A Federated Platform Enabling a Systematic Collaboration Among Devices, Data and Functions for Smart Mobility

Through the vast adoption and application of emerging technologies, the intelligence and autonomy of smart mobility can be substantially elevated to address more diversified demands and supplies. Along with this trend, a systematic collaboration among three essential elements of smart mobility services, namely devices, data and functions, is...

Choice-driven dial-a-ride problem for demand responsive mobility service

Urban mobility services face the challenge of planning their operations efficiently while complying with user preferences. In this paper, we introduce a new mathematical model called a choice-driven dial-a-ride problem (CD-DARP) which is a generalization of the dynamic DARP where passenger behavior is integrated in the operational planning...

SMART mobility via prediction, optimization and personalization

In this chapter, we present a methodological approach for Smart Mobility that integrates three key features: prediction, optimization, and personalization. They are integrated in such a way that when a travel menu is offered, predicted conditions are considered in the attributes of alternatives and optimized system-level policies are maintained....

Intercity truck route choices incorporating toll road alternatives using enhanced GPS data

This research presents the data collection, specification and estimation of a route choice model for intercity truck trips, with a focus on toll road usage. The data was obtained from driver-validated and enhanced GPS records. A mixed logit model with a path-size factor is specified. It accounts for heterogeneity among drivers using...

Real-time predictive control strategy optimization

Urban traffic congestion has led to an increasing emphasis on management measures for more efficient utilization of existing infrastructure. In this context, this paper proposes a novel framework that integrates real-time optimization of control strategies (tolls, ramp metering rates, etc.) with the generation of traffic guidance information...

Endogeneity in adaptive choice contexts: Choice-based recommender systems and adaptive stated preferences surveys

Endogeneity arises in discrete choice models due to several factors and results in inconsistent estimates of the model parameters. In adaptive choice contexts such as choice-based recommender systems and adaptive stated preferences (ASP) surveys, endogeneity is expected because the attributes presented to an individual in a specific menu (or...

Sequential personalized menu optimization through bandit learning

Logit mixture with inter and intra-consumer heterogeneity and flexible mixing distributions

Logit mixture models have gained increasing interest among researchers and practitioners because of their ability to capture unobserved taste heterogeneity. Becker et al. (2018) proposed a Hierarchical Bayes (HB) estimator for logit mixtures with inter- and intra-consumer heterogeneity (defined as taste variations among different individuals...

Context-aware stated preferences with smartphone-based travel surveys

Stated preferences surveys are most commonly used to provide behavioral insights on hypothetical travel scenarios such as new transportation services or attribute ranges beyond those observed in existing conditions. When designing SP surveys, considerable care is needed to balance the statistical objectives with the realism of the experiment....

Online discrete choice models: Applications in personalized recommendations

This paper presents a framework for estimating and updating user preferences in the context of app-based recommender systems. We specifically consider recommender systems which provide personalized menus of options to users. A Hierarchical Bayes procedure is applied in order to account for inter- and intra-consumer heterogeneity, representing...

Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives

This paper presents a systematic way of understanding and modeling traveler behavior in response to on-demand mobility services. We explicitly consider the sequential and yet inter-connected decision-making stages specific to on-demand service usage. The framework includes a hybrid choice model for service subscription, and three logit mixture...

Dynamic toll pricing using Dynamic Traffic Assignment system with online calibration

The paper presents a toll pricing methodology using a dynamic traffic assignment (DTA) system. This methodology relies on the DTA system’s capability to understand and predict traffic conditions, thus enhanced online calibration methodologies are applied to the DTA system, featuring a heuristic technique to calibrate supply parameters online....

Bayesian estimator for Logit Mixtures with inter- and intra-consumer heterogeneity

Estimating discrete choice models on panel data allows for the estimation of preference heterogeneity in the sample. While the Logit Mixture model with random parameters is mostly used to account for variation across individuals, preferences may also vary across different choice situations of the same individual. Up to this point, Logit...