Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study
Abstract
:1. Introduction
- Development of a demand-side cooperative (on-demand) shared automated mobility (DC-SAM) service which can further improve system efficiency.
- Modeling of the proposed system in an open-source and multi-modal microscopic simulation platform in a dynamic environment with more realistic settings, including real-world roadway network, background traffic impacts, SAV dynamics, and customer–SAV interactions. This platform has the potential for extended microscopic traffic modeling and analysis related to MaaS.
2. Background
3. System Framework and Methodology
3.1. System Framework in Simulation
3.2. Alternative PUDO Locations
3.3. Ride Matching
3.3.1. Heuristic Model
3.3.2. Optimization Model with Demand-Side Cooperation (ODC)
- (1)
- Each alternative location node, e.g., the th alternative location for the th request, in either Origin (for pickup) set or Destination (for drop-off) set, is visited at most once by whichever SAV.
- (2)
- For the th SAV, the number of pickup nodes visited within the same service round (or system optimization time window) should not exceed its associated capacity,
- (3)
- From its origin, the th SAV will visit at most one pickup location.
- (4)
- For any SAV, each pickup node has at most one incoming link, which equals to .
- (5)
- For any SAV, each pickup node has at most one outgoing link, which equals to .
- (6)
- After the th SAV picks up all the customers in the origin node set, it will go to the destination node set. In other words, at most, one link will be set up between the origin node set and destination node set.
- (7)
- For any SAV, each drop-off node has at most one incoming link, which equals to
- (8)
- For any SAV, each drop-off node has at most one outgoing link, which equals to .
- (9)
- For any SAV and any request, there is at most one alternative pickup location selected.
- (10)
- For any SAV and any request, there is at most one alternative drop-off location selected.
3.3.3. Optimization Model without Demand-Side Cooperation (ONDC)
3.4. Network Output Metrics
4. Case Study
4.1. Simulation Setup
4.2. Determination of System Optimization Time Window
5. Discussion
5.1. Comparison of Different Ride Matching Strategies
5.2. Sensitivity Analysis
5.2.1. SAM Service Demand
5.2.2. Vehicle Capacity
6. Conclusions and Future Work
6.1. Theoretical and Practical Implications
6.2. Limitations and Future Work
- The simulation scenario and mode choice model are simplified. As one of the future steps, the optimization algorithm and simulation platform will be extended to handle more complex and realistic scenarios, such as cancellations of request, considering customers’ patience and preferences for waiting or walking in the mode choice model.
- Another limitation is the computational efficiency. Due to the nature of the problem (i.e., NP-hard), applying a commercial optimization solver (Gurobi in this study) may not be efficient enough for large-scale studies. Developing a meta-heuristic algorithm (balancing between optimality and computational efficiency) to solve the large-scale ride matching problem considering demand-side cooperation should be a key direction of future research.
- Other emerging and shared modes can be integrated into the current framework, such as fixed-route ridesharing services or micro-mobility services (e.g., e-scooters, mopeds). The proposed simulation platform is flexible enough to accommodate all these modes.
- Integration of zero-emissions vehicle operation, such as the combination of shared autonomous electric vehicles with the management of charging facilities, will be another interesting and important topic for further investigation, as transportation electrification is considered as one of the major global trends in the not-too-distant future.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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PARAM. | Description |
---|---|
ride request | |
th alternative location of request | |
departure time of request at th alternative location | |
th alternative pickup location (node) of request | |
th alternative drop-off location (node) of request | |
size (i.e., the number of customers) of request i at th alternative location; | |
origin of th SAV | |
destination of th SAV | |
capacity of th SAV | |
cost (e.g., travel distance) for traveling from node to node | |
revenue of th alternative location of request served by th SAV | |
total number of requests | |
total number of SAVs | |
total number of alternative locations of request | |
Var. | Description |
binary variable indicates that the th SAV visits th alternative location of request (1-visited; 0-not visited) | |
binary variable indicates that the selects a route from node to node (1-selected; 0-not selected) |
Metrics | Unit | Description |
---|---|---|
VMT | Vehicle-mile | Vehicle miles traveled |
VHT | Vehicle-hour | Vehicle time traveled in hour |
TDF | - | Trip detour factor: customer’s actual trip distance under the pooled TNC service divided by the trip distance with dedicated service (based on the time-dependent shortest path). |
CWT | Second | Average customer’s waiting time; waiting time for the matched vehicle moving to the pickup location and picking the customer up. |
WKT | Second | Customer’s time spent on walking to/from alternative PUDO locations with respect to the origin and destination. |
WKM | Mile | Customer’s walking distance to/from alternative PUDO locations with respect to the origin and destination. |
VEC | Liter (gasoline) | Vehicle energy/fuel consumption for serving all customers. |
500 s | 300 s | 240 s | 180 s | 120 s | 60 s | 1 s | |
---|---|---|---|---|---|---|---|
VMT (vehicle-mile) | 302 | 309 | 293 | 289 | 283 | 304 | 293 |
VHT (vehicle-hour) | 31.2 | 30.1 | 28.8 | 28.2 | 27.9 | 28.1 | 28.4 |
TDF | 4.5 | 4.69 | 4.61 | 4.55 | 4.09 | 4.64 | 4.46 |
CWT (s) | 869 | 836 | 806 | 745 | 817 | 779 | 846 |
WKT (s) | 468 | 479 | 414 | 474 | 476 | 429 | 513 |
WKM (mile) | 0.48 | 0.49 | 0.45 | 0.49 | 0.49 | 0.46 | 0.52 |
VEC | 101.1 | 96.3 | 90.0 | 91.6 | 82.2 | 103.8 | 88.7 |
CPU Time (103) | 18.3 | 12.0 | 11.4 | 14.1 | 9.0 | 11.7 | 12.1 |
Strategy | Performance Metrics | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
VMT | VHT | TDF | CWT | VEC (L) | CO2 (kg) | CO (kg) | HC (g) | NOx (g) | PMx (g) | |
Heuristic | 605.4 | 51.6 | 9.24 | 1159 | 155.3 | 361.2 | 9.33 | 50.4 | 150.6 | 7.06 |
ONDC | 295.6 | 29.2 | 4.45 | 786 | 86.5 | 201.1 | 6.00 | 31.9 | 85.7 | 4.14 |
ODC | 283.0 | 27.9 | 4.09 | 817 | 82.2 | 191.1 | 5.87 | 31.1 | 81.5 | 3.96 |
ONDC vs. Heur. | −51.2% | −43.4% | −51.8% | −32.2% | −44.3% | −44.3% | −35.7% | −36.7% | −43.1% | −41.4% |
ODC vs. Heur. | −53.3% | −45.9% | −55.7% | −29.5% | −47.1% | −47.1% | −37.1% | −38.3% | −45.9% | −43.9% |
ODC vs. ONDC | −4.3% | −4.5% | −8.1% | 3.9% | −5.0% | −5.0% | −2.2% | −2.5% | −4.9% | −4.3% |
Metrics | 20 trips | 60 trips | 140 trips (Benchmark) |
---|---|---|---|
VMT (vehicle-mile) | 58.34 | 133.74 | 283.0 |
VHT (vehicle-hour) | 8.30 | 15.25 | 27.9 |
TDF | 4.53 | 4.26 | 4.09 |
CWT (s) | 800 | 854 | 817 |
WKT (s) | 480 | 464 | 476 |
WKM (mile) | 0.48 | 0.47 | 0.49 |
VEC (L) | 27.1 | 51.1 | 82.2 |
CO2 (kg) | 63.0 | 118.9 | 191.1 |
CO (kg) | 2.54 | 4.43 | 5.87 |
HC (g) | 13.0 | 22.8 | 31.1 |
NOx (g) | 27.4 | 51.3 | 81.5 |
PMx (g) | 1.41 | 2.59 | 3.96 |
Metrics. | 1 seat | 2 seats | 3 seats (Benchmark) |
---|---|---|---|
VMT (vehicle-mile) | 427.5 | 345.8 | 283.0 |
VHT (vehicle-hour) | 43.4 | 33.4 | 27.9 |
TDF | 2.53 | 3.49 | 4.09 |
CWT (second) | 445 | 627 | 817 |
WKT (second) | 384 | 399 | 476 |
WKM (mile) | 0.43 | 0.44 | 0.49 |
VEC (liter) | 122.0 | 103.2 | 82.2 |
CO2 (kg) | 283.7 | 240.0 | 191.1 |
CO (kg) | 10.06 | 7.94 | 5.87 |
HC (g) | 52.3 | 41.5 | 31.1 |
NOx (g) | 122.3 | 102.6 | 81.5 |
PMx (g) | 6.12 | 5.05 | 3.96 |
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Zhu, L.; Zhao, Z.; Wu, G. Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study. Sustainability 2021, 13, 2483. https://doi.org/10.3390/su13052483
Zhu L, Zhao Z, Wu G. Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study. Sustainability. 2021; 13(5):2483. https://doi.org/10.3390/su13052483
Chicago/Turabian StyleZhu, Lei, Zhouqiao Zhao, and Guoyuan Wu. 2021. "Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study" Sustainability 13, no. 5: 2483. https://doi.org/10.3390/su13052483
APA StyleZhu, L., Zhao, Z., & Wu, G. (2021). Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study. Sustainability, 13(5), 2483. https://doi.org/10.3390/su13052483