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Articles 1 - 22 of 22
Full-Text Articles in Physical Sciences and Mathematics
Forest Structure And Composition Alleviate Human Thermal Stress, Loïc Gillerot, Dries Landuyt, Rachel Oh, Winston T. L. Chow, Et Al
Forest Structure And Composition Alleviate Human Thermal Stress, Loïc Gillerot, Dries Landuyt, Rachel Oh, Winston T. L. Chow, Et Al
Research Collection College of Integrative Studies
Current climate change aggravates human health hazards posed by heat stress. Forests can locally mitigate this by acting as strong thermal buffers, yet potential mediation by forest ecological characteristics remains underexplored. We report over 14 months of hourly microclimate data from 131 forest plots across four European countries and compare these to open-field controls using physiologically equivalent temperature (PET) to reflect human thermal perception. Forests slightly tempered cold extremes, but the strongest buffering occurred under very hot conditions (PET >35°C), where forests reduced strong to extreme heat stress day occurrence by 84.1%. Mature forests cooled the microclimate by 12.1 to …
Harmonized Gap-Filled Datasets From 20 Urban Flux Tower Sites, Matthew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow
Harmonized Gap-Filled Datasets From 20 Urban Flux Tower Sites, Matthew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow
Research Collection College of Integrative Studies
A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site–year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects …
Lawbreaker: An Approach For Specifying Traffic Laws And Fuzzing Autonomous Vehicles, Yang Sun, Christopher M. Poskitt, Jun Sun, Yuqi Chen, Zijiang Yang
Lawbreaker: An Approach For Specifying Traffic Laws And Fuzzing Autonomous Vehicles, Yang Sun, Christopher M. Poskitt, Jun Sun, Yuqi Chen, Zijiang Yang
Research Collection School Of Computing and Information Systems
Autonomous driving systems (ADSs) must be tested thoroughly before they can be deployed in autonomous vehicles. High-fidelity simulators allow them to be tested against diverse scenarios, including those that are difficult to recreate in real-world testing grounds. While previous approaches have shown that test cases can be generated automatically, they tend to focus on weak oracles (e.g. reaching the destination without collisions) without assessing whether the journey itself was undertaken safely and satisfied the law. In this work, we propose LawBreaker, an automated framework for testing ADSs against real-world traffic laws, which is designed to be compatible with different scenario …
Towards An Optimal Bus Frequency Scheduling: When The Waiting Time Matters, Songsong Mo, Zhifeng Bao, Baihua Zheng, Zhiyong Peng
Towards An Optimal Bus Frequency Scheduling: When The Waiting Time Matters, Songsong Mo, Zhifeng Bao, Baihua Zheng, Zhiyong Peng
Research Collection School Of Computing and Information Systems
Reorganizing bus frequencies to cater for actual travel demands can significantly save the cost of the public transport system. This paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold can be maximized. We propose two variants of the problem, FAST and FASTCO, to cater for different application needs and prove that both are NP-hard. To solve FAST effectively and efficiently, we first present an …
Learning To Solve Multiple-Tsp With Time Window And Rejections Via Deep Reinforcement Learning, Rongkai Zhang, Cong Zhang, Zhiguang Cao, Wen Song, Puay Siew Tan, Jie Zhang, Bihan Wen, Justin Dauwels
Learning To Solve Multiple-Tsp With Time Window And Rejections Via Deep Reinforcement Learning, Rongkai Zhang, Cong Zhang, Zhiguang Cao, Wen Song, Puay Siew Tan, Jie Zhang, Bihan Wen, Justin Dauwels
Research Collection School Of Computing and Information Systems
We propose a manager-worker framework (the implementation of our model is publically available at: https://github.com/zcaicaros/manager-worker-mtsptwr) based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), i.e. multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections. Particularly, in the proposed framework, a manager agent learns to divide mTSPTWR into sub-routing tasks by assigning customers to each vehicle via a Graph Isomorphism Network (GIN) based policy network. A worker agent learns to solve sub-routing tasks by minimizing the cost in terms of both …
Fed-Ltd: Towards Cross-Platform Ride Hailing Via Federated Learning To Dispatch, Yansheng Wang, Yongxin Tong, Zimu Zhou, Ziyao Ren, Yi Xu, Guobin Wu, Weifeng Lv
Fed-Ltd: Towards Cross-Platform Ride Hailing Via Federated Learning To Dispatch, Yansheng Wang, Yongxin Tong, Zimu Zhou, Ziyao Ren, Yi Xu, Guobin Wu, Weifeng Lv
Research Collection School Of Computing and Information Systems
Learning based order dispatching has witnessed tremendous success in ride hailing. However, the success halts within individual ride hailing platforms because sharing raw order dispatching data across platforms may leak user privacy and business secrets. Such data isolation not only impairs user experience but also decreases the potential revenues of the platforms. In this paper, we advocate federated order dispatching for cross-platform ride hailing, where multiple platforms collaboratively make dispatching decisions without sharing their local data. Realizing this concept calls for new federated learning strategies that tackle the unique challenges on effectiveness, privacy and efficiency in the context of order …
Integrated Assessment Of Urban Overheating Impacts On Human Life, N. Nazarian, E. S. Krayenhoff, B. Bechtel, D. M. Hondula, R. Paolini, J. Vanos, T. Cheung, Winston T. L. Chow, Et Al
Integrated Assessment Of Urban Overheating Impacts On Human Life, N. Nazarian, E. S. Krayenhoff, B. Bechtel, D. M. Hondula, R. Paolini, J. Vanos, T. Cheung, Winston T. L. Chow, Et Al
Research Collection College of Integrative Studies
Urban overheating, driven by global climate change and urban development, is a major contemporary challenge that substantially impacts urban livability and sustainability. Overheating represents a multifaceted threat to the well-being, performance, and health of individuals as well as the energy efficiency and economy of cities, and it is influenced by complex interactions between building, city, and global scale climates. In recent decades, extensive discipline-specific research has characterized urban heat and assessed its implications on human life, including ongoing efforts to bridge neighboring disciplines. The research horizon now encompasses complex problems involving a wide range of disciplines, and therefore comprehensive and …
Time Dependent Orienteering Problem With Time Windows And Service Time Dependent Profits, M. Khodadadian, A. Divsalar, C. Verbeeck, Aldy Gunawan, P. Vansteenwegen
Time Dependent Orienteering Problem With Time Windows And Service Time Dependent Profits, M. Khodadadian, A. Divsalar, C. Verbeeck, Aldy Gunawan, P. Vansteenwegen
Research Collection School Of Computing and Information Systems
This paper addresses the time dependent orienteering problem with time windows and service time dependent profits (TDOPTW-STP). In the TDOPTW-STP, each vertex is assigned a minimum and a maximum service time and the profit collected at each vertex increases linearly with the service time. The goal is to maximize the total collected profit by determining a subset of vertices to be visited and assigning appropriate service time to each vertex, considering a given time budget and time windows. Moreover, travel times are dependent of the departure times. To solve this problem, a mixed integer linear model is formulated and a …
Multi-Agent Reinforcement Learning For Traffic Signal Control Through Universal Communication Method, Qize Jiang, Minhao Qin, Shengmin Shi, Weiwei Sun Sun, Baihua Zheng
Multi-Agent Reinforcement Learning For Traffic Signal Control Through Universal Communication Method, Qize Jiang, Minhao Qin, Shengmin Shi, Weiwei Sun Sun, Baihua Zheng
Research Collection School Of Computing and Information Systems
How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging. Existing approaches only enable the communication in a heuristic manner without considering the content/importance of information to be shared. In this paper, we propose a universal communication form UniComm between intersections. UniComm embeds massive observations collected at one agent into crucial predictions of their impact on its neighbors, which improves the communication efficiency and is universal across existing methods. We also propose a concise network UniLight to make full use of communications enabled by UniComm. Experimental results on real datasets demonstrate that UniComm …
Transportation-Enabled Urban Services: A Brief Discussion, Hai Wang
Transportation-Enabled Urban Services: A Brief Discussion, Hai Wang
Research Collection School Of Computing and Information Systems
Nearly 55% of the world's population lives in urban areas or cities, and is expected to rise above 70% over the coming decades. Rapid urbanization brings steadily more residents and a growing freelancing workforce into cities. The developments of city infrastructure and technologies—for instance, mobile location tracking and computing, autonomous and connected vehicles, wearable devices, robotics and robots, smart appliances, biometric authentication, various internet-of-things devices, and smart monitoring systems—are creating numerous opportunities and inspiring innovative and emerging urban services. Among these innovations, complex systems of urban transportation and logistics have embraced advances in technologies and, consequently, been significantly reshaped (Agatz …
Imagining New Futures Beyond Predictive Systems In Child Welfare: A Qualitative Study With Impacted Stakeholders, Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Ken Holstein, Zhiwei Steven Wu, Haiyi Zhu
Imagining New Futures Beyond Predictive Systems In Child Welfare: A Qualitative Study With Impacted Stakeholders, Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Ken Holstein, Zhiwei Steven Wu, Haiyi Zhu
Research Collection School Of Computing and Information Systems
Child welfare agencies across the United States are turning to datadriven predictive technologies (commonly called predictive analytics) which use government administrative data to assist workers’ decision-making. While some prior work has explored impacted stakeholders’ concerns with current uses of data-driven predictive risk models (PRMs), less work has asked stakeholders whether such tools ought to be used in the first place. In this work, we conducted a set of seven design workshops with 35 stakeholders who have been impacted by the child welfare system or who work in it to understand their beliefs and concerns around PRMs, and to engage them …
Undiscounted Recursive Path Choice Models: Convergence Properties And Algorithms, Tien Mai, Emma Frejinger
Undiscounted Recursive Path Choice Models: Convergence Properties And Algorithms, Tien Mai, Emma Frejinger
Research Collection School Of Computing and Information Systems
Traffic flow predictions are central to a wealth of problems in transportation. Path choice models can be used for this purpose, and in state-of-the-art models—so-called recursive path choice (RPC) models—the choice of a path is formulated as a sequential arc choice process using undiscounted Markov decision process (MDP) with an absorbing state. The MDP has a utility maximization objective with unknown parameters that are estimated based on data. The estimation and prediction using RPC models require repeatedly solving value functions that are solutions to the Bellman equation. Although there are several examples of successful applications of RPC models in the …
Transboundary Air Pollution And Cross-Border Cooperation: Insights From Marine Vessel Emissions Regulations In Hong Kong And Shenzhen, Seung Kyum Kim, Terry Van Gevelt, Paul Joosse, Mia M. Bennett
Transboundary Air Pollution And Cross-Border Cooperation: Insights From Marine Vessel Emissions Regulations In Hong Kong And Shenzhen, Seung Kyum Kim, Terry Van Gevelt, Paul Joosse, Mia M. Bennett
Research Collection College of Integrative Studies
Many coastal cities regulate shipping emissions within their jurisdictions. However, the transboundary nature of air pollution makes such efforts largely ineffective unless they are accompanied by reciprocal, legally-binding regulatory agreements with neighbouring cities. Due to various technical, economic, and institutional barriers, it has thus far been difficult to isolate the effects of legally-binding cross-border cooperation on vessel emissions at the city-level. We exploit the unique administrative characteristics of Hong Kong and its relationship with neighbouring cities in China's Pearl River Delta to isolate the effect of legally-binding cross-border cooperation. Using a regression discontinuity design, we find that Hong Kong's unilateral …
Competition And Third-Party Platform-Integration In Ride-Sourcing Markets, Yaqian Zhou, Hai Yang, Jintao Ke, Hai Wang, Xinwei Li
Competition And Third-Party Platform-Integration In Ride-Sourcing Markets, Yaqian Zhou, Hai Yang, Jintao Ke, Hai Wang, Xinwei Li
Research Collection School Of Computing and Information Systems
Recently, some third-party integrators attempt to integrate the ride services offered by multiple independent ride-sourcing platforms. Accordingly, passengers can request ride through the integrators and receive ride service from any one of the ride-sourcing platforms. This novel business model, termed as third-party platform-integration in this work, has potentials to alleviate market fragmentation cost resulting from demand splitting among multiple platforms. Although most existing studies focus on operation strategies for one single monopolist platform, much less is known about the competition and platform-integration and their implications on operation strategy and system efficiency. In this work, we propose mathematical models to describe …
Hierarchical Value Decomposition For Effective On-Demand Ride Pooling, Hao Jiang, Pradeep Varakantham
Hierarchical Value Decomposition For Effective On-Demand Ride Pooling, Hao Jiang, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
On-demand ride-pooling (e.g., UberPool, GrabShare) services focus on serving multiple different customer requests using each vehicle, i.e., an empty or partially filled vehicle can be assigned requests from different passengers with different origins and destinations. On the other hand, in Taxi on Demand (ToD) services (e.g., UberX), one vehicle is assigned to only one request at a time. On-demand ride pooling is not only beneficial to customers (lower cost), drivers (higher revenue per trip) and aggregation companies (higher revenue), but is also of crucial importance to the environment as it reduces the number of vehicles required on the roads. Since …
Managing The Phaseout Of Coal Power: A Comparison Of Power Decarbonization Pathways In Jilin Province, Weirong Zhang, Zhixu Meng, Jiongjun Yang, Yan Song, Yiou Zhou, Changhong Zhao, Jiahai Yuan
Managing The Phaseout Of Coal Power: A Comparison Of Power Decarbonization Pathways In Jilin Province, Weirong Zhang, Zhixu Meng, Jiongjun Yang, Yan Song, Yiou Zhou, Changhong Zhao, Jiahai Yuan
Research Collection School Of Computing and Information Systems
With the periodic goals of reaching carbon emission peak before 2030 and achieving carbon neutrality before 2060 (“dual carbon” goals), China shows its unprecedented determination to coal power phaseout. This research takes Jilin Province to showcase possible pathways of coal power units’ phaseout on provincial level. We set up four different coal power phaseout scenarios, under which their transition cost and effectiveness would be calculated, respectively. In terms of natural resource endowment and electricity demand, Jilin Province would achieve a complete coal power phaseout by 2045 or even by 2040. However, after assessing the effectiveness of power transition under the …
Estimating Stranded Coal Assets In China's Power Sector, Weirong Zhang, Mengjia Ren, Junjie Kang, Yiou Zhou, Jiahai Yuan
Estimating Stranded Coal Assets In China's Power Sector, Weirong Zhang, Mengjia Ren, Junjie Kang, Yiou Zhou, Jiahai Yuan
Research Collection School Of Computing and Information Systems
China has suffered overcapacity in coal power since 2016. With growing electricity demand and an economic crisis due to the Covid-19 pandemic, China faces a dilemma between easing restrictive policies for short-term growth in coal-fired power production and keeping restrictions in place for long-term sustainability. In this paper, we measure the risks faced by China's coal power units to become stranded in the next decade and estimate the associated economic costs for different shareholders. By implementing restrictive policies on coal power expansion, China can avoid 90% of stranded coal assets by 2025.
A Survey On Modern Deep Neural Network For Traffic Prediction: Trends, Methods And Challenges, David Alexander Tedjopumomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, Kai Qin
A Survey On Modern Deep Neural Network For Traffic Prediction: Trends, Methods And Challenges, David Alexander Tedjopumomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, Kai Qin
Research Collection School Of Computing and Information Systems
In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction. The field of traffic prediction has evolved greatly ever since its inception in the late 70s. Earlier studies mainly use classical statistical models such as ARIMA and its variants. Then, researchers started to focus on machine learning models due to their power and flexibility. As theoretical and technological advances emerge, we enter the era of deep neural network, which gained popularity due to its …
Trust In Robotics: A Multi-Staged Decision-Making Approach To Robots In Community, Wenxi Zhang, Willow Wong, Mark Findlay
Trust In Robotics: A Multi-Staged Decision-Making Approach To Robots In Community, Wenxi Zhang, Willow Wong, Mark Findlay
Centre for AI & Data Governance
Pivoting on the desired outcome of social good within the wider robotics ecosystem, trust is identified as the central adhesive of the HRI interface. However, building trust between humans and robots involves more than improving the machine’s technical reliability or trustworthiness in function. This paper presents a holistic, community-based approach to trust-building, where trust is understood as a multifaceted and multi-staged looped relation that depends heavily on context and human perceptions. Building on past literature that identifies dispositional and learned stages of trust, our proposed Decision to Trust model considers more extensively the human and situational factors influencing how trust …
The Impact Of Ride-Hail Surge Factors On Taxi Bookings, Sumit Agarwal, Ben Charoenwong, Shih-Fen Cheng, Jussi Keppo
The Impact Of Ride-Hail Surge Factors On Taxi Bookings, Sumit Agarwal, Ben Charoenwong, Shih-Fen Cheng, Jussi Keppo
Research Collection School Of Computing and Information Systems
We study the role of ride-hailing surge factors on the allocative efficiency of taxis by combining a reduced-form estimation with structural analyses using machine-learning-based demand predictions. Where other research study the effect of entry on incumbent taxis, we use higher frequency granular data to study how location-time-specific surge factors affect taxi bookings to bound the effect of customer decisions while accounting for various confounding variables. We find that even in a unique market like Singapore, where incumbent taxi companies have app-based booking systems similar to those from ride-hailing companies like Uber, the estimated upper bound on the cross-platform substitution between …
Ecosystem Duties, Green Infrastructure, And Environmental Injustice In Los Angeles, Sayd Randle
Ecosystem Duties, Green Infrastructure, And Environmental Injustice In Los Angeles, Sayd Randle
Research Collection College of Integrative Studies
In Los Angeles, water managers and environmentalist NGOs champion green infrastructure retrofits, installations intended to maximize the water-absorbing capacity of the urban landscape. In such arrangements, the work of water management is necessarily spread among a more-than-human community, including (but certainly not limited to) humans, plants, soils, and gravels. This article analyzes the human labor within these collaborations, tracking when and how this work gets enrolled in networks of water management and circuits of value. I develop the term ecosystem duties to characterize these exertions and as a useful analytic for assessing emergent dynamics of environmental justice.
Heterogeneous Attentions For Solving Pickup And Delivery Problem Via Deep Reinforcement Learning, Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang
Heterogeneous Attentions For Solving Pickup And Delivery Problem Via Deep Reinforcement Learning, Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang
Research Collection School Of Computing and Information Systems
Recently, there is an emerging trend to apply deep reinforcement learning to solve the vehicle routing problem (VRP), where a learnt policy governs the selection of next node for visiting. However, existing methods could not handle well the pairing and precedence relationships in the pickup and delivery problem (PDP), which is a representative variant of VRP. To address this challenging issue, we leverage a novel neural network integrated with a heterogeneous attention mechanism to empower the policy in deep reinforcement learning to automatically select the nodes. In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the …