arXiv subjects

Shuocheng Guo

Publications and source records attributed to Shuocheng Guo.

7 recordsLinked to original sources

arXiv2023

Optimal Drive-by Sensing in Urban Road Networks with Large-scale Ridesourcing Vehicles

The sensing and monitoring of the urban road network contribute to the efficient operation of the urban transportation system and the functionality of urban systems. However, traditional sensing methods, such as inductive loop sensors, roadside cameras, and crowdsourcing data from massive urban travelers (e.g., Google Maps), are often hindered by high costs, limited coverage, and low reliability. This study explores the potential of drive-by sensing, an innovative approach that employs large-scale ridesourcing vehicles (RVs) for urban road network monitoring. We first evaluate RV sensing performance by coverage and reliability through historical road segment visits. Next, we propose an optimal trip-based RV rerouting model to maximize the sensing coverage and reliability while preserving the same level of service for the RVs' mobility service. Furthermore, a scalable column generation-based heuristic is designed to guide the cruising trajectory of RVs, assuming trip independence. The effectiveness of the proposed model is validated through experiments and sensitivity analyses using real-world RV trajectory data of over 20,000 vehicles in New York City. The optimized rerouting strategy has yielded significantly improved results, elevating explicit sensing coverage of the road network by 15.0\% to 17.3\% (varies by time of day) and achieving an impressive enhancement in sensing reliability by at least 24.6\% compared to historical records. Expanding the path-searching space further improved sensing coverage of up to 4.5\% and reliability of over 4.2\%. Moreover, considering incentives for RV drivers, the enhanced sensing performance comes at a remarkably low cost of \$0.10 per RV driver, highlighting its cost-effectiveness.

physics.soc-ph↗

arXiv2023

A branch-cut-and-price algorithm for a dial-a-ride problem with minimum disease-transmission risk

This paper investigates a variant of the dial-a-ride problem (DARP), namely Risk-aware DARP (RDARP). Our RDARP extends the DARP by (1) minimizing a weighted sum of travel cost and disease-transmission risk exposure for onboard passengers and (2) introducing a maximum cumulative exposure risk constraint for each vehicle to ensure a trip with lower external exposure. Both extensions require that the risk exposure of each onboard passenger is propagated in a minimized fashion while satisfying other existing constraints of the DARP. To fully describe the RDARP, we first provide a three-index arc-based formulation and reformulate the RDARP into an equivalent min-max trip-based formulation, which is solved by an exact Branch-Cut-and-Price (BCP) algorithm. For each node in the branching tree, we adopt the column generation method by decomposing the problem into a master problem and a subproblem. The latter takes the form of an elementary shortest path problem with resource constraints and minimized maximum risk (ESPPRCMMR). To solve the ESPPRCMMR efficiently, we develop a new labeling algorithm and establish families of resource extension functions in compliance with the risk-related resources. We adopt a real-world paratransit trip dataset to generate the RDARP instances ranging from 17 to 55 heterogeneous passengers with 3 to 13 vehicles during the morning and afternoon periods. Computational results show that our BCP algorithm can optimally solve all small- and medium-sized instances (32 or fewer passengers) within 5 minutes. For the large instances (39 to 55 passengers), our BCP algorithm can solve 23 of 30 instances optimally within a time limit of one hour.

math.OC↗

arXiv2023

DCA: Delayed Charging Attack on the Electric Shared Mobility System

An efficient operation of the electric shared mobility system (ESMS) relies heavily on seamless interconnections among shared electric vehicles (SEV), electric vehicle supply equipment (EVSE), and the grid. Nevertheless, this interconnectivity also makes the ESMS vulnerable to cyberattacks that may cause short-term breakdowns or long-term degradation of the ESMS. This study focuses on one such attack with long-lasting effects, the Delayed Charge Attack (DCA), that stealthily delays the charging service by exploiting the physical and communication vulnerabilities. To begin, we present the ESMS threat model by highlighting the assets, information flow, and access points. We next identify a linked sequence of vulnerabilities as a viable attack vector for launching DCA. Then, we detail the implementation of DCA, which can effectively bypass the detection in the SEV's battery management system and the cross-verification in the cloud environment. We test the DCA model against various Anomaly Detection (AD) algorithms by simulating the DCA dynamics in a Susceptible-Infectious-Removed-Susceptible process, where the EVSE can be compromised by the DCA or detected for repair. Using real-world taxi trip data and EVSE locations in New York City, the DCA model allows us to explore the long-term impacts and validate the system consequences. The results show that a 10-min delay results in 12-min longer queuing times and 8% more unfulfilled requests, leading to a 10.7% (\$311.7) weekly revenue loss per driver. With the AD algorithms, the weekly revenue loss remains at least 3.8% (\$111.8) with increased repair costs of \$36,000, suggesting the DCA's robustness against the AD.

cs.CR↗

arXiv2022

Understanding charging dynamics of fully-electrified taxi services using large-scale trajectory data

An accurate understanding of "when, where and why" of charging activities is crucial for the optimal planning and operation of E-shared mobility services. In this study, we leverage a unique trajectory of a city-wide fully electrified taxi fleet in Shenzhen, China, and we present one of the first studies to investigate charging behavioral dynamics of a fully electrified shared mobility system from both system-level and individual driver perspectives. The electric taxi (ET) trajectory data contain detailed travel information of over 20,000 ETs over one month period. By combing the trajectory and charging infrastructure data, we reveal remarkable regularities in infrastructure utilization, temporal and spatial charging dynamics as well as individual driver level charging preferences. Specifically, we report that both temporal and spatial distributions of system-level charging activities present strong within-day and daily regularities, and most charging activities are induced from drivers' shift schedules. Further, with 425 charging stations, we observe that the drivers show strong preferences over a small subset of charging stations, and the power-law distribution can well characterize the charging frequency at each charging station. Finally, we show that drivers' shift schedules also dominate the individual charging behavior, and there are strikingly stable daily charging patterns at the individual level. The results and findings of our study represent lessons and insights that may be carried over to the planning and operation of E-shared mobility in other cities and deliver important justifications for future studies on the modeling of E-shared mobility services.

physics.soc-ph↗

arXiv2021

DROP: Deep relocating option policy for optimal ride-hailing vehicle repositioning

In a ride-hailing system, an optimal relocation of vacant vehicles can significantly reduce fleet idling time and balance the supply-demand distribution, enhancing system efficiency and promoting driver satisfaction and retention. Model-free deep reinforcement learning (DRL) has been shown to dynamically learn the relocating policy by actively interacting with the intrinsic dynamics in large-scale ride-hailing systems. However, the issues of sparse reward signals and unbalanced demand and supply distribution place critical barriers in developing effective DRL models. Conventional exploration strategy (e.g., the $ε$-greedy) may barely work under such an environment because of dithering in low-demand regions distant from high-revenue regions. This study proposes the deep relocating option policy (DROP) that supervises vehicle agents to escape from oversupply areas and effectively relocate to potentially underserved areas. We propose to learn the Laplacian embedding of a time-expanded relocation graph, as an approximation representation of the system relocation policy. The embedding generates task-agnostic signals, which in combination with task-dependent signals, constitute the pseudo-reward function for generating DROPs. We present a hierarchical learning framework that trains a high-level relocation policy and a set of low-level DROPs. The effectiveness of our approach is demonstrated using a custom-built high-fidelity simulator with real-world trip record data. We report that DROP significantly improves baseline models with 15.7% more hourly revenue and can effectively resolve the dithering issue in low-demand areas.

cs.MA↗

arXiv2021

Spatiotemporal impacts of human activities and socio-demographics during the COVID-19 outbreak in the U.S

Understanding influencing factors is essential for the surveillance and prevention of infectious diseases, and the factors are likely to vary spatially and temporally as the disease progresses. Taking daily cases and deaths data during the coronavirus disease 2019 (COVID-19) outbreak in the U.S. as a case study, we develop a mobility-augmented geographically and temporally weighted regression (M-GTWR) model to quantify the spatiotemporal impacts of social-demographic factors and human activities on the COVID-19 dynamics. Different from the base GTWR model, we incorporate a mobility-adjusted distance weight matrix where travel mobility is used in addition to the spatial adjacency to capture the correlations among local observations. The model residuals suggest that the proposed model achieves a substantial improvement over other benchmark methods in addressing the spatiotemporal nonstationarity. Our results reveal that the impacts of social-demographic and human activity variables present significant spatiotemporal heterogeneity. In particular, a 1% increase in population density may lead to 0.63% and 0.71% more daily cases and deaths, and a 1% increase in the mean commuting time may result in 0.22% and 0.95% increases in daily cases and deaths. Although increased human activities will, in general, intensify the disease outbreak, we report that the effects of grocery and pharmacy-related activities are insignificant in areas with high population density. And activities at the workplace and public transit are found to either increase or decrease the number of cases and deaths, depending on particular locations. The results of our study establish a quantitative framework for identifying influencing factors during a disease outbreak, and the obtained insights may have significant implications in guiding the policy-making against infectious diseases.

physics.soc-ph↗

arXiv2020

Charging-as-a-Service: On-demand battery delivery for light-duty electric vehicles for mobility service

This study presents an innovative solution for powering electric vehicles, named Charging-as-a-Service (CaaS), that concerns the potential large-scale adoption of light-duty electric vehicles (LDEV) in the Mobility-as-a-Service (MaaS) industry. Analogous to the MaaS, the core idea of the CaaS is to dispatch service vehicles (SVs) that carry modular battery units (MBUs) to provide LDEVs for mobility service with on-demand battery delivery. The CaaS system is expected to tackle major bottlenecks of a large-scale LDEV adoption in the MaaS industry due to the lack of charging infrastructure and excess waiting and charging time. A hybrid agent-based simulation model (HABM) is developed to model the dynamics of the CaaS system with SV agents, and a trip-based stationary charging probability distribution is introduced to simulate the generation of charging demand for LDEVs. Two dispatching algorithms are further developed to support the optimal operation of the CaaS. The model is validated by assuming electrifying all 13,000 yellow taxis in New York City (NYC) that follow the same daily trip patterns. Multiple scenarios are analyzed under various SV fleet sizes and dispatching strategies. The results suggest that optimal deployment of 250 SVs may serve the LDEV fleet in NYC with an average waiting time of 5 minutes, save the travel distance at over 50 miles per minute, and gain considerable profits of up to $50 per minute. This study offers significant insights into the feasibility, service efficiency, and financial sustainability for deploying city-wide CaaS systems to power the electric MaaS industry.

math.OC↗