Browse ORBilu by Open Access
- What it is and what it isn't
- Green Road / Gold Road?
- Ready to Publish. Now What?
- How can I support the OA movement?
- Where can I learn more?
ORBilu is a project developed by
   A Markov Chain Monte Carlo Approach for Estimating Daily Activity PatternsScheffer, Ariane Hélène Marie  ; Bandiera, Claudia ; et alScientific Conference (2021, February) Detailed reference viewed: 32 (4 UL) A Big Data Demand Estimation Model for Urban Congested Networks; Viti, Francesco in Transport and Telecommunication (2020) Detailed reference viewed: 71 (7 UL)Incorporating trip chaining within online demand estimation; ; et al in Transportation Research. Part B, Methodological (2020), 132 Detailed reference viewed: 59 (3 UL)Inferring Urban Mobility and Habits from User Location History; Vitello, Piergiorgio ; et alin Transportation Research Procedia (2020, January), 47 Detailed reference viewed: 92 (11 UL)Inferring Urban Mobility and Habits from User Location History; Vitello, Piergiorgio ; et alin Transportation Research Procedia (2020, January), 47 Detailed reference viewed: 92 (11 UL)Inferring Urban Mobility and Habits from User Location History; Vitello, Piergiorgio ; et alin Transportation Research Procedia (2020, January), 47 Detailed reference viewed: 92 (11 UL)Inferring Urban Mobility and Habits from User Location History; Vitello, Piergiorgio ; et alin Transportation Research Procedia (2020, January), 47 Detailed reference viewed: 92 (11 UL)Leveraging GIS Data and Topological Information to Infer Trip Chaining Behaviour at Macroscopic Level; ; et al Scientific Conference (2019, June) One of the open challenges in transport modelling is to estimate within-day demand flows that reflect the complexity of individual activity-travel behaviour. While disaggregate (Activity-Based) demand ... [more ▼] One of the open challenges in transport modelling is to estimate within-day demand flows that reflect the complexity of individual activity-travel behaviour. While disaggregate (Activity-Based) demand models can recreate realistic daily mobility patterns at an individual level, they usually require an accurate knowledge of individual user behaviour (i.e. via travel surveys), which is not always available. As a result, practitioners often turn to aggregate demand models, that have the advantage of being less demanding in terms of data but typically under represent the demand for secondary activities. In this work, we take research on within-day demand modelling one step forward by proposing a framework that combines traditional methodologies with heterogeneous data sources in order to explicitly represent trip chaining at an aggregated level. We show that the combination of web-based crowd sensed data, network data and behavioural constraints allows to capture complex spatial and temporal correlations between demand patterns. The methodology is applied on the classical Gravity model to show how to incorporate within-day dynamics. Yet, any alternative demand model can be adopted. In our case, Generation and Attraction are used to estimate the systematic demand, that is enriched of information about individual activity patterns, and then a novel definition of impedance function based on Hagestraand ellipse theory plays a central role in spatially distributing locations of trips using geographic relationships and constraints deriving from space-time behaviour. A case study for Luxembourg City has been presented to show the potential of the methodology: the choice of using data from a different spatial context to account for the temporal dimension has been validated through comparisons with official statistics. The results of simulating a workplace relocation show the advantages of this new approach in representing demand related to secondary activities. [less ▲] Detailed reference viewed: 124 (8 UL)Incorporating trip chaining within online demand estimation; ; et al in Transportation Research. Part B, Methodological (2019) Time-dependent Origin–Destination (OD) demand flows are fundamental inputs for Dy- namic Traffic Assignment (DTA) systems and real-time traffic management. This work in- troduces a novel state-space ... [more ▼] Time-dependent Origin–Destination (OD) demand flows are fundamental inputs for Dy- namic Traffic Assignment (DTA) systems and real-time traffic management. This work in- troduces a novel state-space framework to estimate these demand flows in an online con- text. Specifically, we propose to explicitly include trip-chaining behavior within the state- space formulation, which is solved using the well-established Kalman Filtering technique. While existing works already consider structural information and recursive behavior within the online demand estimation problem, this information has been always considered at the OD level. In this study, we introduce this structural information by explicitly representing trip-chaining within the estimation framework. The advantage is twofold. First, all trips belonging to the same tour can be jointly calibrated. Second, given the estimation during a certain time interval, a prediction of the structural deviation over the whole day can be obtained without the need to run additional simulations. The effectiveness of the proposed methodology is demonstrated first on a toy network and then on a large real-world net- work. Results show that the model improves the prediction performance with respect to a conventional Kalman Filtering approach. We also show that, on the basis of the estimation of the morning commute, the model can be used to predict the evening commute without need of running additional simulations. [less ▲] Detailed reference viewed: 95 (2 UL)A Markov Chain Monte Carlo Approach for Estimating Daily Activity PatternsScheffer, Ariane Hélène Marie ; ; et alPoster (2019, January) Determining the purpose of trips brings is a fundamental information to evaluate travel demand during the day and to predict longer-term impacts on the population’s travel behavior. The concept of tours ... [more ▼] Determining the purpose of trips brings is a fundamental information to evaluate travel demand during the day and to predict longer-term impacts on the population’s travel behavior. The concept of tours is the most suited to consider the value of a daily scheduling of individuals and travel interdependencies. However, the meticulous care required for both collecting data of high quality and interpret results of advanced demand models are frequently considered as major drawbacks. The objective of this study is to incorporate into a standard trip-based model some inherent concepts of activity-based models in order to enhance the representation of travel behavior. The main focus of this work is to infer, employing utility theory, the trip purpose of a population, at a zonal level. Making use of Markov Chain Monte Carlo, a set of parameters is estimated in order to retrieve tour-based primitives of the demand. The main advantage of this methodology is the low requirements in terms of data, as no individual information are used, and the good interpretation of the model. Estimated parameters of the priors set a utility-based probability function for departure time, which allows to have a dynamic overview of the demand. In order to account for the tour consistency of travel decisions, a duration constraint is added to the model. The proposed model is applied to the region of Luxembourg city and the results show the potential of the methodologies for dividing an observed demand based on the activity at destination. [less ▲] Detailed reference viewed: 152 (15 UL)Incorporating trip chaining within online demand estimation; ; et al in Transportation Research Procedia (2019), 38 Time-dependent Origin–Destination (OD) demand flows are fundamental inputs for Dynamic Traffic Assignment (DTA) systems and real-time traffic management. This work introduces a novel state-space framework ... [more ▼] Time-dependent Origin–Destination (OD) demand flows are fundamental inputs for Dynamic Traffic Assignment (DTA) systems and real-time traffic management. This work introduces a novel state-space framework to estimate these demand flows in an online context. Specifically, we propose to explicitly include trip-chaining behavior within the state-space formulation, which is solved using the well-established Kalman Filtering technique. While existing works already consider structural information and recursive behavior within the online demand estimation problem, this information has been always considered at the OD level. In this study, we introduce this structural information by explicitly representing trip-chaining within the estimation framework. The advantage is twofold. First, all trips belonging to the same tour can be jointly calibrated. Second, given the estimation during a certain time interval, a prediction of the structural deviation over the whole day can be obtained without the need to run additional simulations. The effectiveness of the proposed methodology is demonstrated first on a toy network and then on a large real-world network. Results show that the model improves the prediction performance with respect to a conventional Kalman Filtering approach. We also show that, on the basis of the estimation of the morning commute, the model can be used to predict the evening commute without need of running additional simulations. [less ▲] Detailed reference viewed: 130 (3 UL) |
||
