A multi-agent system (MAS) is a self-organized computerized system that has many intelligent agents interacting with one another solving problems that are difficult to solve by a single tight and rigid system [35–37]. The agents’ programmed intelli-gence can be procedural, functional, methodical or, in more complicated instances, use various methods, such as finite-state machine learning or algorithmic searches.
This means the agents’ intelligence is scalable from using a simple reactive rule-based decision-making mechanism to choosing the best option given by machine-learning algorithm. The MAS used in papersIIandVare simple and involve agents that only need to make decisions that have binary choices. In paperII, the agents simulating energy brokers only need to decide if they want to participate in the brokering. Likewise, in paperV, the passengers participating in the lift simulation only need to decide on which floor they want to disembark the passenger lift.
In complex situations where there are many considerations and agents can have more than binary choices, data can be synthesized by using a MAS that has agents with built-in artificial intelligence (AI) [38]. Their AI is explainable and determinis-tic. In papersIandIII, a MAS is used to simulate traffic movements on Singapore’s road network. Each vehicle’s velocity was measured while traveling on the road,
and the data was used" to improve clarity and flow here was used to estimate the corresponding energy consumption of all vehicles in Singapore. In paperIV, a MAS is used to simulate the before and after traffic movements of Joensuu, Finland with and without a new bridge. These results could be used in a study to determine the town’s road congestion if, for example, a bridge were closed for repairs.
A MAS can also have emergent behaviors, which are not programmed initially as part of the agents’ original intelligence; instead, they are an outcome of interactions, which result in a cause-and-effect type of relationship among the outcomes [39]. For instance, in papersIandIII, traffic congestion was observed as a result of all drivers on the road converging on common paths in the road network that they have cho-sen. In paperIV, new route choices that did not pass through town were observed due the closing of bridges. These behaviors need not be purposely considered in the simulation. Unlike cases where traditional stochastic modeling is used, the modeler will need to factor in such behaviors. This is an added benefit of using a MAS for modeling [39].
MAS are closed modeling simulations in which every action and reaction in it is deterministic. The agents’ and environment’s behaviors are all pre-programmed into the simulation [38]. This means the interactive agent and environment behaviors are explainable. However, the agent and agent-to-environment interactions may have emergent behaviors that were incorporated into the modeling [39]. For example in paperIVwhen a bridge was closed, some roads became congested due to drivers selecting new routes. In papersI,IIIandIV, MAS was used to synthesize the data required for optimization. Furthermore, due to the model’s complexity, data pre-processing and post-processing were required so that it could be executed using a MAS and raw data produced by the MAS simulation could be meaningfully used.
In paperI, we begin to answer the question of energy load estimation for cars, as mentioned in Chapter 1. We begin to Explore different ways to achieve energy load estimation for cars. We surveyed extensively and found that A MAS could be useful in estimating the energy load of cars driven by humans, modeled after human driving behavior. To ensure that our synthesized data would be similar to actual data, we searched Singapore’s Land Transport Authority database, main-tained by the government agency in charge of managing road traffic in Singapore, for the country’s vehicle statistics. In paperI, actual Singapore’s transportation de-mographics were used (Table 1). In total, there were 957,006 vehicles on Singapore’s roads in 2018. However, not all them were modeled. Essentially, only vehicles that contribute to traffic congestion were chosen. For instance, motorcycles and scooters were not modeled, as the do not contribute significantly to traffic congestion, but other vehicle types were modeled. Some vehicle characteristics are accounted for in the model and can be implemented in the agents’ scheduling in the form of road network entry and exit timings, as explain in paper hjnmI.
The data used in this MAS modeling were combined with additional data, such as Singapore residents’ home locations and manpower distribution with work loca-tions. This combination estimated the number of residents who drove vehicles and where they were likely to drive to during the day. Asorigin-destination(OD) matrices are efficient for keeping track of traffic conditions [40], we produced one with the
Table 1. Distribution of vehicles in Singapore. Image source: Land Transport Authority of Singapore
combined information described above. In addition, assumptions must be added here so that the MAS will account for them during execution. The assumptions be-low were used in building the MAS simulation model in paperI. Subsequently, the same MAS model was used in paperIII.
Assumption 1. All drivers stay in the designated residential areas.
Assumption 2. Jobs are distributed according to Singapore’s manpower statistics.
Assumption 3. All drivers work on the island.
Assumption 4. Every driver in Singapore will drive out at least once per day.
A caveat exists for Assumption 3, as the number of Singaporeans working in nearby Malaysia is small and does not affect traffic conditions significantly. There-fore, we did not consider its effects in the simulation. To improve the simulation’s
accuracy, we added the drivers’ lifestyle information. In papersIandIII, working-class drivers may travel between home and work regularly every day and may visit other places for dinner and other leisure activities after work. Hence, we model three different group of drivers for this simulation. The first group drives to work and home only, making two trips per day. The second group may drive to work and one other destination. The last group drives to multiple locations in a day. This level of detail makes the simulation more similar to real-life daily situations and may be inconsequential at this point in the thesis, but it will impact the processing time consideration later in Section 4.3. The pseudo codes for generating the cars’
departure schedules are provided in Section A.1 of the Appendix. The schedules generated serve as the cars’ entrance time into the road network system and do not define their exit times because we do not want cases of cars disappearing off the road network system due to road congestion and being mysteriously transported to their end destinations.
The most important reason we use a MAS for our study is because each agent can be programmed with intelligence, and each agent is capable of acting indepen-dently according to its surroundings [35]. We want to study human behavior during driving; hence, we only program driving-related behaviors into our MAS. These be-haviors are used for route planning in the pre-journey stage and some reactions required during the journey, such as taking detours due to traffic delays or route unavailability in the original planned route. Intelligence for post-journey behavior is not required, as it does not affect the journey. What drivers do and how they react to situations at their destinations are inconsequential to the journey data in our study. These are high-level driving behaviors. Low-level behaviors, such as vehicle acceleration, turning and stopping should be programmed into the agent if applicable.
Many MAS, such as NetLogo [41], Swarm [42], Repast [43] and Mason [44] are available for our study. These are basic simulators that require users to build ev-erything from scratch. Hence, using them would be excessively time- and effort-consuming for our purposes. Multi-agent simulators specific for traffic studies, such as SuMO [45], FreeSim [46] and MATSim [47] are also available. These sim-ulators are more suitable for our study. They have built-in packages and libraries, including different types of vehicles and roads, as well as junctions and traffic light timing systems specific to traffic simulations. They also have built-in rudimentary driver intelligence,such as acceleration, turning and stopping. These built-in func-tions save tremendous time and effort in creating the traffic model’s environment and the agents’ programmed intelligence. There are many multi-agent simulators built for different purposes, such asMASGriP[48] for smart-grid modeling and MA-SCEM[49] for modeling competitive electricity markets. Hence, choosing the right MAS for the right purpose saves time and effort.
MATSim is used as the default traffic MAS; this simulator can simulate many cars concurrently in a real-world road network system [47]. It was compared to other traffic simulators in paperIand was found to be the best in terms of degrees-of-freedom for programming the cars’ action on road networks. More importantly, MATSim allows users to program the drivers’ intelligence and choose the route creation mechanism for route planning. MATSim plans all routes with the shortest path algorithm [50] but gives users the option to travel by major or minor roads or
avoid tolls. It also allows users to specify whether drivers will take an alternative route when they encounter traffic delays. These behaviors are very human-like. The processes and data described above for the synthesis of traffic data are summarized and shown in Fig. 2.2. Recently, green open vehicle routing problem (GOVRP)"
models were used to optimize route scheduling [51, 52]. Our model is similar, but GOVRP models consider multiple variables to determine the shortest route. Hence, their solutions have considerations from a single dimension.
Figure 2.2:Synthesizing traffic data with multi-agent system.
In paper III, we extend paperI’s work to create data for more days. However, we do not want to create data for arbitrary days. To build a relationship between each day of data, we used the hourly temperatures of consecutive days for the air-conditioning energy load calculation. The air air-conditioning system was found to be a major energy consumer compared to other electrical components in an EV;
temperature control consumes 35%, followed by the power-steering system (5%), the braking system (5%), and other systems (5%) [53]. Table 2 shows estimated energy consumption values for an EV [54].
Table 2. EV Electrical Components
The use of these values for data post-processing will be discussed in Further detail in Section 2.3. The key point of using this method of data synthesis in paperIII is that we can generate continuous daily data for day-ahead energy load forecasting.
This forecast is then used to optimize energy production and achieve the benefits explained in Chapter 1.
In paperIV, we build a new model using the same techniques learned in paper I. However, in paperIV we used the MAS to investigate change in drivers’ behav-ior when the roads they typically use during their work commute are unavailable.
Roads are closed due to, for example, accidents, repairs, and traffic diversions. In paperIV, the problem statement involves investigating changes in Joensuu’s road traffic conditions when one of the bridges crossing Joensuu’s Pielisjoki river is closed for repairs. This is especially important because there are only four bridges crossing Pielisjoki river in Joensuu. "The study in paperIVwas also conducted to compare the similarity of MAS synthesized data to its real-world counterpart, which is ex-plained in further detail in Section 2.4.
After the MAS has finished with simulating the scenario, its raw output data may not be immediately usable. Sometimes, post-processing is required. In papers I-IV, post-processing is applied regardless of whether the MAS was used. The next section elaborates on the post-processing required in each paper.