Adaptive Route Choice Model for Intelligent Route Guidance Using a Rule-Based Approach

Park, Kyounga; Bell, M.G.; Kaparias, Ioannis; Bogenberger, Klaus

doi:10.3141/2000-11

Cited by 9 publications

(9 citation statements)

References 14 publications

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“…Further research is currently being undertaken on the topic of learning user preferences so as to incorporate these in the approach and so devise a personalised reliable dynamic route guidance strategy [26,27].…”

Section: Methodsmentioning

confidence: 99%

ICNavS: a tool for reliable dynamic route guidance

Kaparias

Bell

Chen

et al. 2007

IET Intell. Transp. Syst.

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe aim of this paper is to devise a new reliable dynamic route guidance approach by integrating the A* algorithm, the concept of reliability and an existing route guidance method into a single package.A new purpose-developed software tool, the Imperial College Navigation Software (ICNavS), is presented, so as to implement and demonstrate the new approach on a real road network, using simulated data. A summary of the background of the program is given, followed by a procedure developed in order to model the features of real road networks, as well as missing data. Then, a simulation experiment on a part of West London's road network is carried out and the results are presented.

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Section: Methodsmentioning

confidence: 99%

ICNavS: a tool for reliable dynamic route guidance

Kaparias

Bell

Chen

et al. 2007

IET Intell. Transp. Syst.

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“…A relatively large body of work deals with personalization of routing apps (2,8,(9)(10)(11)(12). However, these papers have in common that their models and algorithms use data that are collected during the running of the service to learn the user's behavior.…”

Section: Previous Work Includes a Methodology For Stages 2 And 3 (1)mentioning

confidence: 99%

Personalization of Routing Services: User Classification as a Method to Overcome the Cold Start Problem for Personalized Models

Rudloff

Leodolter

2017

Transportation Research Record: Journal of the Transportation R

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Mobile technologies are improving constantly, and there are plenty of online routing services around. Although users are willing to try new services and apps, it is hard to retain them as users unless the service meets their expectations straightaway. In multimodal routing, using a generic model might not give appropriate routes to both an environmentally conscious traveler who uses mostly public transport and a car enthusiast. Following a trend in online retailing, in which personalized services are already the norm, research in the personalization of online routing services has shown promising results. However, because users tend to use a new service only a few times if the results are not satisfying right from the start, the initialization of the personalized model is of core importance. The authors propose a methodology that is based on a small initial data collection within the service and combined with two models that classify users into classes, first a latent class model and second a mixed logit approach with a prior clustering step. The authors tested the modeling approaches for their performance in the initialization stage and found that the classification approach can improve the correct detection of users’ chosen routes compared with state-of-the-art methodologies by several percentage points. Furthermore, user reactions to personalized routes included in a routing application were collected. The results show that the personalization of routes is well received by the users.

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“…Other works use artificial intelligence approaches to develop an ATIS capable of some adaptability. For instance, Park et al [30] present an adaptive route choice system using a rule-based decision-tree approach to incorporate an adaptation process into a user's route selection rule. However, this work does not take into account multimodality.…”

Section: B Related Work and Paper Positioningmentioning

confidence: 99%

A Multi-Agent Advanced Traveler Information System for Optimal Trip Planning in a Co-Modal Framework

Dotoli

Zgaya

Russo³

et al. 2017

IEEE Trans. Intell. Transport. Syst.

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We present an advanced traveler information system (ATIS) for public and private transportation, including vehicle sharing and pooling services. The ATIS uses an agentbased architecture and multi-objective optimization to answer trip planning requests from multiple users in a co-modal setting, considering vehicle preferences and conflicting criteria. At each set of users' requests, the transportation network is represented by a co-modal graph that allows decomposing the trip planning problem into smaller tasks: the shortest routes between the network nodes are determined and then combined to obtain possible itineraries. Using multi-objective optimization, the set of uservehicle-route combinations according to the users' preferences is determined, ranking all possible route agents' coalitions. The ATIS is tested for the real case study of the Lille metropolitan area (Nord Pas de Calais, France).

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Adaptive Route Choice Model for Intelligent Route Guidance Using a Rule-Based Approach

Cited by 9 publications

References 14 publications

ICNavS: a tool for reliable dynamic route guidance

ICNavS: a tool for reliable dynamic route guidance

Personalization of Routing Services: User Classification as a Method to Overcome the Cold Start Problem for Personalized Models

A Multi-Agent Advanced Traveler Information System for Optimal Trip Planning in a Co-Modal Framework

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