Tokyo University ofMarine Science and Technology

Agent-based freight simulationand its applications

Summer School of Behavior Modelling
September 18, 2021
Takanori Sakai
Tokyo University of Marine Science and Technology
1Outline• Introduction• SimMobility• SimMobility Freight• Applications• Conclusion

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Freight transport• An integral component of the transportation system.
• Historically, behind in modelling compared with passenger.
• Complexity makes agent-based simulation challenging.
○

Various decision makers○

Shipments vs. vehicle flows○

Diversity in commodity type(e.g., parcels vs. construction materials)

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Freight entities – examples

Source: publicdomainq.net

Source: picjumbo.com

Source: publicdomainq.net

Source: publicdomainq.net
Source: publicdomainq.net

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Freight entities – examples

Source: sg-reality.co.jp

Source: okinawayamato.co.jp

Source: picjumbo.com

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2021 Summer School of Behavior Modelling5FlowsSDCPCCDCCPRMPC : Consumer
CP : Component producer
DC : Distribution center
P : Producer
RMS: Raw material supplier
S : ShopCPSCBDCUrbanPPDC

Modified from Behrends et al. (2008)https://www.tandfonline.com/doi/full/10.1080/03081060802493247

Tokyo University of Marine Science and Technology

2021 Summer School of Behavior Modelling6Motivations for modelling• Increasing commodity flows• E-commerce• Last-mile deliveries• Technological innovations• Passenger-freight interactions

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Freight model classification
Aggregate• Factoring method (Link or OD)• Truck model• Four-step commodity modelDisaggregate• Vehicle touring model (without considering commodity flows)• Logistics model○ Buyer-seller matching, distribution channel, shipment size &inventory, mode choice, shipments-to-vehicle flows
Modified from Chow et al. (2010) https://link.springer.com/content/pdf/10.1007/s11116-010-9281-1.pdf

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2021 Summer School of Behavior Modelling8Aggregate• Truck model• Four-step model
Commodity
generation

Trip generation

Trip distribution

Commodity

Commodity
distribution(Modal split)
Assignment

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VehicleAssignment

2021 Summer School of Behavior Modelling

9Vehicle touring modelStart time

Tour generation

Next stop purpose
Next stop location

Assignment
E.g., Hunt, J.D., Stefan, K.J., 2007. Tour-based
microsimulation of urban commercial movements.
Transport. Res. Part B: Methodol. 41 (9), 981–1013.
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SimMobility

Sakai et al. “SimMobility Freight: An Agent-Based Urban Freight Simulator for Evaluating Logistics Solutions”, Travel Model Improvement
Program (TMIP) Webinar - E-commerce Demand Analysis and Implementation in Urban/Regional Freight Transportation and Supply Chain
Forecasting Models, October 29, 2020.

SimMobility: Overview• SimMobility
An agent-based demand and supply urban transportation
simulation platform including passenger and freight(B-to-B & E-commerce)• Key Features- Temporal dimensions (long-term, mid-term, short-term)- ‘Smart’ mobility services (e.g., on-demand and shared)- Dynamic plan-action activity-based- Supply agents (inc. fleet/infrastructure management)- Open source
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SimMobility agents• Demand- Individuals- Households- Establishments/firms (shippers, receivers)• Supply- Transit operators- Fleet operators/managers(on-demand services, taxis, freight carriers)- Network regulators(pricing, information, traffic control)- E-commerce vendors- Real-estate developers
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SimMobility structure

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SimMobility applications• New modes and services• Traffic management• Last-mile solutions• Post-pandemic scenarios• Disruptions• Land-use• Infrastructure

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Prototype cities

Baltimore
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BostonSingapore
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Urban Freight Modeling

Sakai et al. “SimMobility Freight: An Agent-Based Urban Freight Simulator for Evaluating Logistics Solutions”, Travel Model Improvement
Program (TMIP) Webinar - E-commerce Demand Analysis and Implementation in Urban/Regional Freight Transportation and Supply Chain
Forecasting Models, October 29, 2020.

Freight models
Long-term

Establishments/Fleets/Overnight Parking
Shipments

Mid-term

Preday Logistics Planning
Within-day Vehicle OperationsMesoscopic Traffic Simulation

Short-term
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Microscopic Traffic Simulation

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B-to-B shipments

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E-commerce demand• E-commerce shipments to households• Groceries, HH Goods, and Others• Demand (frequency, expenditure) is sensitive todelivery options (speed, fee, home delivery/pickup)

Example of Home Delivery Options

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E-commerce shipments

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Freight Mid-term

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Illustrative outputs
Industry-to-industry

Delivery Locations

Zone-to-zone

Duration of Vehicle Operation

Source: Alho et al. (2021) https://link.springer.com/chapter/10.1007%2F978-981-15-8983-6_12

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Applications

Recent applications• Overnight freight vehicle parking• Freight consolidation centres• Route restrictions• Congestion pricing• Night/Off-peak deliveries• E-commerce growth• Freight-on-DemandTokyo University of Marine Science and Technology

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Policy analysis: Off-peak deliveries
Sakai et al. (2020). SimMobility Freight: An agent-based urban freight simulator for evaluating logistics solutions. Transportation ResearchPart E: Logistics and Transportation Review, 141, 102017.

• Study area: Singapore• Restrict deliveries at CBD during the morning peak (7-10am)

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Policy analysis: Off-peak deliveries• Peak-hour traffic at CBD decrease; however, through trafficincrease (both pax and freight).
7-10 amWithout policy
CBDPCU-km (Diff. from Scn. Base)-Average speed (km/hr.)1)
City

PCU-km (Diff. from Scn. Base)
Average speed (km/hr.)With policy
-4.7%

28.5
-29.4 (+3.4%)
-1.1%

39.8

40.6 (+1.9%)

1) The average of the trips to the CBD.

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Demonstration: E-commerce demand
Sakai, T., Hara, Y. Seshadri, R., Alho, A., Hasnine, MS., Jing, P., Ben-Akiva, M. (2020) E-Commerce Delivery Demand Modeling Framework
for An Agent-Based Simulation Platform. http://arxiv.org/abs/2010.14375• Study area: Auto Innovative City (Boston, U.S.)• Varying E-commerce adoption rate and pickup availability
Scenario setting (parentheses: change from Base)
Indicator• Adoptionrate (%)•

Groceries
Household
goods
OthersGroceries

Pickupavailability(%)
Household
goods
Others

Base
16.6

S1
S2
28.7 (+70%) 28.7 (+70%)

45.8

68.7 (+50%) 68.7 (+50%)60.9
773.0 (+20%) 73.0 (+20%)
4060

730

4572030

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Network in the Auto Innovative City

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Demonstration: E-commerce demand• Pickup availability curbs the delivery demand
Indicator• No. of homedeliveries(thousand)•No. of pickuporders (thousand)

Groceries
Household goods
Others
Total
Groceries
Household goods
Others
Total

Base
15.4
107.4
271.4
394.2
0.7
2.8
6.7
10.2

S1
22.6 (+47%)
155.4 (+45%)
312.9 (+15%)
490.9 (+25%)
7.0 (+900%)
20.7 (+639%)
24.5 (+266%)
52.2 (+412%)

S2
20.1 (+31%)
153.7 (+43%)304.2 (+12%)478.0 (+21%)
11.2 (+1500%)
34.7 (+1139%)38.9 (+481%)
84.8 (+731%)• Delivery traffic growth is not proportional to delivery growthIndicator
VKT (mil.)Tour type
Only e-commerce shipments
Mixed (e-commerce & other parcels)
Others
Total

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Base
1.86
0.24
6.44
8.54

S1
2.21 (+18.5%)
0.25 (+5.4%)
6.44 (+0.0%)
8.90 (+4.2%)

S2
2.13 (+14.2%)
0.26 (+10.8%)
6.44 (+0.0%)
8.83 (+3.3%)2021 Summer School of Behavior Modelling29

Solution analysis: Freight-On-DemandAlho, A., Sakai, T., Oh, S., Cheng, C., Seshadri, R., Chong, WH., Hara, Y., Caravias, J., Cheah, L., Ben-Akiva, M. A Simulation-Based Evaluationof Impacts of Cargo-Hitching Applied to E-Commerce Using Mobility-on-Demand Vehicles. https://arxiv.org/abs/2010.11585

Source: https://www.straitstimes.com/• E-commerce deliveries○ Increasingly on-demand○ Smart solutions…leverage Mobility-On-Demand (MOD) capacity?
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Solution analysis: Freight-On-Demand• Potential deliveries by MOD vehicles​:- how many deliveries can be handled?
- time gap between request and pickup/delivery?
• Impact on passenger trips: how service levels may change when
adding freight demand?

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Solution analysis: Freight-On-Demand• Singapore 2030
• MOD algorithm in SimMobility- Schedule solo andshared passenger rides• Assign e-commerce shipments
to previously committed
and/or idle MOD vehicles

Tokyo University of Marine Science and TechnologyScenario​

Freight in MOD​

MOD only​(Base)​None​A​

MOD shared​B​

MOD shared andidle vehicles​

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Solution analysis: Freight-On-Demand• Increase in requests handled by the MOD operator- Small change to MOD passenger service.
• Scenario- A: ~50% delivery demand with long waiting times- B: ~100% delivery demand with shorter waiting times• Small reduction in total VKT observed• Potential for emissions reduction by using electric MOD vehicles

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Conclusion• SimMobility is:• a comprehensive platform that jointly simulates passenger, Bto-B, and E-commerce flows• applicable to various types of policies, scenarios, & solutions• Ongoing research:- Enhance E-commerce model (supply-side, trip/E-commerceinteraction)- Post-pandemic scenarios- Application to other metropolitan areas
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Thank you for listening

35

References
Overall
Takanori Sakai, André Alho, Peiyu Jing. “SimMobility Freight: An Agent-Based Urban Freight Simulator for Evaluating Logistics Solutions”,
Travel Model Improvement Program (TMIP) Webinar - E-commerce Demand Analysis and Implementation in Urban/Regional FreightTransportation and Supply Chain Forecasting Models, October 29, 2020.

SimMobility
1.
2.
3.

4.
5.

Adnan, M., Pereira, F. C., Azevedo, C. M. L., Basak, K., Lovric, M., Feliu, S. R., … Ben-Akiva, M. (2016). SimMobility: a multiscale integrated agent-based simulation platform. In: 95th Annual Meeting of the Transportation Research Board, Washington, DC
Oke, J. B., Aboutaleb, Y. M., Akkinepally, A., Azevedo, C. L., Han, Y., Zegras, P. C., ... & Ben-Akiva, M. E. (2019). A novel global
urban typology framework for sustainable mobility futures. Environmental Research Letters, 14(9), 095006.
Oke, J. B., Akkinepally, A. P., Chen, S., Xie, Y., Aboutaleb, Y. M., Azevedo, C. L., ... & Ben-Akiva, M. (2020). Evaluating the systemic effects
of automated mobility-on-demand services via large-scale agent-based simulation of auto-dependent prototype cities. Transportation
Research Part A: Policy and Practice, 140, 98-126.
Oh, S., Seshadri, R., Azevedo, C. L., Kumar, N., Basak, K., & Ben-Akiva, M. (2020). Assessing the impacts of automated mobility-ondemand through agent-based simulation: A study of Singapore. Transportation Research Part A: Policy and Practice, 138, 367-388.
Basu, R., Araldo, A., Akkinepally, A. P., Nahmias Biran, B. H., Basak, K., Seshadri, R., ... & Ben-Akiva, M. (2018). Automated mobility-ondemand vs. mass transit: a multi-modal activity-driven agent-based simulation approach. Transportation Research Record, 2672(8), 608618.

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SimMobility Freight & Applications
1.

2.

3.

4.
5.

6.
7.

Sakai, T., Alho, A. R., Bhavathrathan, B. K., Dalla Chiara, G., Gopalakrishnan, R., Jing, P., ... & Ben-Akiva, M. (2020). SimMobility
Freight: An agent-based urban freight simulator for evaluating logistics solutions. Transportation Research Part E: Logistics andTransportation Review, 141, 102017.
Sakai, T., Hara, Y. Seshadri, R., Alho, A., Hasnine, MS., Jing, P., Ben-Akiva, M. (2020) E-Commerce Delivery Demand Modeling
Framework for An Agent-Based Simulation Platform. http://arxiv.org/abs/2010.14375
Sakai, T., Bhavathrathan, B. K., Alho, A., Hyodo, T., & Ben-Akiva, M. (2020). Commodity flow estimation for a metropolitan scale
freight modeling system: supplier selection considering distribution channel using an error component logit mixture
model. Transportation, 47(2), 997-1025.
Sakai, T., Alho, A., Hyodo, T., & Ben-Akiva, M. (2020). Empirical Shipment Size Model for Urban Freight and its
Implications. Transportation Research Record, 0361198120914890.
Chiara, G. D., Cheah, L., Azevedo, C. L., & Ben-Akiva, M. E. (2020). A policy-sensitive model of parking choice for commercial
vehicles in urban areas. Transportation Science, 54(3), 606-630.
Gopalakrishnan, R., Alho, A. R., Sakai, T., Hara, Y., Cheah, L., & Ben-Akiva, M. (2020). Assessing overnight parking infrastructure
policies for commercial vehicles in cities using agent-based simulation. Sustainability, 12(7), 2673.
Alho, A., Sakai, T., Oh, S., Cheng, C., Seshadri, R., Chong, WH., Hara, Y., Caravias, J., Cheah, L., Ben-Akiva, M. A Simulation-Based
Evaluation of Impacts of Cargo-Hitching Applied to E-Commerce Using Mobility-on-Demand Vehicles.
https://arxiv.org/abs/2010.11585

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