Modeling

Built Environment versus Personal Traits: an Application of Integrated Choice and Latent Variable Model (ICLV) in Understanding Modal Choice in Rome, Italy

Samira Ramezani
Barbara Pizzo
Elizabeth Deakin
2015

In spite of numerous applications of Integrated Choice and Latent Variable (ICLV) modelling in the field of transportation engineering, there is no published work applying it in studies of the effect of built environment on modal choice. This study uses an ICLV model to investigate the relative importance of built environment versus personal traits. It designs a quasiexperiment to elicit a better understanding of the causal effects of several urban form characteristics on modal choice. Findings suggest that built form can compete with the magnitude of the influence of personal traits, if...

Transferable Human Mobility Network Reconstruction with neuroGravity

Yang, Jinming
Huang, Shaoyu
Huang, Zongyuan
Jin, Yaohui
Cao, Xiaokang
Marta Gonzalez
2026

Accurate modeling of human mobility is critical for tackling urban planning and public health challenges. In undeveloped regions, the absence of comprehensive travel surveys necessitates reconstructing mobility networks from publicly available data. Here we develop neuroGravity, a physics-informed deep learning model that reliably reconstructs mobility flows from limited observations and transfers to unobserved cities. Using only urban facility and population distributions, we find that neuroGravity's regional representations strongly correlate with socioeconomic and livability status,...

Reducing Annotation Cost in Vision Language Pedestrian Re Identification via Uncertainty Driven Sampling

Anderson, Michael
Daniel Rodriguez
Chen, Yi
2026

Scaling pedestrian re-identification for autonomous driving is limited by the cost of identity labeling across large camera networks. Inspired by CLIP-based uncertainty modal modeling, this paper proposes an active learning approach that selects labeling candidates using uncertainty in the joint vision–language embedding space. The method combines (i) uncertainty sampling for ambiguous matches, (ii) diversity sampling based on embedding coverage, and (iii) batch acquisition with redundancy control. Experiments are conducted on a large-scale dataset with 400,000 images and 50,000 identities...

DeepTimeGeo: Trajectory Reconstruction From Sparse Data With Transformer

Cao, Shangqing
Wu, Jiaman
Kasliwal, Aparimit
Chen, Baoqi
Perona, Giuseppe
Marta Gonzalez
2026

The completion of sparse Location-Based Service (LBS) data for modeling urban-scale origin-destination (OD) flow is of great importance to transportation planning applications. Sparse trajectories lack realistic human mobility patterns. Only with completed trajectories one can derive urban-scale OD flow that resembles complete travel diaries as those gathered by surveys or actively collecting phone applications. We present DeepTimeGeo (DTG), a transformer encoder-only model that reconstructs complete trajectories from sparse LBS inputs. We adopt a rank-based representation of locations to...

Learning to Recommend in Unknown Games

Alanqary, Arwa
Baba, Zakaria
Wu, Manxi
Alexandre Bayen
2026

We study preference learning through recommendations in multi-agent game settings, where a moderator repeatedly interacts with agents whose utility functions are unknown. In each round, the moderator issues action recommendations and observes whether agents follow or deviate from them. We consider two canonical behavioral feedback models-best response and quantal response-and study how the information revealed by each model affects the learnability of agents' utilities. We show that under quantal-response feedback the game is learnable, up to a positive affine equivalence class, with...

On Stochastic Models of Traffic Assignment

Carlos Daganzo
Sheffi, Yosef
1977

This paper contains a quantitative evaluation of probabilistic traffic assignment models and proposes an alternate formulation. First, the concept of stochastic-user-equilibration (S-U-E) is formalized as an extension of Wardrop's user-equilibration criterion. Then, the stochastic-network-loading (S-N-L) problem (a special case of S-U-E for networks with constant link costs) is analyzed in detail and an expression for the probability of route choice which is based on two general postulates of user behavior is derived. The paper also discusses the weaknesses of existing S-N-L techniques...

Probabilistic Structure of Two-Lane Road Traffic

Carlos Daganzo
1975

In most predictive models for two-lane road traffic, it is assumed that platoons have no physical dimensions, thus restricting their applicability to light traffic where a platoon cannot be long enough to block the progression of the next one. In this paper a model that can be used for heavy traffic is presented. A queueing theory approach in which vehicles are allowed to have physical dimensions yields the platoon length distribution, the delays to fast vehicles, the headway process and the flow density diagram for both the space and time processes. Unlike in other models, the passing...

Multinomial Probit and Qualitative Choice: A Computationally Efficient Algorithm

Carlos Daganzo
Bouthelier, Fernando
Sheffi, Yosef
1977

Even though multinomial probit models have many attractive theoretical features and have been proposed for diverse choice problems (such as modal split and route choice in the transportation field), they have never been used in practice due to the lack of an adequate numerical technique for their application. The purpose of this paper is to introduce such a technique and to demonstrate the feasibility of forecasting with multinominal probit models. Our limited computational experience with the proposed numerical technique indicates that it is accurate, and can be efficiently applied to...

An Approximate Analytic Model of Many-to-Many Demand Responsive Transportation Systems

Carlos Daganzo
1978

This paper presents an analytic model to predict average waiting and ridingtimes in urban transportation systems (such as dial-a-bus and taxicabs), which provide non-transfer door-to-door transportation with a dynamically dispatched fleet of vehicles. Three different dispatching algorithms are analyzed with a simple deterministic model, which is then generalized to capture the most relevant stochastic phenomena. The formulae obtained have been successfully compared with simulated data and are simple enough for hand calculation. They are, thus, tools which enable analysts to avoid...

The Statistical Interpretation of Predictions with Disaggregate Demand Models

Carlos Daganzo
1979

This paper discusses an element of forecasting with disaggregate demand models that has received little attention so far; namely, the extent to which the accuracy of the final prediction depends on the accuracy of the calibration process. The paper introduces a numerical technique to evaluate approximate confidence intervals for the expected number of people using a transportation facility and approximate prediction intervals for the actual usage. It is shown that, unless the magnitude of the variance of the estimated parameters is considerably small, the predictions that result may be...