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.
STG2Seq predicts multi-step passenger demand with graph and hierarchical structure.
Recently, the online car-hailing service, Didi, has emerged as a leader in the sharing economy. Used by passengers and drivers extensive, it becomes increasingly important for the car-hailing service providers to minimize the waiting time of passengers and optimize the vehicle utilization, thus to improve the overall u…
Paper proposes a method for predicting future passenger flow in urban transportation.
Optimizes e-hailing drivers' passenger seeking to reduce congestion and pollution.
Study improves cross-modal bike-share and transit demand prediction.
Neural networks predict airport passenger behavior using WiFi traces.
Proposes a new model for clustering passenger trajectories with graphs.
Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of …
Proposes a new model for clustering passenger trips considering hierarchical and multi-dimensional data.
Paper predicts in-situ metro passenger density using smart card data.
India runs the fourth largest railway transport network size carrying over 8 billion passengers per year. However, the travel experience of passengers is frequently marked by delays, i.e., late arrival of trains at stations, causing inconvenience. In a first, we study the systemic delays in train arrivals using n-order…
Passenger Name Records (PNRs) are at the heart of the travel industry. Created when an itinerary is booked, they contain travel and passenger information. It is usual for airlines and other actors in the industry to inter-exchange and access each other's PNR, creating the challenge of using them without infringing data…
Model predicts passenger origin-destination for online taxi-hailing systems.
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…
Trans-Sense uses smartphones to predict public transit wait times and schedules.
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…
New method improves tensor completion for weakly-dependent spatiotemporal data.
Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their travel route, departure time and travel origin selection, which can b…
Develops a method to predict system behavior under disruptions.
A new method improves ridesharing efficiency using QMIX.
This research optimizes plate structures to reduce vibrations in vehicles and aircraft.
Travel providers such as airlines and on-line travel agents are becoming more and more interested in understanding how passengers choose among alternative itineraries when searching for flights. This knowledge helps them better display and adapt their offer, taking into account market conditions and customer needs. Som…
ATFM predicts traffic flow using attention mechanism and neural networks.
Bayesian framework predicts post-disruption travel times in metro networks.
Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how to best do this: optimal dispatching requires optimizing over several sources of unc…
Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation. We introduce simple closed form kernels that can be used with Gaussian processes to discover patterns and enable extrapolation. These kernels are derived by modelling a spectral dens…
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…
Demand variance can result in a mismatch between planned supply and actual demand. Demand shaping strategies such as pricing can be used to shift elastic demand to reduce the imbalance. In this work, we propose to consider elastic demand in the forecasting phase. We present a method to reallocate the historical elastic…
Modeling shared mobility demand considering supply limitations.
This paper optimizes driver repositioning using MARL and reward design for better service and traffic management.
The paper proposes a new model to better estimate demand from censored data.
Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the general demand foreca…
We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risk-sensitive. In particular, we model risk-sensitivity in a reinforcement learning framework by making use of models of human decision-making having their origins in behavioral psychology, behavioral economics, an…
A new metric optimizes forecasts for lumpy, intermittent demand.
Paper develops deep models for forecasting intermittent demand.
Statistical arbitrageurs have inelastic demand, contrary to classical models.
Implementing a set of microeconomic criteria, we develop price dynamics equations using a function of demand/supply with key symmetry properties. The function of demand/supply can be linear or nonlinear. The type of function determines the nature of the tail of the distribution based on the randomness in the supply and…
Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so as to maximize their values. Users' demands, implying strong purchase intents, can be the most useful way to promote products sales if well …
Novel probabilistic models forecast residential heating and electricity demand at hourly resolution.
Two neural network models analyze bus system efficiency and demand.
Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.
Proposes a compensation mechanism for improving individual forecast confidence.
We consider a firm that sells products over periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. A natural heuristic for this problem, commonly used in practice, is greedy iterative least squares (GILS). At each ti…
Study on inventory control with changing demand, proposing adaptive algorithms.
Model predicts trading strategies based on latent demand and price impact.
Paper optimizes demand aggregation for low-level electricity markets.