Vassilissa LEHOUX — Naver Labs — Multimodal Mobility

Multimodal mobility

Vassilissa Lehoux

8th November 2017

Soph.I.A summit

Š 2018 NAVER LABS. All rights reserved.

NAVER LABS

© 2018 NAVER LABS. All rights reserved.

2

AMBIENT INTELLIGENCE Technology that understands you, contexts, and environment so that you will be given answers and recommendations at the right time without asking

Š 2018 NAVER LABS. All rights reserved.

3

AMBIENT INTELLIGENCE Location Intelligence

© 2018 NAVER LABS. All rights reserved.

Mobility Intelligence

4

Mapping 3D Hi-def indoor mapping by Robot mapper M1 Web-scale cloud mapping Indoor autonomous mobility

Š 2018 NAVER LABS. All rights reserved.

5

Visual Localization Key technology to find accurate position and orientation with images only

Š 2018 NAVER LABS. All rights reserved.

6

Autonomous Driving Future mobility/traffic data platform Real time road condition/ SAE autonomous level 3

Š 2018 NAVER LABS. All rights reserved.

7

South Korea's best GPS navigation.

© 2018 NAVER LABS. All rights reserved.

8

Multimodal Mobility

Š 2018 NAVER LABS. All rights reserved.

What is multimodal mobility?

Public transportation modes • Bus • Train • Tramway …

© 2018 NAVER LABS. All rights reserved.

10

What is multimodal mobility?

Public transportation modes • Bus • Train • Tramway …

And walking between the stations

© 2018 NAVER LABS. All rights reserved.

10

Adding some possibilities

Combining • Personal bicycle, car • Taxi • Bike sharing, car sharing …

With public transportation

© 2018 NAVER LABS. All rights reserved.

10

Itineraries in multimodal networks

Door-to-door with • • • •

Single solution for personal modes and transport providers Modes interleaving in one itinerary Proposing relevant options Arriving at a predictable time

© 2018 NAVER LABS. All rights reserved.

11

Multimodal itineraries Combining different types of modes • • • • • •

Public transportation On-demand public transportation Personnal bike/car, walking Taxi Bike/car sharing …

Into complex mode sequences • Multimodal first and last miles • Multimodal transfers Taxi

© 2018 NAVER LABS. All rights reserved.

12

Combining modes

Based on Points of Mobility (POM) Bike sharing station

• Stations • Parking lots • etc.

Bike sharing station

Taxi

© 2018 NAVER LABS. All rights reserved.

13

Combining the different modes Graph based approaches • Aggregation of one graph per mode • Arcs between modes at POM • Public transportation with time-expended or timedependent graphs • Label constrained min-cost paths

Min path methods

[Pygra et al., 2004] © 2018 NAVER LABS. All rights reserved.

14

Combining the different modes Graph based approaches • Aggregation of one graph per mode • Arcs between modes at POM • Public transportation with time-expended or timedependent graphs • Label constrained min-cost paths

Min path methods Taxi first, then public transport (PT) + Walking

Taxi © 2018 NAVER LABS. All rights reserved.

PT

Walking

UCCH [Dibbelt, Pajor, Wagner, 2012] SDALT [Kirchler, Liberti, Calvo, 2014] 14

Combining the different modes Time-table based approaches • One graph per mode • Arcs between modes at POM • Public transportation with modeling based on trips/connections

Dynamic programming • Allowing one more trip/connection per round

RAPTOR [Delling et al., 2012] CSA [Dibbelt et al., 2013] TBPTR [Witt, 2015] © 2018 NAVER LABS. All rights reserved.

15

Combining the different modes Graph based • Methods for road networks are not as efficient on multimodal networks [Bast, 2009] • Large graphs • Automaton might need to be predefined

Time-table based

A

• Limited precomputations • Sequences can be enforced in the dynamic programming algorithm [Ulloa, Lehoux, Rouland, 2018] Modified RAPTOR with a set of sequences

© 2018 NAVER LABS. All rights reserved.

B

C

23 16

Alternative sets Multiple criteria • Earliest arrival time & number of transfers / profile & number of transfers • Min waiting time, min transfer time, min cost, …

Exponential optimal set

A A

Start

B

B

End © 2018 NAVER LABS. All rights reserved.

17

Alternative sets Approximation - diversified solutions [Ulloa, 2014] • Fastest route

A • Green line is forbidden

A

Start

B

• Violet line is forbidden

End © 2018 NAVER LABS. All rights reserved.

18

Alternative sets Approximation Relevant alternative sets • Constraints and parameters • User preferences and objectives • Diversified sets [Ulloa, Lehoux, Roulland, 2018]

SMIRT Syndicat Mixte Intermodal Régional de Transports

Relevant set sorting • Without personal information • Personalization [Céret, Castellani, Lehoux, 2015] [Wang, Lehoux, Espinouse, Cung, 2018]

© 2018 NAVER LABS. All rights reserved.

19

Predicting arrival times accurately

Historical data and real-time information

Š 2018 NAVER LABS. All rights reserved.

20

Predicting arrival times accurately

Real-time and historical traffic data • Map matching • Traffic patterns • Planned disturbance • Real-time updates • Short term predictions

© 2018 NAVER LABS. All rights reserved.

21

Predicting arrival times accurately

Schedule modes information • Time table predictions • Real-time delays • Real-time trip/route modifications

© 2018 NAVER LABS. All rights reserved.

22

Large scale NAVER LABS • SMIRT (125K stops) • Naver Map (180K stops, 1000K trips)

In the literature • Ile-de-France (110K stops, 290 k trips) • London (21K stops, 133K trips) • Switzerland (25K stops, 1000K trips) • Netherlands (54,5K stops, 620K trips) • Germany (250K stops, 1400K trips)

Graph partition • CSA + METIS [Strasser and Wagner, 2014] • Transfer patterns + partition [Bast et al. 2016] • RAPTOR + hMETIS [Delling et al. 2017] © 2018 NAVER LABS. All rights reserved.

23

Large scale NAVER LABS • SMIRT (125K stops) • Naver Map (180K stops, 1000K trips)

In the literature • Ile-de-France (110K stops, 290 k trips) • London (21K stops, 133K trips) • Switzerland (25K stops, 1000K trips) • Netherlands (54,5K stops, 620K trips) • Germany (250K stops, 1400K trips)

Graph partition • CSA + METIS [Strasser and Wagner, 2014] • Transfer patterns + partition [Bast et al. 2016] • RAPTOR + hMETIS [Delling et al. 2017] © 2018 NAVER LABS. All rights reserved.

24

Large scale NAVER LABS • SMIRT (125K stops) • Naver Map (180K stops, 1000K trips)

In the literature • Ile-de-France (110K stops, 290 k trips) • London (21K stops, 133K trips) • Switzerland (25K stops, 1000K trips) • Netherlands (54,5K stops, 620K trips) • Germany (250K stops, 1400K trips)

Graph partition • CSA + METIS [Strasser and Wagner, 2014] • Transfer patterns + partition [Bast et al. 2016] • RAPTOR + hMETIS [Delling et al. 2017] © 2018 NAVER LABS. All rights reserved.

25

Multimodal trip planning Combining all the available modes • Huge networks and many possibilities • Access to transport provider data

Personalization • Giving choices • Respect of privacy

Real-time information • Current state • Short term predictions

© 2018 NAVER LABS. All rights reserved.

26

Thank you

© 2018 NAVER LABS. All rights reserved.

Bibliography • [Bast, 2009] Hannah Bast. Efficient Algorithms, volume 5760 of Lecture Notes in Computer Science, chapter “Car or Public Transport - Two Worlds”, pages 355-367. Springer, 2009.

• [Delling et al., 2012] Daniel Delling, Julian Dibbelt, Thomas Pajor, Dorothea Wagner, and Renato F. Werneck. “Computing and evaluating multimodal journeys”. Technical Report Karlsruhe Reports in Informatics 2012,20, Karlsruher Institut für Technologie (KIT), 2012.

• [Dibbelt, Pajor, Wagner, 2012] Julian Dibbelt, Thomas Pajor, and Dorothea Wagner. “User-constrained multi-modal route planning”. In Proceedings of the Fourteenth Workshop on Algorithm Engineering and Experiments (ALENEX), pages 118-129, 2012.

• [Dibbelt et al., 2013] Julian Dibbelt, Thomas Pajor, Ben Strasser, and Dorothea Wagner. “Intriguingly simple and fast transit routing”. Experimental Algorithms, Springer Berlin Heidelberg, Berlin, Heidelberg, pages 43-54, 2013.

© 2018 NAVER LABS. All rights reserved.

Bibliography • [Kirchler, Liberti, Calvo, 2014] Dominik Kirchler, Leo Liberti, and Roberto Woler Calvo. “Efficient Computation of Shortest Paths in Time-Dependent MultiModal Networks“. Journal of Experimental Algorithmics (JEA), Volume 19, 2014, Article No. 2.5.

• [Strasser and Wagner, 2014] Ben Strasser and Dorothea Wagner. "Connection scan accelerated”. Proceedings of the Meeting on Algorithm Engineering & Expermiments (ALENEX’14), pages 125-137, Portland, Oregon, 2014.

• [Witt, 2015] Sascha Witt. “Trip-Based Public Transit Routing”. In: Bansal N., Finocchi I. (eds) Algorithms - ESA 2015. Lecture Notes in Computer Science, vol 9294. Springer, Berlin, Heidelberg.

• [Céret, Castellani, Lehoux, 2015] Eric Céret, Stefania Castellani and Vassilissa Lehoux. “System and method for multi-factored-based ranking of trips”. Patent application US20170053209A1, Xerox Corp.

• [Bast et al. 2016] Hannah Bast, Matthias Hertel and Sabine Storandt. “Scalable Transfer Patterns”. In Proceedings of the Eighteenth Workshop on Algorithm Engineering and Experiments (ALENEX), 2016. © 2018 NAVER LABS. All rights reserved.

Bibliography • [Delling et al. 2017] Daniel Delling, Julian Dibbelt, Thomas Pajor, and Tobias Zündorf. “ Faster Transit Routing by Hyper Partitioning”. Proceedings of the 17th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2017), article No. 8; pages 8:1–8:14, Open Access Series in Informatics.

• [Ulloa, Lehoux, Roulland, 2018] Luis Ulloa, Vassilissa Lehoux-Lebacque and Frédéric Roulland. “Trip planning within a multimodal urban mobility”. IET Intelligent Transport Systems, Volume 12, Issue 2, pages 87 –92.

• [Wang, Lehoux, Espinouse, Cung, 2018] Lizhi Wang, Vassilissa Lehoux, Marie-Laure Espinouse and Van-Dat Cung. “Multimodal itineraries ranking using fuzzy logic”, Operations Research 2018 conference (OR2018), Brussels, Belgium, 2018.

© 2018 NAVER LABS. All rights reserved.

SMIRT’s mobility companion plateform

SMIRT’s Request for Proposal (France Nord Region) • Integrates all urban transport service providers information • 14 transportation authorities Main features • Multimodal passenger information / trip planning • Real-time transit information • Personalized trips

© 2018 NAVER LABS. All rights reserved.

Multimodal trip planning

Example of complex mode sequences for SMIRT

Mode 1 Anywhere

POM Type 1

POM Type 4

© 2018 NAVER LABS. All rights reserved.

Mode 2

Scheduled mode

Mode 3 POM Type 2

POM Mode 5 POM Type 5 Type 6

Scheduled modes

Mode 6

POM Type 7

Mode 3

Mode 7

POM Type 3

POM Type 7

Mode 4 Anywhere

Scheduled mode

POM Type 8

Multimodal trip planning

Example of complex mode sequences for SMIRT

Start

Bike sharing station

Bus Stop

Š 2018 NAVER LABS. All rights reserved.

Bike sharing station

Bus Stop

Scheduled modes

Bike sharing station

Bike sharing station

Taxi Station

Tramway Stop

End

Tramway Stop