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.
Deep learning improves trip prediction accuracy in transportation planning.
Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majo…
Trip recommendation is an important location-based service that helps relieve users from the time and efforts for trip planning. It aims to recommend a sequence of places of interest (POIs) for a user to visit that maximizes the user's satisfaction. When adding a POI to a recommended trip, it is essential to understand…
The system recommends hotels based on user preferences.
ProbETA models travel time correlations between trips for better navigation.
New method proves Jones Polynomial's connect sum property.
The paper predicts travel times using tree-based ensembles.
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…
In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temp…
Model predicts next destination for users based on past trips and features.
Real-time estimation of destination and travel time for taxis is of great importance for existing electronic dispatch systems. We present an approach based on trip matching and ensemble learning, in which we leverage the patterns observed in a dataset of roughly 1.7 million taxi journeys to predict the corresponding fi…
Study shows how wealth distribution leads to volatility clustering in speculative markets.
Proposes a new model for clustering passenger trips considering hierarchical and multi-dimensional data.
The paper applies thermodynamics to financial markets to prove no-arbitrage constraints.
Study analyzes how discounts affect train ticket purchases and rescheduling in Switzerland.
In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [23], this paper investigates the use…
Proposes a new model for clustering passenger trajectories with graphs.
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…
In this study, we present a machine learning approach to infer the worker and student mobility flows on daily basis from static censuses. The rapid urbanization has made the estimation of the human mobility flows a critical task for transportation and urban planners. The primary objective of this paper is to complete i…
Enhanced travel time prediction using deep neural networks and road network information.
TripDecoder recovers metro routes and travel times from smart card data.
Paper proposes a risk index combining frequency and severity of abnormal driving patterns.
New model predicts chemical reactions without human input.
The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model. A bi-level optimisation problem is formulated and solved to estimate OD flows from pre-existent demand matrix and historical traffic flow counts. The estimated demand is then …
TRIP detects unreliable feature importance scores in random forests.
Automated scoring prioritizes risky driving behavior in telematic auto insurance policies.
New system assigns vehicles to routes for cost and energy efficiency.
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection…
Method estimates travel times on urban roads using Uber data.
If the group of a 2-knot group has an abelian normal subgroup of rank which is not finitely generated then either has no minimal Seifert hypersurface or is topologically equivalent to Example 10 of Ralph Fox's``{\it A quick trip through knot theory}".
Advances in sensor technology have enabled the collection of large-scale datasets. Such datasets can be extremely noisy and often contain a significant amount of outliers that result from sensor malfunction or human operation faults. In order to utilize such data for real-world applications, it is critical to detect ou…
STAD improves travel time estimation by learning from real traffic data.
In this paper we propose a new method to predict the final destination of vehicle trips based on their initial partial trajectories. We first review how we obtained clustering of trajectories that describes user behaviour. Then, we explain how we model main traffic flow patterns by a mixture of 2d Gaussian distribution…
This paper evaluates various bus arrival time prediction models.
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…
A new prior improves generative models' performance.
A new metric based on hitting probabilities for directed graphs and Markov chains.
We describe our first-place solution to the Animal Behavior Challenge (ABC 2018) on predicting gender of bird from its GPS trajectory. The task consisted in predicting the gender of shearwater based on how they navigate themselves across a big ocean. The trajectories are collected from GPS loggers attached on shearwate…
In this article the Lorenz dynamical system is revived and revisited and the current state of the art results for one step ahead forecasting for the Lorenz trajectories are published. Multitask learning is shown to help learning the hard to learn z trajectory. The article is a reflection upon the evolution of neural ne…
Study improves dynamic PT fleet optimization under noisy demand predictions.
A new neural approach for generating origin-destination matrices in ABMs.
Improves RL planning by proposing sub-goals hierarchically.
New method designs fairer transport plans with uncertainty.
We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then trans…
Study integrates reliability constraints into generation planning models.
Study develops a new model for predicting individual mobility based on activity patterns.
New approach improves black-box planning efficiency by discovering focused macros.
Study motion planning for points avoiding obstacles in a plane.