An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters' responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers' behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief-desire-intention agent architecture. (C) 2002 Elsevier Science Ltd. All rights reserved.

A dynamic sequential route choice model for micro-simulation

Dans les études sur le transport, les modèles de choix de route décrivent la sélection par un utilisateur d’un chemin, depuis son origine jusqu’à sa destination. Plus précisément, il s’agit de trouver dans un réseau composé d’arcs et de sommets la suite d’arcs reliant deux sommets, suivant des critères donnés. Nous considérons dans le présent travail l’application de la programmation dynamique pour représenter le processus de choix, en considérant le choix d’un chemin comme une séquence de choix d’arcs. De plus, nous mettons en œuvre les techniques d’approximation en programmation dynamique afin de représenter la connaissance imparfaite de l’état réseau, en particulier pour les arcs éloignés du point actuel. Plus précisément, à chaque fois qu’un utilisateur atteint une intersection, il considère l’utilité d’un certain nombre d’arcs futurs, puis une estimation est faite pour le restant du chemin jusqu’à la destination. Le modèle de choix de route est implanté dans le cadre d’un modèle de simulation de trafic par événements discrets. Le modèle ainsi construit est testé sur un modèle de réseau routier réel afin d’étudier sa performance.

Optimization of traffic signal controls within equilibrium route choice models : final report to Illinois Department of Transpotation/

Cover title.

Subregional route choice models with link travel times reflecting intersection flows : final report to Illinois Department of Transportation /

"Report no.: FHWA/IL/RC-008."--Documentation page.

European route choice determinants

Different charging zones are found within European airspace. This allows airlines to select different routes between origin and destination that have different lengths and en-route charges. There is a trade- off between the shortest available route and other routes that might have different charges. This paper analyses the routes submitted by airlines to be operated on a given day and compares the associated costs of operating those routes with the shortest available at the time, in terms of en-route charges and fuel consumption. The flights are characterised by different variables with the idea of identifying a behaviour or pattern based on the airline or flight characteristics. Results show that in some areas of the European airspace there might be an incentive to select a longer route, leading to both a lower charge and a lower total cost. However, more variables need to be considered and other techniques used, such as factor analysis, to be able to identify the behaviour within an airline category.

Transitoliikenteen lisäarvopalvelut

Tutkimuksen tarkoituksena on selvittää Suomen transito- eli kauttakulkuliikenteessä käytössä olevia lisäarvopalveluja ja niiden kehitysnäkymiä. Tutkimuksessa selvitetään myös Suomen transitoliikenteen nykytilaa ja tulevaisuutta. Suomen kauttakulkureitin ohella tarkastellaan muita tärkeimpiä Venäjän ulkomaankaupan kuljetusreittejä. Tutkimus jakautuu 1) kauttakulkuliikennettä koskevaan kirjallisuusselvitykseen ja 2) lisäarvopalveluja selvittävään haastattelututkimukseen, jonka yhteydessä haastateltiin Suomessa toimivia kansallisia ja kansainvälisiä transitoliikenteen toimijoita. Tutkimustulosten perusteella Suomi toimii erityisesti itään suuntautuvien arvotavaroiden kauttakulkureittinä. Transitokuljetusten yhteydessä tarvitaan erilaisia lisäarvopalveluja. Haastatteluissa saatiin selville, että tavaratoimituksille on tarjolla Suomessa yli 30 erilaista lisäarvopalvelua, jotka syntyvät yleensä asiakkaan erityistarpeista. Lisäarvopalvelut keskittyvät aineettomiin toimintoihin, kuljetuksiin, laadunhallintaan ja tavarankäsittelyyn. Eniten tarjottuja lisäarvopalveluja ovat räätälöity asiakaspalvelu, IT-palvelut, dokumentointi ja konsultointi. Lisäarvopalvelut eivät yleensä yksistään vaikuta kuljetusreitin valintaan, mutta yhdessä tehokkaiden, laadukkaiden ja turvallisten logistiikkapalvelujen kanssa lisäarvopalvelut muodostavat merkittävän kilpailutekijän Suomen transitoreitille.

On Ukrainian Gas Transit and South Stream. There may be more than meets the eye. CEPS Commentaries, 21 March 2014

The EU relies to a considerable degree on imports to meet its demand for natural gas. Whereas Norwegian export pipelines are directly connected to the EU gas system, a major share of Russian gas flows through the Ukrainian territory before reaching consumers located other consumers located down in the supply chain (e.g. Slovakia, Hungary or Italy). But is the Ukrainian gas transit route still a risk? Will the construction of the South Stream pipeline further reduce the importance of Ukraine as a transit country? Or is there more at stake here than meets the eye?

Human factors requirements for real-time motorist information displays. Volume 6: questionnaire survey of motorist route selection criteria. Final report.

Federal Highway Administration, Office of Research, Washington, D.C.

Spatial-temporal mining of archived automatic vehicle location (AVL) data for transit service planning applications

An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^

Advanced methodologies in dynamic traffic assignment modeling of managed lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. ^ Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. ^ Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. ^ With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.^

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.