Dynamic network models and driver information systems

Ben‐Akiva, Moshe; Palma, André de; Kaysi, Isam

doi:10.1016/0191-2607(91)90142-d

Cited by 432 publications

(197 citation statements)

References 9 publications

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“…An example of dynamic driver behaviour modelling framework is shown in Fig. 1 (Ben-Akiva et al, 1991). Drivers set out with goals to travel between an origin and destination within a given period of time while incurring the lowest possible cost in terms of travel distance or travel time.…”

Section: Dynamic Driver Behaviourmentioning

confidence: 99%

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

Dia¹

2002

Transportation Research Part C: Emerging Technologies

281

109

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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. CommutersÕ responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing driversÕ 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.

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Section: Dynamic Driver Behaviourmentioning

confidence: 99%

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

Dia¹

2002

Transportation Research Part C: Emerging Technologies

281

109

View full text Add to dashboard Cite

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“…Moreover, travel time is easily perceived by the road users so the provision of travel time information may have a great impact on the route choice of travellers (Mahmassani and Liu, 1999). As is turns out, the provision of travel time information may reduce traffic congestion significantly and improve the performance of the whole network system (Ben-Akiva et al, 1991). Because of its critical role in traffic monitoring, extensive research has been conducted on the estimation and prediction of travel times on both freeways and urban roadways (Bhaskar et al, 2011, Coifman, 2002, Coifman and Krishnamurthy, 2007, Du et al, 2012, Ndoye et al, 2011, Sun et al, 2008, van Lint et al, 2005, van Lint and van der Zijpp, 2003.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic travel time progression and its application to automatic vehicle identification data

Nantes

Ngoduy

Miska

et al. 2015

Transportation Research Part B: Methodological

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Travel time has been identified as an important variable to evaluate the performance of a transportation system. Based on the travel time prediction, road users can make their optimal decision in choosing route and departure time. In order to utilize adequately the advanced data collection methods that provide realtime different types of information, this paper is aimed at a novel approach to the estimation of long roadway travel times, using Automatic Vehicle Identification (AVI) technology. Since the long roads contain a large number of scanners, the AVI sample size tends to reduce and, as such, computing the distribution for the total road travel time becomes difficult. In this work, we introduce a probabilistic framework that extends the deterministic travel time progression method to dependent random variables and enables the off-line estimation of road travel time distributions. In the proposed method, the accuracy of the estimation does not depend on the size of the sample over the entire corridor, but only on the amount of historical data that is available for each link. In practice, the system is also robust to small link samples and can be used to detect outliers within the AVI data.

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“…Any congestion game with utility functions as in (8) is a potential game [6]. 8 A congestion game can be generalized further by allowing player utilities to include player specific attributes [31]. For example, each player may have a personal preference over resources, in which case player utilities take the form where is the fixed utility player receives for using resource .…”

Section: A Congestion Gamesmentioning

confidence: 99%

“…It turns out that both JSFP and fading memory JSFP are strongly connected to actual driver behavioral models. Consider the driver adjustment process considered in [8] which is illustrated in Fig. 4.…”

Section: B Distributed Traffic Routingmentioning

confidence: 99%

“…In this setting, observing and responding to the individual actions of all vehicles on a daily basis would be a formidable task for any individual driver. A more realistic measurement on the information tracked and processed by an individual driver is the daily aggregate congestion on the roads that are of interest to that driver [8]. It turns out that JSFP accommodates such information aggregation.…”

Section: Introductionmentioning

confidence: 99%

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Joint Strategy Fictitious Play with Inertia for Potential Games

Marden

Arslan

Shamma

Proceedings of the 44th IEEE Conference on Decision and Control

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Abstract-We consider multi-player repeated games involving a large number of players with large strategy spaces and enmeshed utility structures. In these "large-scale" games, players are inherently faced with limitations in both their observational and computational capabilities. Accordingly, players in large-scale games need to make their decisions using algorithms that accommodate limitations in information gathering and processing. This disqualifies some of the well known decision making models such as "Fictitious Play" (FP), in which each player must monitor the individual actions of every other player and must optimize over a high dimensional probability space. We will show that Joint Strategy Fictitious Play (JSFP), a close variant of FP, alleviates both the informational and computational burden of FP. Furthermore, we introduce JSFP with inertia, i.e., a probabilistic reluctance to change strategies, and establish the convergence to a pure Nash equilibrium in all generalized ordinal potential games in both cases of averaged or exponentially discounted historical data. We illustrate JSFP with inertia on the specific class of congestion games, a subset of generalized ordinal potential games. In particular, we illustrate the main results on a distributed traffic routing problem and derive tolling procedures that can lead to optimized total traffic congestion. Index Terms-Fictitious play (FP), joint strategy fictitious play (JSFP).

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Dynamic network models and driver information systems

Cited by 432 publications

References 9 publications

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

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

Probabilistic travel time progression and its application to automatic vehicle identification data

Joint Strategy Fictitious Play with Inertia for Potential Games

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