The Impact of Predictive Information on Guidance Efficiency: An Analytical Approach

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

doi:10.1007/978-3-642-85256-5_19

Cited by 36 publications

(17 citation statements)

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“…3, April 2011, 199Á216 Theoretical and simulation-based analyses have generally confirmed the need for prediction-based guidance (cf. Ben-Akiva et al 1991, Ben-Akiva et al 1996. Furthermore, Balakrishna et al (2004) confirm some existing findings on overreaction while providing valuable insights into the role played by critical parameters that control simulation-based guidance generation systems.…”

Section: Introductionmentioning

confidence: 62%

Evaluation of diversion strategies using dynamic traffic assignment

Antoniou

Koutsopoulos

Ben‐Akiva

et al. 2011

Transportation Planning and Technology

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A framework for the evaluation of the effectiveness of traffic diversion strategies for non-recurrent congestion, based on predictive guidance and using dynamic traffic assignment, is presented. Predictive guidance is based on a short-term prediction of traffic conditions, incorporating user reaction to information and guidance. A case study of the Lower Westchester County network in New York State, using DynaMIT-P, is presented to illustrate the application of the framework. DynaMIT-P is capable of evaluating diversion strategies based on predicted conditions, which take into account drivers' response to traffic information. The case study simulates the operations of predictive variable message signs positioned in strategic locations. DynaMIT-P is calibrated for the study network and used to establish base conditions for two incident scenarios in the absence of advanced traveller information systems. The effectiveness of predictive diversion strategies is evaluated (using rigorous statistical tests) by comparing traffic conditions with and without diversion strategies. The empirical findings suggest that incident diversion strategies based on predictive guidance result in travel time savings and increased travel time reliability.

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Section: Introductionmentioning

confidence: 62%

Evaluation of diversion strategies using dynamic traffic assignment

Antoniou

Koutsopoulos

Ben‐Akiva

et al. 2011

Transportation Planning and Technology

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“…One of the drawbacks in generating anticipatory route guidance is that the system under consideration is affected by the dissemination of information itself, thus the predictions on which the guidance was based may be invalidated. This problem was introduced by [89]…”

Section: Route Guidance Systems For In-vehicle and Mobile Navigation mentioning

confidence: 99%

Dynamic transport mode estimation subject to joint decisions and spatio-temporal variations

Gkiotsalitis¹

2019

Preprint

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This thesis studies the dynamic route and transport mode optimization for participating in a jointly decided activity subject to spatio-temporal variations. Joint activity participants have to travel from their current locations to a common location which is the location of the joint activity. Apart from the recurrent joint activities (such as work, school etc.), there are several joint leisure activities where activity participants have to decide about the location and the starting time of the joint leisure activity along with the transport mode(s) that each one of them has to use for commuting from his/her current location to the joint leisure activity location.The objective of this thesis is the development of a comprehensive system for the optimization of joint leisure trips that are not related to work by optimizing (a) the location and the starting time of the joint leisure activity (b) the public transport operations (c) the transport mode(s) selection for each activity participant in order to arrive there as fast as possible while satisfying his/her personal trip preferences.Activities not related to work can be responsible for more than 60\% of trips at an urban environment. Non-working travel patterns differ from the more stable, recurrent travel patterns of work-related activities such as trips from/to work, school etc. and the three main differences of those activities are: (1) The location of a leisure activity can differ on a daily basis (it is not static like the location of the working or studying place); (2) The starting time of a leisure activity has greater elasticity and can differ on a daily basis (it is not stable such as the starting time of work, school etc.); (3) The alternative journey options to and from a leisure activity location are not well-known to the users (users are more aware of their journey alternatives when it comes to transfers from/to work-related activities since those activities are re-current) . Given that a significant number of transfers is related to leisure activities, the optimization of the (1) location selection, (2) starting activity time, (3) transport mode selection and (4) route selection are of paramount importance for both the commuters' total travel cost and the transport network performance. In addition, the prediction of non-recurrent activities in time and space can be an important step forward for the tactical and dynamic planning of transport networks since the volume and the non-recurrent nature of such activities lead to significant travel demand variations compared to the more stable, work-related activities. Due to the above, this thesis focuses on: (i) Understanding the State-of-the-Art (SoA) work on utilizing user-generated data for increasing the efficiency level of joint leisure activities and proposing actions towards this direction; (ii) Capturing users' willingness to travel certain distances for participating in different types of activities; (iii) Optimizing the selection of locations and starting times of joint leisure activities (iv) Re-scheduling the starting times of public transportation trips in order to adjust to the joint leisure activity demand without deteriorating the Quality of Service (QoS) for other passengers; (v) Optimizing the journey/path selection of users' who are willing to travel from one point of the network to another for participating at one activity and, possibly, utilize multiple modes while also satisfying their preferences.

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“…Ben-Akiva et al [5] models a reactive route guidance system and compares guidance quality with that of predictive systems. Both systems are modeled in a small sample network, using an analytical network model.…”

Section: Other Modelsmentioning

confidence: 99%

“…As stated in Chapter 1, the objective of the proposed route guidance system is to minimize individual vehicles' travel times; thus we consider travel cost to be equivalent to travel time. Using inter-vehicle communication, the decentralized algorithm can estimate the cost of the portion of the im- 5 Though the prescribed path may changed as guidance is refreshed by the centralized layer.…”

Section: Estimating the Cost Of Pathsmentioning

confidence: 99%