Artificial Intelligence and Robotics Commons^™

Adaptive Operating Hours For Improved Performance Of Taxi Fleets, Rajiv Ranjan Kumar, Pradeep Varakantham, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Taxi fleets and car aggregation systems are an important component of the urban public transportation system. Taxis and cars in taxi fleets and car aggregation systems (e.g., Uber) are dependent on a large number of self-controlled and profit-driven taxi drivers, which introduces inefficiencies in the system. There are two ways in which taxi fleet performance can be optimized: (i) Operational decision making: improve assignment of taxis/cars to customers, while accounting for future demand; (ii) strategic decision making: optimize operating hours of (taxi and car) drivers. Existing research has primarily focused on the operational decisions in (i) and we focus on …

Predicting Bus Travel Times In Washington, Dc Using Artificial Neural Networks (Anns), Stephen Arhin, Babin Manandhar, Hamdiat Baba Adam, Adam Gatiba

Mineta Transportation Institute

Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington …

Zone Path Construction (Zac) Based Approaches For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Real-time ridesharing systems such as UberPool, Lyft Line and GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the “right” requests to travel together in the “right” available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible combinations of requests (with respect to the available delay for customers) as …

Research On Improving Maritime Emergency Management Based On Ai And Vr In Tianjin Port, Shuli Sun

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

Enhanced Traffic Incident Analysis With Advanced Machine Learning Algorithms, Zhenyu Wang

Computational Modeling & Simulation Engineering Theses & Dissertations

Traffic incident analysis is a crucial task in traffic management centers (TMCs) that typically manage many highways with limited staff and resources. An effective automatic incident analysis approach that can report abnormal events timely and accurately will benefit TMCs in optimizing the use of limited incident response and management resources. During the past decades, significant efforts have been made by researchers towards the development of data-driven approaches for incident analysis. Nevertheless, many developed approaches have shown limited success in the field. This is largely attributed to the long detection time (i.e., waiting for overwhelmed upstream detection stations; meanwhile, downstream stations …

Online Traffic Signal Control Through Sample-Based Constrained Optimization, Srishti Dhamija, Alolika Gon, Pradeep Varakantham, William Yeoh

Research Collection School Of Computing and Information Systems

Traffic congestion reduces productivity of individuals by increasing time spent in traffic and also increases pollution. To reduce traffic congestion by better handling dynamic traffic patterns, recent work has focused on online traffic signal control. Typically, the objective in traffic signal control is to minimize expected delay over all vehicles given the uncertainty associated with the vehicle turn movements at intersections. In order to ensure responsiveness in decision making, a typical approach is to compute a schedule that minimizes the delay for the expected scenario of vehicle movements instead of minimizing expected delay over the feasible vehicle movement scenarios. Such …

Zone Path Construction (Zac) Based Approaches For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the "right" requests to travel together in the "right" available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible (with respect to the available delay for customers) combinations of requests as possible …

Pricing And Equilibrium In On-Demand Ride-Pooling Markets, Jintao Ke, Hai Yang, Xinwei Li, Hai Wang, Jieping Ye

Research Collection School Of Computing and Information Systems

With the recent rapid growth of technology-enabled mobility services, ride-sourcing platforms, such as Uber and DiDi, have launched commercial on-demand ride-pooling programs that allow drivers to serve more than one passenger request in each ride. Without requiring the prearrangement of trip schedules, these programs match on-demand passenger requests with vehicles that have vacant seats. Ride-pooling programs are expected to offer benefits for both individual passengers in the form of cost savings and for society in the form of traffic alleviation and emission reduction. In addition to some exogenous variables and environments for ride-sourcing market, such as city size and population …

Bus Frequency Optimization: When Waiting Time Matters In User Satisfaction, Songsong Mo, Zhifeng Bao, Baihua Zheng, Zhiyong Peng

Research Collection School Of Computing and Information Systems

Reorganizing bus frequency to cater for the actual travel demand can save the cost of the public transport system significantly. Many, if not all, existing studies formulate this as a bus frequency optimization problem which tries to minimize passengers’ average waiting time. However, many investigations have confirmed that the user satisfaction drops faster as the waiting time increases. Consequently, 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 …

A Modeling Framework For Urban Growth Prediction Using Remote Sensing And Video Prediction Technologies: A Time-Dependent Convolutional Encoder-Decoder Architecture, Ahmed Hassan Jaad

Civil and Environmental Engineering Theses and Dissertations

Studying the growth pattern of cities/urban areas has received considerable attention during the past few decades. The goal is to identify directions and locations of potential growth, assess infrastructure and public service requirements, and ensure the integration of the new developments with the existing city structure. This dissertation presents a novel model for urban growth prediction using a novel machine learning model. The model treats successive historical satellite images of the urban area under consideration as a video for which future frames are predicted. A time-dependent convolutional encoder-decoder architecture is adopted. The model considers as an input a satellite image …

From The Tree Of Knowledge And The Golem Of Prague To Kosher Autonomous Cars: The Ethics Of Artificial Intelligence Through Jewish Eyes, Nachshon Goltz, John Zeleznikow, Tracey Dowdeswell

Research outputs 2014 to 2021

This article discusses the regulation of artificial intelligence from a Jewish perspective, with an emphasis on the regulation of machine learning and its application to autonomous vehicles and machine learning. Through the Biblical story of Adam and Eve as well as Golem legends from Jewish folklore, we derive several basic principles that underlie a Jewish perspective on the moral and legal personhood of robots and other artificially intelligent agents. We argue that religious ethics in general, and Jewish ethics in particular, show us that the dangers of granting moral personhood to robots and in particular to autonomous vehicles lie not …

Adaptive Large Neighborhood Search For Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu

Research Collection School Of Computing and Information Systems

Cross-docking is considered as a method to manage and control the inventory flow, which is essential in the context of supply chain management. This paper studies the integration of the vehicle routing problem with cross-docking, namely VRPCD which has been extensively studied due to its ability to reducethe overall costs occurring in a supply chain network. Given a fleet of homogeneous vehicles for delivering a single type of product from suppliers to customers through a cross-dock facility, the objective of VRPCD is to determine the number of vehicles used and the corresponding vehicle routes, such that the vehicleoperational and transportation …

Literature Review: How U.S. Government Documents Are Addressing The Increasing National Security Implications Of Artificial Intelligence, Bert Chapman

Libraries Faculty and Staff Scholarship and Research

This article emphasizes the increasing importance of artificial intelligence (AI) in military and national security policy making. It seeks to inform interested individuals about the proliferation of publicly accessible U.S. government and military literature on this multifaceted topic. An additional objective of this endeavor is encouraging greater public awareness of and participation in emerging public policy debate on AI's moral and national security implications..

Hierarchical Multiagent Reinforcement Learning For Maritime Traffic Management, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Increasing global maritime traffic coupled with rapid digitization and automation in shipping mandate developing next generation maritime traffic management systems to mitigate congestion, increase safety of navigation, and avoid collisions in busy and geographically constrained ports (such as Singapore's). To achieve these objectives, we model the maritime traffic as a large multiagent system with individual vessels as agents, and VTS (Vessel Traffic Service) authority as a regulatory agent. We develop a hierarchical reinforcement learning approach where vessels first select a high level action based on the underlying traffic flow, and then select the low level action that determines their future …

Truck Trailer Classification Using Side-Fire Light Detection And Ranging (Lidar) Data, Olcay Sahin

Civil & Environmental Engineering Theses & Dissertations

Classification of vehicles into distinct groups is critical for many applications, including freight and commodity flow modeling, pavement management and design, tolling, air quality monitoring, and intelligent transportation systems. The Federal Highway Administration (FHWA) developed a standardized 13-category vehicle classification ruleset, which meets the needs of many traffic data user applications. However, some applications need high-resolution data for modeling and analysis. For example, the type of commodity being carried must be known in the freight modeling framework. Unfortunately, this information is not available at the state or metropolitan level, or it is expensive to obtain from current resources.

Nevertheless, using …

Artificial Intelligence-Enhanced Decision Support For Informing Global Sustainable Development: A Human-Centric Ai-Thinking Approach, Meng-Leong How, Sin Mei Cheah, Yong-Jiet Chan, Aik Cheow Khor, Eunice Mei Ping Say

CCX Research

Sustainable development is crucial to humanity. Utilization of primary socio-environmental data for analysis is essential for informing decision making by policy makers about sustainability in development. Artificial intelligence (AI)-based approaches are useful for analyzing data. However, it was not easy for people who are not trained in computer science to use AI. The significance and novelty of this paper is that it shows how the use of AI can be democratized via a user-friendly human-centric probabilistic reasoning approach. Using this approach, analysts who are not computer scientists can also use AI to analyze sustainability-related EPI data. Further, this human-centric probabilistic …

Shipbuilding Supply Chain Framework And Digital Transformation: A Project Portfolios Risk Evaluation, Rafael Diaz, Katherine Smith, Rafael Landaeta, Antonio Padovano

VMASC Publications

Program portfolio managers in digital transformation programs have a need for knowledge that can guide decisions related to the alignment of program investments with the sustainability and strategic objectives of the organization. The purpose of this research is to illustrate the utility of a framework capable of clarifying the cost-benefit tradeoffs stemming from assessing digitalization program investment risks in the military shipbuilding sector. Our approach uses Artificial Neural Network to quantify benefits and risks per project while employing scenario analysis to quantify the effects of operational constraints. A Monte Carlo model is used to generate data samples that support the …

A Mathematical Programming Model For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiwan, Aldy Gunawan, Audrey Tedja Widjaja

Research Collection School Of Computing and Information Systems

A green mixed fleet vehicle routing with realistic energy consumption and partial recharges problem (GMFVRP-REC-PR) is addressed in this paper. This problem involves a fixed number of electric vehicles and internal combustion vehicles to serve a set of customers. The realistic energy consumption which depends on several variables is utilized to calculate the electricity consumption of an electric vehicle and fuel consumption of an internal combustion vehicle. Partial recharging policy is included into the problem to represent the real life scenario. The objective of this problem is to minimize the total travelled distance and the total emission produced by internal …

Developing Algorithms To Detect Incidents On Freeways From Loop Detector And Vehicle Re-Identification Data, Biraj Adhikari

Civil & Environmental Engineering Theses & Dissertations

A new approach for testing incident detection algorithms has been developed and is presented in this thesis. Two new algorithms were developed and tested taking California #7, which is the most widely used algorithm to date, and SVM (Support Vector Machine), which is considered one of the best performing classifiers, as the baseline for comparisons. Algorithm #B in this study uses data from Vehicle Re-Identification whereas the other three algorithms (California #7, SVM and Algorithm #A) use data from a double loop detector for detection of an incident. A microscopic traffic simulator is used for modeling three types of incident …

Zac: A Zone Path Construction Approach For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the right requests to travel in available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. The most relevant existing work has focussed on generating as many relevant feasible (with respect to available delay for customers) combinations of requests (referred to as trips) as possible in real-time. …

Entrans: Leveraging Kinetic Energy Harvesting Signal For Transportation Mode Detection, Guohao Lan, Weitao Xu, Dong Ma, Sara Khalifa, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, as well as providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by …

Re-Org: An Online Repositioning Guidance Agent, Muralidhar Konda, Pradeep Varakantham, Aayush Saxena, Meghna Lowalekar

Research Collection School Of Computing and Information Systems

No abstract provided.

Route Planning For A Fleet Of Electric Vehicles With Waiting Times At Charging Stations, Baoxiang Li, Shashi Shekhar Jha, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Electric Vehicles (EVs) are the next wave of technology in the transportation industry. EVs are increasingly becoming common for personal transport and pushing the boundaries to become the mainstream mode of transportation. Use of such EVs in logistic fleets for delivering customer goods is not far from becoming reality. However, managing such fleet of EVs bring new challenges in terms of battery capacities and charging infrastructure for efficient route planning. Researchers have addressed such issues considering different aspects of the EVs such as linear battery charging/discharging rate, fixed travel times, etc. In this paper, we address the issue of waiting …

Taxis Strike Back: A Field Trial Of The Driver Guidance System, Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram

Research Collection School Of Computing and Information Systems

Traditional taxi fleet operators world-over have been facing intense competitions from various ride-hailing services such as Uber and Grab (specific to the Southeast Asia region). Based on our studies on the taxi industry in Singapore, we see that the emergence of Uber and Grab in the ride-hailing market has greatly impacted the taxi industry: the average daily taxi ridership for the past two years has been falling continuously, by close to 20% in total. In this work, we discuss how efficient real-time data analytics and large-scale multi-agent optimization technology could potentially help taxi drivers compete against more technologically advanced service …

Resource-Constrained Scheduling For Maritime Traffic Management, Lucas Agussurja, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of mitigating congestion and preventing hotspots in busy water areas such as Singapore Straits and port waters. Increasing maritime traffic coupled with narrow waterways makes vessel schedule coordination for just-in-time arrival critical for navigational safety. Our contributions are: 1) We formulate the maritime traffic management problem based on the real case study of Singapore waters; 2) We model the problem as a variant of the resource-constrained project scheduling problem (RCPSP), and formulate mixed-integer and constraint programming (MIP/CP) formulations; 3) To improve the scalability, we develop a combinatorial Benders (CB) approach that is significantly more effective than …

Pricing For A Last-Mile Transportation System, Yiwei Chen, Hai Wang

Research Collection School Of Computing and Information Systems

The Last-Mile Problem refers to the provision of travel service from the nearest public transportation node to a home or other destination. Last-Mile Transportation System (LMTS), which has recently emerged, provide on-demand shared transportation. We consider an LMTS with multiple passenger types—adults, senior citizens, children, and students. The LMTS designer determines the price for the passengers, last-mile service vehicle capacity, and service fleet size (number of vehicles) for each last-mile region to maximize the social welfare generated by the LMTS. The level of last-mile service (in terms of passenger waiting time) is approximated by using a batch arrival, batch service, …

Efficient Gate System Operations For A Multipurpose Port Using Simulation Optimization, Ketki Kulkarni, Trong Khiem Tran, Hai Wang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Port capacity is determined by three major infrastructural resources namely, berths, yards and gates. Theadvertised capacity is constrained by the least of the capacities of the three resources. While a lot ofattention has been paid to optimizing berth and yard capacities, not much attention has been given toanalyzing the gate capacity. The gates are a key node between the land-side and sea-side operations in anocean-to-cities value chain. The gate system under consideration, located at an important port in an Asiancity, is a multi-class parallel queuing system with non-homogeneous Poisson arrivals. It is hard to obtaina closed form analytic approach for …

Incentivizing The Use Of Bike Trailers For Dynamic Repositioning In Bike Sharing Systems, Supriyo Ghosh, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Bike Sharing System (BSS) is a green mode of transportation that is employed extensively for short distance travels in major cities of the world. Unfortunately, the users behaviour driven by their personal needs can often result in empty or full base stations, thereby resulting in loss of customer demand. To counter this loss in customer demand, BSS operators typically utilize a fleet of carrier vehicles for repositioning the bikes between stations. However, this fuel burning mode of repositioning incurs a significant amount of routing, labor cost and further increases carbon emissions. Therefore, we propose a potentially self-sustaining and environment friendly …

Augmenting Decisions Of Taxi Drivers Through Reinforcement Learning For Improving Revenues, Tanvi Verma, Pradeep Varakantham, Sarit Kraus, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Taxis (which include cars working with car aggregation systems such as Uber, Grab, Lyft etc.) have become a critical component in the urban transportation. While most research and applications in the context of taxis have focused on improving performance from a customer perspective, in this paper,we focus on improving performance from a taxi driver perspective. Higher revenues for taxi drivers can help bring more drivers into the system thereby improving availability for customers in dense urban cities.Typically, when there is no customer on board, taxi driverswill cruise around to find customers either directly (on thestreet) or indirectly (due to a …

Tackling Large-Scale Home Health Care Delivery Problem With Uncertainty, Cen Chen, Zachary Rubinstein, Stephen Smith, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this work, we investigate a multi-period Home HealthCare Scheduling Problem (HHCSP) under stochastic serviceand travel times. We first model the deterministic problemas an integer linear programming model that incorporatesreal-world requirements, such as time windows, continuityof care, workload fairness, inter-visit temporal dependencies.We then extend the model to cope with uncertainty in durations,by introducing chance constraints into the formulation.We propose efficient solution approaches, which providequantifiable near-optimal solutions and further handlethe uncertainties by employing a sampling-based strategy. Wedemonstrate the effectiveness of our proposed approaches oninstances synthetically generated by real-world dataset forboth deterministic and stochastic scenarios.