**5. Exploratory study**

Using a systems model allows us to simulate different scenarios, varying the values assigned to exogenous parameters. In this section, we will explore in greater detail the potential parameters of a shuttle service operating on a ring basis, focusing on two major investigative variables: technology and regulation.

## **5.1 The strategic domain**

These two investigative variables represent the two dimensions of our strategic domain. We will now cross-compare four different levels of technology and three potential regulatory regimes, giving 12 possible scenarios.

These levels of technology in fact represent successive technological generations which succeed one another in a process of progressive accumulation. Level zero, the 'pre-platform' generation, corresponds to the situation which prevailed before the emergence of platform-based services. For each trip, we take into account a user transaction time of 2 minutes as an additional ride time in the generalised cost. The first level corresponds to the platform era, defined by the advent of platform technologies and the radical simplification of transaction operations for customers. We use the abbreviation PF to represent this level. The 2nd generation of the platform uses electric vehicles, better suited to the urban environment and more economical when used intensively. We can abbreviate this as PF + EV, where EV stands for Electric Vehicles. The 3rd generation of the platform might incorporate self-driving shuttles, drastically reducing the primary cost of production. We can abbreviate this to PF + EV + AD, where AD stands for Autonomous Driving.

The regulatory axis comprises three possible regimes: monopoly (MO), first-best system optimum (SO), second-best system optimum (S2). Abbreviation MO stands for an unregulated monopoly, with a single operator dedicated to maximising profit. Abbreviation SO stands for a socio-economic system optimum of both supply and demand; then the objective function includes the net profit to the operator and the net surplus for users (the total reserve price less the generalised cost). Abbreviation S2 stands for an optimised system subject to the constraint of balancing the production budget. The objective function is the same as for SO, but optimisation is restricted to all values of the paired parameters (fleet size and price) which ensure a non-negative return for the service provider. As such, S2 is an intermediate solution between SO and MO.

#### **5.2 The projected service**

We have chosen to focus on a collective service running shuttles whose capacity *K* ¼ 12 places. The ring format is consistent with the service cycle principle, and the vehicle capacity corresponds to the shared occupancy associated with public transport.

The limited capacity of the vehicles offers several advantages. First, as for the physical form of the vehicles, limiting their size should allow for a certain degree of agility on the road network, and reduce the disruption of other traffic caused by the shuttles stopping to pick up/drop off passengers. Second, per user ride, limiting

*Towards Shared Mobility Services in Ring Shape DOI: http://dx.doi.org/10.5772/intechopen.94410*

*<sup>τ</sup>*uð Þ *<sup>x</sup>* . Each of the other curves represents a specific pricing policy adopted by the producer: proposed price *<sup>τ</sup>*oð Þ *<sup>x</sup>* . Optimising production consists of levelling up demanded price and proposed price: for each producer behaviour, the corresponding

*Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

, τ<sup>∗</sup>

Using a systems model allows us to simulate different scenarios, varying the values assigned to exogenous parameters. In this section, we will explore in greater detail the potential parameters of a shuttle service operating on a ring basis, focusing on two major investigative variables: technology and regulation.

These two investigative variables represent the two dimensions of our strategic domain. We will now cross-compare four different levels of technology and three

These levels of technology in fact represent successive technological generations which succeed one another in a process of progressive accumulation. Level zero, the 'pre-platform' generation, corresponds to the situation which prevailed before the emergence of platform-based services. For each trip, we take into account a user transaction time of 2 minutes as an additional ride time in the generalised cost. The first level corresponds to the platform era, defined by the advent of platform technologies and the radical simplification of transaction operations for customers. We use the abbreviation PF to represent this level. The 2nd generation of the platform uses electric vehicles, better suited to the urban environment and more economical when used intensively. We can abbreviate this as PF + EV, where EV stands for Electric Vehicles. The 3rd generation of the platform might incorporate self-driving shuttles, drastically reducing the primary cost of production. We can abbreviate this to PF + EV + AD, where AD stands for Autonomous Driving.

The regulatory axis comprises three possible regimes: monopoly (MO), first-best system optimum (SO), second-best system optimum (S2). Abbreviation MO stands for an unregulated monopoly, with a single operator dedicated to maximising profit. Abbreviation SO stands for a socio-economic system optimum of both supply and demand; then the objective function includes the net profit to the operator and the net surplus for users (the total reserve price less the generalised cost). Abbreviation S2 stands for an optimised system subject to the constraint of balancing the production budget. The objective function is the same as for SO, but optimisation is restricted to all values of the paired parameters (fleet size and price) which ensure a non-negative return for the service provider. As such, S2 is an

We have chosen to focus on a collective service running shuttles whose capacity *K* ¼ 12 places. The ring format is consistent with the service cycle principle, and the vehicle capacity corresponds to the shared occupancy associated with public

The limited capacity of the vehicles offers several advantages. First, as for the physical form of the vehicles, limiting their size should allow for a certain degree of agility on the road network, and reduce the disruption of other traffic caused by the shuttles stopping to pick up/drop off passengers. Second, per user ride, limiting

potential regulatory regimes, giving 12 possible scenarios.

intermediate solution between SO and MO.

**5.2 The projected service**

transport.

**176**

) which ensures that *<sup>τ</sup>*oð Þ¼ *<sup>x</sup> <sup>τ</sup>*uð Þ *<sup>x</sup>* .

solution is the point of intersection (x<sup>∗</sup>

**5. Exploratory study**

**5.1 The strategic domain**

capacity enables us to reduce the number of stops required to pick up/drop off other passengers, thus increasing the usage speed. Third, in order to satisfy a given volume of ridership, the fleet size will need to be bigger than it would if using vehicles with a larger capacity (e.g. around fifty passengers in a standard city bus); this makes it possible to increase the frequency of the service, and thus to reduce the time users wait to access it. Fourth, on an industrial level, the manufacturing of larger fleets of vehicles allows for greater economies of scale and drives down the cost price per vehicle. We might also expect to see greater flexibility in the internal logistics of the service: recharging, cleaning, maintenance and repairs. Fifth, regarding the environment, the impact of the fleet of vehicles over its whole life cycle may be reduced: this applies to the construction phase, due to the industrial efficiency gained, and the usage phase, thanks to the increased operating speed.

Nevertheless, using collective vehicles of a smaller size presents at least two disadvantages. The first is that the cost of driving the vehicles is increased proportionally to 1*=K*. The second is the sacrifice of potential economies of scale for vehicles powered by combustion engines. However, these disadvantages could feasibly be swept away by the advent of self-driving vehicles and the rise of electric motors.

For all of the simulation scenarios envisaged here, the common parameters are as follows. The territorial aspects involve:


For demand:


Production parameters:


#### **5.3 Results and discussion**

**Table 3** presents the main results for each of the 12 scenarios simulated, with one scenario per column. The scenarios are grouped by technological generation, in ascending order of technological progress. For each generation, the three regulatory regimes are presented in the order MO – S2 – SO.

Taking all of these scenarios into consideration, the following indicators emerge. The number of trips per day varies from 455 to 8000 depending on the scenario. The size of the fleet varies from 4 vehicles, for an MO regime in the pre-platform



era, to 60 for an SO system running PF + EV + AD: this fifteen-fold increase needs to be seen in light of the respective passenger numbers. The number of trips provided by vehicle and by day varies from 95 for self-driving technologies to 220 for the generation PF + EV. In the simulated range of traffic conditions, the 'Load factor' is almost equivalent to the average number of passengers per shuttle. The values range from 1.4 to 4.0, suggesting that the capacity of 12 seats per shuttle is

Average access time varies from one hundredth of an hour to one tenth of an hour. It is systematically lower in SO and S2 regimes than in MO systems using the same technology. Average journey time does not vary so substantially, ranging from 0.22 to 0.28 hours. For each technology, this time is systematically lower in MO systems than it is in SO regimes. Fare per trip varies from €0.7 to €9. For each generation of technology, it is systematically much higher in MO than in SO and S2, from the platform generation onwards. Furthermore, the pre-platform fares in S2 systems are lower than those charged in the MO regime with the most advanced technology PF + EV + AD, which goes to show that offering an efficiently-organised service, combining a ring format with a suitable regulatory regime, is more important than the generation of technology deployed. The generalised cost per trip ranges from €2.6 to €15, obtained by adding the fare and the average ride and access

The commercial revenue is large, varying from €3400 to €8000 per day. Both of

these extremes are found in the most advanced technological generation. Daily production costs are also substantial, ranging from €2600 to €9400 and depending on the generation of technology used and the operating system (via fleet size). Daily operating profit is nil for S2 regimes, highly positive for MO (increasingly so as technology advances) and negative for SO, with lower profits for the most

advanced technologies compared with intermediate technologies. The daily surplus of demand is very large: double the total generalised cost to passengers, due to the specific elasticity of the demand function. For each generation of technology, the SO and S2 operating regimes are much more beneficial to demand than the MO system, while the difference between SO and S2 is relatively slight. The socioeconomic benefits of the service ('system profit Puo') are massive: these benefits increase with each technological generation, and within each generation of technology the profit is greater for system optimal regimes than for the unregulated

In summary, the configuration of the service requires a large fleet of vehicles, indeed a very large fleet to make the system 'optimal'. Optimising operations gives us a number of trips per shuttle and per day ranging from one to two hundred. This gives us an idea of scale. A vehicle capacity of 6 or 8 places should be sufficient. Generally speaking, technological progress leads to quantitative and qualitative improvement of the service. However, switching to fully self-driving technology alters the economics of production and reconfigures certain parameters: for some indicators, the variation in performance as technology advances is a U-shaped curve rather than a steady increase. The regulatory regime has a major influence, which becomes increasingly important with each successive generation of technology. S2 systems are most compatible with achieving financial equilibrium in the production of the service, while also getting as close as possible to the system's first-best socioeconomic optimum. They allow for relatively moderate tariffs compared with monopoly scenarios (which operate at the same rate as pre-platform era taxis).

The target value for the most advanced generation of technology is €0.7 per trip,

for an average length of 3.7 km. For intermediate technological generations, S2 prices are around €3.5 per trip. These values provide points of reference which can

help us to gauge the current economics of public transport services.

**179**

monopoly (in keeping with the definition of the system optimum).

excessive and a 6 or 8-seater alternative would be sufficient.

*Towards Shared Mobility Services in Ring Shape DOI: http://dx.doi.org/10.5772/intechopen.94410*

times (weighted by their specific time values).

### *Towards Shared Mobility Services in Ring Shape DOI: http://dx.doi.org/10.5772/intechopen.94410*

era, to 60 for an SO system running PF + EV + AD: this fifteen-fold increase needs to be seen in light of the respective passenger numbers. The number of trips provided by vehicle and by day varies from 95 for self-driving technologies to 220 for the generation PF + EV. In the simulated range of traffic conditions, the 'Load factor' is almost equivalent to the average number of passengers per shuttle. The values range from 1.4 to 4.0, suggesting that the capacity of 12 seats per shuttle is excessive and a 6 or 8-seater alternative would be sufficient.

Average access time varies from one hundredth of an hour to one tenth of an hour. It is systematically lower in SO and S2 regimes than in MO systems using the same technology. Average journey time does not vary so substantially, ranging from 0.22 to 0.28 hours. For each technology, this time is systematically lower in MO systems than it is in SO regimes. Fare per trip varies from €0.7 to €9. For each generation of technology, it is systematically much higher in MO than in SO and S2, from the platform generation onwards. Furthermore, the pre-platform fares in S2 systems are lower than those charged in the MO regime with the most advanced technology PF + EV + AD, which goes to show that offering an efficiently-organised service, combining a ring format with a suitable regulatory regime, is more important than the generation of technology deployed. The generalised cost per trip ranges from €2.6 to €15, obtained by adding the fare and the average ride and access times (weighted by their specific time values).

The commercial revenue is large, varying from €3400 to €8000 per day. Both of these extremes are found in the most advanced technological generation. Daily production costs are also substantial, ranging from €2600 to €9400 and depending on the generation of technology used and the operating system (via fleet size). Daily operating profit is nil for S2 regimes, highly positive for MO (increasingly so as technology advances) and negative for SO, with lower profits for the most advanced technologies compared with intermediate technologies. The daily surplus of demand is very large: double the total generalised cost to passengers, due to the specific elasticity of the demand function. For each generation of technology, the SO and S2 operating regimes are much more beneficial to demand than the MO system, while the difference between SO and S2 is relatively slight. The socioeconomic benefits of the service ('system profit Puo') are massive: these benefits increase with each technological generation, and within each generation of technology the profit is greater for system optimal regimes than for the unregulated monopoly (in keeping with the definition of the system optimum).

In summary, the configuration of the service requires a large fleet of vehicles, indeed a very large fleet to make the system 'optimal'. Optimising operations gives us a number of trips per shuttle and per day ranging from one to two hundred. This gives us an idea of scale. A vehicle capacity of 6 or 8 places should be sufficient.

Generally speaking, technological progress leads to quantitative and qualitative improvement of the service. However, switching to fully self-driving technology alters the economics of production and reconfigures certain parameters: for some indicators, the variation in performance as technology advances is a U-shaped curve rather than a steady increase. The regulatory regime has a major influence, which becomes increasingly important with each successive generation of technology. S2 systems are most compatible with achieving financial equilibrium in the production of the service, while also getting as close as possible to the system's first-best socioeconomic optimum. They allow for relatively moderate tariffs compared with monopoly scenarios (which operate at the same rate as pre-platform era taxis).

The target value for the most advanced generation of technology is €0.7 per trip, for an average length of 3.7 km. For intermediate technological generations, S2 prices are around €3.5 per trip. These values provide points of reference which can help us to gauge the current economics of public transport services.

**Technology**

**178**

**Regulation**

Load factor *x*

Ride price *τ*

Gen. cost *g*

Demand

Fleet size

Costs

*C* Revenues *R* System profit

**Table 3.** *Principal results for the simulated scenarios.*

 8270

 12550

 13760

 9193

 13900

 15040

 8100

 12200

 13500

 19000

 26400

 29000

3980

 5210

 4640

 4450

 5860

 5200

 3400

 5500

 5060

 8000

 7100

 6000

2600

 5210

 8280

 2855

 5860

 9300

 2800

 5500

 9400

 3000

 7100

 8200

*N*

4.0

 8.9

 14.4

 4.5

 10.1

 16.4

 4

8

14

20

50

60

*Q*

455

 1510

 2913

 554

 1858

 3500

 450

 1500

 3100

 1900

 6800

 8000

14.6

 8.3

 5.93

 13.7

 7.5

 5.45

 15

8.5

5.8

7.4

4.0

2.6

*Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

8.75

 3.45

 1.59

 8.02

 3.2

 1.55

 9.0

 3.8

1.7

4.4

1.1

0.7

 **MO** 1.72

 2.82

 3.5

 1.9

 3.08

 3.8

 1.85

 3.1

 4.0

1.4

2.0

 2.05

 **S2**

 **SO**

 **MO**

 **S2**

**SO**

 **MO**

 **S2**

**SO**

 **MO**

**S2**

**SO**

**0:** 

**preplatform**

**1: platform**

**2: PF + EV**

**3: PF + EV + AD**
