Price-and-Time-Aware Dynamic Ridesharing

Lu Chen, Qilu Zhong, Xiaokui Xiao, Yunjun Gao, Pengfei Jin, Christian Søndergaard Jensen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

7 Citationer (Scopus)

Resumé

Ridesharing refers to a transportation scenario where travellers with similar itineraries and time schedules share a vehicle for a trip and split the travel cost, which may include fuel, tolls, and parking fees. Ridesharing is popular among travellers because it can reduce their travel costs, and it also holds the potential to reduce travel time, congestion, air pollution, and overall fuel consumption. However, existing ridesharing systems often offer each traveller only one choice that aims to minimize system-wide vehicle travel distance or time. We propose a solution that offers more options. Specifically, we do this by considering both pick-up time and price, so that travellers are able to choose the vehicle that matches their preferences best. In order to identify quickly vehicles that satisfy incoming ridesharing requests, we propose two efficient matching algorithms that follow the single-side and dual-side search paradigms, respectively. To further accelerate the matching, indexes on the road network and vehicles are developed, based on which several pruning heuristics are designed. Extensive experiments on a large Shanghai taxi dataset offer insights into the performance of our proposed techniques and compare with a baseline that extends the state-of-The art method. © 2018 IEEE.
OriginalsprogEngelsk
TitelProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Antal sider12
ForlagIEEE
Publikationsdato24 okt. 2018
Sider1061-1072
Artikelnummer8509320
ISBN (Trykt)978-1-5386-5521-4
ISBN (Elektronisk)978-1-5386-5520-7
DOI
StatusUdgivet - 24 okt. 2018
Begivenhed34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, Frankrig
Varighed: 16 apr. 201819 apr. 2018

Konference

Konference34th IEEE International Conference on Data Engineering, ICDE 2018
LandFrankrig
ByParis
Periode16/04/201819/04/2018
NavnProceedings of the International Conference on Data Engineering
ISSN1063-6382

Fingerprint

Parking
Travel time
Air pollution
Fuel consumption
Costs
Experiments

Citer dette

Chen, L., Zhong, Q., Xiao, X., Gao, Y., Jin, P., & Jensen, C. S. (2018). Price-and-Time-Aware Dynamic Ridesharing. I Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018 (s. 1061-1072). [8509320] IEEE. Proceedings of the International Conference on Data Engineering https://doi.org/10.1109/ICDE.2018.00099
Chen, Lu ; Zhong, Qilu ; Xiao, Xiaokui ; Gao, Yunjun ; Jin, Pengfei ; Jensen, Christian Søndergaard. / Price-and-Time-Aware Dynamic Ridesharing. Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018. IEEE, 2018. s. 1061-1072 (Proceedings of the International Conference on Data Engineering).
@inproceedings{32c1c5d38597408d87d3b01129c68cf6,
title = "Price-and-Time-Aware Dynamic Ridesharing",
abstract = "Ridesharing refers to a transportation scenario where travellers with similar itineraries and time schedules share a vehicle for a trip and split the travel cost, which may include fuel, tolls, and parking fees. Ridesharing is popular among travellers because it can reduce their travel costs, and it also holds the potential to reduce travel time, congestion, air pollution, and overall fuel consumption. However, existing ridesharing systems often offer each traveller only one choice that aims to minimize system-wide vehicle travel distance or time. We propose a solution that offers more options. Specifically, we do this by considering both pick-up time and price, so that travellers are able to choose the vehicle that matches their preferences best. In order to identify quickly vehicles that satisfy incoming ridesharing requests, we propose two efficient matching algorithms that follow the single-side and dual-side search paradigms, respectively. To further accelerate the matching, indexes on the road network and vehicles are developed, based on which several pruning heuristics are designed. Extensive experiments on a large Shanghai taxi dataset offer insights into the performance of our proposed techniques and compare with a baseline that extends the state-of-The art method. {\circledC} 2018 IEEE.",
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Chen, L, Zhong, Q, Xiao, X, Gao, Y, Jin, P & Jensen, CS 2018, Price-and-Time-Aware Dynamic Ridesharing. i Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018., 8509320, IEEE, Proceedings of the International Conference on Data Engineering, s. 1061-1072, Paris, Frankrig, 16/04/2018. https://doi.org/10.1109/ICDE.2018.00099

Price-and-Time-Aware Dynamic Ridesharing. / Chen, Lu; Zhong, Qilu; Xiao, Xiaokui; Gao, Yunjun; Jin, Pengfei; Jensen, Christian Søndergaard.

Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018. IEEE, 2018. s. 1061-1072 8509320 (Proceedings of the International Conference on Data Engineering).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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AU - Zhong, Qilu

AU - Xiao, Xiaokui

AU - Gao, Yunjun

AU - Jin, Pengfei

AU - Jensen, Christian Søndergaard

PY - 2018/10/24

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N2 - Ridesharing refers to a transportation scenario where travellers with similar itineraries and time schedules share a vehicle for a trip and split the travel cost, which may include fuel, tolls, and parking fees. Ridesharing is popular among travellers because it can reduce their travel costs, and it also holds the potential to reduce travel time, congestion, air pollution, and overall fuel consumption. However, existing ridesharing systems often offer each traveller only one choice that aims to minimize system-wide vehicle travel distance or time. We propose a solution that offers more options. Specifically, we do this by considering both pick-up time and price, so that travellers are able to choose the vehicle that matches their preferences best. In order to identify quickly vehicles that satisfy incoming ridesharing requests, we propose two efficient matching algorithms that follow the single-side and dual-side search paradigms, respectively. To further accelerate the matching, indexes on the road network and vehicles are developed, based on which several pruning heuristics are designed. Extensive experiments on a large Shanghai taxi dataset offer insights into the performance of our proposed techniques and compare with a baseline that extends the state-of-The art method. © 2018 IEEE.

AB - Ridesharing refers to a transportation scenario where travellers with similar itineraries and time schedules share a vehicle for a trip and split the travel cost, which may include fuel, tolls, and parking fees. Ridesharing is popular among travellers because it can reduce their travel costs, and it also holds the potential to reduce travel time, congestion, air pollution, and overall fuel consumption. However, existing ridesharing systems often offer each traveller only one choice that aims to minimize system-wide vehicle travel distance or time. We propose a solution that offers more options. Specifically, we do this by considering both pick-up time and price, so that travellers are able to choose the vehicle that matches their preferences best. In order to identify quickly vehicles that satisfy incoming ridesharing requests, we propose two efficient matching algorithms that follow the single-side and dual-side search paradigms, respectively. To further accelerate the matching, indexes on the road network and vehicles are developed, based on which several pruning heuristics are designed. Extensive experiments on a large Shanghai taxi dataset offer insights into the performance of our proposed techniques and compare with a baseline that extends the state-of-The art method. © 2018 IEEE.

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BT - Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018

PB - IEEE

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Chen L, Zhong Q, Xiao X, Gao Y, Jin P, Jensen CS. Price-and-Time-Aware Dynamic Ridesharing. I Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018. IEEE. 2018. s. 1061-1072. 8509320. (Proceedings of the International Conference on Data Engineering). https://doi.org/10.1109/ICDE.2018.00099