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Articles 1 - 30 of 168
Full-Text Articles in Public Affairs, Public Policy and Public Administration
Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia
Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia
Research Collection School Of Computing and Information Systems
The proliferation of smart personal devices and mobile internet access has fueled numerous advancements in on-demand transportation services. These services are facilitated by online digital platforms and range from providing rides to delivering products. Their influence is transforming transportation systems and leaving a mark on changing individual mobility, activity patterns, and consumption behaviors. For instance, on-demand transportation companies such as Uber, Lyft, Grab, and DiDi have become increasingly vital for meeting urban transportation needs by connecting available drivers with passengers in real time. The recent surge in door-to-door food delivery (e.g., Uber Eats, DoorDash, Meituan); grocery delivery (e.g., Amazon Fresh, …
Conditional Neural Heuristic For Multiobjective Vehicle Routing Problems, Mingfeng Fan, Yaoxin Wu, Zhiguang Cao, Wen Song, Guillaume Sartoretti, Huan Liu, Guohua Wu
Conditional Neural Heuristic For Multiobjective Vehicle Routing Problems, Mingfeng Fan, Yaoxin Wu, Zhiguang Cao, Wen Song, Guillaume Sartoretti, Huan Liu, Guohua Wu
Research Collection School Of Computing and Information Systems
Existing neural heuristics for multiobjective vehicle routing problems (MOVRPs) are primarily conditioned on instance context, which failed to appropriately exploit preference and problem size, thus holding back the performance. To thoroughly unleash the potential, we propose a novel conditional neural heuristic (CNH) that fully leverages the instance context, preference, and size with an encoder–decoder structured policy network. Particularly, in our CNH, we design a dual-attention-based encoder to relate preferences and instance contexts, so as to better capture their joint effect on approximating the exact Pareto front (PF). We also design a size-aware decoder based on the sinusoidal encoding to explicitly …
T-Pickseer: Visual Analysis Of Taxi Pick-Up Point Selection Behavior, Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang, Haipeng Zeng
T-Pickseer: Visual Analysis Of Taxi Pick-Up Point Selection Behavior, Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang, Haipeng Zeng
Research Collection School Of Computing and Information Systems
Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hotspot regions of pick-up points, which can make it easier for drivers to pick-up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory …
Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao
Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao
Research Collection School Of Computing and Information Systems
The cooperative delivery of trucks and drones promises considerable advantages in delivery efficiency and environmental friendliness over pure fossil fuel fleets. As the prosperity of rural B2C e-commerce grows, this study intends to explore the prospect of this cooperation mode for rural last-mile delivery by developing a green vehicle routing problem with drones that considers the presence of steep roads (GVRPD-SR). Realistic energy consumption calculations for trucks and drones that both consider the impacts of general factors and steep roads are incorporated into the GVRPD-SR model, and the objective is to minimize the total energy consumption. To solve the proposed …
Segac: Sample Efficient Generalized Actor Critic For The Stochastic On-Time Arrival Problem, Honglian Guo, Zhi He, Wenda Sheng, Zhiguang Cao, Yingjie Zhou, Weinan Gao
Segac: Sample Efficient Generalized Actor Critic For The Stochastic On-Time Arrival Problem, Honglian Guo, Zhi He, Wenda Sheng, Zhiguang Cao, Yingjie Zhou, Weinan Gao
Research Collection School Of Computing and Information Systems
This paper studies the problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely. Different from almost all canonical sota solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, segac offers the following appealing characteristics. segac updates the ego vehicle’s navigation policy in a sample efficient manner, reduces the variance of both value network and policy network during training, and is automatically adaptive to new destinations. Furthermore, the pre-trained segac policy network enables its real-time decision-making ability within seconds, outperforming state-of-the-art sota algorithms in simulations across various transportation networks. We also successfully deploy segac …
Vision Paper: Advancing Of Ai Explainability For The Use Of Chatgpt In Government Agencies: Proposal Of A 4-Step Framework, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh
Vision Paper: Advancing Of Ai Explainability For The Use Of Chatgpt In Government Agencies: Proposal Of A 4-Step Framework, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh
Research Collection School Of Computing and Information Systems
This paper explores ChatGPT’s potential in aiding government agencies, drawing from a case study based on a government agency in Singapore. While ChatGPT’s text generation abilities offer promise, it brings inherent challenges, including data opacity, potential misinformation, and occasional errors. These issues are especially critical in government decision-making.Public administration’s core values of transparency and accountability magnify these concerns. Ensuring AI alignment with these principles is imperative, given the potential repercussions on policy outcomes and citizen trust.AI explainability plays a central role in ChatGPT’s adoption within government agencies. To address these concerns, we propose strategies like prompt engineering, data governance, and …
Neural Airport Ground Handling, Yaoxin Wu, Jianan Zhou, Yunwen Xia, Xianli Zhang, Zhiguang Cao, Jie Zhang
Neural Airport Ground Handling, Yaoxin Wu, Jianan Zhou, Yunwen Xia, Xianli Zhang, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, …
Spatial Data Management For Green Mobility, Christophe Claramunt, Christine Bassem, Demetrios Zeinalipour-Yazti, Baihua Zheng, Goce Trajcevski, Kristian Torp
Spatial Data Management For Green Mobility, Christophe Claramunt, Christine Bassem, Demetrios Zeinalipour-Yazti, Baihua Zheng, Goce Trajcevski, Kristian Torp
Research Collection School Of Computing and Information Systems
While many countries are developing appropriate actions towards a greener future and moving towards adopting sustainable mobility activities, the real-time management and planning of innovative transportation facilities and services in urban environments still require the development of advanced mobile data management infrastructures. Novel green mobility solutions, such as electric, hybrid, solar and hydrogen vehicles, as well as public and gig-based transportation resources are very likely to reduce the carbon footprint. However, their successful implementation still needs efficient spatio-temporal data management resources and applications to provide a clear picture and demonstrate their effectiveness. This paper discusses the major data management challenges, …
Privacy-Preserving Arbitrary Geometric Range Query In Mobile Internet Of Vehicles, Yinbin Miao, Lin Song, Xinghua Li, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. Deng
Privacy-Preserving Arbitrary Geometric Range Query In Mobile Internet Of Vehicles, Yinbin Miao, Lin Song, Xinghua Li, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. Deng
Research Collection School Of Computing and Information Systems
The mobile Internet of Vehicles (IoVs) has great potential for intelligent transportation, and creates spatial data query demands to realize the value of data. Outsourcing spatial data to a cloud server eliminates the need for local computation and storage, but it leads to data security and privacy threats caused by untrusted third-parties. Existing privacy-preserving spatial range query solutions based on Homomorphic Encryption (HE) have been developed to increase security. However, in the single server model, the private key is held by the query user, which incurs high computation and communication burdens on query users due to multiple rounds of interactions. …
Decentralized Multimedia Data Sharing In Iov: A Learning-Based Equilibrium Of Supply And Demand, Jiani Fan, Minrui Xu, Jiale Guo, Lwin Khin Shar, Jiawen Kang, Dusit Niyato, Kwok-Yan Lam
Decentralized Multimedia Data Sharing In Iov: A Learning-Based Equilibrium Of Supply And Demand, Jiani Fan, Minrui Xu, Jiale Guo, Lwin Khin Shar, Jiawen Kang, Dusit Niyato, Kwok-Yan Lam
Research Collection School Of Computing and Information Systems
The Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications. Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs. However, decentralized data sharing may not achieve the expected efficiency if there are IoV users who only want to consume the shared data but are not willing to contribute their own data to the community, resulting in incomplete information observed by other vehicles and infrastructure, which can introduce additional transmission latency. Therefore, in this paper, by modeling the …
Quantifying Taxi Drivers' Behaviors With Behavioral Game Theory, Mengyu Ji, Yuhong Xu, Shih-Fen Cheng
Quantifying Taxi Drivers' Behaviors With Behavioral Game Theory, Mengyu Ji, Yuhong Xu, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
With their flexibility and convenience, taxis play a vital role in urban transportation systems. Understanding how human drivers make decisions in a context of uncertainty and competition is crucial for taxi fleets that depend on drivers to provide their services. As part of this paper, we propose modeling taxi drivers’ behaviors based on behavioral game theory. Based on real-world data, we demonstrate that the behavioral game theory model we select is superior to state-of-the-art baselines. These results provide a solid foundation for improving taxi fleet efficiency in the future.
An Adaptive Large Neighborhood Search For Heterogeneous Vehicle Routing Problem With Time Windows, Minh Pham Kien Nguyen, Aldy Gunawan, Vincent F. Yu, Mustafa Misir
An Adaptive Large Neighborhood Search For Heterogeneous Vehicle Routing Problem With Time Windows, Minh Pham Kien Nguyen, Aldy Gunawan, Vincent F. Yu, Mustafa Misir
Research Collection School Of Computing and Information Systems
The heterogeneous vehicle routing problem with time windows (HVRPTW) employs various vehicles with different capacities to serve upcoming pickup and delivery orders. We introduce a HVRPTW variant for reflecting the practical needs of crowd-shipping by considering the mass-rapid-transit stations, as the additional terminal points. A mixed integer linear programming model is formulated. An Adaptive Large Neighborhood Search based meta-heuristic is also developed by utilizing a basic probabilistic selection strategy, i.e. roulette wheel, and Simulated Annealing. The proposed approach is empirically evaluated on a new set of benchmark instances. The computational results revealed that ALNS shows its clear advantage on the …
Estimation Of Recursive Route Choice Models With Incomplete Trip Observations, Tien Mai, The Viet Bui, Quoc Phong Nguyen, Tho V. Le
Estimation Of Recursive Route Choice Models With Incomplete Trip Observations, Tien Mai, The Viet Bui, Quoc Phong Nguyen, Tho V. Le
Research Collection School Of Computing and Information Systems
This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue could be intractable because enumerating all paths between unconnected links (or nodes) in a real network is typically not possible. We exploit an expectation–maximization (EM) method that allows dealing with the missing-data issue by alternatively performing two steps of sampling the missing segments in the observations and solving maximum likelihood estimation problems. Moreover, observing that the EM method could be expensive, we propose a …
Imitation Improvement Learning For Large-Scale Capacitated Vehicle Routing Problems, The Viet Bui, Tien Mai
Imitation Improvement Learning For Large-Scale Capacitated Vehicle Routing Problems, The Viet Bui, Tien Mai
Research Collection School Of Computing and Information Systems
Recent works using deep reinforcement learning (RL) to solve routing problems such as the capacitated vehicle routing problem (CVRP) have focused on improvement learning-based methods, which involve improving a given solution until it becomes near-optimal. Although adequate solutions can be achieved for small problem instances, their efficiency degrades for large-scale ones. In this work, we propose a newimprovement learning-based framework based on imitation learning where classical heuristics serve as experts to encourage the policy model to mimic and produce similar or better solutions. Moreover, to improve scalability, we propose Clockwise Clustering, a novel augmented framework for decomposing large-scale CVRP into …
A Lightweight Privacy-Preserving Path Selection Scheme In Vanets, Guojun Wang, Huijie Yang
A Lightweight Privacy-Preserving Path Selection Scheme In Vanets, Guojun Wang, Huijie Yang
Research Collection School Of Computing and Information Systems
With the rapid development of edge computing, artificial intelligence and other technologies, intelligent transportation services in the vehicular ad hoc networks (VANETs) such as in-vehicle navigation and distress alert are increasingly being widely used in life. Currently, road navigation is an essential service in the vehicle network. However, when a user employs the road navigation service, his private data maybe exposed to roadside nodes. Meanwhile, when the trusted authorization sends the navigation route data to the user, the user can obtain all the road data. Especially, other unrequested data might be related to the military. Therefore, how to achieve secure …
A Data-Driven Approach For Scheduling Bus Services Subject To Demand Constraints, Brahmanage Janaka Chathuranga Thilakarathna, Thivya Kandappu, Baihua Zheng
A Data-Driven Approach For Scheduling Bus Services Subject To Demand Constraints, Brahmanage Janaka Chathuranga Thilakarathna, Thivya Kandappu, Baihua Zheng
Research Collection School Of Computing and Information Systems
Passenger satisfaction is extremely important for the success of a public transportation system. Many studies have shown that passenger satisfaction strongly depends on the time they have to wait at the bus stop (waiting time) to get on a bus. To be specific, user satisfaction drops faster as the waiting time increases. Therefore, service providers want to provide a bus to the waiting passengers within a threshold to keep them satisfied. It is a two-pronged problem: (a) to satisfy more passengers the transport planner may increase the frequency of the buses, and (b) in turn, the increased frequency may impact …
Testing Automated Driving Systems By Breaking Many Laws Efficiently, Xiaodong Zhang, Wei Zhao, Yang Sun, Jun Sun, Yulong Shen, Xuewen Dong, Zijiang Yang
Testing Automated Driving Systems By Breaking Many Laws Efficiently, Xiaodong Zhang, Wei Zhao, Yang Sun, Jun Sun, Yulong Shen, Xuewen Dong, Zijiang Yang
Research Collection School Of Computing and Information Systems
An automated driving system (ADS), as the brain of an autonomous vehicle (AV), should be tested thoroughly ahead of deployment. ADS must satisfy a complex set of rules to ensure road safety, e.g., the existing traffic laws and possibly future laws that are dedicated to AVs. To comprehensively test an ADS, we would like to systematically discover diverse scenarios in which certain traffic law is violated. The challenge is that (1) there are many traffic laws (e.g., 13 testable articles in Chinese traffic laws and 16 testable articles in Singapore traffic laws, with 81 and 43 violation situations respectively); and …
Singapore's Ai Applications In The Public Sector: Six Examples, Steven M. Miller
Singapore's Ai Applications In The Public Sector: Six Examples, Steven M. Miller
Research Collection School Of Computing and Information Systems
Steven M. Miller describes six instances in which Singapore has applied AI in the public sector, illustrating different ways of improving its engagement with the public by making government services more accessible, anywhere, anytime, and speeding its responses to public processes and feedback. He illustrates how its leaders made the city a living lab for AI use, and what they learned.
Neighborhood Retail Amenities And Taxi Trip Behavior: A Natural Experiment In Singapore, Kwan Ok Lee, Shih-Fen Cheng
Neighborhood Retail Amenities And Taxi Trip Behavior: A Natural Experiment In Singapore, Kwan Ok Lee, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
While a small change in land use planning in existing neighborhoods may significantly reduce private vehicle trips, we do not have a great understanding of the magnitude of the project- and shock-based causal change in travel behaviors, especially for the retail purpose. We analyze the impact of newly developed malls on the retail trip behavior of nearby residents for shopping, dining or services. Using the difference-in-differences approach and big data from a major taxi company in Singapore, we find that households residing within 800 m from a new mall are significantly less likely to take taxis to other retail destinations …
A Diversity-Enhanced Memetic Algorithm For Solving Electric Vehicle Routing Problems With Time Windows And Mixed Backhauls, Jianhua Xiao, Jingguo Du, Zhiguang Cao, Xingyi Zhang, Yunyun Niu
A Diversity-Enhanced Memetic Algorithm For Solving Electric Vehicle Routing Problems With Time Windows And Mixed Backhauls, Jianhua Xiao, Jingguo Du, Zhiguang Cao, Xingyi Zhang, Yunyun Niu
Research Collection School Of Computing and Information Systems
The electric vehicle routing problem (EVRP) has been studied increasingly because of environmental concerns. However, existing studies on the EVRP mainly focus on time windows and sole linehaul customers, which might not be practical as backhaul customers are also ubiquitous in reality. In this study, we investigate an EVRP with time windows and mixed backhauls (EVRPTWMB), where both linehaul and backhaul customers exist and can be served in any order. To address this challenging problem, we propose a diversity-enhanced memetic algorithm (DEMA) that integrates three types of novel operators, including genetic operators based on adaptive selection mechanism, a selection operator …
Quantifying Stranded Assets Of The Coal-Fired Power In China Under The Paris Agreement Target, Weirong Zhang, Yiou Zhou, Zhen Gong, Junjie Kang, Changhong Zhao, Zhixu Meng, Jian Zhang, Tao Zhang, Jiahai Yuan
Quantifying Stranded Assets Of The Coal-Fired Power In China Under The Paris Agreement Target, Weirong Zhang, Yiou Zhou, Zhen Gong, Junjie Kang, Changhong Zhao, Zhixu Meng, Jian Zhang, Tao Zhang, Jiahai Yuan
Research Collection School Of Computing and Information Systems
Coal-fired power plays a critical role in China's compliance with the Paris Agreement. This research quantifies China's stranded coal assets under different coal capacity expansion scenarios with an integrated approach and high-precision coal-fired power database. From a top-down perspective, firstly, the pathway of China's coal-fired power capacity consistent with the global 2 degrees C scenario is outlined and then those stranded coal-fired power plants are identified with a bottom-up perspective. Stranded value is estimated based upon a cash flow algorithm. Results show that if coal capacity stabilizes during 2020-2030, China will only incur a sizeable yet manageable stranded asset loss …
Coordinating Multi-Party Vehicle Routing With Location Congestion Via Iterative Best Response, Waldy Joe, Hoong Chuin Lau
Coordinating Multi-Party Vehicle Routing With Location Congestion Via Iterative Best Response, Waldy Joe, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This work is motivated by a real-world problem of coordinating B2B pickup-delivery operations to shopping malls involving multiple non-collaborative logistics service providers (LSPs) in a congested city where space is scarce. This problem can be categorized as a vehicle routing problem with pickup and delivery, time windows and location congestion with multiple LSPs (or ML-VRPLC in short), and we propose a scalable, decentralized, coordinated planning approach via iterative best response. We formulate the problem as a strategic game where each LSP is a self-interested agent but is willing to participate in a coordinated planning as long as there are sufficient …
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 …
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 …
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 …
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 …
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 …
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 …
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 …