A Heuristic for Multi-modal Route Planning

Bucher, Dominik; Jonietz, David; Raubal, Martin

doi:10.1007/978-3-319-47289-8_11

Cited by 18 publications

(11 citation statements)

References 26 publications

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“…Route planning is a wide field for research [36]. Also, multi-modal route planning has been subject to a large number of publications [37,38]. Fortunately, there are good implementations around for multi-modal route planning and there exist routing providers with a public API.…”

Section: (Cross-border) Multi-modal Routingmentioning

confidence: 99%

Mobility Choices—An Instrument for Precise Automatized Travel Behavior Detection & Analysis

et al. 2021

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Within the Mobility Choices (MC) project we have developed an app that allows users to record their travel behavior and encourages them to try out new means of transportation that may better fit their preferences. Tracks explicitly released by the users are anonymized and can be analyzed by authorized institutions. For recorded tracks, the freely available app automatically determines the segments with their transportation mode; analyzes the track according to the criteria environment, health, costs, and time; and indicates alternative connections that better fit the criteria, which can individually be configured by the user. In the second step, the users can edit their tracks and release them for further analysis by authorized institutions. The system is complemented by a Web-based analysis program that helps authorized institutions carry out specific evaluations of traffic flows based on the released tracks of the app users. The automatic transportation mode detection of the system reaches an accuracy of 97%. This requires only minimal corrections by the user, which can easily be done directly in the app before releasing a track. All this enables significantly more accurate surveys of transport behavior than the usual time-consuming manual (non-automated) approaches, based on questionnaires.

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Section: (Cross-border) Multi-modal Routingmentioning

confidence: 99%

Mobility Choices—An Instrument for Precise Automatized Travel Behavior Detection & Analysis

et al. 2021

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“…Parkbob http://www.parkbob.com/), safety warning (Y. Li, Wang, and Zhang 2015), multimodal routing (Bucher, Jonietz, and Raubal 2017) have appeared. There are also studies using LBS to promote more healthy, greener (lower CO 2 emission), and more active mobility behaviours ).…”

Section: More Diverse Applicationsmentioning

confidence: 99%

Location based services: ongoing evolution and research agenda

Huang

Gärtner

Krisp

et al. 2018

Journal of Location Based Services

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249

132

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We are now living in a mobile information era, which is fundamentally changing science and society. Location Based Services (LBS), which deliver information depending on the location of the (mobile) device and user, play a key role in this mobile information era. This article first reviews the ongoing evolution and research trends of the scientific field of LBS in the past years. To motivate further LBS research and stimulate collective efforts, this article then presents a series of key research challenges that are essential to advance the development of LBS, setting a research agenda for LBS to 'positively' shape the future of our mobile information society. These research challenges cover issues related to the core of LBS development (e.g. positioning, modelling, and communication), evaluation, and analysis of LBS-generated data, as well as social, ethical, and behavioural issues that rise as LBS enter into people's daily lives.

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“…The central system operation is the following: (1) detect the 'environment state' via the detectors, (2) convert this state in to an integer string (e.g. 42403015), (3) determine an integer-labelled action based on LCS internal structure, (4) implement the action in the environment, (5) obtain feedback via the feedback sensor, (6) use feedback to adapt internal structure to model the environment, (7) re-start at (1). In order to operate in the above manner XCSI is put together from a number of components, governed by a central algorithm (see [8] for more detail).…”

Section: Applying Xcsi To Provision Of Recommendationsmentioning

confidence: 99%

“…Rule A corresponds to action 2 (recommend a shared taxi) and thus in this situation the recommended action supplied to the user (in the environment) is to book a shared taxi. 1…”

Section: Rule Matchingmentioning

confidence: 99%

“…Present-day 'live' services such as Citymapper (citymapper.com) suggest routes, times and modes, but do not consider passenger preferences. Research in this direction is limited, with recent work proposing Bayesian networks [4], heuristics and traditional routing [1] and 'case-based reasoning' [9,6], which provide customised recommendations without actual specification of passenger preferences. In previous work [8] we have applied the XCS variant [2] of LCS to study controllability [10] in Random Boolean Networks.…”

Section: Related Workmentioning

confidence: 99%

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Learning Condition–Action Rules for Personalised Journey Recommendations

Karlsen

Moschoyiannis

2018

Rules and Reasoning

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We apply a learning classifier system, XCSI, to the task of providing personalised suggestions for passenger onward journeys. Learning classifier systems combine evolutionary computation with rule-based machine learning, altering a population of rules to achieve a goal through interaction with the environment. Here XCSI interacts with a simulated environment of passengers travelling around the London Underground network, subject to disruption. We show that XCSI successfully learns individual passenger preferences and can be used to suggest personalised adjustments to the onward journey in the event of disruption.

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A Heuristic for Multi-modal Route Planning

Cited by 18 publications

References 26 publications

Mobility Choices—An Instrument for Precise Automatized Travel Behavior Detection & Analysis

Mobility Choices—An Instrument for Precise Automatized Travel Behavior Detection & Analysis

Location based services: ongoing evolution and research agenda

Learning Condition–Action Rules for Personalised Journey Recommendations

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