A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Polestar optimizes public transportation routes for efficiency and user satisfaction.
Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although these are usually planned well in advance, their impact is difficult to predict, even when organisers and transportation operators coordinat…
Two neural network models analyze bus system efficiency and demand.
Study uses neural networks to predict travel times for public transportation.
Increasing urban concentration raises operational challenges that can benefit from integrated monitoring and decision support. Such complex systems need to leverage the full stack of analytical methods, from state estimation using multi-sensor fusion for situational awareness, to prediction and computation of optimal r…
We extend martingale transport results to weak martingale transport.
Study improves dynamic PT fleet optimization under noisy demand predictions.
Public transport is one of the major forms of transportation in the world. This makes it vital to ensure that public transport is efficient. This research presents a novel real-time GPS bus transit data for over 500 routes of buses operating in New Delhi. The data can be used for modeling various timetable optimization…
Developing countries suffer from traffic congestion, poorly planned road/rail networks, and lack of access to public transportation facilities. This context results in an increase in fuel consumption, pollution level, monetary losses, massive delays, and less productivity. On the other hand, it has a negative impact on…
This article presents results from the first statistically significant study of cost escalation in transportation infrastructure projects. Based on a sample of 258 transportation infrastructure projects worth US$90 billion and representing different project types, geographical regions, and historical periods, it is fou…
Model predicts missing boarding stops in smart card data.
Transportation systems can be conceptualized as an instrument of spreading people and resources over the territory, playing an important role in developing sustainable cities. The current rationale of transport provision is based on population demand, disregarding land use and socioeconomic information. To meet the cha…
Within a broad class of generative adversarial networks, we show that discriminator optimization process increases a lower bound of the dual cost function for the Wasserstein distance between the target distribution and the generator distribution . It implies that the trained discriminator can approximate opti…
CAST predicts distribution-valued time series by stabilizing and transporting simplex-supported successors.
Transportation agencies have an opportunity to leverage increasingly-available trajectory datasets to improve their analyses and decision-making processes. However, this data is typically purchased from vendors, which means agencies must understand its potential benefits beforehand in order to properly assess its value…
This paper surveys differential privacy methods for transportation spatiotemporal data.
A sufficient knowledge of the demographics of a commuting public is essential in formulating and implementing more targeted transportation policies, as commuters exhibit different ways of traveling. With the advent of the Automated Fare Collection system (AFC), probing the travel patterns of commuters has become less i…
This article presents results from the first statistically significant study of causes of cost escalation in transport infrastructure projects. The study is based on a sample of 258 rail, bridge, tunnel and road projects worth US$90 billion. The focus is on the dependence of cost escalation on (1) length of project imp…
Transportation modes prediction is a fundamental task for decision making in smart cities and traffic management systems. Traffic policies designed based on trajectory mining can save money and time for authorities and the public. It may reduce the fuel consumption and commute time and moreover, may provide more pleasa…
Study improves cross-modal bike-share and transit demand prediction.
FairPOT balances fairness and AUC performance by selectively transforming risk scores.
The preponderance of connected devices provides unprecedented opportunities for fine-grained monitoring of the public infrastructure. However while classical models expect high quality application-specific data streams, the promise of the Internet of Things (IoT) is that of an abundance of disparate and noisy datasets …
This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$59 billion. The study shows with very high statistical significance that forecasters gener…
VisitHGNN predicts visit probabilities between neighborhoods and POIs using graph neural networks.
Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relativ…
Proposes FairDRL-ST for fair spatio-temporal mobility prediction.
Survey of robust clustering methods for hotspot detection.
Paper predicts in-situ metro passenger density using smart card data.
Accurate and reliable travel time predictions in public transport networks are essential for delivering an attractive service that is able to compete with other modes of transport in urban areas. The traditional application of this information, where arrival and departure predictions are displayed on digital boards, is…
Study examines ridesourcing patterns in Chicago using K-prototypes segmentation.
The objective of this work is to take advantage of deep neural networks in order to make next day crime count predictions in a fine-grain city partition. We make predictions using Chicago and Portland crime data, which is augmented with additional datasets covering weather, census data, and public transportation. The c…
Accurate expected time of arrival (ETA) information is crucial in maintaining the quality of service of public transit. Recent advances in artificial intelligence (AI) has led to more effective models for ETA estimation that rely heavily on a large GPS datasets. More importantly, these are mainly cabs based datasets wh…
We consider learning problems where the training set consists of two types of examples: private and public. The goal is to design a learning algorithm that satisfies differential privacy only with respect to the private examples. This setting interpolates between private learning (where all examples are private) and cl…
Study public-data assisted private stochastic optimization with labeled or unlabeled public data.
Public pretraining improves private model training even in extreme distribution shift scenarios.
Private estimation with public data reduces sample complexity.
Algorithm selects public datasets for private machine learning.
Developed Merton's model for public companies using observed liabilities.
Private distribution learning with public data, leveraging sample compression schemes.
Study uses AI to predict changes in international public finances based on US markets.
New methods estimate transport-growth pairs in unbalanced optimal transport.
Social networking sites such as Twitter have provided a great opportunity for organizations such as public libraries to disseminate information for public relations purposes. However, there is a need to analyze vast amounts of social media data. This study presents a computational approach to explore the content of twe…
Paper develops a method for valid inference using language model predictions from verbal autopsy narratives.
Predicts node sequences in graphs using multi-order network models.
Study private query release with public data, reducing sample sizes.
Introduces statistical optimal transport for probabilistic lectures.
Optimal DP model training with public data improves privacy and accuracy.
Study shows how optimal transport behaves in higher dimensions.