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.
Enhanced travel time prediction using deep neural networks and road network information.
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 …
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…
TripDecoder recovers metro routes and travel times from smart card data.
ProbETA models travel time correlations between trips for better navigation.
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…
Deep learning improves trip prediction accuracy in transportation planning.
New method proves Jones Polynomial's connect sum property.
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 uses vehicle trajectory data to predict traffic incidents on highways.
This study is a detailed analysis of Speculation Game, a minimal agent-based model of financial markets, in which the round-trip trading and the dynamic wealth evolution with variable trading volumes are implemented. Instead of herding behavior, we find that the emergence of volatility clustering can be induced by the …
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.
Proposes AtCoR for predicting bike station usage, improving station network reconfiguration.
Study analyzes how discounts affect train ticket purchases and rescheduling in Switzerland.
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…
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.
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…
Paper proposes a risk index combining frequency and severity of abnormal driving patterns.
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.
New HDP-HMM model accurately segments Internet path delays.
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.
Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on pred…
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…
A new prior improves generative models' performance.
Ride sharing has important implications in terms of environmental, social and individual goals by reducing carbon footprints, fostering social interactions and economizing commuter costs. The ride sharing systems that are commonly available lack adaptive and scalable techniques that can simultaneously learn from the la…
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.
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…
Study develops a new model for predicting individual mobility based on activity patterns.
Study examines ridesourcing patterns in Chicago using K-prototypes segmentation.
Unified HS and related methods with explicit modeling assumptions.
Paper combines multiple ETA models into a stacked ensemble for better ETA predictions.
The link between different psychophysiological measures during emotion episodes is not well understood. To analyse the functional relationship between electroencephalography (EEG) and facial electromyography (EMG), we apply historical function-on-function regression models to EEG and EMG data that were simultaneously r…
ArtificialReplay improves data efficiency in bandits using historical data.
ADR helps LLMs find and use historical analogies for foresight analysis.