The Royal Borough of Greenwich (RBG) is one of thirty-three local authority administrative areas in London. It represents a typical urban area where AMoD services might complement the existing public transport services. As part of the MERGE Greenwich project (Addison Lee 2018; MERGE Greenwich 2017) different configurations of the IMSim ride-share model were tested to evaluate the potential viability of alternative future AMoD service-blueprints, from the operator-, city- and traveller- perspectives.
The agent-based nature of the overall IMSim model can generate very detailed results with wide applicability. These allow different stakeholders to focus on different aspects of the results. For example, a fleet operator will tend to focus on operational issues, such as fleet size, service performance, occupancy per vehicle, etc. City authorities will focus on the wider, social, legal, transport and administrative borough-level responsibilities, including the effects on congestion, modal shift from private and public transport, and fairness and accessibility for the broader community etc. This section presents a case study based on the RBG that shows how our tool can be used to address a variety of issues from both the operator and city perspectives.
Although the focus area for the AMoD ride-share service was limited to the Royal Borough of Greenwich, the whole of London (see Fig. 3) had to be modelled to some extent, to capture the trips into, out of, and across Greenwich. The overall MATSim model therefore included 65 zones, of which 33 are within Greenwich, and 32 for the other London boroughs. The network model was comprised of 14,063 nodes and 26,312 links.
The total simulated demand involving Greenwich as origin, destination or via point was 640,641 trips, of which 27% (165,813) take place wholly within Greenwich. Only these “in-Greenwich” trips were allowed to switch to the AMoD service. The rest of the trips were private car trips crossing Greenwich and were simulated so that congestion effects could be properly evaluated.
The MERGE Greenwich case study: the service blueprints
The MATSim–IMSim simulator was used to analyse different market propositions as part of the MERGE Greenwich project. The market propositions were called “service blueprints” and were differentiated by the available vehicle types (fleets or tiers), the level of service offered, and the price to the consumer.
Separate vehicle fleets provide the different service tiers. This study considered two tiers of fleet:
- 1.
Minibus (8-seater), and
- 2.
Standard (4-seater).
Like any service operator, the model allowed the delivery a (defined) service levels for each tier:
- 1.
Minibus: 80% of trips served; 500% detour ratioFootnote 3 tolerated; 25 min max waiting time.
- 2.
Standard: 95% of trips served; 300% detour ratio tolerated; 15 min max waiting time.
Three Service Blueprints were considered:
- 1.
Accessibility Blueprint:
- 2.
Convenience Blueprint:
- 3.
Combined Blueprint:
The configurable parameters for each simulation can be grouped in four classes:
The MERGE Greenwich case study: calibration and validation
Many approaches having been suggested for the calibration of agent-based models (Balakrishna et al. 2007; Fabretti 2014; Bianchi et al. 2007). From a transport modelling perspective, interest seems to have centred around the calibration of microsimulation models under Dynamic Traffic Assignment (DTA). Recent studies (Ben-Akiva et al. 2012; Oh et al. 2019) have suggested variations of the Simultaneous Perturbation Stochastic Approximation (SPSA) to estimate the requisite model parameters that minimise the discrepancy between the predicted and observed mode shares (e.g. by using traffic counts, surveys, etc.). Specific to the calibration of MATSim, the Cadyts calibration framework (Flötteröd 2009) offers a calibration approach that adjusts all the available parameters (such as departure time and mode choice) so that traffic counts are matched.
Despite the existing body of research, the authors believe that significantly more research is required to improve confidence in the calibration and validation of agent-based models. For this study, the proposed model was calibrated using a trial and error approach until the mode shares of the permitted simulated modes (prior to the introduction of the AMoD service) matched the corresponding values estimated by NTEM. According to NTEM the mode share for the year 2025 for private car is roughly 75% while for bus 25%. The transport mode-shares in Greenwich were calculated omitting modes not modelled (such as trips completed by overground or underground train).
Validation of the MERGE Greenwich models against prevailing travel demand and traffic conditions was performed through a cordon analysis and travel-time comparison process. UK Department for Transport data was used to compare the number of trips entering and exiting Greenwich against the corresponding number calculated by the model. Although there is room for improvement, actual trip numbers differed from the predicted trip numbers by less than 10%. A travel time comparison showed Google Maps® data to be broadly comparable with our model prediction (Fig. 5).
Even though our results could have been improved, they were considered adequate for the purposes of this research project. We believe that a significantly more detailed calibration and validation exercise is required before the model can be used for production purposes. However, this was beyond the scope of this study which was mostly focused on developing the required tools and methodology for the assessment of AMoD services.
The MERGE Greenwich case study: the operator’s perspective
Fleet-sizing
The full MERGE Greenwich blueprint has four different tiers or fleets. In this experiment we studied how the number of vehicles affected the level of service in a tier. We assumed long-range ICE vehicles and studied the number of vehicles in a single fleet, one fleet at a time. The vehicles were assumed to have the following power-plant parameters:
Fuel Capacity: 70.5 L (i.e. 18.62 gallons or 0.0705 m3);
Fuel Consumption rate: 20.9 L/100 km, (i.e. 11.25 mpg or 2.09 × 10−7 m3/m);
Refuelling time: 3 min (i.e. 180 s).
The full City Perspective analysis allows the MATSim model to adapt: the demand changes according to the customer-experience of using the fleets. For fleet-sizing studies, however, this creates a closed loop between demand (trips) and vehicles (supply). Convergence is not guaranteed, and the more complex analysis is beyond the scope of this paper. For fleet-sizing we therefore freeze MATSim’s daily demand for AV ride-share (AVRS), as shown in Fig. 6, to study the ability of different sized fleets to serving the demand with different numbers of vehicles.
By fixing the number of vehicles for three of the tiers and only changing the number of vehicles in one fleet tier, (e.g. the minibuses), it is possible to see how adding more vehicles better serves the number of trip-requests. Figures 7 and 8 show this effect in the form of occupancy distribution graphs for the standard and minibus tiers for the three different minibus fleet sizes. The figures show the proportion of the fleet with a given number of passengers-on-board for 30-min time intervals throughout the day, where the proportion is measured by counting the number of vehicles with a specific head count and dividing by the total number of vehicles in the specified tier. The vehicle occupancy is indicated by colour (0-blue, 1-orange, 2-green, 3-red, 4-purple, 5-brown). With a small minibus fleet, ride-sharing is more evident: more purple in the histograms. With a larger minibus fleet, ride-sharing is less evident: less purple.
Collectively the curves like those shown in Figs. 7 and 8 have been useful in estimating the necessary fleet sizes that give the required levels of service. We estimated ‘Base Case’ fleet sizes as requiring the following number of vehicles to give the indicated level of service (i.e. trips-served):
Electrification
In the second set of experiments we studied the impact of using electric vehicles (EV), replacing the ICE minibus tier with an EV minibus fleet with the following vehicle specifications:
Energy Capacity: 80 kWh (c.f. 70.5 L of Petrol or 0.0705 m3);
Energy Consumption rate: 1440 J/m (c.f. 11.25 mpg or 2.09 × 10−7 m3/m, Petrol.)
Range: 200 km (c.f. 330 km, Petrol)
Recharge time: 30 min (c.f. 3 min, Petrol)
All the other parameters, such as fleet size and vehicle specifications for the remaining tiers were kept constant.
Figure 9 show the vehicles’ states over time: Idle, On-Call, In-Service, Waiting-Passenger, Dead-Running-To-Depot, Dead-Running-To-Recharge, Recharging. Each horizontal ‘bar’ indicates the state of an individual vehicle as it moves through the simulated day. The difference between the electric and the petrol tier is highlighted by the recharging pattern (in purple) in the case of the electric fleet.
The bands of purple indicate when the EV minibuses return for recharge. Potentially this points to an operational problem as no minibuses will be available during parts of the morning and evening peaks. It is interesting to note that the service level only dropped by 4% so the overall service target was still satisfied. However, some of the ‘minibuses’ trips were served by the standard tier, compensating for the absent minibuses when they are recharging. This demonstrates that the ride-share model and vehicle allocation is behaving as intended. The service levels achieved for Experiment 2 are listed below (compared to the base case):
Minibus: 82% (vs. 86%)
Standard: 94% (vs. 91%)
Ride-share versus taxi
In the third set of experiments, we compared a non-ride-share simulation against the base ride-share case. All the parameters were kept constant except for the ride-share dispatcher components that were switched off and substituted by a traditional taxi-like, nearest-vehicle allocation of a single rider per vehicle. As a result, service levels consistently drop, as indicated below (compared to the base case):
Minibus: 44% (vs. 86%)
Standard: 40% (vs. 91%)
The observed drop happens mainly because ride-sharing allows a fleet to serve a greater number of rides. For a taxi-like service, the fleet is less utilised, as each vehicle now only allows one passenger at a time and trip-requests get cancelled once the maximum waiting time is exceeded:
Minibus: 25 min
Standard: 15 min
The number of trips served is now lower than the total trip-requests causing the observed drop in service level.
The MERGE Greenwich case study: the city’s perspective
This section focuses on outcomes of most interest to the city and transport authorities, such as RBG and Transport for London. These authorities need to develop a detailed understanding of how nascent AMoD services will affect citizens and travellers. Impact assessments—using simulation tools—can help authorities to understand the effect of new mobility services on, for example, the existing road network performance, accessibility, and shifts in public transport. Extending the earlier fleet-sizing and operating characteristics experiments, we can assess the various MERGE Greenwich project service blueprints. Now, with fleet sizes fixed, the demand floats freely and permit shifts in travel mode according to the MATSim methodology described previously. Table 1 summarises the effect on key performance indicators (KPIs) resulting from introducing AMoD ride-share services in Greenwich.
Table 1 Scenario (Blueprints) comparison from a city-perspective Table 1 shows that the three proposed blueprints give considerably different outcomes. The most successful blueprint in terms of total trip-requests is the Accessibility Blueprint while the least is the Convenience Blueprint. This seems to be driven by the low cost of the Accessibility Service coupled with a high level of service. Regardless of the blueprint, accessibility levels improve significantly because the travel-time reduction between buses and AMoD services is between 41 and 21%. The AMoD services also show a great potential for reducing the parking space requirements because of a 16–38% reduction in the number of trips requiring parking.
Nevertheless, the new AMoD services may not always have a positive effect. For instance, although the private car-usage is reduced by between 6 and 15%, bus trips are also reduced by 8–34%. This highlights the potential threat from new services to core public transport patronage. Somewhat surprisingly, the total number of kms driven by all vehicles (including the AMoD fleet) throughout the network also increases significantly by 57%. So, although travel times for private car users are reduced by 4%, CO2 emissions increase by 24% because of the overall increase in total vehicle distances driven.
Despite the apparently negative impact of some aspects of the proposed AMoD services, we believe the model has achieved its primary goal: the realistic simulation of AMoD fleet services in a city compatible environment. Clearly the potential for negative impact needs to be understood. Further research is clearly required. However, we believe that we have presented a tool to facilitate the forward-looking research: our MATSim–IMSim co-simulation tool will allow robust scenarios to be developed to test the transport environment of the future.