Personalised optimal speed advice to cyclists approaching an intersection with uncertain green time

Azita Dabiri; Andreas Hegyi

doi:10.23919/ECC.2018.8550416

Personalised optimal speed advice to cyclists approaching an intersection with uncertain green time

Transport and Planning

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

6 Citations (Scopus)

Abstract

When travelling in urban areas with signalised intersections, cyclists are currently unable to optimize their speed profile according to their preferences. By integrating information from traffic signal phase and timing and cyclists' preferences, in this paper an algorithm is developed that generates personalised optimal speed advice for cyclists. Results from the simulation experiment indicate that in spite of stochastic nature of traffic light phase and timing, with the developed algorithm based on stochastic dynamic programming, an speed profile can be advised that meets the cyclist's preferences in terms of travel time, energy consumption, or the number of stops.

Original language	English
Title of host publication	2018 European Control Conference, ECC 2018
Editors	Marios Polycarpou, Thomas Parisini, Christos Panayiotou, Christoforos Hadjicostis
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1666-1671
Number of pages	6
ISBN (Electronic)	978-3-9524-2698-2
DOIs	https://doi.org/10.23919/ECC.2018.8550416
Publication status	Published - 2018
Event	16th European Control Conference, ECC 2018 - Limassol, Cyprus Duration: 12 Jun 2018 → 15 Jun 2018 http://www.ecc18.eu/

Conference

Conference	16th European Control Conference, ECC 2018
Abbreviated title	ECC 2018
Country/Territory	Cyprus
City	Limassol
Period	12/06/18 → 15/06/18
Internet address	http://www.ecc18.eu/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.23919/ECC.2018.8550416

Cite this

Dabiri, A., & Hegyi, A. (2018). Personalised optimal speed advice to cyclists approaching an intersection with uncertain green time. In M. Polycarpou, T. Parisini, C. Panayiotou, & C. Hadjicostis (Eds.), 2018 European Control Conference, ECC 2018 (pp. 1666-1671). Article 8550416 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.23919/ECC.2018.8550416

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title = "Personalised optimal speed advice to cyclists approaching an intersection with uncertain green time",

abstract = "When travelling in urban areas with signalised intersections, cyclists are currently unable to optimize their speed profile according to their preferences. By integrating information from traffic signal phase and timing and cyclists' preferences, in this paper an algorithm is developed that generates personalised optimal speed advice for cyclists. Results from the simulation experiment indicate that in spite of stochastic nature of traffic light phase and timing, with the developed algorithm based on stochastic dynamic programming, an speed profile can be advised that meets the cyclist's preferences in terms of travel time, energy consumption, or the number of stops.",

author = "Azita Dabiri and Andreas Hegyi",

year = "2018",

doi = "10.23919/ECC.2018.8550416",

language = "English",

pages = "1666--1671",

editor = "Polycarpou, {Marios } and { Parisini}, Thomas and Panayiotou, {Christos } and Hadjicostis, {Christoforos }",

booktitle = "2018 European Control Conference, ECC 2018",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "16th European Control Conference, ECC 2018, ECC 2018 ; Conference date: 12-06-2018 Through 15-06-2018",

url = "http://www.ecc18.eu/",

}

Dabiri, A & Hegyi, A 2018, Personalised optimal speed advice to cyclists approaching an intersection with uncertain green time. in M Polycarpou, T Parisini, C Panayiotou & C Hadjicostis (eds), 2018 European Control Conference, ECC 2018., 8550416, Institute of Electrical and Electronics Engineers (IEEE), pp. 1666-1671, 16th European Control Conference, ECC 2018, Limassol, Cyprus, 12/06/18. https://doi.org/10.23919/ECC.2018.8550416

Personalised optimal speed advice to cyclists approaching an intersection with uncertain green time. / Dabiri, Azita ; Hegyi, Andreas.
2018 European Control Conference, ECC 2018. ed. / Marios Polycarpou; Thomas Parisini; Christos Panayiotou; Christoforos Hadjicostis. Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 1666-1671 8550416.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Hegyi, Andreas

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N2 - When travelling in urban areas with signalised intersections, cyclists are currently unable to optimize their speed profile according to their preferences. By integrating information from traffic signal phase and timing and cyclists' preferences, in this paper an algorithm is developed that generates personalised optimal speed advice for cyclists. Results from the simulation experiment indicate that in spite of stochastic nature of traffic light phase and timing, with the developed algorithm based on stochastic dynamic programming, an speed profile can be advised that meets the cyclist's preferences in terms of travel time, energy consumption, or the number of stops.

AB - When travelling in urban areas with signalised intersections, cyclists are currently unable to optimize their speed profile according to their preferences. By integrating information from traffic signal phase and timing and cyclists' preferences, in this paper an algorithm is developed that generates personalised optimal speed advice for cyclists. Results from the simulation experiment indicate that in spite of stochastic nature of traffic light phase and timing, with the developed algorithm based on stochastic dynamic programming, an speed profile can be advised that meets the cyclist's preferences in terms of travel time, energy consumption, or the number of stops.

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T2 - 16th European Control Conference, ECC 2018

Y2 - 12 June 2018 through 15 June 2018

ER -

Personalised optimal speed advice to cyclists approaching an intersection with uncertain green time

Abstract

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UN SDGs

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