# Towards Sustainable Liveable City: Management Operations of Shared Autonomous Cargo-Bike Fleets

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## Abstract

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## 1. Introduction

## 2. Literature Review

#### 2.1. Vehicle Relocation Problem

- The static and deterministic repositioning problem (SDRP): where all the inputs are known beforehand with certainty. This is the case for the bike relocation, which is operated overnight and does not integrate demand forecasting. It can be viewed as a static, many-to-many pickup and delivery problem. We can find a survey of the static bicycle repositioning problem in [28].
- The static and stochastic repositioning problem (SSRP): where the inputs arepartially known, with a defined probability. One example is the maximum expected covering problem (MEXCLP), which was applied to solve the relocation of medical service vehicles [29]. A probability function was included to consider the possibility that a requested vehicle is not available because of previous demands.
- The dynamic and deterministic repositioning problem (DDRP): where the problem is solved periodically (or in real-time) but the future input is unknown and there is no stochastic information. The value of inputs is known only when they appear. One example is the ambulance relocation problem solved by [30], where the dispatching problem should be solved each time a request appears.
- The dynamic and stochastic repositioning problem (DSRP): where the input is stochastic information, and the problem is solved in real-time. One application is the intraday bike-sharing rebalancing, which considers the upcoming demand during the day [31].

#### 2.2. Shared Autonomous Vehicles Fleet Management

- User-based strategies: for this type, the customer is given incentives to move from an area of high demand to an area of low demand where there is an offer. These incentives could be a free ride, a lower price, or extra free time.
- Operator-based strategies: these consist of relocating vehicles with the intervention of the system manager. The rebalancing operation is performed based on a criterion reflecting the statistics calculation of demand and supply in the different stations or areas in the system. The criteria could be, for example, the number of available and needed vehicles, a calculated index, or waiting times. These strategies are the most studied in the literature. We can find various papers with different assumptions regarding the type of network, reservation structure, and fleet characteristics. Many studies consider a grid network, where the operating area is devided into blocks and the imbalance of each block is evaluated. Thereafter, vehicles should move from surplus blocks to deficit blocks. Fagnant and Kockelman [35] implemented four strategies in a subsequent order for each relocation period. First, they spread idle vehicles according to block balance. Second, they moved excess idle vehicles (any with more than two per zone) to zones unoccupied by idle vehicles. Chen et al. [36] built from the framework in [35] to investigate the operations of a SAV fleet under various vehicle range and charging infrastructure assumptions. Winter et al. [20] simulated an AMoD service on a generic grid network to study the impact of five different rebalancing policies for idle vehicles on parking needs, passenger waiting time, and empty mileage. The limit of such a network is that the traveling cost is roughly estimated. They assume that vehicles move from one grid to another at a fixed cost. Other papers consider a graph network where stations are nodes and the edges represent the traveling cost. Spieser et al. [37] extended a linear program formulation from [38] to compare the relocation that only responds to the current state of the system, the relocation that integrates future demand estimation, and the relocation with certain demand information. The demand destination is assumed to be known. Tsao et al. [39] studied the rebalancing of an AMoD system with uncertain travel demand forecasts. They formulated the problem as an integer program and developed a relaxed approach to solving it. Azevedo et al. [40] solved a linear program each hour to find the number of vehicles that move from an oversupplied station to an undersupplied station while minimizing the traveling cost. These different models mainly concern the periodic relocation, which occurs in each defined period T, and ignores the possible relocation between periods when a vehicle becomes idle.To the best of our knowledge, only two papers were found where a road network was used. Wallar et al. [41] presented a method to divide the operating area into a set of relocation regions and estimate the demand in each region. The rebalancing algorithm optimizes the relocation of idle vehicles between these regions using integer linear programming. Marczuk et al. [42] adopted the same formulation as in [40] to analyze the importance of rebalancing the system fleet size and the customer waiting time.Few papers considered a real network (maps) in their simulation. Winter et al. [21] used the map from Open Street Map (OSM) and compared three proactive rebalancing heuristics for SAVs under parking constraints. Brendel et al. [43] used Google Maps to examine and adapt existing carsharing rebalancing policies for SAVs. They combined operation-based relocation (ObR) strategies (relocation between stations) and user-based relocation (UbR) strategies (relocation after rental) in one model to show the positive impact of UbR on the number of served customers.

- Most papers only evaluate periodic relocation. The immediate relocation after rental is considered in only one paper, with the limitation that only one rebalancing operation is possible for the bike when it becomes idle (either periodic or after rental). In this paper, we extend the work of Brendel et al. [43] by allowing for multiple rebalancing operations for the bike.
- Many studies consider open requests, where the customer demand never quits the system if it is not fulfilled. We assume that this representation cannot reflect the real-world case, as the customer cannot wait for too long. In our simulation model, we reject each request that cannot be satisfied within a 10 min waiting time.
- Vehicle routing is not explicitly simulated on a real road network but roughly estimated in a regional manner. Most papers work on a complete graph with a relatively small number of nodes. In this work, we use a detailed routing with a real road network, based on an Open Street Map. Unlike the SAVs, our bikes use the bicycle lanes and have a limited speed of 25 km/h. In addition, each routing mode (manual and autonomous mode) has its own requirements. Thus, we developed our specific routing calculation according to the system requirements.
- For SAVs, the destination of the customer at the time of renting the vehicle is assumed to be known, as the customer books a defined trip. This facilitates fleet optimization and the calculation of the fleet imbalance for the coming period. In our case, the customer books a bike. The trip duration and the customer’s destination are unknown till the customer leaves the bike.
- Operational constraints, such as parking and recharging, are rarely simulated, as in this work.
- The demand profiles used are generally computer-generated demands, which do not accurately capture the complex distribution of origins and destinations in a real transport system. In this work, a detailed population-based demand pattern is used, which imitates the real biking demand in high temporal and spatial detail.
- Small demand and fleet sizes are mostly used in the simulation scenario to manage computational time. Those scalings have unclear effects on the accuracy of the results. In this work, we simulate full demand and calculate the needed fleet size.

## 3. Simulation Setup

#### 3.1. Demand Generation

#### 3.2. Model Description

- Demand generation: we generated the demand data as an origin–destination pair for each region. We fed these demand data as an input to the simulation model, which schedules the customer arrivals. The customer arrives at a random location in the origin region. Then, the customer generates the request for the autonomous cargo bike with the constraints of a 10-min waiting time.
- Matching request: The matching algorithm receives the customer request and checks for available cargo bikes from the fleet near the customer location. It assigns the suitable cargo bike to the customer if available, based on the route calculation.
- Route calculation: The routing algorithm generates the best possible bike route for the selected bike to the customer. Anylogic has an inbuilt routing engine that can provide an online route for the bike. However, we developed a custom route provider using the Graphhopper routing engine to provide autonomous routing and manual routing for our cargo bike. Graphhopper is an open-source java routing engine, which parses OpenStreetMap data and allows for caluclation of the shortest path algorithm between two points [56]. Given two coordinations, GraphHoppers allows the best route for the chosen vehicle profile to be found, considering the chosen weight function. We developed two vehicle profiles, one for the autonomous mode and one for the manual mode, as the routes and the energy consumption differs between the two modes. For a reliable routing, we parsed the specific cycling paths our cargo bike can use using tGraphhopper, by avoiding footways, platforms, pedestrians, steps, and non-paved routes. We also defined the speed of the bike depending on the type of surface tag and highway tag in OSM. To calculate the best route, we implemented our own weighting function, which allows us to determine the route with less energy consumption.
- Bike movement: The cargo bike drives autonomously to the assigned customer. As the customer has the destination region information, the customer drives manually to the random destination location using the manual routing mode.
- Rebalancing: Once the customer reaches the destination, he or she drops the cargo bike. If there is a nearby request, then the bike moves directly to the next customer; otherwise, the chosen rebalancing strategy relocates the bike to the suitable waiting station.
- Output indicators: As an output of the simulation, we collect a list of statistics including the number of customer requests, the number of customers served, service level, time utilization of the cargo bike with the customer and without the customer, energy consumption during autonomous driving and manual driving.

#### 3.3. Fleet Management Strategies

- Relocation after rental: refers to the rebalancing operation applied when a bike becomes idle after serving a customer.
- Periodic relocation: refers to the rebalancing operation which occurs each period T to distribute bikes between the different stations according to future demand.

- Stations distribution: We need to discretize the operating area into a set of rebalancing regions (cells) and locate a station in each region. In this work, we adopt the same discretization of statistical cells, for two reasons. First, because the demand scenarios are defined according to the statistical cells. The second reason is that each statistical cell can be covered in 10 min if we place a station in its center. Therefore, we have placed 14 stations (a station in the center of each region).
- Demand estimation: Predicting upcoming demand has been recognized as an important task [57]. However, as we aim to test different relocation strategies and are not interested in evaluating or developing novel forecasting methods, we assume that we know the mean of the demand distribution (generated according to poisson distribution). This assumption has no impact on our comparison study.
- Imbalance calculation: For each relocation type, we have a specific imbalance calculation.The imbalance for periodic relocation: For each region i, at the period T, we define the periodic imbalance $P{I}_{i}\left(T\right)$ according to Equation (1).$$P{I}_{i}\left(T\right)={N}_{i}\left(T\right)-{D}_{i}\left(T\right)$$
- ${N}_{i}\left(T\right)$: Number of bikes at the beginning of the period T in the cell i.
- ${D}_{i}\left(T\right)$: Estimated number of bikes needed for the current period in the region i.

The imbalance for relocation after rental: For each region i, and for each period T, we calculate the imbalance according to the Equation (2).$${P}_{i}\left(t\right)={N}_{i}\left(t\right)-{D}_{i}\left(t\right)$$- For each period T, T <= t < T + 1
- ${N}_{i}\left(t\right)$: Number of bikes available at t in the region i. This value is updated each time a bike from the region i is assigned to a customer (We decrease the value by 1) or a bike is relocated to the station of region i (we increase the value by 1).
- ${D}_{i}\left(t\right)$: Estimated number of bikes needed at t till the end of the current period T. The value of ${D}_{i}\left(t\right)$ is updated continuously (it decreases when requests are appearing).

- Bike relocation: we assign each idle bike to a station using imbalance calculation. We have defined three different rebalancing strategies and one reference case where no rebalancing strategy is applied:
- No Relocation: this is the case where less idle mileage is traveled, where we do not consider the future demand. For the periodic relocation, no action is needed. However, for the relocation after rental, we move the bike to the waiting station of the customer destination region (bikes need to always be in a waiting station).
- Relocation in the vicinity: for this strategy, bikes can move only to neighboring regions. For the periodic relocation, we relocate bikes from a region with oversupply to undersupplied regions in its vicinity only. The priority is given to the region with the highest deficit in the neighborhood. For relocation after rental, the bike should stay in the customer destination region if it has already a negative imbalance; otherwise, it moves to the region with the highest deficit in the vicinity, If no region has a deficit, the bike is assigned to the waiting station of the destination region.
- Relocate to any undersupplied cell: in this case, we mainly relocate bikes based on the imbalance value. Each region with an undersupply can get bikes from any region with an oversupply (even if it is distant). However, this operation is optimized by selecting the nearest available bike to the undersupplied region. After the rental, the bike should stay in the customer destination region if it has already a negative imbalance. Otherwise, it goes to the region with the biggest deficit.
- Mixed relocation: here, we combine the strategies “Relocation in the vicinity” and “relocation to any”. For cells with a significant undersupply (imbalance ≥ 5), relocation is possible from any cell. However, for cells with a moderate undersupply (imbalance < 5), only relocation from neighboring cells is allowed.

## 4. Case Study Results

#### 4.1. Simulation Settings

- Global service level: percentage of served requests.
- Service level during peak hours: the service level in the evening peak (between 18 h and 19 h).
- Empty vehicle mileage Traveled (VMT) for relocation: total mileage driven to redistribute bikes (for relocation purposes) in km.
- Empty mileage Traveled for relocation per bike in km.
- Energy consumption for relocation: the energy consumed for relocation operation in kWh.
- Energy consumption for relocation per bike in kWh.

- $\overline{x}$ is the sample mean.
- $\u03f5$ allowable percentage error of the estimate.
- t is the inverse of Student’s t CDF evaluated at $1-\alpha /2$ with $n-1$ degrees of freedom.
- $\alpha $ is the level of significance.

#### 4.2. Simulation Results

#### 4.2.1. Simulation of Working Day

#### 4.2.2. Weekend Simulation

#### 4.3. Cost Analysis

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Share | ||||

Time | Monday | Tuesday | Wednesday | Thursday |

0–1 | 1.08% | 1.21% | 1.40% | 1.67% |

1–2 | 0.69% | 0.72% | 0.87% | 1.03% |

2–3 | 0.39% | 0.37% | 0.44% | 0.60% |

3–4 | 0.27% | 0.23% | 0.27% | 0.39% |

3–4 | 0.23% | 0.16% | 0.19% | 0.26% |

5–6 | 0.39% | 0.35% | 0.34% | 0.37% |

6–7 | 1.27% | 1.38% | 1.25% | 1.28% |

7–8 | 4.40% | 4.98% | 4.72% | 4.55% |

8–9 | 8.37% | 9.24% | 8.92% | 8.54% |

9–10 | 5.10% | 5.49% | 5.33% | 5.26% |

10–11 | 3.15% | 3.15% | 3.10% | 3.11% |

11–12 | 3.56% | 3.43% | 3.36% | 3.50% |

12–13 | 4.75% | 4.71% | 4.69% | 4.85% |

13–14 | 5.20% | 4.97% | 5.01% | 5.13% |

14–15 | 5.20% | 4.87% | 4.88% | 5.03% |

15–16 | 6.23% | 5.82% | 5.71% | 5.89% |

16–17 | 7.90% | 7.47% | 7.47% | 7.42% |

17–18 | 10.48% | 9.98% | 9.88% | 9.50% |

18–19 | 10.65% | 10.14% | 10.11% | 9.66% |

19–20 | 7.49% | 7.23% | 7.36% | 7.18% |

20–21 | 4.95% | 5.04% | 5.16% | 5.00% |

21–22 | 3.57% | 3.78% | 3.79% | 3.75% |

22–23 | 2.77% | 3.08% | 3.23% | 3.27% |

23–24 | 1.91% | 2.19% | 2.51% | 2.78% |

Share | ||||

Time | Friday | Saturday | Sunday | |

0–1 | 2.09% | 3.52% | 3.69% | |

1–2 | 1.47% | 2.97% | 3.42% | |

2–3 | 0.93% | 2.15% | 2.65% | |

3–4 | 0.63% | 1.53% | 1.99% | |

4–5 | 0.41% | 1.10% | 1.50% | |

5–6 | 0.43% | 0.80% | 1.11% | |

6–7 | 1.16% | 0.62% | 0.82% | |

7–8 | 3.98% | 0.78% | 0.86% | |

8–9 | 7.43% | 1.46% | 1.29% | |

9–10 | 4.81% | 2.69% | 2.33% | |

10–11 | 3.16% | 3.86% | 3.57% | |

11–12 | 3.63% | 5.27% | 4.88% | |

12–13 | 4.94% | 6.43% | 6.41% | |

13–14 | 5.60% | 7.33% | 7.66% | |

14–15 | 5.94% | 7.75% | 8.42% | |

15–16 | 6.66% | 7.77% | 8.52% | |

16–17 | 7.82% | 7.38% | 8.28% | |

17–18 | 8.51% | 7.48% | 8.05% | |

18–19 | 7.93% | 6.96% | 7.08% | |

19–20 | 6.45% | 6.22% | 5.86% | |

20–21 | 5.18% | 5.16% | 4.24% | |

21–22 | 4.01% | 3.98% | 3.04% | |

22–23 | 3.48% | 3.36% | 2.48% | |

23–24 | 3.33% | 3.43% | 1.85% |

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**Figure 1.**The basic concept of OSABS [9].

Distance d (km) | Share Distance (%) | Share Foot (%) | Share Bike (%) | Share Public Transport (%) | Share Car (%) |
---|---|---|---|---|---|

$0\le d<1$ | 18.32 | 75.26 | 9.54 | 9.95 | 5.24 |

$1\le d<2$ | 58.74 | 62.11 | 11.65 | 16.35 | 9.88 |

$2\le d<3$ | 12.84 | 39.52 | 18.29 | 27.36 | 14.82 |

$d\ge 3$ | 10.10 | 20.69 | 21.64 | 31.48 | 26.18 |

Relocation Strategy | Relocation VMT (km) | Relocation VMT per Bike (km) | Relocation Energy (kWh) | Relocation Energy per Bike (kWh) |
---|---|---|---|---|

No rebalancing | 3100.785 | 28.189 | 107.154 | 0.974 |

To vicinity | 4736.338 | 43.058 | 155.249 | 1.411 |

To any | 5736.59 | 52.151 | 176.198 | 1.602 |

Mixed rebalancing | 5474.413 | 49.767 | 173.315 | 1.576 |

**Table 3.**Average mileage traveled per bike for the different rebalancing strategies with different fleet sizes (in km).

Relocation Strategy | 95 Bikes | 90 Bikes | 85 Bikes | 80 Bikes | 75 Bikes |
---|---|---|---|---|---|

No rebalancing | 27.2 | 26.74 | 28.24 | 28.41 | 28.76 |

To vicinity | 43.52 | 44.2 | 44.53 | 45.43 | 43.98 |

To any | 56.16 | 55.17 | 55.18 | 55.01 | 52.93 |

Mixed rebalancing | 52.34 | 51.82 | 53 | 51.26 | 51.14 |

**Table 4.**Average energy consumption per bike for the different rebalancing strategies with different fleet sizes (in kWh).

Relocation Strategy | 95 Bikes | 90 Bikes | 85 Bikes | 80 Bikes | 75 Bikes |
---|---|---|---|---|---|

No rebalancing | 0.94 | 0.93 | 0.98 | 0.99 | 1 |

To vicinity | 1.42 | 1.44 | 1.45 | 1.47 | 1.43 |

To any | 1.7 | 1.68 | 1.69 | 1.68 | 1.62 |

Mixed rebalancing | 1.62 | 1.61 | 1.65 | 1.6 | 1.6 |

Bike Capital | Bike Maintenance | Charging Station Implementation | Charging Station Maintenance | Electricity (per kWh) | |
---|---|---|---|---|---|

Costs in € | 30,000 € | 300 €/month | 45,000 € | 150 €/month | 0.3 €/kWh |

Costs per hour in € | 0.856 € | 0.417 € | 0.514 € | 0.208 € |

Rebalancing Strategy | In-Use Energy Consumption in kWh | Idle Energy Consumption in kWh | Energy Consumption Costs | Fleet Size | Fixed Costs |
---|---|---|---|---|---|

No rebalancing | 412.553 | 479.668 | 267.667 € | 105 | 2113.014 € |

To vicinity | 464.963 | 349.095 | 244.217 € | 90 | 2070.844 € |

To any | 522.414 | 338.20 | 258.184 € | 90 | 2084.811 € |

Mixed | 497.808 | 342.937 | 252.223 € | 90 | 2078.85 € |

Rebalancing Strategy | Total Costs | Number of Satisfied Trips | Cost per Trip |
---|---|---|---|

No rebalancing | 2380.681 € | 8072 | 0.295 € |

To vicinity | 2070.844 € | 8017 | 0.258 € |

To any | 2084.811 € | 8014 | 0.260 € |

Mixed | 2078.85 € | 8013 | 0.259 € |

Bike-Sharing System | Rebalancing Costs per Hour and Bike |
---|---|

OSABS | 0.03 €/h-bike |

Traditional | 0.028 €/h-bike |

Electrical | 0.09 €/h-bike |

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## Share and Cite

**MDPI and ACS Style**

Haj Salah, I.; Mukku, V.D.; Kania, M.; Assmann, T.
Towards Sustainable Liveable City: Management Operations of Shared Autonomous Cargo-Bike Fleets. *Future Transp.* **2021**, *1*, 505-532.
https://doi.org/10.3390/futuretransp1030027

**AMA Style**

Haj Salah I, Mukku VD, Kania M, Assmann T.
Towards Sustainable Liveable City: Management Operations of Shared Autonomous Cargo-Bike Fleets. *Future Transportation*. 2021; 1(3):505-532.
https://doi.org/10.3390/futuretransp1030027

**Chicago/Turabian Style**

Haj Salah, Imen, Vasu Dev Mukku, Malte Kania, and Tom Assmann.
2021. "Towards Sustainable Liveable City: Management Operations of Shared Autonomous Cargo-Bike Fleets" *Future Transportation* 1, no. 3: 505-532.
https://doi.org/10.3390/futuretransp1030027