Negin Alisoltani @ séminaire IA & Mobilité

1. Research Engineer
GeoTwin
PhD in civil engineering
COSYS department
GRETTIA lab., Paris
LICIT lab., Lyon
Transportation Modeling and
Optimization Specialist
Email: negin.alisoltani@geotwin.io
Séminaire IA & Mobilité
March 2021
Negin Alisoltani
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2. GeoTwin
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3. GeoTwin
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4. GeoTwin
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5. GeoTwin
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6. GeoTwin
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7. • N. Alisoltani, L. Leclercq, M. Zargayouna, “Can dynamic ride-sharing reduce traffic congestion?” Transportation research part B: methodological 145
(2021): 212-246.
• N. Alisoltani, M. Zargayouna and L. Leclercq, "A Sequential Clustering Method for the Taxi-Dispatching Problem Considering Traffic Dynamics,"
IEEE Intelligent Transportation Systems Magazine 12.4 (2020): 169-181
• N.Alisoltani, M.Zargayouna, and L.Leclercq. "A Multi-agent System for Real-Time Ride Sharing in Congested Networks." Agents and Multi-agent
Systems: Technologies and Applications 2019. Springer, Singapore, 2019. 333-342.
• N. Alisoltani, M. Zargayouna, L. Leclercq, “Real-Time Autonomous Taxi Service: An Agent Based Simulation”, Agents and Multi-agent Systems:
Technologies and Applications 2020. Springer, Singapore, 2020. 199-207.
• N. Alisoltani, L. Leclercq, M. Zargayouna, “Optimal fleet management for real-time ride-sharing service considering network congestion” The 97th
Transportation Research Board Annual Meeting (TRB), Washington, DC, USA, January 7-11, 2018.
• N. Alisoltani, L. Leclercq, M. Zargayouna, “Network performance under different levels of ride-sharing: A simulation study” The Tenth Triennial
Symposium on Transportation Analysis (Tristan 2019), Hamilton Island, Australia, 17-21 June.
• N. Alisoltani, L. Leclercq, M. Zargayouna, “Real-time ride-sharing systems performance considering network congestion” Heart 2019 Symposium,
Budapest, Hungary, September 4-6, 2019.
• N. Alisoltani, M. Zargayouna, L. Leclercq, “Data-Oriented Approach for the Dial-A-Ride Problem” 16th ACS/IEEE International Conference on
Computer Systems and Applications AICCSA 2019, November 3rd to November 7th, 2019.
Université Gustave Eiffel
Simulation-Based Optimization Frameworks for Dynamic Ride-Sharing
COSYS-GRETTIA and LICIT (2017-2020)
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8. Saving travel cost
Conserving fuel
Reducing air pollution
Reducing traffic congestion
Shared-mobility advantages
Shared-mobility services
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9. Dynamic ride-sharing
Main research gaps:
• The performance of ride-sharing
on the network demand
• The impact of the network on the
ride-sharing system and vice versa
What is ride-sharing?
Shared services
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10. • Optimizer
• Demand characteristics
• Service characteristics
• Simulator
• Plant model
• Prediction model
System components
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11. Solution method
Optimal algorithm
Branch-and-Cut algorithm
Performance of ride-sharing on the network demand
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12. Heuristic 1: Rolling horizon and re-scheduling
Rolling horizon
Step 1: Solving the problem over [𝑡 , 𝑡 +𝑇𝐻]
Step 2: Running the simulation between 𝑡 and 𝑡 +
𝑇𝐻
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Step 3: Updating the predicted travel times at 𝑡′
for the next period [𝑡′
, 𝑡′
+ 𝑇𝐻]
Performance of ride-sharing on the network demand
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13. Heuristic 1: Rolling horizon and re-scheduling
Re-scheduling
Performance of ride-sharing on the network demand
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14. Heuristic 2: FOSH method
Performance of ride-sharing on the network demand
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15. Heuristic 3: Clustering method
1- Shareability Function
2- Shareability Matrix
3- Clustering based on the matrix
M 54 23 … 10
54 M 84 … 31
23 84 M … 47
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. .
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. .
.
. .
.
. .
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10 31 47 … M
Performance of ride-sharing on the network demand
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16. Lyon 6 + Villeurbanne
Scale of 25 km2
62450 requests
11235 Service requests
1,883 nodes and 3383 links
6:30 to 10:30 AM (morning peak)
Rolling horizon: 20 min
Optimization time step: 10 min
Test cases
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17. Lyon
Scale of 80 km2
484,690 requests
205,308 Service requests
11,314 origin/destination set points
6 to 10 AM (morning peak)
Rolling horizon: 20 min
Optimization time step: 10 min
Test cases
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18. Optimizer evaluation
Comparing the final algorithm with the existing methods
Impact of the network on the ride-sharing system and vice versa
Compare different clustering methods (market-share = 50%) 18

19. Impact of dynamic ride-sharing on medium-scale network
Number of sharing
Traffic situation for market-share 100% with different numbers of sharing
Impact of the network on the ride-sharing system and vice versa
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20. Impact of dynamic ride-sharing on large-scale network
Number of sharing
Market-share = 100% : Number of requests : 205308
Traffic situation for market-share 100% with different numbers of sharing (large-scale)
Number of
sharing
Number of trips Number of cars
0 205124 17102
1 105745 9489
2 72160 6826
3 69790 6595
Impact of the network on the ride-sharing system and vice versa
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21. Impact of dynamic ride-sharing on large-scale network
Capacity of vehicles
Regular vehicle: capacity = 4, nshare = 3
Big vehicle: capacity = 6, nshare = 5
Van-pooling: capacity = 10, nshare = 9
Shuttle-sharing: capacity = 20, nshare = 19
Traffic situation for different vehicle capacity (market-share = 100%)
Configuration
Shared vehicles
Number of trips
Number of
cars
MS: 100%
Capacity = 4 69790 6595
Capacity = 6 63304 5714
Capacity = 10 46448 4253
Capacity = 20 30004 2785
Impact of the network on the ride-sharing system and vice versa
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22. Thank you for your attention
negin.alisoltani@geotwin.io
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