating fare-free service. The results from dynamic models reveal earlier implementation and extended durations of fare-free transit in the social welfare-aware regime, driven by lower thresholds compared to the social equity-aware regime.

math.OC↗

arXiv2025

Optimized dynamic scheduling of an exclusive bus lane or high occupancy vehicle lane in a bimodal traffic corridor

Efficient management of traffic corridors is critical for sustaining urban mobility. Exclusive bus lane (EBL) and high occupancy vehicle lane (HOVL) are two prominent strategies for enhancing public transit services and alleviating congestion. EBLs prioritize bus transit by providing dedicated lanes for faster travel times, while HOVLs encourage carpooling by reserving lanes for high-occupancy vehicles. However, static implementations of these policies may underutilize road resources and disrupt general-purpose lanes. Dynamic implementation, based on real-time demand, can potentially maximize road efficiency and minimize negative impacts. This study first compares the mixed traffic policy (MTP), exclusive bus lane policy (EBLP), and high occupancy vehicle lane policy (HOVLP) by formulating the total system costs in the context of a bimodal traffic corridor involving private cars and public buses. Under each lane policy, the bus frequency is optimized together with the modal split equilibrium derived separately. Based on dynamic demand simulated using an Ornstein-Uhlenbeck (O-U) process, switching thresholds are then derived to identify optimal periods for implementing each policy. Results reveal significant reductions in total system costs with the proposed dynamic policy schedules. Compared to static implementations, the dynamic policy schedules achieve cost reductions of 11.9%, 6.70%, and 43.64% relative to MTP-only, EBLP-only, and HOVLP-only scenarios, respectively. Additionally, in two real case studies of existing EBL and HOVL operations in Seattle, the proposed dynamic policy reduces total costs by 32.5% and 28.6%, respectively. The findings provide valuable insights for policymakers and transit planners, offering a robust framework for dynamically scheduling and integrating EBL and HOVL policies to optimize urban corridor efficiency and reduce overall system costs.

math.OC↗

arXiv2025

Dynamic Switching Models for Truck-only Delivery and Drone-assisted Truck Delivery under Demand Uncertainty

Integrating drones into truck delivery systems can improve customer accessibility, reduce operational costs, and increase delivery efficiency. However, drone deployment incurs costs, including procurement, maintenance, and energy consumption, and its benefits depend on service demand. In low-demand areas, drone-assisted trucks may underutilize resources due to high upfront costs. Accurately predicting demand is challenging due to uncertainties from unforeseen events or infrastructure disruptions. To address this, a market entry and exit real option approach is used to optimize switching between truck-only and drone-assisted delivery under stochastic demand. Results show that deploying multiple drones per truck offers significant cost advantages in high-demand regions. Using the proposed dynamic switching model, deterministic and stochastic approaches reduce costs by 17.4% and 31.3%, respectively, compared to immediate cost-saving switching. Sensitivity analysis reveals asymmetric effects of stochastic parameters on entry and exit timings. A stochastic multiple-options model is further developed to dynamically switch between truck-only and drone-assisted delivery with varying drone numbers. Applying these models to Miami-Dade County, we evaluate dynamic switching costs for three major logistics operators. This study highlights the potential benefits of dynamic delivery switching and provides insights for optimizing logistics operations.

eess.SY↗

arXiv2025

Q-RESTORE: Quantum-Driven Framework for Resilient and Equitable Transportation Network Restoration

Efficient and socially equitable restoration of transportation networks post disasters is crucial for community resilience and access to essential services. The ability to rapidly recover critical infrastructure can significantly mitigate the impacts of disasters, particularly in underserved communities where prolonged isolation exacerbates vulnerabilities. Traditional restoration methods prioritize functionality over computational efficiency and equity, leaving low-income communities at a disadvantage during recovery. To address this gap, this research introduces a novel framework that combines quantum computing technology with an equity-focused approach to network restoration. Optimization of road link recovery within budget constraints is achieved by leveraging D Wave's hybrid quantum solver, which targets the connectivity needs of low, average, and high income communities. This framework combines computational speed with equity, ensuring priority support for underserved populations. Findings demonstrate that this hybrid quantum solver achieves near instantaneous computation times of approximately 8.7 seconds across various budget scenarios, significantly outperforming the widely used genetic algorithm. It offers targeted restoration by first aiding low-income communities and expanding aid as budgets increase, aligning with equity goals. This work showcases quantum computing's potential in disaster recovery planning, providing a rapid and equitable solution that elevates urban resilience and social sustainability by aiding vulnerable populations in disasters.

cs.MA↗