scholarly journals On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

2017 ◽  
Vol 114 (3) ◽  
pp. 462-467 ◽  
Author(s):  
Javier Alonso-Mora ◽  
Samitha Samaranayake ◽  
Alex Wallar ◽  
Emilio Frazzoli ◽  
Daniela Rus
Keyword(s):  
New York ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
High Capacity ◽  
Fleet Size ◽  
Ride Sharing ◽  
Large Numbers ◽  
General Mathematical Model ◽  
Travel Delay

Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.

scholarly journals Real-Time Dispatching of Large-Scale Ride-Sharing Systems: Integrating Optimization, Machine Learning, and Model Predictive Control

2020 ◽  
Author(s):  
Connor Riley ◽  
Pascal van Hentenryck ◽  
Enpeng Yuan
Keyword(s):  
Machine Learning ◽  
New York ◽  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Large Scale ◽  
Waiting Times ◽  
Service Guarantees ◽  
Ride Sharing ◽  
Dispatching Algorithm

This paper considers the dispatching of large-scale real-time ride-sharing systems to address congestion issues faced by many cities. The goal is to serve all customers (service guarantees) with a small number of vehicles while minimizing waiting times under constraints on ride duration. This paper proposes an end-to-end approach that tightly integrates a state-of-the-art dispatching algorithm, a machine-learning model to predict zone-to-zone demand over time, and a model predictive control optimization to relocate idle vehicles. Experiments using historic taxi trips in New York City indicate that this integration decreases average waiting times by about 30% over all test cases and reaches close to 55% on the largest instances for high-demand zones.

scholarly journals Space-time clustering-based method to optimize shareability in real-time ride-sharing

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262499
Author(s):  
Negin Alisoltani ◽  
Mostafa Ameli ◽  
Mahdi Zargayouna ◽  
Ludovic Leclercq
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Computation Time ◽  
Clustering Method ◽  
Matching Problem ◽  
Solution Quality ◽  
Mobility Service ◽  
Ride Sharing ◽  
Large Scale Problems ◽  
Spatio Temporal

Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.

scholarly journals The High Capacity Expanding Lifeboat HiCEL – Meeting the Modern SAR Challenge

2018 ◽  
Author(s):  
J R E Wright ◽  
G E Payne
Keyword(s):  
Large Scale ◽  
High Capacity ◽  
Cost Effective ◽  
Second World War ◽  
Concept Design ◽  
Small Vessels ◽  
Large Numbers ◽  
Cost Effective Alternative ◽  
Operational Envelope ◽  
Second World

The Mediterranean migrant crisis has resulted in the highest population displacement since the Second World War. In 2016 alone, over one million made the journey across the sea. Since 2013 over 15,000 have died as a result of this journey. Small vessels such as wooden fishing boats and RIBs are commonly used by smugglers as transport. These are often unseaworthy and filled with numbers of passengers far exceeding their intended capacity. When failure occurs, rescues are typically conducted by the nearest available vessel. These vessels are often ill-equipped for a large-scale Search and Rescue (SAR) operation making it highly dangerous for all involved.  The size and quantity of lifeboats available are often insufficient for the large numbers of people to be rescued; as a result, repeat journeys are required, making the rescue process slow, inefficient and hazardous. This paper outlines a novel solution to this problem. A concept design is presented for a rapidly expandable lifeboat capable of holding large numbers of passengers, whilst still fitting into the operational envelope of common davits. The unique inflatable design can be deployed quickly from a range of vessels and aeroplanes offering an immediate platform from which disembarkation onto a suitable vessel can be achieved. CONOPS are outlined along with the required capabilities of the design. Drop stitch technology is identified as a viable means of manufacturing the large inflatable platforms. Finally, the paper discusses an alternative solution, retrofitting existing enclosed lifeboats with the solution to offer a more cost-effective alternative.  

Using Deficit Function to Determine the Minimum Fleet Size of an Autonomous Modular Public Transit System

2020 ◽  
Vol 2674 (11) ◽  
pp. 532-541 ◽  
Author(s):  
Tao Liu ◽  
Avishai (Avi) Ceder ◽  
Andreas Rau
Keyword(s):  
Autonomous Vehicles ◽  
Large Scale ◽  
Public Transit ◽  
Programming Model ◽  
Real Life ◽  
Single Line ◽  
Continuum Approximation ◽  
Transit System ◽  
Fleet Size ◽  
Minimum Number

Emerging technologies, such as connected and autonomous vehicles, electric vehicles, and information and communication, are surrounding us at an ever-increasing pace, which, together with the concept of shared mobility, have great potential to transform existing public transit (PT) systems into far more user-oriented, system-optimal, smart, and sustainable new PT systems with increased service connectivity, synchronization, and better, more satisfactory user experiences. This work analyses such a new PT system comprised of autonomous modular PT (AMPT) vehicles. In this analysis, one of the most challenging tasks is to accurately estimate the minimum number of vehicle modules, that is, its minimum fleet size (MFS), required to perform a set of scheduled services. The solution of the MFS problem of a single-line AMPT system is based on a graphical method, adapted from the deficit function (DF) theory. The traditional DF model has been extended to accommodate the definitions of an AMPT system. Some numerical examples are provided to illustrate the mathematical formulations. The limitations of traditional continuum approximation models and the equivalence between the extended DF model and an integer programming model are also provided. The extended DF model was applied, as a case study, to a single line of an AMPT system, the dynamic autonomous road transit (DART) system in Singapore. The results show that the extended DF model is effective in solving the MFS problem and has the potential to be applied to solving real-life MFS problems of large-scale, multi-line and multi-terminal AMPT systems.

scholarly journals MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology

2019 ◽  
Author(s):  
Zach Werkhoven ◽  
Christian Rohrsen ◽  
Chuan Qin ◽  
Björn Brembs ◽  
Benjamin de Bivort
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
High Throughput ◽  
Large Scale ◽  
Closed Loop ◽  
Optomotor Response ◽  
Behavioral Experiments ◽  
Loop Control ◽  
Flexible Control ◽  
Large Numbers

AbstractFast object tracking in real time allows convenient tracking of very large numbers of animals and closed-loop experiments that control stimuli for multiple animals in parallel. We developed MARGO, a real-time animal tracking suite for custom behavioral experiments. We demonstrated that MARGO can rapidly and accurately track large numbers of animals in parallel over very long timescales. We incorporated control of peripheral hardware, and implemented a flexible software architecture for defining new experimental routines. These features enable closed-loop delivery of stimuli to many individuals simultaneously. We highlight MARGO’s ability to coordinate tracking and hardware control with two custom behavioral assays (measuring phototaxis and optomotor response) and one optogenetic operant conditioning assay. There are currently several open source animal trackers. MARGO’s strengths are 1) robustness, 2) high throughput, 3) flexible control of hardware and 4) real-time closed-loop control of sensory and optogenetic stimuli, all of which are optimized for large-scale experimentation.

scholarly journals Shared Autonomous Taxi System and Utilization of Collected Travel-Time Information

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Zhiguang Liu ◽  
Tomio Miwa ◽  
Weiliang Zeng ◽  
Michael G. H. Bell ◽  
Takayuki Morikawa
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Traffic Information ◽  
Path Finding ◽  
Fleet Size ◽  
Real Time Traffic ◽  
Total Travel Time ◽  
The Mean ◽  
Time Information

Shared autonomous taxi systems (SATS) are being regarded as a promising means of improving travel flexibility. Each shared autonomous taxi (SAT) requires very precise traffic information to independently and accurately select its route. In this study, taxis were replaced with ride-sharing autonomous vehicles, and the potential benefits of utilizing collected travel-time information for path finding in the new taxi system examined. Specifically, four categories of available SATs for every taxi request were considered: currently empty, expected-empty, currently sharable, and expected-sharable. Two simulation scenarios—one based on historical traffic information and the other based on real-time traffic information—were developed to examine the performance of information use in a SATS. Interestingly, in the historical traffic information-based scenario, the mean travel time for taxi requests and private vehicle users decreased significantly in the first several simulation days and then remained stable as the number of simulation days increased. Conversely, in the real-time information-based scenario, the mean travel time was constant. As the SAT fleet size increased, the total travel time for taxi requests significantly decreased, and convergence occurred earlier in the historical information-based scenario. The results demonstrate that historical traffic information is better than real-time traffic information for path finding in SATS.

Dynamic real-time high-capacity ride-sharing model with subsequent information

2020 ◽  
Vol 14 (7) ◽  
pp. 742-752
Author(s):  
Yanglan Wang ◽  
Yi Zhang ◽  
Yi Zhang ◽  
Jiangshan Ma
Keyword(s):  
Real Time ◽  
High Capacity ◽  
Ride Sharing

scholarly journals Parking Line Based SLAM Approach Using AVM/LiDAR Sensor Fusion for Rapid and Accurate Loop Closing and Parking Space Detection

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4811 ◽  
Author(s):  
Gyubeom Im ◽  
Minsung Kim ◽  
Jaeheung Park
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Outdoor Environment ◽  
Parking Space ◽  
Parking Space Detection ◽  
Localization Performance ◽  
Line Features ◽  
Loop Closing

Parking is a challenging task for autonomous vehicles and requires a centimeter level precision of distance measurement for safe parking at a destination to avoid collisions with nearby vehicles. In order to avoid collisions with parked vehicles while parking, real-time localization performance should be maintained even when loop closing occurs. This study proposes a simultaneous localization and mapping (SLAM) method, using around view monitor (AVM)/light detection and ranging (LiDAR) sensor fusion, that provides rapid loop closing performance. We extract the parking line features by utilizing the sensor fusion data for sparse feature-based pose graph optimization that boosts the loop closing speed. Hence, the proposed method can perform the loop closing within a few milliseconds to compensate for the accumulative errors even in a large-scale outdoor environment, which is much faster than other LiDAR-based SLAM algorithms. Therefore, it easily satisfies real-time localization performance. Furthermore, thanks to the parking line features, the proposed method can detect a parking space by utilizing the accumulated parking lines in the map. The experiment was performed in three outdoor parking lots to validate the localization performance and parking space detection performance. All of the proposed methods can be operated in real-time in a single-CPU environment.

Computer Analyses in Preventive Health Research

1967 ◽  
Vol 06 (01) ◽  
pp. 8-14 ◽  
Author(s):  
M. F. Collen
Keyword(s):  
Medical Research ◽  
Preventive Medicine ◽  
Health Research ◽  
Large Scale ◽  
Preventive Health ◽  
New Era ◽  
Large Numbers ◽  
Normal Test ◽  
Computer Analyses

The utilization of an automated multitest laboratory as a data acquisition center and of a computer for trie data processing and analysis permits large scale preventive medical research previously not feasible. Normal test values are easily generated for the particular population studied. Long-term epidemiological research on large numbers of persons becomes practical. It is our belief that the advent of automation and computers has introduced a new era of preventive medicine.

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