publications

2024

1. Expert with Clustering: Hierarchical Online Preference Learning Framework

   Tianyue Zhou , Jung-Hoon Cho , Babak Rahimi Ardabili , and 2 more authors

   Jan 2024

   arXiv:2401.15062 [cs]

   Abs arXiv

   Emerging mobility systems are increasingly capable of recommending options to mobility users, to guide them towards personalized yet sustainable system outcomes. Even more so than the typical recommendation system, it is crucial to minimize regret, because 1) the mobility options directly affect the lives of the users, and 2) the system sustainability relies on sufficient user participation. In this study, we consider accelerating user preference learning by exploiting a low-dimensional latent space that captures the mobility preferences of users. We introduce a hierarchical contextual bandit framework named Expert with Clustering (EWC), which integrates clustering techniques and prediction with expert advice. EWC efficiently utilizes hierarchical user information and incorporates a novel Loss-guided Distance metric. This metric is instrumental in generating more representative cluster centroids. In a recommendation scenario with \N users, \T rounds per user, and \K options, our algorithm achieves a regret bound of Ø(N}sqrt{T}log K} + NT)\. This bound consists of two parts: the first term is the regret from the Hedge algorithm, and the second term depends on the average loss from clustering. The algorithm performs with low regret, especially when a latent hierarchical structure exists among users. This regret bound underscores the theoretical and experimental efficacy of EWC, particularly in scenarios that demand rapid learning and adaptation. Experimental results highlight that EWC can substantially reduce regret by 27.57% compared to the LinUCB baseline. Our work offers a data-efficient approach to capturing both individual and collective behaviors, making it highly applicable to contexts with hierarchical structures. We expect the algorithm to be applicable to other settings with layered nuances of user preferences and information.

2023

1. PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems

   Aamir Hasan , Neeloy Chakraborty , Haonan Chen , and 3 more authors

   Aug 2023

   arXiv:2308.00864 [cs]

   Abs arXiv

   Intelligent driving systems can be used to mitigate congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these systems assume precise control over autonomous vehicle fleets, and are hence limited in practice as they fail to account for uncertainty in human behavior. Piecewise Constant (PC) Policies address these issues by structurally modeling the likeness of human driving to reduce traffic congestion in dense scenarios to provide action advice to be followed by human drivers. However, PC policies assume that all drivers behave similarly. To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP. PeRP advises drivers to behave in ways that mitigate traffic congestion. We first infer the driver’s intrinsic traits on how they follow instructions in an unsupervised manner with a variational autoencoder. Then, a policy conditioned on the inferred trait adapts the action of the PC policy to provide the driver with a personalized recommendation. Our system is trained in simulation with novel driver modeling of instruction adherence. We show that our approach successfully mitigates congestion while adapting to different driver behaviors, with 4 to 22% improvement in average speed over baselines.

2. Incentive Design for Eco-driving in Urban Transportation Networks

   M. Umar B. Niazi , Jung-Hoon Cho , Munther A. Dahleh , and 2 more authors

   Nov 2023

   arXiv:2311.03682 [cs, eess, math]

   Abs arXiv

   Eco-driving emerges as a cost-effective and efficient strategy to mitigate greenhouse gas emissions in urban transportation networks. Acknowledging the persuasive influence of incentives in shaping driver behavior, this paper presents the ‘eco-planner,’ a digital platform devised to promote eco-driving practices in urban transportation. At the outset of their trips, users provide the platform with their trip details and travel time preferences, enabling the eco-planner to formulate personalized eco-driving recommendations and corresponding incentives, while adhering to its budgetary constraints. Upon trip completion, incentives are transferred to users who comply with the recommendations and effectively reduce their emissions. By comparing our proposed incentive mechanism with a baseline scheme that offers uniform incentives to all users, we demonstrate that our approach achieves superior emission reductions and increased user compliance with a smaller budget.

3. Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy

   Jung-Hoon Cho , Sirui Li , Jeongyun Kim , and 1 more author

   Nov 2023

   arXiv:2312.09436 [cs]

   Abs arXiv

   The recent development of connected and automated vehicle (CAV) technologies has spurred investigations to optimize dense urban traffic. This paper considers advisory autonomy, in which real-time driving advisories are issued to drivers, thus blending the CAV and the human driver. Due to the complexity of traffic systems, recent studies of coordinating CAVs have resorted to leveraging deep reinforcement learning (RL). Advisory autonomy is formalized as zero-order holds, and we consider a range of hold duration from 0.1 to 40 seconds. However, despite the similarity of the higher frequency tasks on CAVs, a direct application of deep RL fails to be generalized to advisory autonomy tasks. We introduce Temporal Transfer Learning (TTL) algorithms to select source tasks, systematically leveraging the temporal structure to solve the full range of tasks. TTL selects the most suitable source tasks to maximize the performance of the range of tasks. We validate our algorithms on diverse mixed-traffic scenarios, demonstrating that TTL more reliably solves the tasks than baselines. This paper underscores the potential of coarse-grained advisory autonomy with TTL in traffic flow optimization.

2022

1. Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition

   Jung-Hoon Cho , Dong-Kyu Kim , and Eui-Jin Kim

   Physica A: Statistical Mechanics and its Applications, Aug 2022

   Abs

   The global spread of the coronavirus disease 2019 (COVID-19) pandemic has affected the world in many ways. Due to the communicable nature of the disease, it is difficult to investigate the causal reason for the epidemic’s spread sufficiently. This study comprehensively investigates the causal relationship between the spread of COVID-19 and mobility level on a multi time-scale and its influencing factors, by using ensemble empirical mode decomposition (EEMD) and the causal decomposition approach. Linear regression analysis investigates the significance and importance of the influential factors on the intrastate and interstate causal strength. The results of an EEMD analysis indicate that the mid-term and long-term domain portrays the macroscopic component of the states’ mobility level and COVID-19 cases, which represents overall intrinsic characteristics. In particular, the mobility level is highly associated with the long-term variations of COVID-19 cases rather than short-term variations. Intrastate causality analysis identifies the significant effects of median age and political orientation on the causal strength at a specific time-scale, and some of them cannot be identified from the existing method. Interstate causality results show a negative association with the interstate distance and the positive one with the airline traffic in the long-term domain. Clustering analysis confirms that the states with the higher the gross domestic product and the more politically democratic tend to more adhere to social distancing. The findings of this study can provide practical implications to the policymakers that whether the social distancing policies are effectively working or not should be monitored by long-term trends of COVID-19 cases rather than short-term.

2021

1. Spatiotemporal Demand Prediction Model for E-Scooter Sharing Services with Latent Feature and Deep Learning

   Seung Woo Ham , Jung-Hoon Cho , Sangwoo Park , and 1 more author

   Transportation Research Record, Nov 2021

   Publisher: SAGE Publications Inc

   Abs

   The electric scooter (e-scooter) sharing service has attracted significant attention because of its extensive usage and eco-friendliness. Since e-scooters are mostly accessed by foot, the presence of e-scooters within walking distance has a crucial effect on the service quality. Therefore, to maintain appropriate service quality, relocation strategies are often used to properly distribute e-scooters within service areas. There are extensive literatures on demand forecasting for an efficient relocation. However, the study of the relocation of small-scale spatial units within walking distance level is still inadequate because of the sparsity of demand data. This research aims to establish an effective methodology for predicting the demand for e-scooters in high spatial resolution. A new grid-based spatial setting was created with the usage data. The model in the methodology predicts not only the identified demand but also the unmet demand to increase practicality. A convolutional autoencoder is used to obtain the latent feature that can reduce the problem of representing sparse data. An encoder–recurrent neural network–decoder (ERD) framework with a convolutional autoencoder resulted in a huge improvement in predicting spatiotemporal events. This new ERD framework shows enhanced prediction performance, reducing the mean squared error loss to 0.00036 from 0.00679 compared with the baseline long short-term memory model. This methodological strategy has its significance in that it can solve any prediction issue with spatiotemporal data, even those with sparse data problems.

2. Efficiency Comparison of Public Bike-Sharing Repositioning Strategies Based on Predicted Demand Patterns

   Jung-Hoon Cho , Young-Hyun Seo , and Dong-Kyu Kim

   Transportation Research Record: Journal of the Transportation Research Board, Nov 2021

   Abs

   Public bike-sharing systems are used worldwide, and the imbalance between supply and demand for bicycles and operational inefficiency is becoming increasingly severe. For a system to operate efficiently, it is necessary to relocate bicycles among rental stations to minimize a lack of bikes at the station causing unmet demand. Recent studies have presented various repositioning strategies for bike-sharing systems and compared their efficiency. However, little consideration has been paid to the strategy of the spatial and temporal patterns of bike-sharing demand and the inventory level. This study aims to analyze the spatiotemporal patterns of the forecasted demand for the bike-sharing system and to compare the efficiency of different repositioning strategies to choose the most efficient one. We use three repositioning strategies with different additional constraints related to unbalanced stations and present computational results with real data in Seoul. Two indices represent the temporal variation of predicted inventory at each station and the coefficients of the spatial variation for hourly unmet demand. Linear classifiers are derived by linear discriminant analysis to classify the efficiency of each strategy according to developed indices. The study reveals that adding constraints of imbalanced stations to the strategy according to the spatiotemporal characteristics of forecasted inventory can help to reduce unmet demand. The result of this study enables proactive decision-making using proposed indices in operating bike-sharing systems and contributes to improving the efficiency and reliability of systems.

3. Enhancing the Accuracy of Peak Hourly Demand in Bike-Sharing Systems using a Graph Convolutional Network with Public Transit Usage Data

   Jung-Hoon Cho , Seung Woo Ham , and Dong-Kyu Kim

   Transportation Research Record: Journal of the Transportation Research Board, Oct 2021

   Abs

   With the growth of the bike-sharing system, the problem of demand forecasting has become important to the bike-sharing system. This study aims to develop a novel prediction model that enhances the accuracy of the peak hourly demand. A spatiotemporal graph convolutional network (STGCN) is constructed to consider both the spatial and temporal features. One of the model’s essential steps is determining the main component of the adjacency matrix and the node feature matrix. To achieve this, 131 days of data from the bike-sharing system in Seoul are used and experiments conducted on the models with various adjacency matrices and node feature matrices, including public transit usage. The results indicate that the STGCN models reflecting the previous demand pattern to the adjacency matrix show outstanding performance in predicting demand compared with the other models. The results also show that the model that includes bus boarding and alighting records is more accurate than the model that contains subway records, inferring that buses have a greater connection to bike-sharing than the subway. The proposed STGCN with public transit data contributes to the alleviation of unmet demand by enhancing the accuracy in predicting peak demand.