2023

1. Sijing Duan*, Feng Lyu, Xin Zhu, Yi Ding, Haotian Wang, Desheng Zhang, Xue Liu, Yaoxue Zhang, Ju Ren.
   VeLP: Vehicle Loading Plan Learning from Human Behavior in Nationwide Logistics System.
   To Appear in VLDB 2023.
   


2. Hua Yan*, Hao Wang, Desheng Zhang, Yu Yang.
   Identifying Regional Driving Risks via Transductive Cross-City Transfer Learning Under Negative Transfer.
   To Appear in ACM CIKM 2023.
   


3. Guang Yang*, Yuequn Zhang*, Jinquan Hang*, Xinyue Feng*, Zejun Xie*, Desheng Zhang, Yu Yang.
   CARPG: Cross-City Knowledge Transfer for Traffic Accident Prediction via Attentive Region-Level Parameter Generation.
   To Appear in ACM CIKM 2023.
   


4. Xiaohui Zhao*, Shuai Wang, Hai Wang, Tian He, Desheng Zhang, Guang Wang.
   HST-GT: Heterogeneous Spatial-Temporal Graph Transformer for Delivery Time Estimation in Warehouse-Distribution Integration E-Commerce.
   To Appear in ACM CIKM 2023.
   


5. Shuxin Zhong*, William Yubeaton, Wenjun Lyu*, Guang Wang, Desheng Zhang, Yu Yang.
   RLIFE: Remaining Lifespan Prediction for E-scooters.
   To Appear in ACM CIKM 2023.
   


6. Zhiqing Hong*, Dongjiang Cao, Haotian Wang, Guang Wang, Tian He, Desheng Zhang.
   AutoBuild: Automatic Community Building Labeling for Last-mile Delivery.
   To Appear in ACM CIKM 2023.
   


7. Zhiqing Hong*, Haotian Wang, Wenjun Lyu*, Hai Wang*, Yunhuai Liu, Guang Wang, Tian He, Desheng Zhang.
   Urban-scale POI Updating with Crowd Intelligence.
   To Appear in ACM CIKM 2023.
   


8. Hua Yan*, Yingqiang Ge, Haotian Wang, Desheng Zhang, Yu Yang.
   Logistics Audience Expansion via Temporal Knowledge Graph.
   To Appear in ACM CIKM 2023.
   


9. Baoshen Guo*, Shuai Wang, Haotian Wang, Yunhuai Liu, Fanshuo Kong, Desheng Zhang, Tian He.
   FairCod: A Fairness-aware Concurrent Dispatch System for Large-scale Instant Delivery Services.
   To Appear in ACM KDD 2023.
   


10. Lin Jiang*, Shuai Wang, Baoshen Guo*, Hai Wang*, Desheng Zhang, Guang Wang.
    FairCod: A Fairness-aware Concurrent Dispatch System for Large-scale Instant Delivery Services.
    To Appear in ACM KDD 2023.
    


11. Kaiwen Xia*, Li Lin, Shuai Wang, Haotian Wang, Desheng Zhang, Tian He.
    A Predict-Then-Optimize Couriers Allocation Framework for Emergency Last-mile Logistics.
    To Appear in ACM KDD 2023.
    


12. Wenjun Lyu*, Haotian Wang, Yiwei Song, Yunhuai Liu, Tian He, Desheng Zhang.
    A Prediction-and-Scheduling Framework for Efficient Order Transfer in Logistics.
    To Appear in IJCAI 2023.
    


13. Wenjun Lyu*, Haotian Wang, Zhiqing Hong*, Guang Wang, Yu Yang, Yunhuai Liu, Desheng Zhang.
    REDE: Exploring Relay Transportation for Efficient Last-mile Delivery.
    To Appear in IEEE ICDE 2023.
    


14. Hai Wang*, Shuai Wang, Yu Yang, Desheng Zhang.
    GCRL: Efficient Delivery Area Assignment for Last-mile Logistics with Group-based Cooperative Reinforcement Learning.
    To Appear in IEEE ICDE 2023.
    


15. Guanzhou Zhu*, Dong Zhao, Yizong Wang, Haotian Wang, Desheng Zhang, Huadong Ma.
    COME: Learning to Coordinate Crowdsourcing and Regular Couriers for Offline Delivery During Online Mega Sale Days.
    To Appear in IEEE ICDE 2023.
    

2022

1. Yi Ding*, Dongzhe Jiang, Yu Yang, Yunhuai Liu, Tian He, Desheng Zhang.
   P2-Loc: A Person-2-Person Indoor Localization System in On-Demand Delivery.
   To Appear in ACM UbiComp 2022.
   


2. Baoshen Guo*, Weijian Zuo, Shuai Wang, Wenjun Lyu*, Zhiqing Hong*, Yi Ding*, Tian He, Desheng Zhang.
   WePos: Weak-supervised Indoor Positioning with Unlabeled WiFi for On-demand Delivery.
   To Appear in ACM UbiComp 2022.
   


3. Xiaoyang Xie*, Zhiqing Hong*, Zhou Qin*, Zhihan Fang*, Yuan Tian, Desheng Zhang.
   TransRisk: Mobility Privacy Risk Prediction based on Transferred Knowledge.
   To Appear in ACM UbiComp 2022.
   


4. Zhiqing Hong*, Guang Wang, Wenjun Lyu*, Baoshen Guo*, Yi Ding*, Haotian Wang, Shuai Wang, Yunhuai Liu, Desheng Zhang.
   CoMiner: nationwide behavior-driven unsupervised spatial coordinate mining from uncertain delivery events.
   To Appear in ACM SIGSPATIAL 2022. 38/160= 23.8%.
   


5. Zhiqing Hong*, Heng Yang, Haotian Wang, Wenjun Lyu*, Yu Yang, Guang Wang, Yunhuai Liu, Yang Wang, Desheng Zhang.
   real-time abnormal address detection via contrastive augmentation for location-based services.
   To Appear inACM SIGSPATIAL 2022. 38/160= 23.8%.
   


6. Wenjun Lyu*, Kexin Zhang, Baoshen Guo*, Zhiqing Hong*, Guang Yang*, Guang Wang, Yu Yang, Yunhuai Liu, Desheng Zhang.
   Towards Fair Workload Assessment via Homogeneous Order Grouping in Last-mile Delivery.
   To Appear inACM CIKM 2022. 621/2257= 28%.
   


7. Shuxin Zhong*, Wenjun Lyu*, Desheng Zhang, Yu Yang.
   BikeCAP: Deep Spatial-temporal Capsule Network for Multi-step Bike Demand Prediction.
   To Appear in IEEE ICDCS 2022. 114/573= 19.9%.
   


8. Hua Yan, Shuai Wang, Yu Yang, Baoshen Guo*, Tian He, Desheng Zhang
   SiteRec: Store Site Recommendation under the O2O Model via Multi-graph Attention Networks.
   To Appear in IEEE ICDE 2022.
   


9. Wei Liu, Yi Ding*, Shuai Wang, Yu Yang, Desheng Zhang
   Para-Pred: Addressing Heterogeneity for City-Wide Indoor Status Estimation in On-Demand Delivery.
   To Appear in ACM SIGKDD 2022. 254/1695= 15%.
   

2021

1. Yi Ding*, Yu Yang*, W.Jiang, Y.Liu, Tian He, Desheng Zhang.
   Nationwide Deployment and Operation of a Virtual Arrival Detection System in the Wild.
   To Appear in ACM SIGCOMM 2021. [Paper] 55/241=22%.
   


2. Yi Ding*, Ling Liu, Yu Yang*, Yunhuai Liu, Desheng Zhang, Tian He.
   From Conception to Retirement: a Lifetime Story of a 3-Year-Old Operational Wireless Beacon System in the Wild.
   To Appear in USENIX NSDI 2021. [Paper] [BibTex]
   @inproceedings {262019, author = {Yi Ding and Ling Liu and Yu Yang and Yunhuai Liu and Desheng Zhang and Tian He}, title = {From Conception to Retirement: a Lifetime Story of a 3-Year-Old Wireless Beacon System in the Wild}, booktitle = {18th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 21)}, year = {2021}, isbn = {978-1-939133-21-2}, pages = {859--872}, url = {https://www.usenix.org/conference/nsdi21/presentation/ding}, publisher = {{USENIX} Association}, month = apr, }

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   19/114=16%.
   


3. Zhihan Fang*, G. Yang*, D. Zhang, X. Xie*, G.Wang*, Y.Yang*, F.Zhang* and Desheng Zhang.
   MoCha: Large-Scale Driving Pattern Characterization for Usage-based Insurance
   In ACM SIGKDD 2021. [Paper] 138/705=19%.
   


4. Guang Wang*, Zhou Qin*, Shuai Wang, Huijun Sun, Zheng Dong, and Desheng Zhang.
   Joint Real-Time Repositioning and Charging for Electric Carsharing with Dynamic Deadlines
   In ACM SIGKDD 2021. [Paper] 138/705=19%.
   


5. Zhihan Fang*, Guang Wang*, Xiaoyang Xie*, Fan Zhang, Desheng Zhang.
   Urban Map Inference by Pervasive Vehicular Sensing Systems with Complementary Mobility
   To Appear in ACM UbiComp 2021. [Paper] [BibTex]
   @article{10.1145/3448076, author = {Fang, Zhihan and Wang, Guang and Xie, Xiaoyang and Zhang, Fan and Zhang, Desheng}, title = {Urban Map Inference by Pervasive Vehicular Sensing Systems with Complementary Mobility}, year = {2021}, issue_date = {March 2021}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {5}, number = {1}, url = {https://doi.org/10.1145/3448076}, doi = {10.1145/3448076}, abstract = {Accurate and up-to-date digital road maps are the foundation of many mobile applications, such as navigation and autonomous driving. A manually-created map suffers from the high cost for creation and maintenance due to constant road network updating. Recently, the ubiquity of GPS devices in vehicular systems has led to an unprecedented amount of vehicle sensing data for map inference. Unfortunately, accurate map inference based on vehicle GPS is challenging for two reasons. First, it is challenging to infer complete road structures due to the sensing deviation, sparse coverage, and low sampling rate of GPS of a fleet of vehicles with similar mobility patterns, e.g., taxis. Second, a road map requires various road properties such as road categories, which is challenging to be inferred by just GPS locations of vehicles. In this paper, we design a map inference system called coMap by considering multiple fleets of vehicles with Complementary Mobility Features. coMap has two key components: a graph-based map sketching component, a learning-based map painting component. We implement coMap with the data from four type-aware vehicular sensing systems in one city, which consists of 18 thousand taxis, 10 thousand private vehicles, 6 thousand trucks, and 14 thousand buses. We conduct a comprehensive evaluation of coMap with two state-of-the-art baselines along with ground truth based on OpenStreetMap and a commercial map provider, i.e., Baidu Maps. The results show that (i) for the map sketching, our work improves the performance by 15.9%; (ii) for the map painting, our work achieves 74.58% of average accuracy on road category classification.}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = mar, articleno = {47}, numpages = {24}, keywords = {heterogeneous vehicular fleets, map painting, GPS traces, map sketching} }

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6. Guang Wang*, Harsh Rajkumar Vaish*, Huijun Sun, Jianjun Wu, Shuai Wang, Desheng Zhang.
   Mixing Qualitative and Quantitative User Studies for Online Car Sharing Systems.
   To Appear in ACM UbiComp 2021. [Paper] [BibTex]
   @article{10.1145/3432200, author = {Wang, Guang and Vaish, Harsh Rajkumar and Sun, Huijun and Wu, Jianjun and Wang, Shuai and Zhang, Desheng}, title = {Understanding User Behavior in Car Sharing Services Through The Lens of Mobility: Mixing Qualitative and Quantitative Studies}, year = {2020}, issue_date = {December 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {4}, number = {4}, url = {https://doi.org/10.1145/3432200}, doi = {10.1145/3432200}, abstract = {Qualitative and quantitative user studies can reveal valuable insights into user behavior, which in turn can assist system designers in providing better user experiences. Car sharing (e.g., Zipcar and car2go), as an emerging App-based online shared mobility mode, has been increasing dramatically worldwide in recent years. However, to date, comprehensive user behavior in car sharing systems has not been investigated, which is essential for understanding their characteristics and promotion roadblocks. With the goal of understanding various facets of user behavior in online car sharing systems, in this paper, we performed a qualitative and quantitative user study by adopting a mixed-methods approach. We first designed an attitude-aware online survey with a set of qualitative questions to perceive people's subjective attitudes to online car sharing, where a total of 185 participants (68 females) completed the survey. Next, we quantitatively analyzed a one-year real-world car sharing operation dataset collected from the Chinese city Beijing, which involves over 68,000 unique users and over 587,850 usage records. We dissected this attitude-free dataset to understand the objective car sharing user behavior from different dimensions, e.g., spatial, temporal, and demographic. Furthermore, we conducted a comparative study by utilizing one-year data from other two representative Chinese city Fuzhou and Lanzhou to show if the obtained findings from Beijing data may be generalizable to other cities having different urban features, e.g., different city size, population density, wealth, and climate conditions. We also do a case study by designing a user behavior-aware usage prediction model (i.e., BeXGBoost) based on findings from our user study (e.g., unbalanced spatiotemporal usage patterns, weekly regularity, demographic-related usage difference, and low-frequency revisitation), which is the basis for car sharing service station deployment and vehicle rebalancing. Finally, we summarize a set of findings obtained from our study about the unique user behavior in online car sharing systems, combined with some detailed discussions about implications for design.}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = dec, articleno = {156}, numpages = {30}, keywords = {User behavior, shared mobility, quantitative, qualitative, car sharing} }

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7. Zhou Qin*, YikunXian, Fan Zhang, Desheng Zhang.
   MIMU: Mobile WiFi Usage Inference by Mining Diverse User Behaviors.
   To Appear in ACM UbiComp 2021. [Paper] [BibTex]
   @article{10.1145/3432226, author = {Qin, Zhou and Xian, Yikun and Zhang, Fan and Zhang, Desheng}, title = {MIMU: Mobile WiFi Usage Inference by Mining Diverse User Behaviors}, year = {2020}, issue_date = {December 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {4}, number = {4}, url = {https://doi.org/10.1145/3432226}, doi = {10.1145/3432226}, abstract = {Mobile WiFi is a newly emerging service in recent years, which provides convenience for users to access online resources and increases revenues for operators via services such as advertisements and application promotions. However, in practice, the prohibitively high system implementation and operational costs, especially the costs of perpetual data traffic, hinder the further deployment of mobile WiFi services. In this paper, we present MIMU, a usage inference system for data traffic saving suitable for ubiquitous mobile WiFi services. We demonstrate the performance of the system via an example from the real-world nationwide edge computing mobile WiFi infrastructure. To address the impact of diverse user behaviors, we investigate the WiFi network usage from the perspective of users and devices, focusing on two unique features of mobile WiFi: user mobility regularity and access irregularity. In particular, we first design a deep learning-based two-dimension usage predictor to infer the future mobile WiFi usage with 1) a user dimension model with temporal attention addressing dominant users with heavy bus WiFi usage, and 2) a device dimension model with spatial attention addressing diverse WiFi usage and connection. Based on the results of the predictor, an application of content caching is implemented in an iterative fashion to save the data traffic. We evaluate MIMU by real-world bus WiFi system data sets of three major cities with 6,643 bus WiFi devices and 150k daily active users in total. Our results show that MIMU outperforms state-of-the-art methods in terms of usage inference. Moreover, we summarize the lessons learned from our large-scale bus WiFi system investigation.}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = dec, articleno = {149}, numpages = {22}, keywords = {Mobile Networks, Deep Learning, Bus WiFi, Behavior} }

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8. Dong Zhao, Zijian Cao, Chen Ju, Desheng Zhang, Huadong Ma.
   D2Park: Diversified Demand-aware On-street Parking Guidance.
   To Appear in ACM UbiComp 2021. [Paper] [BibTex]
   @article{10.1145/3432214, author = {Zhao, Dong and Cao, Zijian and Ju, Chen and Zhang, Desheng and Ma, Huadong}, title = {D2Park: Diversified Demand-Aware On-Street Parking Guidance}, year = {2020}, issue_date = {December 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {4}, number = {4}, url = {https://doi.org/10.1145/3432214}, doi = {10.1145/3432214}, abstract = {To address the increasingly serious parking pain, numerous mobile Apps have emerged to help drivers to find a convenient parking spot with various auxiliary information. However, the phenomenon of "multiple cars chasing the same spot" still exists, especially for on-street parking. Existing reservation-based resource allocation solutions could address the parking competition issue to some extent, but it is impractical to treat all spots as reservable resources. This paper first conducts a qualitative investigation based on the online survey data, which identifies diversified parking requirements involving i) reserved users, who request guaranteed spots with a reservation fee, ii) normal users, who request non-guaranteed spots with a "best-effort" service, and iii) external users, who do not use any guidance service. To this end, we design the D2Park system for diversified demand-aware parking guidance services. We formulate the problem as a novel Heterogeneous-Agent Dynamic Resource Allocation (HADRA) problem, which considers both current and future parking demands, and different constraints for diversified requirements. Two main modules are used in the system: 1) multi-step parking prediction, which makes multi-step parking inflow and occupancy rate predictions given the current parking events data and external factors; and 2) diversified parking guidance, which integrates the cooperation-based and competition-based resource allocation mechanisms based on a model predictive control framework to achieve a better performance balance among different user groups. Extensive experiments with a four-month real-world on-street parking dataset from the Chinese city Shenzhen demonstrate the effectiveness and efficiency of D2Park.}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = dec, articleno = {163}, numpages = {25}, keywords = {Dynamic Resource Allocation, Diversified Requirements, On-street Parking Guidance} }

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9. Song Yiwei*, Yunhuai Liu, Wenqing Qiu, Zhou Qin, Chang Tan, Can Yang, Desheng Zhang.
   MIFF: Human Mobility Extractions with Cellular Signaling Data.
   To Appear in ACM UbiComp 2021. [Paper] [BibTex]
   @article{10.1145/3432238, author = {Song, Yiwei and Liu, Yunhuai and Qiu, Wenqing and Qin, Zhou and Tan, Chang and Yang, Can and Zhang, Desheng}, title = {MIFF: Human Mobility Extractions with Cellular Signaling Data under Spatio-Temporal Uncertainty}, year = {2020}, issue_date = {December 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {4}, number = {4}, url = {https://doi.org/10.1145/3432238}, doi = {10.1145/3432238}, abstract = {Human Mobility Extraction with cellular Signaling Data (SD) is essential for human mobility understanding, epidemic control, and wireless network planning. SD log the detailed interactions between cellphones and cellular towers, but suffer from a spatio-temporal uncertainty problem due to cellular network tower-level load rebalancing (switching users between towers) and cellphone usage activities. To date, most models focus on utilizing better data like RSSI or GPS, do not directly address uncertainty. To address the SD uncertainty issue, we utilize two insights based on (i) individuals' regular mobility patterns and (ii) common co-movement mobility patterns between cellphone users as suggested by fundamental human mobility nature. Accordingly, we design a Multi-Information Fusion Framework (MIFF) to assist in extracting road-level human mobility based on cell-tower level traces. To evaluate the effectiveness of MIFF, we conduct experiments on one-month SD obtained from a cellular service operator, and SD manually collected by handheld mobile devices in two cities in China. Four transportation modes, namely railways, cars, buses, and bikes are evaluated. Experimental results show that with MIFF, our road-level trajectory extraction accuracy can be improved by 5.0% on Point correct matching index and 68.5% on Geographic Error on average.}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = dec, articleno = {159}, numpages = {19}, keywords = {Multiple Traces Fusion, Regular Pattern Exploration, Homogeneous Traces Search, Signaling Data, Map Matching} }

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10. Guang Wang*, Shuxin Zhong, Shuai Wang, Fei Miao, Zheng Dong, Desheng Zhang.
    Data-Driven Fairness-Aware Vehicle Displacement for Large-Scale Electric Taxi Fleets.
    To Appear in IEEE ICDE 2021. [Paper] [BibTex]
    @inproceedings{inproceedings, author = {Wang, Guang and Zhong, Shuxin and Wang, Shuai and Miao, Fei and Dong, Zheng and Zhang, Desheng}, year = {2021}, month = {04}, pages = {}, title = {Data-Driven Fairness-Aware Vehicle Displacement for Large-Scale Electric Taxi Fleets}, doi = {10.1109/ICDE51399.2021.00108} }

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11. Yukun Yuan, Meiyi Ma, S. Han, Desheng Zhang, Fei Miao, John A. Stankovic and Shan Lin.
    DeResolver: A Decentralized Negotiation and Conflict Resolution Framework for City Services.
    To Appear in ACM ICCPS 2021. 26% [Paper] [BibTex]
    @inbook{10.1145/3450267.3450538, author = {Yuan, Yukun and Ma, Meiyi and Han, Songyang and Zhang, Desheng and Miao, Fei and Stankovic, John and Lin, Shan}, title = {DeResolver: A Decentralized Negotiation and Conflict Resolution Framework for Smart City Services}, year = {2021}, isbn = {9781450383530}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3450267.3450538}, abstract = {As various smart services are increasingly deployed in modern cities, many unexpected conflicts arise due to various physical world couplings. Existing solutions for conflict resolution often rely on centralized control to enforce predetermined and fixed priorities of different services, which is challenging due to the inconsistent and private objectives of the services. Also, the centralized solutions miss opportunities to more effectively resolve conflicts according to their spatiotemporal locality of the conflicts. To address this issue, we design a decentralized negotiation and conflict resolution framework named DeResolver, which allows services to resolve conflicts by communicating and negotiating with each other to reach a Pareto-optimal agreement autonomously and efficiently. Our design features a two-level semi-supervised learning-based algorithm to predict acceptable proposals and their rankings of each opponent through the negotiation. Our design is evaluated with a smart city case study of three services: intelligent traffic light control, pedestrian service, and environmental control. In this case study, a data-driven evaluation is conducted using a large data set consisting of the GPS locations of 246 surveillance cameras and an automatic traffic monitoring system with more than 3 million records per day to extract real-world vehicle routes. The evaluation results show that our solution achieves much more balanced results, i.e., only increasing the average waiting time of vehicles, the measurement metric of intelligent traffic light control service, by 6.8% while reducing the weighted sum of air pollutant emission, measured for environment control service, by 12.1%, and the pedestrian waiting time, the measurement metric of pedestrian service, by 33.1%, compared to priority-based solution.}, booktitle = {Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems}, pages = {98–109}, numpages = {12} }

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2020

1. Yu Yang*, Ding Yi, D.Yuan, Guang Wang*, Xiaoyang Xie*, Y. Liu, T. He and Desheng Zhang.
   TransLoc: Transparent Indoor Localization with Uncertain Human Participation.
   In ACM MobiCom'20. [Paper] 63/384=16%,
   


2. Desheng Zhang.
   Mobile Cyber Physical Systems for Smart Cities.
   In ACM WWW'20 Sideway WorkShop. Invited Keynote
   


3. Zhihan Fang*, Boyang Fu*, Zhou Qin*, Fan Zhang, and Desheng Zhang.
   PrivateBus: Privacy Identification and Protection in Large-Scale Bus WiFi Systems
   In ACM UbiComp 2020. [Paper] [BibTex]
   @article{10.1145/3380990, author = {Fang, Zhihan and Fu, Boyang and Qin, Zhou and Zhang, Fan and Zhang, Desheng}, title = {PrivateBus: Privacy Identification and Protection in Large-Scale Bus WiFi Systems}, year = {2020}, issue_date = {March 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {4}, number = {1}, url = {https://doi.org/10.1145/3380990}, doi = {10.1145/3380990}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = mar, articleno = {9}, numpages = {23}, keywords = {finger traces, bus WiFi, uniqueness, hybrid traces, foot traces} }

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4. Xiaoyang Xie*, Zhihan Fang*, Yang Wang, Fan Zhang, and Desheng Zhang.
   RISC: Resource-Constrained Urban Sensing Task Scheduling based on Commercial Fleets
   In ACM UbiComp 2020. [Paper] [BibTex]
   @article{10.1145/3397337, author = {Xie, Xiaoyang and Fang, Zhihan and Wang, Yang and Zhang, Fan and Zhang, Desheng}, title = {RISC: Resource-Constrained Urban Sensing Task Scheduling Based on Commercial Fleets}, year = {2020}, issue_date = {June 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {4}, number = {2}, url = {https://doi.org/10.1145/3397337}, doi = {10.1145/3397337}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = jun, articleno = {62}, numpages = {20}, keywords = {Vehicle Sensing, Heterogeneous Fleets, Mobility Patterns} }

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5. Guang Wang*, Yongfeng Zhang, Zhihan Fang*, Fan Zhang, and Desheng Zhang.
   FairCharge: Data-Driven Fairness-Aware Charging Recommendation for Electric Taxi Fleets
   In ACM UbiComp 2020. [Paper] [BibTex]
   @article{10.1145/3381003, author = {Wang, Guang and Zhang, Yongfeng and Fang, Zhihan and Wang, Shuai and Zhang, Fan and Zhang, Desheng}, title = {FairCharge: A Data-Driven Fairness-Aware Charging Recommendation System for Large-Scale Electric Taxi Fleets}, year = {2020}, issue_date = {March 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {4}, number = {1}, url = {https://doi.org/10.1145/3381003}, doi = {10.1145/3381003}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = mar, articleno = {28}, numpages = {25}, keywords = {recommendation system, Electric taxi, charging recommendation, fairness-aware, Pareto efficiency} }

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6. Zhou Qin*, Fang Cao, Yu Yang*, Shuai Wang, Yunhuai Liu, Chang Tan, and Desheng Zhang.
   CellPred: A Behavior-aided Scheme for Cellular Data Usage Prediction
   In ACM UbiComp 2020. [Paper] [BibTex]
   @article{10.1145/3380982, author = {Qin, Zhou and Cao, Fang and Yang, Yu and Wang, Shuai and Liu, Yunhuai and Tan, Chang and Zhang, Desheng}, title = {CellPred: A Behavior-Aware Scheme for Cellular Data Usage Prediction}, year = {2020}, issue_date = {March 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {4}, number = {1}, url = {https://doi.org/10.1145/3380982}, doi = {10.1145/3380982}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = mar, articleno = {40}, numpages = {24}, keywords = {Prediction, Cellular networks, Urban Computing, Behaviors, Deep Learning} }

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7. Zhihan Fang*, Gang Wang*, Shuang Wang, Chaoji Zuo, Fan Zhang, and Desheng Zhang.
   CellRep: Usage Representativeness Modeling and Correction Based on Multiple City-Scale Cellular Networks
   In the Web Conference (WWW) 2020. 217/1129=19%, Oral Presentation. [Paper] [BibTex]
   @inproceedings{10.1145/3366423.3380141, author = {Fang, Zhihan and Wang, Guang and Wang, Shuai and Zuo, Chaoji and Zhang, Fan and Zhang, Desheng}, title = {CellRep: Usage Representativeness Modeling and Correction Based on Multiple City-Scale Cellular Networks}, year = {2020}, isbn = {9781450370233}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3366423.3380141}, doi = {10.1145/3366423.3380141}, booktitle = {Proceedings of The Web Conference 2020}, pages = {584–595}, numpages = {12}, keywords = {Cellular Networks, Correction, Representativeness}, location = {Taipei, Taiwan}, series = {WWW ’20} }

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8. Sihong He, Lynn Pepin, Guang Wang, Desheng Zhang and Fei Miao.
   Data-Driven Distributionally Robust Electric Vehicle Balancing for Mobility-on-Demand Systems under Demand and Supply Uncertainties
   In In IEEE/RSJ IROS 2020.[Paper] [BibTex]
   @article{Miao2017DataDrivenDR, title={Data-Driven Distributionally Robust Vehicle Balancing Using Dynamic Region Partitions}, author={Fei Miao and Shuo Han and Abdeltawab M. Hendawi and Mohamed E. Khalefa and John A. Stankovic and George J. Pappas}, journal={2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems (ICCPS)}, year={2017}, pages={261-272} }

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2019

1. Yu Yang*, Xiaoyang Xie*, Zhihan Fang*, Fan Zhang, Yang Wang, and Desheng Zhang.
   VeMo: Enabling Vehicular Mobility Modeling at Individual Levels with Full Penetration
   In ACM MobiCom 2019. [Paper][BibTex]
   @inproceedings{10.1145/3300061.3300130, author = {Yang, Yu and Xie, Xiaoyang and Fang, Zhihan and Zhang, Fan and Wang, Yang and Zhang, Desheng}, title = {VeMo: Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration}, year = {2019}, isbn = {9781450361699}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3300061.3300130}, doi = {10.1145/3300061.3300130}, booktitle = {The 25th Annual International Conference on Mobile Computing and Networking}, articleno = {11}, numpages = {16}, keywords = {toll systems, route prediction, vehicular mobility modeling, speed prediction, static sensors, destination prediction}, location = {Los Cabos, Mexico}, series = {MobiCom ’19} }

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2. Guang Wang*, Xiuyuan Chen*, Fan Zhang, Yang Wang, and Desheng Zhang.
   Experience: Understanding Long-Term Evolving Patterns of Shared Electric Vehicle Fleets
   In ACM MobiCom 2019. [Paper][BibTex]
   @inproceedings{10.1145/3300061.3300132, author = {Wang, Guang and Chen, Xiuyuan and Zhang, Fan and Wang, Yang and Zhang, Desheng}, title = {Experience: Understanding Long-Term Evolving Patterns of Shared Electric Vehicle Networks}, year = {2019}, isbn = {9781450361699}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3300061.3300132}, doi = {10.1145/3300061.3300132}, booktitle = {The 25th Annual International Conference on Mobile Computing and Networking}, articleno = {9}, numpages = {12}, keywords = {mobility pattern, charging pattern, evolving experience, electric vehicle, shared autonomous vehicle}, location = {Los Cabos, Mexico}, series = {MobiCom ’19} }

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3. Zhihan Fang*, Yu Yang*, Shuai Wang, B Fu, Z, Song, Fan Zhang, and Desheng Zhang.
   MAC: Measuring the Impacts of Anomalies on Travel Time of Multiple Transportation Systems
   In ACM UbiComp 2019. [Paper][BibTex]
   @article{10.1145/3328913, author = {Fang, Zhihan and Yang, Yu and Wang, Shuai and Fu, Boyang and Song, Zixing and Zhang, Fan and Zhang, Desheng}, title = {MAC: Measuring the Impacts of Anomalies on Travel Time of Multiple Transportation Systems}, year = {2019}, issue_date = {June 2019}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {3}, number = {2}, url = {https://doi.org/10.1145/3328913}, doi = {10.1145/3328913}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = jun, articleno = {42}, numpages = {24}, keywords = {travel time components, anomalies, cyber physical systems} }

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4. Xiaoyang Xie*, Zhihan Fang*, Yu Yang*, Yunhuai Liu, and Desheng Zhang.
   coSense: Collaborative Urban-Scale Vehicle Sensing based on Heterogeneous Fleets
   In ACM UbiComp 2019. [Paper][BibTex]
   @article{10.1145/3287074, author = {Xie, Xiaoyang and Yang, Yu and Fang, Zhihan and Wang, Guang and Zhang, Fan and Zhang, Fan and Liu, Yunhuai and Zhang, Desheng}, title = {CoSense: Collaborative Urban-Scale Vehicle Sensing Based on Heterogeneous Fleets}, year = {2018}, issue_date = {December 2018}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {2}, number = {4}, url = {https://doi.org/10.1145/3287074}, doi = {10.1145/3287074}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = dec, articleno = {196}, numpages = {25}, keywords = {Heterogeneous Fleets, Mobility Patterns, Vehicle Sensing} }

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5. Guang Wang*, W.Li, J.Zhang, Y.Ge, Z.Fu, F.Zhang, Y.Wang,and Desheng Zhang.
   sharedCharging: Data-Driven Shared Charging for Large-Scale Heterogeneous Electric Vehicles
   In ACM UbiComp 2019. [Paper][BibTex]
   @article{10.1145/3351266, author = {Wang, Guang and Li, Wenzhong and Zhang, Jun and Ge, Yingqiang and Fu, Zuohui and Zhang, Fan and Wang, Yang and Zhang, Desheng}, title = {SharedCharging: Data-Driven Shared Charging for Large-Scale Heterogeneous Electric Vehicle Fleets}, year = {2019}, issue_date = {September 2019}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {3}, number = {3}, url = {https://doi.org/10.1145/3351266}, doi = {10.1145/3351266}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = sep, articleno = {108}, numpages = {25}, keywords = {Data-driven, electric vehicle, electric taxi, charging scheduling, electric bus} }

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6. Yi Zhao, Xu Wang, Jianbo Li, Zheng Yang, and Desheng Zhang.
   CellTrans: Private Car or Public Transportation? Infer Users' Main Transportation Modes
   In ACM UbiComp 2019. [Paper][BibTex]
   @article{10.1145/3351283, author = {Zhao, Yi and Wang, Xu and Li, Jianbo and Zhang, Desheng and Yang, Zheng}, title = {CellTrans: Private Car or Public Transportation? Infer Users’ Main Transportation Modes at Urban Scale with Cellular Data}, year = {2019}, issue_date = {September 2019}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {3}, number = {3}, url = {https://doi.org/10.1145/3351283}, doi = {10.1145/3351283}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = sep, articleno = {125}, numpages = {26}, keywords = {main transportation mode, cellular networks, human mobility} }

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7. Yan Zhang, Yunhuai Liu, Y.Ding, G.Li, N.Chen, H.Zhang, T.He and Desheng Zhang.
   Route Prediction for Instant Delivery
   In ACM UbiComp 2019. [Paper][BibTex]
   @article{10.1145/3351282, author = {Zhang, Yan and Liu, Yunhuai and Li, Genjian and Ding, Yi and Chen, Ning and Zhang, Hao and He, Tian and Zhang, Desheng}, title = {Route Prediction for Instant Delivery}, year = {2019}, issue_date = {September 2019}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {3}, number = {3}, url = {https://doi.org/10.1145/3351282}, doi = {10.1145/3351282}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = sep, articleno = {124}, numpages = {25}, keywords = {machine learning, instant delivery, route prediction, perceived distance} }

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8. Guangyan Hu, Sandro Rigo, Desheng Zhang, and Thu Nguyen.
   Approximation with Error Bounds in Spark
   In IEEE MASCOTS 2019. [Paper][BibTex]
   @article{Hu2018ApproximationWE, title={Approximation with Error Bounds in Spark}, author={Guangyan Hu and Desheng Zhang and Sandro Rigo and Thu D. Nguyen}, journal={ArXiv}, year={2018}, volume={abs/1812.01823} }

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9. Yukun Yuan, Desheng Zhang, Fei Miao, Jiming Chen, Tian He and Shan Lin.
   p^2Charging: Proactive Partial Charging for Electric Taxi Systems
   In IEEE ICDCS 2019. 19.6% [Paper][BibTex]
   @article{Kong2016OnLineES, title={On-Line Event-Driven Scheduling for Electric Vehicle Charging via Park-and-Charge}, author={Fanxin Kong and Qiao Xiang and Linghe Kong and Xue Liu}, journal={2016 IEEE Real-Time Systems Symposium (RTSS)}, year={2016}, pages={69-78} }

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10. Shuai Wang, Tian He, Desheng Zhang, Y.Liu, Sang H. Son..
    Towards Efficient Sharing: A Usage Balancing Mechanism for Bike Sharing Systems
    In WWW 2019. 225/1247=18%, [Best Papers Award Nominee]. [Paper][BibTex]
    @inproceedings{10.1145/3308558.3313441, author = {Wang, Shuai and He, Tian and Zhang, Desheng and Liu, Yunhuai and H. Son, Sang}, title = {Towards Efficient Sharing: A Usage Balancing Mechanism for Bike Sharing Systems}, year = {2019}, isbn = {9781450366748}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3308558.3313441}, doi = {10.1145/3308558.3313441}, booktitle = {The World Wide Web Conference}, pages = {2011–2021}, numpages = {11}, keywords = {usage balancing, sharing economy, Bike sharing, urban data}, location = {San Francisco, CA, USA}, series = {WWW ’19} }

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2018

1. Zhou Qin*, Zhihan Fang*, Yunhuai Liu, Chang Tan, Wei Chang, and Desheng Zhang.
   EXIMIUS: A Measurement Framework for Explicit and Implicit Urban Traffic Sensing
   In ACM SenSys 2018. [Paper][BibTex]
   @inproceedings{10.1145/3274783.3274850, author = {Qin, Zhou and Fang, Zhihan and Liu, Yunhuai and Tan, Chang and Chang, Wei and Zhang, Desheng}, title = {EXIMIUS: A Measurement Framework for Explicit and Implicit Urban Traffic Sensing}, year = {2018}, isbn = {9781450359528}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3274783.3274850}, doi = {10.1145/3274783.3274850}, booktitle = {Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems}, pages = {1–14}, numpages = {14}, keywords = {measurement, transportation, Telecommunication, network}, location = {Shenzhen, China}, series = {SenSys ’18} }

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2. Guang Wang*, Xiaoyang Xie*, Fan Zhang, Yunhuai Liu, and Desheng Zhang.
   bCharge: Data-Driven Real-Time ChargingScheduling for Large-Scale Electric Bus Fleets
   In IEEE RTSS 2018. [Paper] [BibTex]
   @inproceedings{56cb09ccf5a34bdda58931d52afe7022, title = "BCharge: Data-Driven Real-Time Charging Scheduling for Large-Scale Electric Bus Fleets", abstract = "We are witnessing a rapid growth of electrified vehicles because of the ever-increasing concerns over urban air quality and energy security. Compared with other electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to the high owning and operating costs, long charging time, and the uneven distribution of charging facilities. Moreover, the highly dynamic environment factors such as the unpredictable traffic congestions, different passenger demands, and even changing weather, can significantly affect electric bus charging efficiency and potentially hinder further development of large-scale electric bus fleets. To deal with these issues, in this paper, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines and 5,562 bus stops, which is obtained from the Chinese city Shenzhen, who has the first and the largest full electric bus network for public transit. Then we investigate the electric bus network to understand its operating and charging patterns, and further verify the feasibility and necessity of a real-time charging scheduling. With such understanding, we design bCharge, a real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of bCharge, we implement it with the real-world streaming dataset from Shenzhen, which includes GPS data of the electric bus fleet, the bus lines and stops data, coupled with the 376 electric bus charging stations data. The evaluation results show that bCharge can dramatically reduce the charging cost by 23.7% and 12.8% electricity usage simultaneously.", keywords = "Electric bus, Markov decision process, charging scheduling, data-driven", author = "Guang Wang and Xiaoyang Xie and Fan Zhang and Yunhuai Liu and Desheng Zhang", year = "2019", month = jan day = "4", doi = "10.1109/RTSS.2018.00015", language = "English (US)", series = "Proceedings - Real-Time Systems Symposium", publisher = "Institute of Electrical and Electronics Engineers Inc.", pages = "45--55", booktitle = "Proceedings - 39th IEEE Real-Time Systems Symposium, RTSS 2018", address = "United States", note = "39th IEEE Real-Time Systems Symposium, RTSS 2018 ; Conference date: 11-12-2018 Through 14-12-2018", }

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3. Zhihan Fang*, Fan Zhang*, Ling Yin, and Desheng Zhang.
   MutliCell: Urban Population Modeling based on Multiple Cellphone Networks
   In ACM UbiComp 2018. [Paper] [BibTex]
   @article{10.1145/3264916, author = {Fang, Zhihan and Zhang, Fan and Yin, Ling and Zhang, Desheng}, title = {MultiCell: Urban Population Modeling Based on Multiple Cellphone Networks}, year = {2018}, issue_date = {September 2018}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {2}, number = {3}, url = {https://doi.org/10.1145/3264916}, doi = {10.1145/3264916}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = sep, articleno = {106}, numpages = {25}, keywords = {cellphone networks, data fusion, data analysis} }

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4. Xiaoyang Xie*, Fan Zhang*, and Desheng Zhang.
   PrivateHunt: Multi-source Data-driven Dispatching in For-Hire Vehicle Systems
   In ACM UbiComp 2018. [Paper] [BibTex]
   @article{10.1145/3191777, author = {Xie, Xiaoyang and Zhang, Fan and Zhang, Desheng}, title = {PrivateHunt: Multi-Source Data-Driven Dispatching in For-Hire Vehicle Systems}, year = {2018}, issue_date = {March 2018}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {2}, number = {1}, url = {https://doi.org/10.1145/3191777}, doi = {10.1145/3191777}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = mar, articleno = {45}, numpages = {26}, keywords = {Urban Computing, Mobility Models, Multi-modal Interaction, Design, Transportation} }

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5. Yu Yang*, Fan Zhang, and Desheng Zhang.
   SharedEdge: GPS-Free Fine-Grained Travel Time Estimation in State-Level Highway Systems
   In ACM UbiComp 2018. [Paper] [BibTex]
   @article{10.1145/3191780, author = {Yang, Yu and Zhang, Fan and Zhang, Desheng}, title = {SharedEdge: GPS-Free Fine-Grained Travel Time Estimation in State-Level Highway Systems}, year = {2018}, issue_date = {March 2018}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {2}, number = {1}, url = {https://doi.org/10.1145/3191780}, doi = {10.1145/3191780}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = mar, articleno = {48}, numpages = {26}, keywords = {Travel Time Estimation, Cyber-physical System, Highway System} }

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6. S. Wang, T. He, Desheng Zhang, Y.Shu, Y.Liu, Y.Gu, C.Liu, J.Lee, and S.Son.
   BRAVO: Improving the Rebalancing Operation in Bike Sharing System
   In ACM UbiComp 2018. [Paper] [BibTex]
   @article{10.1145/3191776, author = {Wang, Shuai and He, Tian and Zhang, Desheng and Shu, Yuanchao and Liu, Yunhuai and Gu, Yu and Liu, Cong and Lee, Haengju and Son, Sang H.}, title = {BRAVO: Improving the Rebalancing Operation in Bike Sharing with Rebalancing Range Prediction}, year = {2018}, issue_date = {March 2018}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {2}, number = {1}, url = {https://doi.org/10.1145/3191776}, doi = {10.1145/3191776}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = mar, articleno = {44}, numpages = {22}, keywords = {bike rebalancing system, Bike sharing, urban data, demand prediction} }

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7. Y. Yuan, Desheng Zhang, Fei Miao, John A. Stankovic, Tian He, George Pappas and Shan Lin.
   Dynamic Integration of Heterogeneous Transportation Modes under Disruptive Events
   In ACM ICCPS 2018. [Paper] [BibTex]
   @article{10.1145/3191776, author = {Wang, Shuai and He, Tian and Zhang, Desheng and Shu, Yuanchao and Liu, Yunhuai and Gu, Yu and Liu, Cong and Lee, Haengju and Son, Sang H.}, title = {BRAVO: Improving the Rebalancing Operation in Bike Sharing with Rebalancing Range Prediction}, year = {2018}, issue_date = {March 2018}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {2}, number = {1}, url = {https://doi.org/10.1145/3191776}, doi = {10.1145/3191776}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = mar, articleno = {44}, numpages = {22}, keywords = {demand prediction, Bike sharing, urban data, bike rebalancing system} }

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2017

1. Ruilin Liu, Yu Yang*, Daehan Kwak, Desheng Zhang, Liviu Iftode, and Badri Nath.
   Towards Fine-Grained Parking Availability Crowdsourcing Using Parking Decision Models
   In ACM UbiComp 2017. (Acceptance Rate: 23/268=8.6% , 1 of 9 Discussion Papers) [Paper] [BibTex]
   @article{10.1145/3130942, author = {Liu, Ruilin and Yang, Yu and Kwak, Daehan and Zhang, Desheng and Iftode, Liviu and Nath, Badri}, title = {Your Search Path Tells Others Where to Park: Towards Fine-Grained Parking Availability Crowdsourcing Using Parking Decision Models}, year = {2017}, issue_date = {September 2017}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {1}, number = {3}, url = {https://doi.org/10.1145/3130942}, doi = {10.1145/3130942}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = sep, articleno = {78}, numpages = {27}, keywords = {Crowdsourcing, Mobile Sensing, Parking, Human Decision Modeling} }

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2. Haengju Lee, Desheng Zhang, Tian He, and Sang Son
   MetroTime: Travel Time Decomposition under Stochastic Time Table for Metro Networks
   In IEEE SmartComp 2017. (Invited Paper) [Paper] [BibTex]
   @INPROCEEDINGS{7947021, author={H. {Lee} and D. {Zhang} and T. {He} and S. H. {Son}}, booktitle={2017 IEEE International Conference on Smart Computing (SMARTCOMP)}, title={MetroTime: Travel Time Decomposition under Stochastic Time Table for Metro Networks}, year={2017}, volume={}, number={}, pages={1-8},}

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2011-2016

Ph.D

1. Desheng Zhang, Fan Zhang, and Tian He.
   MultiCalib:National-Scale Traffic Model Calibration with Multi-source Incomplete Data
   In ACM SIGSPATIAL 2016. (Acceptance Rate: 40/217=18%) [Paper][BibTex]
   @inproceedings{b51e0aeebc4242019f4292ca14767a56, title = "MultiCalib: National-scale traffic model calibration in real time with multi-source incomplete data", abstract = "Real-time traffic modeling at national scale is essential to many applications, but its calibration is extremely challenging due to its large spatial and fine temporal coverage. The existing work mostly is focused on urban-scale calibration with complete field data from single data sources (e.g., loop sensors or taxis), which cannot be generalized to national scale, because complete single-source field data at national scale are almost impossible to obtain. To address this challenge, in this paper, we design MultiCalib, a model calibration framework to optimize traffic models based on multiple incomplete data sources at national scale in real time. Instead of naively combining multi-source data, we theoretically formulate a multi-source model calibration problem based on real-world contexts and multi-view learning. More importantly, we implement and evaluate MultiCalib with two heterogeneous nationwide vehicle networks with 340,000 vehicles to infer traffic conditions on 36 expressways and 119 highways, along with 4 cities across China. The results show that MultiCalib outperforms state-of-theart calibration by 25% on average with same input data.", keywords = "Incomplete Data, Model Calibration", author = "Desheng Zhang and Fan Zhang and Tian He", year = "2016", month = oct, day = "31", doi = "10.1145/2996913.2996918", language = "English (US)", series = "GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems", publisher = "Association for Computing Machinery", editor = "Matthias Renz and Mohamed Ali and Shawn Newsam and Matthias Renz and Siva Ravada and Goce Trajcevski", booktitle = "24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016", note = "24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 ; Conference date: 31-10-2016 Through 03-11-2016", }

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2. Desheng Zhang, Juanjuan Zhao, Fan Zhang, and Tian He.
   coMobile: Real-time Human Mobility Modeling at Urban Scale by Multi-View Learning
   In ACM SIGSPATIAL 2015. (Acceptance Rate: 38/216=17%) [Paper][BibTex]
   @article{10.1145/3092692, author = {Zhang, Desheng and He, Tian and Zhang, Fan}, title = {Real-Time Human Mobility Modeling with Multi-View Learning}, year = {2017}, issue_date = {February 2018}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {9}, number = {3}, issn = {2157-6904}, url = {https://doi.org/10.1145/3092692}, doi = {10.1145/3092692}, journal = {ACM Trans. Intell. Syst. Technol.}, month = dec, articleno = {22}, numpages = {25}, keywords = {mobility model, Smart cities, model integration} }

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3. F. Miao, S. Lin, S. Munir, J. Stankovic, H. Huang, Desheng Zhang, T. He, and G. J. Pappas.
   Taxi Dispatch with Real-Time Data in Metropolitan Areas
   In ACM ICCPS 2015. [Best Paper Nominee] [BibTex]
   @ARTICLE{7438925, author={F. {Miao} and S. {Han} and S. {Lin} and J. A. {Stankovic} and D. {Zhang} and S. {Munir} and H. {Huang} and T. {He} and G. J. {Pappas}}, journal={IEEE Transactions on Automation Science and Engineering}, title={Taxi Dispatch With Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach}, year={2016}, volume={13}, number={2}, pages={463-478},}

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4. Desheng Zhang, Juanjuan Zhao, Fan Zhang, Ruobing Jiang, and Tian He.
   Feeder: Supporting Last-Mile Transit with Extreme-Scale Urban Infrastructure Data
   In ACM IPSN 2015. (Acceptance Rate: 27/110=24%) [Paper] [BibTex]
   @ARTICLE{7438925, author={F. {Miao} and S. {Han} and S. {Lin} and J. A. {Stankovic} and D. {Zhang} and S. {Munir} and H. {Huang} and T. {He} and G. J. {Pappas}}, journal={IEEE Transactions on Automation Science and Engineering}, title={Taxi Dispatch With Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach}, year={2016}, volume={13}, number={2}, pages={463-478},}

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5. Desheng Zhang, Juanjuan Zhao, Fan Zhang, and Tian He.
   UrbanCPS: a Cyber-Physical System Based on Multi-Source Data with Model Integration
   In ACM/IEEE ICCPS 2015. (Acceptance Rate: 25/91=27%) [Paper] [BibTex]
   @inproceedings{0b043f9e1106455189799b4a889e6276, title = "UrbanCPS: A cyber-Physical system based on multi-Source big infrastructure data for heterogeneous model integration", abstract = "Data-driven modeling usually suffers from data sparsity, especially for large-scale modeling for urban phenomena based on single-source urban infrastructure data under fine-grained spatial-temporal contexts. To address this challenge, we motivate, design and implement UrbanCPS, a cyber-physical system with heterogeneous model integration, based on extremely-large multi-source infrastructures in a Chinese city Shenzhen, involving 42 thousand vehicles, 10 million residents, and 16 million smartcards. Based on temporal, spatial and contextual contexts, we formulate an optimization problem about how to optimally integrate models based on highly-diverse datasets, under three practical issues, i.e., heterogeneity of models, input data sparsity or unknown ground truth. We further propose a real-world application called Speedometer, inferring real-time traffic speeds in urban areas. The evaluation results show that compared to a state-of-the-art system, Speedometer increases the inference accuracy by 21% on average.", keywords = "Cyber-physical system, Model integration", author = "Desheng Zhang and Tian He and Juanjuan Zhao and Fan Zhang", year = "2015", month = apr, day = "14", doi = "10.1145/2735960.2735985", language = "English (US)", series = "ACM/IEEE 6th International Conference on Cyber-Physical Systems, ICCPS 2015", publisher = "Association for Computing Machinery, Inc", pages = "238--247", booktitle = "ACM/IEEE 6th International Conference on Cyber-Physical Systems, ICCPS 2015", note = "6th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2015 ; Conference date: 14-04-2015 Through 16-04-2015", }

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6. Desheng Zhang,Ruobing Jiang, Shuai Wang, Yanmin Zhu, Bo Yang, Tian He, and Jian Cao.
   Everyone Counts: Fine-Grained Digital Media Advertising in Urban Metro Systems
   In IEEE BIGDATA 2015. (Acceptance Rate: 63/363=17%) [Paper][BibTex]
   @inproceedings{10.1145/3366424.3382121, author = {Zhang, Desheng}, title = {Mobile Cyber-Physical Systems for Smart Cities}, year = {2020}, isbn = {9781450370240}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3366424.3382121}, doi = {10.1145/3366424.3382121}, booktitle = {Companion Proceedings of the Web Conference 2020}, pages = {546–548}, numpages = {3}, keywords = {Human Mobility, Smart Cities, Cyber-Physical Systems}, location = {Taipei, Taiwan}, series = {WWW ’20} }

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7. Desheng Zhang, and Tian He.
   Collaborative Sensing and Control in Large-Scale Transportation Systems
   In CTS 2014. [Invited Paper][BibTex]
   @INPROCEEDINGS{6867575, author={D. {Zhang} and T. {He}}, booktitle={2014 International Conference on Collaboration Technologies and Systems (CTS)}, title={Collaborative sensing and control in large-scale transportation systems}, year={2014}, volume={}, number={}, pages={275-276},}

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8. Desheng Zhang, Jun Huang, Ye Li, Fan Zhang, Chengzhong Xu, and Tian He.
   Exploring Human Mobility with Multi-Source Data at Extremely Large Metropolitan Scales
   In ACM MobiCom 2014. (Acceptance Rate: 36/220=16%) [Paper][BibTex]
   @inproceedings{10.1145/2639108.2639116, author = {Zhang, Desheng and Huang, Jun and Li, Ye and Zhang, Fan and Xu, Chengzhong and He, Tian}, title = {Exploring Human Mobility with Multi-Source Data at Extremely Large Metropolitan Scales}, year = {2014}, isbn = {9781450327831}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2639108.2639116}, doi = {10.1145/2639108.2639116}, booktitle = {Proceedings of the 20th Annual International Conference on Mobile Computing and Networking}, pages = {201–212}, numpages = {12}, keywords = {cellphone networks, urban transit networks, mobility pattern}, location = {Maui, Hawaii, USA}, series = {MobiCom ’14} }

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9. Desheng Zhang, Tian He, Shan Lin, and John A. Stankovic.
   Dmodel: Online Taxicab Passenger Demand Model from Large Roving Sensor Networks
   In IEEE BIGDATA 2014. (Acceptance Rate: 38/200=19%) [Paper][BibTex]
   @article{Hu2018ApproximationWE, title={Approximation with Error Bounds in Spark}, author={Guangyan Hu and Desheng Zhang and Sandro Rigo and Thu D. Nguyen}, journal={ArXiv}, year={2018}, volume={abs/1812.01823} }

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10. Desheng Zhang, Tian He, Yunhuai Liu, and John A.Stankovic.
    CallCab:A Unified Recommendation System for Carpooling and Regular Taxicab Services
    In IEEE BIGDATA 2013. (Acceptance Rate: 45/259=17%) [Paper][BibTex]
    @INPROCEEDINGS{6691605, author={D. {Zhang} and T. {He} and Y. {Liu} and J. A. {Stankovic}}, booktitle={2013 IEEE International Conference on Big Data}, title={CallCab: A unified recommendation system for carpooling and regular taxicab services}, year={2013}, volume={}, number={}, pages={439-447},}

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11. Desheng Zhang, Ye Li, Fan Zhang, Mingming Lu, Yunhuai Liu, and Tian He.
    coRide:Carpool Service with a Win-Win Fare Model for Large-Scale Taxicab Networks
    In ACM SenSys 2013. (Acceptance Rate: 21/123=17%) [Paper][BibTex]
    @inproceedings{10.1145/2517351.2517361, author = {Zhang, Desheng and Li, Ye and Zhang, Fan and Lu, Mingming and Liu, Yunhuai and He, Tian}, title = {CoRide: Carpool Service with a Win-Win Fare Model for Large-Scale Taxicab Networks}, year = {2013}, isbn = {9781450320276}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2517351.2517361}, doi = {10.1145/2517351.2517361}, booktitle = {Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems}, articleno = {9}, numpages = {14}, location = {Roma, Italy}, series = {SenSys ’13} }

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12. Desheng Zhang, and Tian He.
    pCruise:Reducing Cruising Miles for Taxicab Networks
    In IEEE RTSS 2012. (Acceptance Rate: 35/157=22%) [Fast Track to IEEE TBD][Paper][BibTex]
    @article{Zhang2012pCruiseRC, title={pCruise: Reducing Cruising Miles for Taxicab Networks}, author={Desheng Zhang and Tian He}, journal={2012 IEEE 33rd Real-Time Systems Symposium}, year={2012}, pages={85-94} }

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13. Desheng Zhang, Tian He, Yunhuai Liu, Yu Gu, Fan Ye, Raghu K. Ganti, and Hui Lei.
    Acc:Generic On-Demand Accelerations for Neighbor Discovery in Mobile Applications
    In ACM SenSys 2012. (Acceptance Rate: 23/123=18%) [Paper][BibTex]
    @inproceedings{ef0b32c7562d47e9aa902ce4c931e2ae, title = "Acc: Generic on-demand accelerations for neighbor discovery in mobile applications", abstract = "As a supporting primitive of many mobile device applications, neighbor discovery identifies nearby devices so that they can exchange information and collaborate in a peer-topeer manner. To date, discovery schemes trade a long latency for energy efficiency and require a collaborative duty cycle pattern, and thus they are not suitable for interactive mobile applications where a user is unable to configure others' devices. In this paper, we propose Acc, which serves as an on-demand generic discovery accelerating middleware for many existing neighbor discovery schemes. Acc leverages the discovery capabilities of neighbor devices, supporting both direct and indirect neighbor discoveries. Our evaluations show that Acc-assisted discovery schemes reduce latency by a maximum of 51.8%, compared with the schemes consuming the same amount of energy. We further present and evaluate a Crowd-Alert application where Acc can be employed by taxi drivers to accelerate selection of a direction with fewer competing taxis and more potential passengers, based on a 10 GB dataset of more than 15,000 taxis in a metropolitan area.", keywords = "Mobile applications, Neighbor discovery, Protocol", author = "Desheng Zhang and Tian He and Yunhuai Liu and Yu Gu and Fan Ye and Ganti, {Raghu K.} and Hui Lei", year = "2012", month = dec, day = "1", doi = "10.1145/2426656.2426674", language = "English (US)", isbn = "9781450311694", series = "SenSys 2012 - Proceedings of the 10th ACM Conference on Embedded Networked Sensor Systems", pages = "169--182", booktitle = "SenSys 2012 - Proceedings of the 10th ACM Conference on Embedded Networked Sensor Systems", note = "10th ACM Conference on Embedded Networked Sensor Systems, SenSys 2012 ; Conference date: 06-11-2012 Through 09-11-2012", }

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14. Desheng Zhang, Tian He, Fan Ye, Raghu K. Ganti, and Hui Lei.
    EQS:Neighbor Discovery and Rendezvous Maintenance with Extended Quorum System
    In IEEE ICDCS 2012. (Acceptance Rate: 71/515=13%) [Paper][BibTex]
    @INPROCEEDINGS{6257980, author={D. {Zhang} and T. {He} and F. {Ye} and R. K. {Ganti} and H. {Lei}}, booktitle={2012 IEEE 32nd International Conference on Distributed Computing Systems}, title={EQS: Neighbor Discovery and Rendezvous Maintenance with Extended Quorum System for Mobile Sensing Applications}, year={2012}, volume={}, number={}, pages={72-81},}

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2009-2011

Master

1. Jinbao Li, Desheng Zhang, Longjiang Guo.
   OCO: A Multi-channel MAC Protocol with Opportunistic Cooperation for Wireless Sensor Networks.
   In IEEE EUC 2011. [Paper][BibTex]
   @inproceedings{10.1109/EUC.2010.45, author = {Li, Jinbao and Zhang, Desheng and Guo, Longjiang}, title = {OCO: A Multi-Channel MAC Protocol with Opportunistic Cooperation for Wireless Sensor Networks}, year = {2010}, isbn = {9780769543222}, publisher = {IEEE Computer Society}, address = {USA}, url = {https://doi.org/10.1109/EUC.2010.45}, doi = {10.1109/EUC.2010.45}, booktitle = {Proceedings of the 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing}, pages = {260–267}, numpages = {8}, series = {EUC ’10} }

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2. Jinbao Li, Desheng Zhang, Longjiang Guo.
   OMA: A Multi-channel MAC Protocol with Opportunistic Media Access in Wireless Sensor Networks.
   In IEEE MSN 2011. [Paper][BibTex]
   @inproceedings{10.1109/MSN.2010.8, author = {Li, Jinbao and Zhang, Desheng and Guo, Longjiang}, title = {OMA: A Multi-Channel MAC Protocol with Opportunistic Media Access in Wireless Sensor Networks}, year = {2010}, isbn = {9780769543154}, publisher = {IEEE Computer Society}, address = {USA}, url = {https://doi.org/10.1109/MSN.2010.8}, doi = {10.1109/MSN.2010.8}, booktitle = {Proceedings of the 2010 Sixth International Conference on Mobile Ad-Hoc and Sensor Networks}, pages = {7–13}, numpages = {7}, series = {MSN ’10} }

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3. Desheng Zhang, Jinbao Li, and Longjiang Guo.
   MCR:A Dynamic and Optimal Duty Cycle Based MAC Protocol for Wireless Sensor Networks
   In CWSN 2010. [Best Paper Award][Paper][BibTex]
   @inproceedings{10.1109/CyberC.2010.29, author = {Li, Jinbao and Zhang, Desheng}, title = {M&M: A Multi-Channel MAC Protocol with Multiple Channel Reservation for Wireless Sensor Networks}, year = {2010}, isbn = {9780769542355}, publisher = {IEEE Computer Society}, address = {USA}, url = {https://doi.org/10.1109/CyberC.2010.29}, doi = {10.1109/CyberC.2010.29}, booktitle = {Proceedings of the 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery}, pages = {113–120}, numpages = {8}, series = {CYBERC ’10} }

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4. Jinbao Li, Desheng Zhang.
   M&M: A Multi-Channel MAC Protocol with Multiple Channel Reservation for Sensor Networks
   In CyberC 2010. [Honorable Paper Award][Paper][BibTex]
   @inproceedings{10.1109/CyberC.2010.29, author = {Li, Jinbao and Zhang, Desheng}, title = {M&M: A Multi-Channel MAC Protocol with Multiple Channel Reservation for Wireless Sensor Networks}, year = {2010}, isbn = {9780769542355}, publisher = {IEEE Computer Society}, address = {USA}, url = {https://doi.org/10.1109/CyberC.2010.29}, doi = {10.1109/CyberC.2010.29}, booktitle = {Proceedings of the 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery}, pages = {113–120}, numpages = {8}, series = {CYBERC ’10} }

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5. Jinbao Li, and Desheng Zhang.
   RCO: A Multi-channel MAC Protocol with Random Cooperation for Sensor Networks.
   In IEEE UIC 2010.[Paper][BibTex]
   @inproceedings{10.5555/1929661.1929686, author = {Li, Jinbao and Zhang, Desheng}, title = {RCO: A Multi-Channel MAC Protocol with Random Cooperation for Sensor Networks}, year = {2010}, isbn = {3642163548}, publisher = {Springer-Verlag}, address = {Berlin, Heidelberg}, booktitle = {Proceedings of the 7th International Conference on Ubiquitous Intelligence and Computing}, pages = {228–242}, numpages = {15}, keywords = {random cooperation, MAC protocols, multi-channel, WSNs}, location = {Xi'an, China}, series = {UIC’10} }

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6. Jinbao Li, and Desheng Zhang, Shouling Ji, Longjiang Guo.
   RCS: A Random Channel Selection with Probabilistic Backoff for Multi-Channel MAC Protocols in WSNs.
   In IEEE GLOBECOM 2010.[Paper][BibTex]
   @inproceedings{inproceedings, author = {Li, Jinbao and Zhang, Desheng and Ji, Shouling and Guo, Longjiang}, year = {2010}, month = {12}, pages = {1-5}, title = {RCS: A Random Channel Selection with Probabilistic Backoff for Multi-Channel MAC Protocols in WSNs}, doi = {10.1109/GLOCOM.2010.5683094} }

   [Close]



7. Jinbao Li, Desheng Zhang, Longjiang Guo, Shouling Ji, Yingshu Li.
   M-cube: A Duty Cycle Based Multi-channel MAC Protocol with Multiple Channel Reservation for WSNs.
   In IEEE ICPADS 2009.[Paper][BibTex]
   @INPROCEEDINGS{5695592, author={J. {Li} and D. {Zhang} and L. {Guo} and S. {Ji} and Y. {Li}}, booktitle={2010 IEEE 16th International Conference on Parallel and Distributed Systems}, title={M-cube: A Duty Cycle Based Multi-channel MAC Protocol with Multiple Channel Reservation for WSNs}, year={2010}, volume={}, number={}, pages={107-114},}

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8. Desheng Zhang and Jinbao Li.
   HM-MAC: A Multi-Channel MAC Protocol for Sensor Networks with Broadcast Supporting
   In CWSN 2009. [Fast Track to JCRD][Paper][BibTex]
   @article{article, author = {Zhang, D. and Li, J. and Guo, L. and Ji, S. and Wang, Y.}, year = {2009}, month = {12}, pages = {2024-2031}, title = {HM-MAC: A multi-channel MAC protocol for sensor network with broadcast supporting}, volume = {46} }

   [Close]

Selected

Journal

Papers

1. Guang Wang, Zhou Qin, Shuai Wang, Huijun Sun, Zheng Dong, Desheng Zhang
   Towards Accessible Shared Autonomous Electric Mobility With Dynamic Deadlines.
   In IEEE TMC 2024.
   


2. Sihong He, Zhili Zhang, Shuo Han, Lynn Pepin, Guang Wang, Desheng Zhang, John A. Stankovic, Fei Miao.
   Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems Under Demand and Supply Uncertainties.
   In IEEE TITS 2023.
   


3. Yizong Wang, Dong Zhao, Yajie Ren, Desheng Zhang, Huadong Ma.
   SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New City.
   In ACM TKDD 2023.
   


4. Shuai Wang, Xin Zhu, Guang Wang, Desheng Zhang, Lai Tu, Tian He.
   W2Parking: A Data-Driven Win-Win Contract Parking Sharing Mechanism Under Both Supply and Demand Uncertainties.
   In IEEE TKDE 2023.
   


5. Shuai Wang, Xin Zhu, Guang Wang, Yunhuai Liu, Tian He, Desheng Zhang.
   eShare+: A Data-Driven Balancing Mechanism for Bike Sharing Systems Considering Both Quality of Service and Maintenance.
   In IEEE TKDE 2023.
   


6. Guang Wang, Yuefei Chen, Shuai Wang, Fan Zhang, Desheng Zhang.
   ForETaxi: Data-Driven Fleet-Oriented Charging Resource Allocation in Large-Scale Electric Taxi Networks.
   In ACM TOSN 2023.
   


7. Yi Ding, Yu Yang, Wenchao Jiang, Yunhuai Liu, Tian He, Desheng Zhang.
   Nationwide Deployment and Operation of a Virtual Arrival Detection System in the Wild.
   In IEEE/ACM TON 2023.
   


8. Yukun Yuan, Desheng Zhang, Fei Miao, John A. Stankovic, Tian He, George J. Pappas, Shan Lin.
   Mobility-Driven Integration of Heterogeneous Urban Cyber-Physical Systems Under Disruptive Events.
   In IEEE TMC 2023.
   


9. Ruobing Jiang, Zhenni Feng, Desheng Zhang, Shuai Wang, Yanmin Zhu, Fan Zhang, Tian He.
   From Conception to Retirement: A Lifetime Story of a 3-Year-Old Wireless Beacon System in the Wild.
   In IEEE/ACM TON 2022.
   


10. Yi Ding, Ling Liu, Yu Yang, Yunhuai Liu, Desheng Zhang, Tian He.
    From Conception to Retirement: A Lifetime Story of a 3-Year-Old Wireless Beacon System in the Wild.
    In IEEE/ACM TON 2022.
    


11. Sijing Duan, Feng Lyu, Ju Ren, Yifeng Wang, Peng Yang, Desheng Zhang, Yaoxue Zhang.
    Multitype Highway Mobility Analytics for Efficient Learning Model Design: A Case of Station Traffic Prediction.
    In IEEE TITS 2022.
    


12. Dong Zhao, Chen Ju, Guanzhou Zhu, Jing Ning, Dan Luo, Desheng Zhang, Huadong Ma.
    MePark: Using Meters as Sensors for Citywide On-Street Parking Availability Prediction.
    In IEEE TITS 2022.
    


13. Yukun Yuan, Meiyi Ma, Songyang Han, Desheng Zhang, Fei Miao, John A. Stankovic, Shan Lin.
    DeResolver: A Decentralized Conflict Resolution Framework with Autonomous Negotiation for Smart City Services.
    In ACM TCPS 2022.
    


14. Qin Zhou*, Zhihan Fang*, Yunhuai Liu, Chang Tan, Wei Chang, and Desheng Zhang
    A Measurement Framework for Explicit and Implicit Urban Traffic Sensing
    In ACM TOSN 2021.


15. Yu Yang*, X.Xie*, Z.Fang*, F.Zhang, Y.Wang, and Desheng Zhang
    Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration
    In IEEE TMC 2020.


16. Guang Wang*, Zhihan Fang, Xiaoyang Xie, Shuai Wang, Fan Zhang, Y. Liu, Desheng Zhang
    Pricing-Aware Real-Time Charging Scheduling for Large-Scale Electric Buses
    In ACM TIST, 2020.


17. Guang Wang*, Fan Zhang, Yang Wang, Desheng Zhang
    Understanding the Long-Term Evolution of Electric Taxi Networks
    In ACM TIST, 2020.


18. Dan Luo, Dong Zhao, Q. Ke, X. You, L. Liu, Desheng Zhang, H. Ma, X. Zuo
    Fine-grained Service-level Passenger Flow Prediction for Bus Transit Systems Based on Multitask Deep Learning
    In IEEE TITS 2020 .[Paper][BibTex]
    @ARTICLE{9126198, author={D. {Luo} and D. {Zhao} and Q. {Ke} and X. {You} and L. {Liu} and D. {Zhang} and H. {Ma} and X. {Zuo}}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={Fine-Grained Service-Level Passenger Flow Prediction for Bus Transit Systems Based on Multitask Deep Learning}, year={2020}, volume={}, number={}, pages={1-16},}

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19. Guang Wang, Fan Zhang, Yang Wang, Desheng Zhang.
    Understanding the Long-Term Evolution of Electric Taxi Networks
    In ACM TIST 2020.[Paper][BibTex]
    @article{10.1145/3393671, author = {Wang, Guang and Zhang, Fan and Sun, Huijun and Wang, Yang and Zhang, Desheng}, title = {Understanding the Long-Term Evolution of Electric Taxi Networks: A Longitudinal Measurement Study on Mobility and Charging Patterns}, year = {2020}, issue_date = {July 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {11}, number = {4}, issn = {2157-6904}, url = {https://doi.org/10.1145/3393671}, doi = {10.1145/3393671}, journal = {ACM Trans. Intell. Syst. Technol.}, month = may, articleno = {48}, numpages = {27}, keywords = {Electric taxi, charging pattern, evolution experience, mobility pattern, shared autonomous vehicle} }

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20. Lai Tu, Shuai Wang, Desheng Zhang, Fan Zhang, and Tian He.
    ViFi-MobiScanner: Observe Human Mobility via Vehicular Internet Service
    In IEEE TITS 2019.[Paper][BibTex]
    @ARTICLE{8924933, author={L. {Tu} and S. {Wang} and D. {Zhang} and F. {Zhang} and T. {He}}, journal={IEEE Transactions on Intelligent Transportation Systems}, title={ViFi-MobiScanner: Observe Human Mobility via Vehicular Internet Service}, year={2019}, volume={}, number={}, pages={1-13},}

    [Close]



21. Desheng Zhang, Tian He, Fan Zhang, and Chengzhong Xu.
    Urban-Scale Human Mobility Modeling with Multi-Source Urban Network Data
    In IEEE/ACM ToN 2018.[Paper][BibTex]
    @ARTICLE{8319925, author={D. {Zhang} and T. {He} and F. {Zhang} and C. {Xu}}, journal={IEEE/ACM Transactions on Networking}, title={Urban-Scale Human Mobility Modeling With Multi-Source Urban Network Data}, year={2018}, volume={26}, number={2}, pages={671-684},}

    [Close]



22. Desheng Zhang, Tian He, and Fan Zhang.
    National-Scale Traffic Model Calibration with Multi-source Incomplete Data
    In ACM TCPS 2018.[Paper][BibTex]
    @article{10.1145/3300186, author = {Zhang, Desheng and He, Tian and Zhang, Fan}, title = {National-Scale Traffic Model Calibration in Real Time with Multi-Source Incomplete Data}, year = {2019}, issue_date = {March 2019}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {3}, number = {2}, issn = {2378-962X}, url = {https://doi.org/10.1145/3300186}, doi = {10.1145/3300186}, journal = {ACM Trans. Cyber-Phys. Syst.}, month = feb, articleno = {21}, numpages = {26}, keywords = {incomplete data, Cyber-physical system, model calibration} }

    [Close]



23. Desheng Zhang, Ruobing Jiang, Shuai Wang, Yanmin Zhu, Bo Yang, Tian He, and Jian Cao.
    Data-Driven Digital Advertising with Uncertain Demand Model in Metro Networks
    In IEEE TBD 2017.[Paper][BibTex]
    @ARTICLE{7974780, author={R. {Jiang} and Z. {Feng} and D. {Zhang} and S. {Wang} and Y. {Zhu} and F. {Zhang} and T. {He}}, journal={IEEE Transactions on Big Data}, title={Data-Driven Digital Advertising with Uncertain Demand Model in Metro Networks}, year={2017}, volume={}, number={}, pages={1-1},}

    [Close]



24. Desheng Zhang, Tian He and Fan Zhang.
    Real-time Human Mobility Modeling at Urban Scale by Multi-View Learning
    In ACM TIST 2017.[Paper][BibTex]
    @article{10.1145/3092692, author = {Zhang, Desheng and He, Tian and Zhang, Fan}, title = {Real-Time Human Mobility Modeling with Multi-View Learning}, year = {2017}, issue_date = {February 2018}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {9}, number = {3}, issn = {2157-6904}, url = {https://doi.org/10.1145/3092692}, doi = {10.1145/3092692}, journal = {ACM Trans. Intell. Syst. Technol.}, month = dec, articleno = {22}, numpages = {25}, keywords = {model integration, Smart cities, mobility model} }

    [Close]



25. Desheng Zhang, Tian He, Shan Lin, Sirajum Munir, and John A. Stankovic.
    Taxi Passenger Demand Modeling from a Roving Sensor Network
    In IEEE TBD 2016.[Paper][BibTex]
    @ARTICLE{7740911, author={D. {Zhang} and T. {He} and S. {Lin} and S. {Munir} and J. A. {Stankovic}}, journal={IEEE Transactions on Big Data}, title={Taxi-Passenger-Demand Modeling Based on Big Data from a Roving Sensor Network}, year={2017}, volume={3}, number={3}, pages={362-374},}

    [Close]



26. Desheng Zhang, Juanjuan Zhao, Fan Zhang, and Tian He.
    Neighbor Discovery and Maintenance with Extended Quorum Systems
    In IEEE TMC 2016.[Paper][BibTex]
    D. Zhang, T. He, F. Ye, R. K. Ganti and H. Lei, "Neighbor Discovery and Rendezvous Maintenance with Extended Quorum Systems for Mobile Applications," in IEEE Transactions on Mobile Computing, vol. 16, no. 7, pp. 1967-1980, 1 July 2017, doi: 10.1109/TMC.2016.2612200.

    [Close]



27. Desheng Zhang, Juanjuan Zhao, Fan Zhang, and Tian He.
    Heterogeneous Model Integration for Multi-source Infrastructure Data
    In ACM TCPS 2016.[Paper][BibTex]
    @article{10.1145/2967503, author = {Zhang, Desheng and Zhao, Juanjuan and Zhang, Fan and He, Tian and Lee, Haengju and Son, Sang H.}, title = {Heterogeneous Model Integration for Multi-Source Urban Infrastructure Data}, year = {2016}, issue_date = {February 2017}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {1}, number = {1}, issn = {2378-962X}, url = {https://doi.org/10.1145/2967503}, doi = {10.1145/2967503}, journal = {ACM Trans. Cyber-Phys. Syst.}, month = nov, articleno = {4}, numpages = {26}, keywords = {model integration, Cyber-physical system} }

    [Close]



28. Desheng Zhang, Juanjuan Zhao, Fan Zhang, Ruobing Jiang and Tian He.
    Last-Mile Transit Service with Urban Infrastructure Data
    In ACM TCPS 2016.[Paper][BibTex]
    @article{10.1145/2823326, author = {Zhang, Desheng and Zhao, Juanjuan and Zhang, Fan and Jiang, Ruobing and He, Tian and Papanikolopoulos, Nikos}, title = {Last-Mile Transit Service with Urban Infrastructure Data}, year = {2016}, issue_date = {February 2017}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {1}, number = {2}, issn = {2378-962X}, url = {https://doi.org/10.1145/2823326}, doi = {10.1145/2823326}, journal = {ACM Trans. Cyber-Phys. Syst.}, month = nov, articleno = {6}, numpages = {26}, keywords = {graph theory, Taxicab carpool, mobile applications} }

    [Close]



29. Desheng Zhang, Fan Zhang, Ming Lu, Yunhuai Liu, and Tian He.
    Carpool Service for Large-Scale Taxicab Networks
    In ACM TOSN 2016.[Paper][BibTex]
    @article{10.1145/2897517, author = {Zhang, Desheng and He, Tian and Zhang, Fan and Lu, Mingming and Liu, Yunhuai and Lee, Haengju and Son, Sang H.}, title = {Carpooling Service for Large-Scale Taxicab Networks}, year = {2016}, issue_date = {August 2016}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {12}, number = {3}, issn = {1550-4859}, url = {https://doi.org/10.1145/2897517}, doi = {10.1145/2897517}, journal = {ACM Trans. Sen. Netw.}, month = aug, articleno = {18}, numpages = {35}, keywords = {taxi network, application, fare model, Ridesharing} }

    [Close]



30. Desheng Zhang, Tian He, Yunhuai Liu, Yu Gu, Fan Ye, Raghu K. Ganti, and Hui Lei.
    Generic Neighbor Discovery in Mobile Applications
    In ACM TOSN 2015.[Paper][BibTex]
    @article{10.1145/2832914, author = {Zhang, Desheng and He, Tian and Liu, Yunhuai and Gu, Yu and Ye, Fan and Ganti, Raghu K. and Lei, Hui}, title = {Generic Neighbor Discovery Accelerations in Mobile Applications}, year = {2015}, issue_date = {December 2015}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {11}, number = {4}, issn = {1550-4859}, url = {https://doi.org/10.1145/2832914}, doi = {10.1145/2832914}, journal = {ACM Trans. Sen. Netw.}, month = nov, articleno = {63}, numpages = {35}, keywords = {Protocol, mobile applications, neighbor discovery} }

    [Close]



31. Desheng Zhang, Tian He, Lin Shan, Sirajum Munir, and John A. Stankovic.
    Online Cruising Mile Reduction for Large-Scale Taxicab Networks
    In IEEE TPDS 2015.[Paper][BibTex]
    @ARTICLE{6930792, author={D. {Zhang} and T. {He} and S. {Lin} and S. {Munir} and J. A. {Stankovic}}, journal={IEEE Transactions on Parallel and Distributed Systems}, title={Online Cruising Mile Reduction in Large-Scale Taxicab Networks}, year={2015}, volume={26}, number={11}, pages={3122-3135},}

    [Close]



32. Desheng Zhang, Tian He, Yunhuai Liu, and John A. Stankovic.
    A Unified Recommendation System for Carpooling and Regular Taxicab Services
    In IEEE TETC 2014.[Paper][BibTex]
    @INPROCEEDINGS{6691605, author={D. {Zhang} and T. {He} and Y. {Liu} and J. A. {Stankovic}}, booktitle={2013 IEEE International Conference on Big Data}, title={CallCab: A unified recommendation system for carpooling and regular taxicab services}, year={2013}, volume={}, number={}, pages={439-447},}

    [Close]



33. Desheng Zhang, Jinbao Li, and Longjiang Guo.
    Study on Asynchronous Multi-channel MAC Protocol for WSNs
    In Journal of Software 2012.[Paper][BibTex]
    @article{da184c07f2934f97ad57c1a2620e0a68, title = "Asynchronous multi-channel MAC protocol for WSNs", abstract = "To tackle control channel saturation and triple hidden terminal problems, this paper proposes RIM, a receiver-initiated multi-channel MAC protocol with duty cycling for WSNs. By adopting a receiver-initiated transmission scheme and probability-based random channel selection, RIM effectively alleviates, if not completely eliminates, control channel saturation and triple hidden terminal problems. In addition, RIM exploits a simple but reliable asynchronous broadcast scheme to solve the problem of broadcast data loss with reliable broadcast-intensive applications. More importantly, RIM is fully distributed with no requirements of time synchronization or multi-radio. Therefore, RIM is very easily implemented in resource-constrained sensor nodes. Via the theoretical analysis, the optimal duty cycle are obtained, respectively. The simulation and real testbed experimental results show that RIM achieves significant improvement in energy efficiency with increasing benefit when the number of channels and traffic loads increase, while maintaining higher throughput. Moreover, RIM exhibits a prominent ability to enhance its broadcast reliability.", keywords = "Control channel saturation, Multi-channel MAC protocol, Multi-channel terminal, Multi-hop terminal, Sleep hidden terminal, WSN", author = "Zhang, {De Sheng} and Li, {Jin Bao} and Guo, {Long Jiang}", year = "2012", month = mar, day = "1", doi = "10.3724/SP.J.1001.2012.03984", language = "English (US)", volume = "23", pages = "613--628", journal = "Ruan Jian Xue Bao/Journal of Software", issn = "1000-9825", publisher = "Chinese Academy of Sciences", number = "3", }

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34. Desheng Zhang, Jinbao Li, and Longjiang Guo.
    Study on Multi-channel Reservation Based MAC Protocol for Wireless Sensor Networks
    In Journal on Communications 2011.[Paper][BibTex]
    @article{article, author = {Zhang, D.-S and Li, J.-B and Guo, L.-J}, year = {2011}, month = {04}, pages = {126-137}, title = {Study on multi-channel reservation based MAC protocol for sensor networks}, volume = {32} }

    [Close]



35. Desheng Zhang, Jinbao Li, Longjiang Guo, Shouling Ji, and Yu Wang.
    HM-MAC: A Multi-Channel MAC Protocol for Sensor Networks with Broadcast Supporting
    In Journal of Computer Research and Development 2009.[Paper][BibTex]
    @article{Zhang Desheng:2024, author = {Zhang Desheng, Li Jinbao, Guo Longjiang, Ji Shouling, and Wang Yu}, title = {HM-MAC: A Multi-Channel MAC Protocol for Sensor Network with Broadcast Supporting}, publisher = {Journal of Computer Research and Development}, year = {2009}, journal = {Journal of Computer Research and Development}, volume = {46}, number = {12}, eid = {2024}, numpages = {8}, pages = {2024}, keywords = {WSN; multi-channel MAC protocol; multi-channel hidden terminal; time synchronization; multi-channel broadcast}, url = {http://crad.ict.ac.cn/EN/abstract/article_1152.shtml}, doi = {} }

    [Close]



36. F. Miao, S. Han, S. Lin, J. Stankovic, Q. Wang, Desheng Zhang, T. He, and G. J. Pappas.
    Data-Driven Approaches for Modeling Taxi Demand Uncertainties
    In IEEE TCST 2017.[Paper][BibTex]
    @article{DBLP:journals/corr/MiaoHLWSHZHP16, author = {Fei Miao and Shuo Han and Shan Lin and Qian Wang and John A. Stankovic and Abdeltawab M. Hendawi and Desheng Zhang and Tian He and George J. Pappas}, title = {Data-Driven Robust Taxi Dispatch under Demand Uncertainties}, journal = {CoRR}, volume = {abs/1603.06263}, year = {2016}, url = {http://arxiv.org/abs/1603.06263}, archivePrefix = {arXiv}, eprint = {1603.06263}, timestamp = {Mon, 13 Aug 2018 16:48:10 +0200}, biburl = {https://dblp.org/rec/journals/corr/MiaoHLWSHZHP16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }

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37. F. Miao, S. Lin, S. Munir, J. Stankovic, H. Huang, Desheng Zhang, T. He, and G. J. Pappas.
    Taxi Dispatch with Real-Time Data in Metropolitan Areas
    In ACM ICCPS 2015. [Best Paper Nominee] [BibTex]
    @ARTICLE{7438925, author={F. {Miao} and S. {Han} and S. {Lin} and J. A. {Stankovic} and D. {Zhang} and S. {Munir} and H. {Huang} and T. {He} and G. J. {Pappas}}, journal={IEEE Transactions on Automation Science and Engineering}, title={Taxi Dispatch With Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach}, year={2016}, volume={13}, number={2}, pages={463-478},}

    [Close]

Peer-Reviewed

Conference

Posters

1. Zhihan Fang, Guang Wang, and Desheng Zhang.
   Modeling Fine-Grained Human Mobility on Cellular Networks
   In WWW 2020.


2. Guang Wang and Desheng Zhang.
   Understanding Long-Term Mobility and Charging Evolving of Shared Electric Vehicle Networks
   In ACM MobiCom 2019.


3. Zhihan Fang, Fan Zhang, Desheng Zhang.
   Fine-grained travel time sensing in heterogeneous mobile networks
   In ACM SenSys 2019.


4. Zhou Qin, Yikun Xian, Desheng Zhang.
   A neural networks based caching scheme for mobile edge networks
   In ACM SenSys 2019.


5. Shuxin Zhong, Desheng Zhang.
   Conflict Detection for Smart Cities Services
   In ACM SenSys 2019.


6. Fan Zhang, Desheng Zhang.
   Privacy-aware synthesis of sensing data based on learning model at metropolitan scale
   In ACM SenSys 2019.


7. Guang Wang, Fan Zhang, Desheng Zhang.
   tCharge - A fleet-oriented real-time charging scheduling system for electric taxi fleets
   In ACM SenSys 2019.


8. Xiaoyang Xie, Fan Zhang, Desheng Zhang.
   Understanding real-time interaction in heterogeneous vehicular sensing
   In ACM SenSys 2019.


9. Yu Yang, Fan Zhang, Desheng Zhang.
   Vehicular mobility modeling based on heterogeneous sensor networks
   In ACM SenSys 2019.


10. Zhihan Fang and Desheng Zhang.
    Human Mobility Modeling on Metropolitan Scale Based on Multiple Cellphone Networks
    In ACM/IEEE IoTDI 2017.


11. Desheng Zhang, and Tian He.
    USN: an Extremely Large Sensor Network based on Urban Infrastructures for Smart Cities
    In ACM SenSys 2016.


12. F.Miao, S.Han, S.Lin, J.Stankovic, Q.Wang, Desheng Zhang, T.He and G.Pappas.
    Data-Driven Robust Taxi Dispatch Approaches
    In ACM ICCPS 2016.


13. Desheng Zhang, and Tian He.
    Improving Efficiency of Metropolitan-Scale Transit Systems with Multi-Mode Data Feeds
    In ACM MobiSys 2014.


14. Desheng Zhang, Tian He, Fan Ye, Raghu K. Ganti, and Hui Lei.
    Neighbor Discovery with Distributed Quorum System
    In ACM SenSys 2011.


15. Desheng Zhang, Tian He, Fan Ye, Raghu K. Ganti, and Hui Lei.
    Where Is the Crowd?: Crowdedness Detection Scheme for Mobile Crowdsensing Applications
    In IEEE INFOCOM 2011.